Design of a Modular E-Core Flux Concentrating Axial Flux Machine: Preprint

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

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-08-24

In this paper a novel E-Core axial flux machine is proposed. The machine has a double-stator, single-rotor configuration with flux-concentrating ferrite magnets and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis. facilitatingmore » simpler construction. This paper presents a single-phase and a three-phase version of the E-Core machine. Case studies for a 1.1-kW, 400-rpm machine for both the single-phase and three-phase axial flux machines are presented. The results are verified through 3D finite element analysis.« less

Dynamically allocated virtual clustering management system

NASA Astrophysics Data System (ADS)

Marcus, Kelvin; Cannata, Jess

2013-05-01

The U.S Army Research Laboratory (ARL) has built a "Wireless Emulation Lab" to support research in wireless mobile networks. In our current experimentation environment, our researchers need the capability to run clusters of heterogeneous nodes to model emulated wireless tactical networks where each node could contain a different operating system, application set, and physical hardware. To complicate matters, most experiments require the researcher to have root privileges. Our previous solution of using a single shared cluster of statically deployed virtual machines did not sufficiently separate each user's experiment due to undesirable network crosstalk, thus only one experiment could be run at a time. In addition, the cluster did not make efficient use of our servers and physical networks. To address these concerns, we created the Dynamically Allocated Virtual Clustering management system (DAVC). This system leverages existing open-source software to create private clusters of nodes that are either virtual or physical machines. These clusters can be utilized for software development, experimentation, and integration with existing hardware and software. The system uses the Grid Engine job scheduler to efficiently allocate virtual machines to idle systems and networks. The system deploys stateless nodes via network booting. The system uses 802.1Q Virtual LANs (VLANs) to prevent experimentation crosstalk and to allow for complex, private networks eliminating the need to map each virtual machine to a specific switch port. The system monitors the health of the clusters and the underlying physical servers and it maintains cluster usage statistics for historical trends. Users can start private clusters of heterogeneous nodes with root privileges for the duration of the experiment. Users also control when to shutdown their clusters.

Design and Construction Multi Output Power Transmition with Single Prime Mover on Agricultural Products Machine

NASA Astrophysics Data System (ADS)

Koten, V. K.; Tanamal, C. E.

2017-03-01

Manufacturing agricultural products by the farmers, people or person who involve in medium industry, small industry, and households industry still be done in separately. Although the power on primemover is enough, in operations, primemover was only to move one of several agricultural products machine. This study attempts to design and construct power transmition multi output with single primemover; a single construction that allows primemover move some agricultur products machine in the same or not. This study begins with the determination of production capacity and the power to destroy products, the determination of resources and rotation, normalization of resources and rotation, the determination of the type material used, the size determination of each machine elements, construction machine elements, and assemble machine elements into a construction multi output power transmition with single primemover on agricultural products machine. The results show that with a input normalization 4 PK (2984 Watt), rotation 2000 rpm, the strength of material 60 kg/mm2, and several operating consideration, thus obtained size of machine elements through calculation. Based on the size, the machine elements is made through the use of some machine tools and assembled to form a multi output power transmition with single primemover.

Machine learning on-a-chip: a high-performance low-power reusable neuron architecture for artificial neural networks in ECG classifications.

PubMed

Sun, Yuwen; Cheng, Allen C

2012-07-01

Artificial neural networks (ANNs) are a promising machine learning technique in classifying non-linear electrocardiogram (ECG) signals and recognizing abnormal patterns suggesting risks of cardiovascular diseases (CVDs). In this paper, we propose a new reusable neuron architecture (RNA) enabling a performance-efficient and cost-effective silicon implementation for ANN. The RNA architecture consists of a single layer of physical RNA neurons, each of which is designed to use minimal hardware resource (e.g., a single 2-input multiplier-accumulator is used to compute the dot product of two vectors). By carefully applying the principal of time sharing, RNA can multiplexs this single layer of physical neurons to efficiently execute both feed-forward and back-propagation computations of an ANN while conserving the area and reducing the power dissipation of the silicon. A three-layer 51-30-12 ANN is implemented in RNA to perform the ECG classification for CVD detection. This RNA hardware also allows on-chip automatic training update. A quantitative design space exploration in area, power dissipation, and execution speed between RNA and three other implementations representative of different reusable hardware strategies is presented and discussed. Compared with an equivalent software implementation in C executed on an embedded microprocessor, the RNA ASIC achieves three orders of magnitude improvements in both the execution speed and the energy efficiency. Copyright © 2012 Elsevier Ltd. All rights reserved.

Ground Fault Overvoltage With Inverter-Interfaced Distributed Energy Resources

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

Ropp, Michael; Hoke, Anderson; Chakraborty, Sudipta

Ground Fault Overvoltage can occur in situations in which a four-wire distribution circuit is energized by an ungrounded voltage source during a single phase to ground fault. The phenomenon is well-documented with ungrounded synchronous machines, but there is considerable discussion about whether inverters cause this phenomenon, and consequently whether inverters require effective grounding. This paper examines the overvoltages that can be supported by inverters during single phase to ground faults via theory, simulation and experiment, identifies the relevant physical mechanisms, quantifies expected levels of overvoltage, and makes recommendations for optimal mitigation.

Stability Assessment of a System Comprising a Single Machine and Inverter with Scalable Ratings

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

Johnson, Brian B; Lin, Yashen; Gevorgian, Vahan

From the inception of power systems, synchronous machines have acted as the foundation of large-scale electrical infrastructures and their physical properties have formed the cornerstone of system operations. However, power electronics interfaces are playing a growing role as they are the primary interface for several types of renewable energy sources and storage technologies. As the role of power electronics in systems continues to grow, it is crucial to investigate the properties of bulk power systems in low inertia settings. In this paper, we assess the properties of coupled machine-inverter systems by studying an elementary system comprised of a synchronous generator,more » three-phase inverter, and a load. Furthermore, the inverter model is formulated such that its power rating can be scaled continuously across power levels while preserving its closed-loop response. Accordingly, the properties of the machine-inverter system can be assessed for varying ratios of machine-to-inverter power ratings and, hence, differing levels of inertia. After linearizing the model and assessing its eigenvalues, we show that system stability is highly dependent on the interaction between the inverter current controller and machine exciter, thus uncovering a key concern with mixed machine-inverter systems and motivating the need for next-generation grid-stabilizing inverter controls.« less

Exploring cluster Monte Carlo updates with Boltzmann machines

NASA Astrophysics Data System (ADS)

Wang, Lei

2017-11-01

Boltzmann machines are physics informed generative models with broad applications in machine learning. They model the probability distribution of an input data set with latent variables and generate new samples accordingly. Applying the Boltzmann machines back to physics, they are ideal recommender systems to accelerate the Monte Carlo simulation of physical systems due to their flexibility and effectiveness. More intriguingly, we show that the generative sampling of the Boltzmann machines can even give different cluster Monte Carlo algorithms. The latent representation of the Boltzmann machines can be designed to mediate complex interactions and identify clusters of the physical system. We demonstrate these findings with concrete examples of the classical Ising model with and without four-spin plaquette interactions. In the future, automatic searches in the algorithm space parametrized by Boltzmann machines may discover more innovative Monte Carlo updates.

Single molecule detection, thermal fluctuation and life

PubMed Central

YANAGIDA, Toshio; ISHII, Yoshiharu

2017-01-01

Single molecule detection has contributed to our understanding of the unique mechanisms of life. Unlike artificial man-made machines, biological molecular machines integrate thermal noises rather than avoid them. For example, single molecule detection has demonstrated that myosin motors undergo biased Brownian motion for stepwise movement and that single protein molecules spontaneously change their conformation, for switching to interactions with other proteins, in response to thermal fluctuation. Thus, molecular machines have flexibility and efficiency not seen in artificial machines. PMID:28190869

Bellco Formula Domus Home Care System.

PubMed

Trewin, Elizabeth

2004-01-01

There are certain characteristics in a dialysis machine that would be desirable for use in home and limited care environments. These features relate to safety, ease of use, consideration of physical space, and reliability. The Bellco Formula Domus Home Care System was designed to meet all these requirements. Bellco's philosophy of patient treatment centers on global biocompatibility. This is evident in the design of the Formula Domus Home Care System. It has the smallest hydraulic fluid pathway of any dialysis machine on the market. Formula is capable of preparing ultrapure dialysate. The ultrafiltration measurement mechanism, the patented Coriolis flow meter, measures the mass of the dialysate, not the volume. For this reason it is the only dialysis machine that detects actual backfiltration, not just the theoretical possibility of it based on transmembrane pressure. The Coriolis flow meter also ensures that dialysate flow is a true single pass. The operator interface is a single window operating control. It is possible to select up to 14 different languages. There is an online help key to assist patients with troubleshooting. Programmable start-up and shutdown times save time for the patient. Formula is the only dialysis machine to offer a backup battery feature. Formula is capable of communicating with any software available. The focus on global biocompatibility ensures the best quality dialysis treatments for a population of patients who will likely remain on dialysis for a longer period of time than conventional dialysis patients.

New Single Piece Blast Hardware design

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

Ulrich, Andri; Steinzig, Michael Louis; Aragon, Daniel Adrian

W, Q and PF engineers and machinists designed and fabricated, on the new Mazak i300, the first Single Piece Blast Hardware (unclassified design shown) reducing fabrication and inspection time by over 50%. The first DU Single Piece is completed and will be used for Hydro Test 3680. Past hydro tests used a twopiece assembly due to a lack of equipment capable of machining the complex saddle shape in a single piece. The i300 provides turning and milling 5-axis machining on one machine. The milling head on the i300 can machine past 90 relative to the spindle axis. This makes itmore » possible to machine the complex saddle surface on a single piece. Going to a single piece eliminates tolerance problems, such as tilting and eccentricity, that typically occurred when assembling the two pieces together« less

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.

Network of time-multiplexed optical parametric oscillators as a coherent Ising machine

NASA Astrophysics Data System (ADS)

Marandi, Alireza; Wang, Zhe; Takata, Kenta; Byer, Robert L.; Yamamoto, Yoshihisa

2014-12-01

Finding the ground states of the Ising Hamiltonian maps to various combinatorial optimization problems in biology, medicine, wireless communications, artificial intelligence and social network. So far, no efficient classical and quantum algorithm is known for these problems and intensive research is focused on creating physical systems—Ising machines—capable of finding the absolute or approximate ground states of the Ising Hamiltonian. Here, we report an Ising machine using a network of degenerate optical parametric oscillators (OPOs). Spins are represented with above-threshold binary phases of the OPOs and the Ising couplings are realized by mutual injections. The network is implemented in a single OPO ring cavity with multiple trains of femtosecond pulses and configurable mutual couplings, and operates at room temperature. We programmed a small non-deterministic polynomial time-hard problem on a 4-OPO Ising machine and in 1,000 runs no computational error was detected.

Design of a Modular E-Core Flux Concentrating Axial Flux Machine

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

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal

2015-09-02

In this paper a novel E-Core axial flux machine is proposed. The machine has a double stator-single rotor configuration with flux concentrating ferrite magnets, and pole windings across each leg of an E-Core stator. E-Core stators with the proposed flux-concentrating rotor arrangement result in better magnet utilization and higher torque density. The machine also has a modular structure facilitating simpler construction. This paper presents a single phase and a three-phase version of the E-Core machine. Case study for a 1.1 kW, 400 rpm machine for both the single phase and three-phase axial flux machine is presented. The results are verifiedmore » through 3D finite element analysis.« less

Ensemble Methods for Classification of Physical Activities from Wrist Accelerometry.

PubMed

Chowdhury, Alok Kumar; Tjondronegoro, Dian; Chandran, Vinod; Trost, Stewart G

2017-09-01

To investigate whether the use of ensemble learning algorithms improve physical activity recognition accuracy compared to the single classifier algorithms, and to compare the classification accuracy achieved by three conventional ensemble machine learning methods (bagging, boosting, random forest) and a custom ensemble model comprising four algorithms commonly used for activity recognition (binary decision tree, k nearest neighbor, support vector machine, and neural network). The study used three independent data sets that included wrist-worn accelerometer data. For each data set, a four-step classification framework consisting of data preprocessing, feature extraction, normalization and feature selection, and classifier training and testing was implemented. For the custom ensemble, decisions from the single classifiers were aggregated using three decision fusion methods: weighted majority vote, naïve Bayes combination, and behavior knowledge space combination. Classifiers were cross-validated using leave-one subject out cross-validation and compared on the basis of average F1 scores. In all three data sets, ensemble learning methods consistently outperformed the individual classifiers. Among the conventional ensemble methods, random forest models provided consistently high activity recognition; however, the custom ensemble model using weighted majority voting demonstrated the highest classification accuracy in two of the three data sets. Combining multiple individual classifiers using conventional or custom ensemble learning methods can improve activity recognition accuracy from wrist-worn accelerometer data.

The study on the nanomachining property and cutting model of single-crystal sapphire by atomic force microscopy.

PubMed

Huang, Jen-Ching; Weng, Yung-Jin

2014-01-01

This study focused on the nanomachining property and cutting model of single-crystal sapphire during nanomachining. The coated diamond probe is used to as a tool, and the atomic force microscopy (AFM) is as an experimental platform for nanomachining. To understand the effect of normal force on single-crystal sapphire machining, this study tested nano-line machining and nano-rectangular pattern machining at different normal force. In nano-line machining test, the experimental results showed that the normal force increased, the groove depth from nano-line machining also increased. And the trend is logarithmic type. In nano-rectangular pattern machining test, it is found when the normal force increases, the groove depth also increased, but rather the accumulation of small chips. This paper combined the blew by air blower, the cleaning by ultrasonic cleaning machine and using contact mode probe to scan the surface topology after nanomaching, and proposed the "criterion of nanomachining cutting model," in order to determine the cutting model of single-crystal sapphire in the nanomachining is ductile regime cutting model or brittle regime cutting model. After analysis, the single-crystal sapphire substrate is processed in small normal force during nano-linear machining; its cutting modes are ductile regime cutting model. In the nano-rectangular pattern machining, due to the impact of machined zones overlap, the cutting mode is converted into a brittle regime cutting model. © 2014 Wiley Periodicals, Inc.

Artificial muscles driven by the cooperative actuation of electrochemical molecular machines. Persistent discrepancies and challenges

NASA Astrophysics Data System (ADS)

Otero

2017-10-01

Here we review the persisting conceptual discrepancies between different research groups working on artificial muscles based on conducting polymers and other electroactive material. The basic question is if they can be treated as traditional electro-mechanical (physical) actuators driven by electric fields and described by some adaptation of their physical models or if, replicating natural muscles, they are electro-chemo-mechanical actuators driven by electrochemical reaction of the constitutive molecular machines: the polymeric chains. In that case the charge consumed by the reaction will control the volume variation of the muscular material and the motor displacement, following the basic and single Faraday's laws: the charge consumed by the reaction determines the number of exchanged ions and solvent, the film volume variation to lodge/expel them and the amplitude of the movement. Deviations from the linear relationships are due to the osmotic exchange of solvent and to the presence of parallel reactions from the electrolyte, which originate creeping effects. Challenges and limitations are underlined.

A single-phase axially-magnetized permanent-magnet oscillating machine for miniature aerospace power sources

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Cheng, Luming; Wang, Weinan; Liu, Jiaqi

2017-05-01

A single-phase axially-magnetized permanent-magnet (PM) oscillating machine which can be integrated with a free-piston Stirling engine to generate electric power, is investigated for miniature aerospace power sources. Machine structure, operating principle and detent force characteristic are elaborately studied. With the sinusoidal speed characteristic of the mover considered, the proposed machine is designed by 2D finite-element analysis (FEA), and some main structural parameters such as air gap diameter, dimensions of PMs, pole pitches of both stator and mover, and the pole-pitch combinations, etc., are optimized to improve both the power density and force capability. Compared with the three-phase PM linear machines, the proposed single-phase machine features less PM use, simple control and low controller cost. The power density of the proposed machine is higher than that of the three-phase radially-magnetized PM linear machine, but lower than the three-phase axially-magnetized PM linear machine.

Quantification of microscopic surface features of single point diamond turned optics with subsequent chemical polishing

NASA Astrophysics Data System (ADS)

Cardenas, Nelson; Kyrish, Matthew; Taylor, Daniel; Fraelich, Margaret; Lechuga, Oscar; Claytor, Richard; Claytor, Nelson

2015-03-01

Electro-Chemical Polishing is routinely used in the anodizing industry to achieve specular surface finishes of various metals products prior to anodizing. Electro-Chemical polishing functions by leveling the microscopic peaks and valleys of the substrate, thereby increasing specularity and reducing light scattering. The rate of attack is dependent of the physical characteristics (height, depth, and width) of the microscopic structures that constitute the surface finish. To prepare the sample, mechanical polishing such as buffing or grinding is typically required before etching. This type of mechanical polishing produces random microscopic structures at varying depths and widths, thus the electropolishing parameters are determined in an ad hoc basis. Alternatively, single point diamond turning offers excellent repeatability and highly specific control of substrate polishing parameters. While polishing, the diamond tool leaves behind an associated tool mark, which is related to the diamond tool geometry and machining parameters. Machine parameters such as tool cutting depth, speed and step over can be changed in situ, thus providing control of the spatial frequency of the microscopic structures characteristic of the surface topography of the substrate. By combining single point diamond turning with subsequent electro-chemical etching, ultra smooth polishing of both rotationally symmetric and free form mirrors and molds is possible. Additionally, machining parameters can be set to optimize post polishing for increased surface quality and reduced processing times. In this work, we present a study of substrate surface finish based on diamond turning tool mark spatial frequency with subsequent electro-chemical polishing.

Dynamic Testing of Signal Transduction Deregulation During Breast Cancer Initiation

DTIC Science & Technology

2011-07-01

1 at a chamber pressure of ~3 × 10-6 Torr for the electron beam evaporated films. A Hitachi FB2100 Focused Ion Beam milling machine with a gallium ...immobilization. These include physical absorption, layer-by-layer (LBL) assembly, and covalent attachment, and eventually chose the covalent attachment...testing real-time signaling in live breast cancer cells, it is important to evaluate the nanosensors to monitor fluorescent compounds in single

Mortality risk score prediction in an elderly population using machine learning.

PubMed

Rose, Sherri

2013-03-01

Standard practice for prediction often relies on parametric regression methods. Interesting new methods from the machine learning literature have been introduced in epidemiologic studies, such as random forest and neural networks. However, a priori, an investigator will not know which algorithm to select and may wish to try several. Here I apply the super learner, an ensembling machine learning approach that combines multiple algorithms into a single algorithm and returns a prediction function with the best cross-validated mean squared error. Super learning is a generalization of stacking methods. I used super learning in the Study of Physical Performance and Age-Related Changes in Sonomans (SPPARCS) to predict death among 2,066 residents of Sonoma, California, aged 54 years or more during the period 1993-1999. The super learner for predicting death (risk score) improved upon all single algorithms in the collection of algorithms, although its performance was similar to that of several algorithms. Super learner outperformed the worst algorithm (neural networks) by 44% with respect to estimated cross-validated mean squared error and had an R2 value of 0.201. The improvement of super learner over random forest with respect to R2 was approximately 2-fold. Alternatives for risk score prediction include the super learner, which can provide improved performance.

Vowel Imagery Decoding toward Silent Speech BCI Using Extreme Learning Machine with Electroencephalogram

PubMed Central

Kim, Jongin; Park, Hyeong-jun

2016-01-01

The purpose of this study is to classify EEG data on imagined speech in a single trial. We recorded EEG data while five subjects imagined different vowels, /a/, /e/, /i/, /o/, and /u/. We divided each single trial dataset into thirty segments and extracted features (mean, variance, standard deviation, and skewness) from all segments. To reduce the dimension of the feature vector, we applied a feature selection algorithm based on the sparse regression model. These features were classified using a support vector machine with a radial basis function kernel, an extreme learning machine, and two variants of an extreme learning machine with different kernels. Because each single trial consisted of thirty segments, our algorithm decided the label of the single trial by selecting the most frequent output among the outputs of the thirty segments. As a result, we observed that the extreme learning machine and its variants achieved better classification rates than the support vector machine with a radial basis function kernel and linear discrimination analysis. Thus, our results suggested that EEG responses to imagined speech could be successfully classified in a single trial using an extreme learning machine with a radial basis function and linear kernel. This study with classification of imagined speech might contribute to the development of silent speech BCI systems. PMID:28097128

Evidence of end-effector based gait machines in gait rehabilitation after CNS lesion.

PubMed

Hesse, S; Schattat, N; Mehrholz, J; Werner, C

2013-01-01

A task-specific repetitive approach in gait rehabilitation after CNS lesion is well accepted nowadays. To ease the therapists' and patients' physical effort, the past two decades have seen the introduction of gait machines to intensify the amount of gait practice. Two principles have emerged, an exoskeleton- and an endeffector-based approach. Both systems share the harness and the body weight support. With the end-effector-based devices, the patients' feet are positioned on two foot plates, whose movements simulate stance and swing phase. This article provides an overview on the end-effector based machine's effectiveness regarding the restoration of gait. For the electromechanical gait trainer GT I, a meta analysis identified nine controlled trials (RCT) in stroke subjects (n = 568) and were analyzed to detect differences between end-effector-based locomotion + physiotherapy and physiotherapy alone. Patients practising with the machine effected in a superior gait ability (210 out of 319 patients, 65.8% vs. 96 out of 249 patients, 38.6%, respectively, Z = 2.29, p = 0.020), due to a larger training intensity. Only single RCTs have been reported for other devices and etiologies. The introduction of end-effector based gait machines has opened a new succesful chapter in gait rehabilitation after CNS lesion.

Detectors for single-molecule fluorescence imaging and spectroscopy

PubMed Central

MICHALET, X.; SIEGMUND, O.H.W.; VALLERGA, J.V.; JELINSKY, P.; MILLAUD, J.E.; WEISS, S.

2010-01-01

Single-molecule observation, characterization and manipulation techniques have recently come to the forefront of several research domains spanning chemistry, biology and physics. Due to the exquisite sensitivity, specificity, and unmasking of ensemble averaging, single-molecule fluorescence imaging and spectroscopy have become, in a short period of time, important tools in cell biology, biochemistry and biophysics. These methods led to new ways of thinking about biological processes such as viral infection, receptor diffusion and oligomerization, cellular signaling, protein-protein or protein-nucleic acid interactions, and molecular machines. Such achievements require a combination of several factors to be met, among which detector sensitivity and bandwidth are crucial. We examine here the needed performance of photodetectors used in these types of experiments, the current state of the art for different categories of detectors, and actual and future developments of single-photon counting detectors for single-molecule imaging and spectroscopy. PMID:20157633

THRESHOLD LOGIC SYNTHESIS OF SEQUENTIAL MACHINES.

DTIC Science & Technology

The application of threshold logic to the design of sequential machines is the subject of this research. A single layer of threshold logic units in...advantages of fewer components because of the use of threshold logic, along with very high-speed operation resulting from the use of only a single layer of...logic. In some instances, namely for asynchronous machines, the only delay need be the natural delay of the single layer of threshold elements. It is

Science 101: Q--What Is the Physics behind Simple Machines?

ERIC Educational Resources Information Center

Robertson, Bill

2013-01-01

Bill Robertson thinks that questioning the physics behind simple machines is a great idea because when he encounters the subject of simple machines in textbooks, activities, and classrooms, he seldom encounters, a scientific explanation of how they work. Instead, what one often sees is a discussion of load, effort, fulcrum, actual mechanical…

Industrial femtosecond lasers for machining of heat-sensitive polymers (Conference Presentation)

NASA Astrophysics Data System (ADS)

Hendricks, Frank; Bernard, Benjamin; Matylitsky, Victor V.

2017-03-01

Heat-sensitive materials, such as polymers, are used increasingly in various industrial sectors such as medical device manufacturing and organic electronics. Medical applications include implantable devices like stents, catheters and wires, which need to be structured and cut with minimum heat damage. Also the flat panel display market moves from LCD displays to organic LED (OLED) solutions, which utilize heat-sensitive polymer substrates. In both areas, the substrates often consist of multilayer stacks with different types of materials, such as metals, dielectric layers and polymers with different physical characteristic. The different thermal behavior and laser absorption properties of the materials used makes these stacks difficult to machine using conventional laser sources. Femtosecond lasers are an enabling technology for micromachining of these materials since it is possible to machine ultrafine structures with minimum thermal impact and very precise control over material removed. An industrial femtosecond Spirit HE laser system from Spectra-Physics with pulse duration <400 fs, pulse energies of >120 μJ and average output powers of >16 W is an ideal tool for industrial micromachining of a wide range of materials with highest quality and efficiency. The laser offers process flexibility with programmable pulse energy, repetition rate, and pulse width. In this paper, we provide an overview of machining heat-sensitive materials using Spirit HE laser. In particular, we show how the laser parameters (e.g. laser wavelength, pulse duration, applied energy and repetition rate) and the processing strategy (gas assisted single pass cut vs. multi-scan process) influence the efficiency and quality of laser processing.

Parametric analysis of plastic strain and force distribution in single pass metal spinning

NASA Astrophysics Data System (ADS)

Choudhary, Shashank; Tejesh, Chiruvolu Mohan; Regalla, Srinivasa Prakash; Suresh, Kurra

2013-12-01

Metal spinning also known as spin forming is one of the sheet metal working processes by which an axis-symmetric part can be formed from a flat sheet metal blank. Parts are produced by pressing a blunt edged tool or roller on to the blank which in turn is mounted on a rotating mandrel. This paper discusses about the setting up a 3-D finite element simulation of single pass metal spinning in LS-Dyna. Four parameters were considered namely blank thickness, roller nose radius, feed ratio and mandrel speed and the variation in forces and plastic strain were analysed using the full-factorial design of experiments (DOE) method of simulation experiments. For some of these DOE runs, physical experiments on extra deep drawing (EDD) sheet metal were carried out using En31 tool on a lathe machine. Simulation results are able to predict the zone of unsafe thinning in the sheet and high forming forces that are hint to the necessity for less-expensive and semi-automated machine tools to help the household and small scale spinning workers widely prevalent in India.

Study of a Variable Mass Atwood's Machine Using a Smartphone

ERIC Educational Resources Information Center

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-01-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses…

Development of the self-learning machine for creating models of microprocessor of single-phase earth fault protection devices in networks with isolated neutral voltage above 1000 V

NASA Astrophysics Data System (ADS)

Utegulov, B. B.; Utegulov, A. B.; Meiramova, S.

2018-02-01

The paper proposes the development of a self-learning machine for creating models of microprocessor-based single-phase ground fault protection devices in networks with an isolated neutral voltage higher than 1000 V. Development of a self-learning machine for creating models of microprocessor-based single-phase earth fault protection devices in networks with an isolated neutral voltage higher than 1000 V. allows to effectively implement mathematical models of automatic change of protection settings. Single-phase earth fault protection devices.

Diamond turning of Si and Ge single crystals

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

Blake, P.; Scattergood, R.O.

Single-point diamond turning studies have been completed on Si and Ge crystals. A new process model was developed for diamond turning which is based on a critical depth of cut for plastic flow-to-brittle fracture transitions. This concept, when combined with the actual machining geometry for single-point turning, predicts that {open_quotes}ductile{close_quotes} machining is a combined action of plasticity and fracture. Interrupted cutting experiments also provide a meant to directly measure the critical depth parameter for given machining conditions.

Origin of acoustic emission produced during single point machining

NASA Astrophysics Data System (ADS)

Heiple, C. R.; Carpenter, S. H.; Armentrout, D. L.

1991-05-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emission produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent.

Parametric modelling design applied to weft knitted surfaces and its effects in their physical properties

NASA Astrophysics Data System (ADS)

Oliveira, N. P.; Maciel, L.; Catarino, A. P.; Rocha, A. M.

2017-10-01

This work proposes the creation of models of surfaces using a parametric computer modelling software to obtain three-dimensional structures in weft knitted fabrics produced on single needle system machines. Digital prototyping, another feature of digital modelling software, was also explored in three-dimensional drawings generated using the Rhinoceros software. With this approach, different 3D structures were developed and produced. Physical characterization tests were then performed on the resulting 3D weft knitted structures to assess their ability to promote comfort. From the obtained results, it is apparent that the developed structures have potential for application in different market segments, such as clothing and interior textiles.

Non-symmetric approach to single-screw expander and compressor modeling

NASA Astrophysics Data System (ADS)

Ziviani, Davide; Groll, Eckhard A.; Braun, James E.; Horton, W. Travis; De Paepe, M.; van den Broek, M.

2017-08-01

Single-screw type volumetric machines are employed both as compressors in refrigeration systems and, more recently, as expanders in organic Rankine cycle (ORC) applications. The single-screw machine is characterized by having a central grooved rotor and two mating toothed starwheels that isolate the working chambers. One of the main features of such machine is related to the simultaneous occurrence of the compression or expansion processes on both sides of the main rotor which results in a more balanced loading on the main shaft bearings with respect to twin-screw machines. However, the meshing between starwheels and main rotor is a critical aspect as it heavily affects the volumetric performance of the machine. To allow flow interactions between the two sides of the rotor, a non-symmetric modelling approach has been established to obtain a more comprehensive model of the single-screw machine. The resulting mechanistic model includes in-chamber governing equations, leakage flow models, heat transfer mechanisms, viscous and mechanical losses. Forces and moments balances are used to estimate the loads on the main shaft bearings as well as on the starwheel bearings. An 11 kWe single-screw expander (SSE) adapted from an air compressor operating with R245fa as working fluid is used to validate the model. A total of 60 steady-steady points at four different rotational speeds have been collected to characterize the performance of the machine. The maximum electrical power output and overall isentropic efficiency measured were 7.31 kW and 51.91%, respectively.

Single-shot high aspect ratio bulk nanostructuring of fused silica using chirp-controlled ultrafast laser Bessel beams

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

Bhuyan, M. K.; Velpula, P. K.; Colombier, J. P.

2014-01-13

We report single-shot, high aspect ratio nanovoid fabrication in bulk fused silica using zeroth order chirp-controlled ultrafast laser Bessel beams. We identify a unique laser pulse length and energy dependence of the physical characteristics of machined structures over which nanovoids of diameter in the range 200–400 nm and aspect ratios exceeding 1000 can be fabricated. A mechanism based on the axial energy deposition of nonlinear ultrashort Bessel beams and subsequent material densification or rarefaction in fused silica is proposed, intricating the non-diffractive nature with the diffusing character of laser-generated free carriers. Fluid flow through nanochannel is also demonstrated.

High Energy Colliders

NASA Astrophysics Data System (ADS)

Palmer, R. B.; Gallardo, J. C.

INTRODUCTION PHYSICS CONSIDERATIONS GENERAL REQUIRED LUMINOSITY FOR LEPTON COLLIDERS THE EFFECTIVE PHYSICS ENERGIES OF HADRON COLLIDERS HADRON-HADRON MACHINES LUMINOSITY SIZE AND COST CIRCULAR e^{+}e^- MACHINES LUMINOSITY SIZE AND COST e^{+}e^- LINEAR COLLIDERS LUMINOSITY CONVENTIONAL RF SUPERCONDUCTING RF AT HIGHER ENERGIES γ - γ COLLIDERS μ ^{+} μ^- COLLIDERS ADVANTAGES AND DISADVANTAGES DESIGN STUDIES STATUS AND REQUIRED R AND D COMPARISION OF MACHINES CONCLUSIONS DISCUSSION

An ion accelerator for undergraduate research and teaching

NASA Astrophysics Data System (ADS)

Monce, Michael

1997-04-01

We have recently upgraded our 400kV, single beam line ion accelerator to a 1MV, multiple beam line machine. This upgrade has greatly expanded the opportunities for student involvement in the laboratory. We will describe four areas of work in which students now participate. The first is the continuing research being conducted in excitations produced in ion-molecule collisions, which recently involved the use of digital imaging. The second area of research now opened up by the new accelerator involves PIXE. We are currently beginning a cross disciplinary study of archaeological specimens using PIXE and involving students from both anthropology and physics. Finally, two beam lines from the accelerator will be used for basic work in nuclear physics: Rutherford scattering and nuclear resonances. These two nuclear physics experiments will be integrated into our sophomore-junior level, year-long course in experimental physics.

Some single-piston closed-cycle machines and Peter Tailer's thermal lag engine

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

West, C.D.

1993-01-01

Peter Tailer has devised, built, and operated a beautifully simple engine with a closed working gas cycle, external heating, and only a single piston. The aim of this paper is to cast some light on the possible modes of operation for his machine. The methods develops to analyze certain aspects of Stirling cycle engines, and especially the thermodynamic losses incurred in systems that are neither perfectly isothermal nor perfectly adiabatic, can be applied to Tailer's system. The results identify two idealized cycles fr such machines; relate those cycles to a single piston, ported cylinder machine proposed earlier; and offer amore » possible explanation for the success of the thermal lag engine.« less

5 CFR 532.279 - Special wage schedules for printing positions.

Code of Federal Regulations, 2011 CFR

2011-01-01

... Opaquer 4 Offset Press Helper 5 Bindery Machine Operator (Helper) 5 Film Assembler-Stripper (Single Flat-Single Color) 5 Platemaker (Single Color) 5 Film Assembler-Stripper (Partial and Composite Flats) 7... Cutter) 8 Bindery Machine Operator (Power Folder) 8 Film Assembler-Stripper (Multiple Flat-Multiple Color...

5 CFR 532.279 - Special wage schedules for printing positions.

Code of Federal Regulations, 2012 CFR

2012-01-01

... Opaquer 4 Offset Press Helper 5 Bindery Machine Operator (Helper) 5 Film Assembler-Stripper (Single Flat-Single Color) 5 Platemaker (Single Color) 5 Film Assembler-Stripper (Partial and Composite Flats) 7... Cutter) 8 Bindery Machine Operator (Power Folder) 8 Film Assembler-Stripper (Multiple Flat-Multiple Color...

Effect of Width of Kerf on Machining Accuracy and Subsurface Layer After WEDM

NASA Astrophysics Data System (ADS)

Mouralova, K.; Kovar, J.; Klakurkova, L.; Prokes, T.

2018-02-01

Wire electrical discharge machining is an unconventional machining technology that applies physical principles to material removal. The material is removed by a series of recurring current discharges between the workpiece and the tool electrode, and a `kerf' is created between the wire and the material being machined. The width of the kerf is directly dependent not only on the diameter of the wire used, but also on the machine parameter settings and, in particular, on the set of mechanical and physical properties of the material being machined. To ensure precise machining, it is important to have the width of the kerf as small as possible. The present study deals with the evaluation of the width of the kerf for four different metallic materials (some of which were subsequently heat treated using several methods) with different machine parameter settings. The kerf is investigated on metallographic cross sections using light and electron microscopy.

Basic Machines - The "Nuts and Bolts" of Technical Physics Minicourse, Career Oriented Pre-Technical Physics. Preliminary Edition.

ERIC Educational Resources Information Center

Bullock, Bob; And Others

This minicourse was prepared for use with secondary physics students in the Dallas Independent School District and is one option in a physics program which provides for the selection of topics on the basis of student career needs and interests. This minicourse was aimed at two levels in the study of basic machines. The "light" level…

OptiCentric lathe centering machine

NASA Astrophysics Data System (ADS)

Buß, C.; Heinisch, J.

2013-09-01

High precision optics depend on precisely aligned lenses. The shift and tilt of individual lenses as well as the air gap between elements require accuracies in the single micron regime. These accuracies are hard to meet with traditional assembly methods. Instead, lathe centering can be used to machine the mount with respect to the optical axis. Using a diamond turning process, all relevant errors of single mounted lenses can be corrected in one post-machining step. Building on the OptiCentric® and OptiSurf® measurement systems, Trioptics has developed their first lathe centering machines. The machine and specific design elements of the setup will be shown. For example, the machine can be used to turn optics for i-line steppers with highest precision.

Smart material screening machines using smart materials and controls

NASA Astrophysics Data System (ADS)

Allaei, Daryoush; Corradi, Gary; Waigand, Al

2002-07-01

The objective of this product is to address the specific need for improvements in the efficiency and effectiveness in physical separation technologies in the screening areas. Currently, the mining industry uses approximately 33 billion kW-hr per year, costing 1.65 billion dollars at 0.05 cents per kW-hr, of electrical energy for physical separations. Even though screening and size separations are not the single most energy intensive process in the mining industry, they are often the major bottleneck in the whole process. Improvements to this area offer tremendous potential in both energy savings and production improvements. Additionally, the vibrating screens used in the mining processing plants are the most costly areas from maintenance and worker health and safety point of views. The goal of this product is to reduce energy use in the screening and total processing areas. This goal is accomplished by developing an innovative screening machine based on smart materials and smart actuators, namely smart screen that uses advanced sensory system to continuously monitor the screening process and make appropriate adjustments to improve production. The theory behind the development of Smart Screen technology is based on two key technologies, namely smart actuators and smart Energy Flow ControlT (EFCT) strategies, developed initially for military applications. Smart Screen technology controls the flow of vibration energy and confines it to the screen rather than shaking much of the mass that makes up the conventional vibratory screening machine. Consequently, Smart Screens eliminates and downsizes many of the structural components associated with conventional vibratory screening machines. As a result, the surface area of the screen increases for a given envelope. This increase in usable screening surface area extends the life of the screens, reduces required maintenance by reducing the frequency of screen change-outs and improves throughput or productivity.

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,…

Radiation tolerant combinational logic cell

NASA Technical Reports Server (NTRS)

Maki, Gary R. (Inventor); Whitaker, Sterling (Inventor); Gambles, Jody W. (Inventor)

2009-01-01

A system has a reduced sensitivity to Single Event Upset and/or Single Event Transient(s) compared to traditional logic devices. In a particular embodiment, the system includes an input, a logic block, a bias stage, a state machine, and an output. The logic block is coupled to the input. The logic block is for implementing a logic function, receiving a data set via the input, and generating a result f by applying the data set to the logic function. The bias stage is coupled to the logic block. The bias stage is for receiving the result from the logic block and presenting it to the state machine. The state machine is coupled to the bias stage. The state machine is for receiving, via the bias stage, the result generated by the logic block. The state machine is configured to retain a state value for the system. The state value is typically based on the result generated by the logic block. The output is coupled to the state machine. The output is for providing the value stored by the state machine. Some embodiments of the invention produce dual rail outputs Q and Q'. The logic block typically contains combinational logic and is similar, in size and transistor configuration, to a conventional CMOS combinational logic design. However, only a very small portion of the circuits of these embodiments, is sensitive to Single Event Upset and/or Single Event Transients.

Origin of acoustic emission produced during single point machining

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

Heiple, C.R,.; Carpenter, S.H.; Armentrout, D.L.

1991-01-01

Acoustic emission was monitored during single point, continuous machining of 4340 steel and Ti-6Al-4V as a function of heat treatment. Acoustic emission produced during tensile and compressive deformation of these alloys has been previously characterized as a function of heat treatment. Heat treatments which increase the strength of 4340 steel increase the amount of acoustic emission produced during deformation, while heat treatments which increase the strength of Ti-6Al-4V decrease the amount of acoustic emission produced during deformation. If chip deformation were the primary source of acoustic emission during single point machining, then opposite trends in the level of acoustic emissionmore » produced during machining as a function of material strength would be expected for these two alloys. Trends in rms acoustic emission level with increasing strength were similar for both alloys, demonstrating that chip deformation is not a major source of acoustic emission in single point machining. Acoustic emission has also been monitored as a function of machining parameters on 6061-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, lead, and teflon. The data suggest that sliding friction between the nose and/or flank of the tool and the newly machined surface is the primary source of acoustic emission. Changes in acoustic emission with tool wear were strongly material dependent. 21 refs., 19 figs., 4 tabs.« less

Some single-piston closed-cycle machines and Peter Tailer`s thermal lag engine

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

West, C.D.

1993-06-01

Peter Tailer has devised, built, and operated a beautifully simple engine with a closed working gas cycle, external heating, and only a single piston. The aim of this paper is to cast some light on the possible modes of operation for his machine. The methods develops to analyze certain aspects of Stirling cycle engines, and especially the thermodynamic losses incurred in systems that are neither perfectly isothermal nor perfectly adiabatic, can be applied to Tailer`s system. The results identify two idealized cycles fr such machines; relate those cycles to a single piston, ported cylinder machine proposed earlier; and offer amore » possible explanation for the success of the thermal lag engine.« less

The Intersection of Physics and Biology

ScienceCinema

Liphardt, Jan

2017-12-22

In April 1953, Watson and Crick largely defined the program of 20th century biology: obtaining the blueprint of life encoded in the DNA. Fifty years later, in 2003, the sequencing of the human genome was completed. Like any major scientific breakthrough, the sequencing of the human genome raised many more questions than it answered. I'll brief you on some of the big open problems in cell and developmental biology, and I'll explain why approaches, tools, and ideas from the physical sciences are currently reshaping biological research. Super-resolution light microscopies are revealing the intricate spatial organization of cells, single-molecule methods show how molecular machines function, and new probes are clarifying the role of mechanical forces in cell and tissue function. At the same time, Physics stands to gain beautiful new problems in soft condensed matter, quantum mechanics, and non-equilibrium thermodynamics.

VIEW EASTLEFTBUILDING 2 PHYSICAL TESTING HOUSE (1928) RIGHTBUILDING 7 MACHINE ...

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

VIEW EAST-LEFT-BUILDING 2 PHYSICAL TESTING HOUSE (1928) RIGHT-BUILDING 7 MACHINE SHOP (1901 SECTION) - John A. Roebling's Sons Company & American Steel & Wire Company, South Broad, Clark, Elmer, Mott & Hudson Streets, Trenton, Mercer County, NJ

Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses

NASA Astrophysics Data System (ADS)

Huang, Haiping

2017-05-01

Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistical mechanics analysis of the unsupervised learning in a restricted Boltzmann machine with binary synapses. A message passing equation to infer the hidden feature is derived, and furthermore, variants of this equation are analyzed. A statistical analysis by replica theory describes the thermodynamic properties of the model. Our analysis confirms an entropy crisis preceding the non-convergence of the message passing equation, suggesting a discontinuous phase transition as a key characteristic of the restricted Boltzmann machine. Continuous phase transition is also confirmed depending on the embedded feature strength in the data. The mean-field result under the replica symmetric assumption agrees with that obtained by running message passing algorithms on single instances of finite sizes. Interestingly, in an approximate Hopfield model, the entropy crisis is absent, and a continuous phase transition is observed instead. We also develop an iterative equation to infer the hyper-parameter (temperature) hidden in the data, which in physics corresponds to iteratively imposing Nishimori condition. Our study provides insights towards understanding the thermodynamic properties of the restricted Boltzmann machine learning, and moreover important theoretical basis to build simplified deep networks.

The Trail Making test: a study of its ability to predict falls in the acute neurological in-patient population.

PubMed

Mateen, Bilal Akhter; Bussas, Matthias; Doogan, Catherine; Waller, Denise; Saverino, Alessia; Király, Franz J; Playford, E Diane

2018-05-01

To determine whether tests of cognitive function and patient-reported outcome measures of motor function can be used to create a machine learning-based predictive tool for falls. Prospective cohort study. Tertiary neurological and neurosurgical center. In all, 337 in-patients receiving neurosurgical, neurological, or neurorehabilitation-based care. Binary (Y/N) for falling during the in-patient episode, the Trail Making Test (a measure of attention and executive function) and the Walk-12 (a patient-reported measure of physical function). The principal outcome was a fall during the in-patient stay ( n = 54). The Trail test was identified as the best predictor of falls. Moreover, addition of other variables, did not improve the prediction (Wilcoxon signed-rank P < 0.001). Classical linear statistical modeling methods were then compared with more recent machine learning based strategies, for example, random forests, neural networks, support vector machines. The random forest was the best modeling strategy when utilizing just the Trail Making Test data (Wilcoxon signed-rank P < 0.001) with 68% (± 7.7) sensitivity, and 90% (± 2.3) specificity. This study identifies a simple yet powerful machine learning (Random Forest) based predictive model for an in-patient neurological population, utilizing a single neuropsychological test of cognitive function, the Trail Making test.

Personifying self in physics problem situations involving forces as a student help strategy

NASA Astrophysics Data System (ADS)

Tabor-Morris, A. E.

2013-03-01

How can physics teachers best guide students regarding physics problem situations involving forces? A suggestion is made here to personify oneself as the object in question, that is, to pretend to be the object undergoing forces and then qualify and quantify those forces according to their vectors for the system at hand. This personification is not meant to empower the object to act, just to sense the forces it is experiencing. This strategy may be especially useful to beginning physics learners attacking problems that involve both multiple forces AND multiple objects, since each object acted upon needs to be considered separately, using the idea that one cannot be two places at once. An example of this type of problem expounded on here is Atwood's machine: two weights hung over a pulley with a single rope. Another example given is electromagnetic forces on one charge caused by other charges in the vicinity. Discussion is made on implementation of classroom strategies. Department of Physics

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults

PubMed Central

Verschueren, Sabine M. P.; Degens, Hans; Morse, Christopher I.; Onambélé, Gladys L.

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual’s physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry. PMID:29155839

Performance of thigh-mounted triaxial accelerometer algorithms in objective quantification of sedentary behaviour and physical activity in older adults.

PubMed

Wullems, Jorgen A; Verschueren, Sabine M P; Degens, Hans; Morse, Christopher I; Onambélé, Gladys L

2017-01-01

Accurate monitoring of sedentary behaviour and physical activity is key to investigate their exact role in healthy ageing. To date, accelerometers using cut-off point models are most preferred for this, however, machine learning seems a highly promising future alternative. Hence, the current study compared between cut-off point and machine learning algorithms, for optimal quantification of sedentary behaviour and physical activity intensities in the elderly. Thus, in a heterogeneous sample of forty participants (aged ≥60 years, 50% female) energy expenditure during laboratory-based activities (ranging from sedentary behaviour through to moderate-to-vigorous physical activity) was estimated by indirect calorimetry, whilst wearing triaxial thigh-mounted accelerometers. Three cut-off point algorithms and a Random Forest machine learning model were developed and cross-validated using the collected data. Detailed analyses were performed to check algorithm robustness, and examine and benchmark both overall and participant-specific balanced accuracies. This revealed that the four models can at least be used to confidently monitor sedentary behaviour and moderate-to-vigorous physical activity. Nevertheless, the machine learning algorithm outperformed the cut-off point models by being robust for all individual's physiological and non-physiological characteristics and showing more performance of an acceptable level over the whole range of physical activity intensities. Therefore, we propose that Random Forest machine learning may be optimal for objective assessment of sedentary behaviour and physical activity in older adults using thigh-mounted triaxial accelerometry.

Relative Performance of Hardwood Sawing Machines

Treesearch

Philip H. Steele; Michael W. Wade; Steven H. Bullard; Philip A. Araman

1991-01-01

Only limited information has been available to hardwood sawmillers on the performance of their sawing machines. This study analyzes a large database of individual machine studies to provide detailed information on 6 machine types. These machine types were band headrig, circular headrig, band linebar resaw, vertical band splitter resaw, single arbor gang resaw and...

Identification of Synchronous Machine Stability - Parameters: AN On-Line Time-Domain Approach.

NASA Astrophysics Data System (ADS)

Le, Loc Xuan

1987-09-01

A time-domain modeling approach is described which enables the stability-study parameters of the synchronous machine to be determined directly from input-output data measured at the terminals of the machine operating under normal conditions. The transient responses due to system perturbations are used to identify the parameters of the equivalent circuit models. The described models are verified by comparing their responses with the machine responses generated from the transient stability models of a small three-generator multi-bus power system and of a single -machine infinite-bus power network. The least-squares method is used for the solution of the model parameters. As a precaution against ill-conditioned problems, the singular value decomposition (SVD) is employed for its inherent numerical stability. In order to identify the equivalent-circuit parameters uniquely, the solution of a linear optimization problem with non-linear constraints is required. Here, the SVD appears to offer a simple solution to this otherwise difficult problem. Furthermore, the SVD yields solutions with small bias and, therefore, physically meaningful parameters even in the presence of noise in the data. The question concerning the need for a more advanced model of the synchronous machine which describes subtransient and even sub-subtransient behavior is dealt with sensibly by the concept of condition number. The concept provides a quantitative measure for determining whether such an advanced model is indeed necessary. Finally, the recursive SVD algorithm is described for real-time parameter identification and tracking of slowly time-variant parameters. The algorithm is applied to identify the dynamic equivalent power system model.

Application verification research of cloud computing technology in the field of real time aerospace experiment

NASA Astrophysics Data System (ADS)

Wan, Junwei; Chen, Hongyan; Zhao, Jing

2017-08-01

According to the requirements of real-time, reliability and safety for aerospace experiment, the single center cloud computing technology application verification platform is constructed. At the IAAS level, the feasibility of the cloud computing technology be applied to the field of aerospace experiment is tested and verified. Based on the analysis of the test results, a preliminary conclusion is obtained: Cloud computing platform can be applied to the aerospace experiment computing intensive business. For I/O intensive business, it is recommended to use the traditional physical machine.

The Physics of Ultrabroadband Frequency Comb Generation and Optimized Combs for Measurements in Fundamental Physics

DTIC Science & Technology

2016-07-02

beams Superresolution machining Threshold effect of ablation means that structure diameter is less than the beam diameter fs pulses at 800 nm yield 200...Approved for public release: distribution unlimited. Applications of Bessel beams Superresolution machining Threshold effect of ablation means that... Superresolution machining Threshold effect of ablation means that structure diameter is less than the beam diameter fs pulses at 800 nm yield 200 nm

Quantum adiabatic machine learning

NASA Astrophysics Data System (ADS)

Pudenz, Kristen L.; Lidar, Daniel A.

2013-05-01

We develop an approach to machine learning and anomaly detection via quantum adiabatic evolution. This approach consists of two quantum phases, with some amount of classical preprocessing to set up the quantum problems. In the training phase we identify an optimal set of weak classifiers, to form a single strong classifier. In the testing phase we adiabatically evolve one or more strong classifiers on a superposition of inputs in order to find certain anomalous elements in the classification space. Both the training and testing phases are executed via quantum adiabatic evolution. All quantum processing is strictly limited to two-qubit interactions so as to ensure physical feasibility. We apply and illustrate this approach in detail to the problem of software verification and validation, with a specific example of the learning phase applied to a problem of interest in flight control systems. Beyond this example, the algorithm can be used to attack a broad class of anomaly detection problems.

FEM analysis of an single stator dual PM rotors axial synchronous machine

NASA Astrophysics Data System (ADS)

Tutelea, L. N.; Deaconu, S. I.; Popa, G. N.

2017-01-01

The actual e - continuously variable transmission (e-CVT) solution for the parallel Hybrid Electric Vehicle (HEV) requires two electric machines, two inverters, and a planetary gear. A distinct electric generator and a propulsion electric motor, both with full power converters, are typical for a series HEV. In an effort to simplify the planetary-geared e-CVT for the parallel HEV or the series HEV we hereby propose to replace the basically two electric machines and their two power converters by a single, axial-air-gap, electric machine central stator, fed from a single PWM converter with dual frequency voltage output and two independent PM rotors. The proposed topologies, the magneto-motive force analysis and quasi 3D-FEM analysis are the core of the paper.

A novel single-phase flux-switching permanent magnet linear generator used for free-piston Stirling engine

NASA Astrophysics Data System (ADS)

Zheng, Ping; Sui, Yi; Tong, Chengde; Bai, Jingang; Yu, Bin; Lin, Fei

2014-05-01

This paper investigates a novel single-phase flux-switching permanent-magnet (PM) linear machine used for free-piston Stirling engines. The machine topology and operating principle are studied. A flux-switching PM linear machine is designed based on the quasi-sinusoidal speed characteristic of the resonant piston. Considering the performance of back electromotive force and thrust capability, some leading structural parameters, including the air gap length, the PM thickness, the ratio of the outer radius of mover to that of stator, the mover tooth width, the stator tooth width, etc., are optimized by finite element analysis. Compared with conventional three-phase moving-magnet linear machine, the proposed single-phase flux-switching topology shows advantages in less PM use, lighter mover, and higher volume power density.

Changes in the physical status of the typical and leached chernozems of Kursk oblast within 40 years

NASA Astrophysics Data System (ADS)

Kuznetsova, I. V.

2013-04-01

The changes in the physical properties of the chernozems in the Central Russian province of the forest-steppe zone (Kursk oblast) that took place from 1964 to 2002 are analyzed in relation to the corresponding changes in the agrotechnology, agroeconomy, and agroecology. Three periods of the soil transformation are distinguished. The first period was characterized by the use of machines with relatively small pressure on the soil and by the dynamic equilibrium between the physical state of the soils and the processes of the humification-mineralization of the soil organic matter. The use of power-intensive machines in the next period resulted in greater soil compaction with negative changes in the soil physical properties. At the same time, the physical properties of the chernozems remained close to optimum on the fields where heavy machines were not used. The third period was characterized by the use of heavy machines and by the decrease in the rates of the organic and mineral fertilizers and certain disturbances in the crop rotation systems because of the economic difficulties. The negative tendencies of the changes in the soil physical properties observed during the preceding period continued.

Development of a low energy micro sheet forming machine

NASA Astrophysics Data System (ADS)

Razali, A. R.; Ann, C. T.; Shariff, H. M.; Kasim, N. I.; Musa, M. A.; Ahmad, A. F.

2017-10-01

It is expected that with the miniaturization of materials being processed, energy consumption is also being `miniaturized' proportionally. The focus of this study was to design a low energy micro-sheet-forming machine for thin sheet metal application and fabricate a low direct current powered micro-sheet-forming machine. A prototype of low energy system for a micro-sheet-forming machine which includes mechanical and electronic elements was developed. The machine was tested for its performance in terms of natural frequency, punching forces, punching speed and capability, energy consumption (single punch and frequency-time based). Based on the experiments, the machine can do 600 stroke per minute and the process is unaffected by the machine's natural frequency. It was also found that sub-Joule of power was required for a single stroke of punching/blanking process. Up to 100micron thick carbon steel shim was successfully tested and punched. It concludes that low power forming machine is feasible to be developed and be used to replace high powered machineries to form micro-products/parts.

Quantum Inference on Bayesian Networks

NASA Astrophysics Data System (ADS)

Yoder, Theodore; Low, Guang Hao; Chuang, Isaac

2014-03-01

Because quantum physics is naturally probabilistic, it seems reasonable to expect physical systems to describe probabilities and their evolution in a natural fashion. Here, we use quantum computation to speedup sampling from a graphical probability model, the Bayesian network. A specialization of this sampling problem is approximate Bayesian inference, where the distribution on query variables is sampled given the values e of evidence variables. Inference is a key part of modern machine learning and artificial intelligence tasks, but is known to be NP-hard. Classically, a single unbiased sample is obtained from a Bayesian network on n variables with at most m parents per node in time (nmP(e) - 1 / 2) , depending critically on P(e) , the probability the evidence might occur in the first place. However, by implementing a quantum version of rejection sampling, we obtain a square-root speedup, taking (n2m P(e) -1/2) time per sample. The speedup is the result of amplitude amplification, which is proving to be broadly applicable in sampling and machine learning tasks. In particular, we provide an explicit and efficient circuit construction that implements the algorithm without the need for oracle access.

ERDDAP: Reducing Data Friction with an Open Source Data Platform

NASA Astrophysics Data System (ADS)

O'Brien, K.

2017-12-01

Data friction is not just an issue facing interdisciplinary research. Often times, even within disciplines, significant data friction can exist. Issues of differing formats, limited metadata and non-existent machine-to-machine data access are all issues that exist within disciplines and make it that much harder for successful interdisciplinary cooperation. Therefore, reducing data friction within disciplines is crucial first step in providing better overall collaboration. ERDDAP, an open source data platform developed at NOAA's Southwest Fisheries Center, is well poised to improve data useability and understanding and reduce data friction, both in single and multi-disciplinary research. By virtue of its ability to integrate data of varying formats and provide RESTful-based user access to data and metadata, use of ERDDAP has grown substantially throughout the ocean data community. ERDDAP also supports standards such as the DAP data protocol, the Climate and Forecast (CF) metadata conventions and the Bagit document standard for data archival. In this presentation, we will discuss the advantages of using ERDDAP as a data platform. We will also show specific use cases where utilizing ERDDAP has reduced friction within a single discipline (physical oceanography) and improved interdisciplinary collaboration as well.

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.

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.

The East, the West and the universal machine.

PubMed

Marchal, Bruno

2017-12-01

After reviewing the basic of theology of Universal Numbers/Machines, as detailed in Marchal (2007), I illustrate how that body of thought might be used to shed some light upon the apparent dichotomy in Eastern/Western spirituality. This paper relies entirely on my previous interdisciplinary work in mathematical logic, computer science and machine's theology, where "theology" is used here in the sense of Plato: it is the truth, or the "truth-theory" (in the sense of logicians) about a machine that the machine can either deduce from some of its primitive beliefs, or can be intuited in some sense that eventually is made clear through the modal logic of machine self-reference. Such a theology appears to be testable, because it has been shown that physics has to be necessarily retrieved from it when we assume the mechanist hypothesis in the cognitive sciences, and this in a unique precise (introspective) way, so that we only need to compare the physics of the introspective machine with the physics inferred from the human observation; and up to now, it is the only theory known to fit both the existence of personal "consciousness" (undoubtable yet unjustifiable truth) and quanta and quantum relationships (Marchal, 1998; Marchal, 2004; Marchal, 2013; Marchal, 2015). Copyright © 2017 Elsevier Ltd. All rights reserved.

Children's Beliefs about the Fantasy/Reality Status of Hypothesized Machines

ERIC Educational Resources Information Center

Cook, Claire; Sobel, David M.

2011-01-01

Four-year-olds, 6-year-olds, and adults were asked to make judgments about the reality status of four different types of machines: real machines that children and adults interact with on a daily basis, real machines that children and adults interact with rarely (if at all), and impossible machines that violated a real-world physical or biological…

Tool setting device

DOEpatents

Brown, Raymond J.

1977-01-01

The present invention relates to a tool setting device for use with numerically controlled machine tools, such as lathes and milling machines. A reference position of the machine tool relative to the workpiece along both the X and Y axes is utilized by the control circuit for driving the tool through its program. This reference position is determined for both axes by displacing a single linear variable displacement transducer (LVDT) with the machine tool through a T-shaped pivotal bar. The use of the T-shaped bar allows the cutting tool to be moved sequentially in the X or Y direction for indicating the actual position of the machine tool relative to the predetermined desired position in the numerical control circuit by using a single LVDT.

Detecting Abnormal Word Utterances in Children With Autism Spectrum Disorders: Machine-Learning-Based Voice Analysis Versus Speech Therapists.

PubMed

Nakai, Yasushi; Takiguchi, Tetsuya; Matsui, Gakuyo; Yamaoka, Noriko; Takada, Satoshi

2017-10-01

Abnormal prosody is often evident in the voice intonations of individuals with autism spectrum disorders. We compared a machine-learning-based voice analysis with human hearing judgments made by 10 speech therapists for classifying children with autism spectrum disorders ( n = 30) and typical development ( n = 51). Using stimuli limited to single-word utterances, machine-learning-based voice analysis was superior to speech therapist judgments. There was a significantly higher true-positive than false-negative rate for machine-learning-based voice analysis but not for speech therapists. Results are discussed in terms of some artificiality of clinician judgments based on single-word utterances, and the objectivity machine-learning-based voice analysis adds to judging abnormal prosody.

Real-time single-molecule imaging of quantum interference.

PubMed

Juffmann, Thomas; Milic, Adriana; Müllneritsch, Michael; Asenbaum, Peter; Tsukernik, Alexander; Tüxen, Jens; Mayor, Marcel; Cheshnovsky, Ori; Arndt, Markus

2012-03-25

The observation of interference patterns in double-slit experiments with massive particles is generally regarded as the ultimate demonstration of the quantum nature of these objects. Such matter-wave interference has been observed for electrons, neutrons, atoms and molecules and, in contrast to classical physics, quantum interference can be observed when single particles arrive at the detector one by one. The build-up of such patterns in experiments with electrons has been described as the "most beautiful experiment in physics". Here, we show how a combination of nanofabrication and nano-imaging allows us to record the full two-dimensional build-up of quantum interference patterns in real time for phthalocyanine molecules and for derivatives of phthalocyanine molecules, which have masses of 514 AMU and 1,298 AMU respectively. A laser-controlled micro-evaporation source was used to produce a beam of molecules with the required intensity and coherence, and the gratings were machined in 10-nm-thick silicon nitride membranes to reduce the effect of van der Waals forces. Wide-field fluorescence microscopy detected the position of each molecule with an accuracy of 10 nm and revealed the build-up of a deterministic ensemble interference pattern from single molecules that arrived stochastically at the detector. In addition to providing this particularly clear demonstration of wave-particle duality, our approach could also be used to study larger molecules and explore the boundary between quantum and classical physics.

Real-time single-molecule imaging of quantum interference

NASA Astrophysics Data System (ADS)

Juffmann, Thomas; Milic, Adriana; Müllneritsch, Michael; Asenbaum, Peter; Tsukernik, Alexander; Tüxen, Jens; Mayor, Marcel; Cheshnovsky, Ori; Arndt, Markus

2012-05-01

The observation of interference patterns in double-slit experiments with massive particles is generally regarded as the ultimate demonstration of the quantum nature of these objects. Such matter-wave interference has been observed for electrons, neutrons, atoms and molecules and, in contrast to classical physics, quantum interference can be observed when single particles arrive at the detector one by one. The build-up of such patterns in experiments with electrons has been described as the ``most beautiful experiment in physics''. Here, we show how a combination of nanofabrication and nano-imaging allows us to record the full two-dimensional build-up of quantum interference patterns in real time for phthalocyanine molecules and for derivatives of phthalocyanine molecules, which have masses of 514 AMU and 1,298 AMU respectively. A laser-controlled micro-evaporation source was used to produce a beam of molecules with the required intensity and coherence, and the gratings were machined in 10-nm-thick silicon nitride membranes to reduce the effect of van der Waals forces. Wide-field fluorescence microscopy detected the position of each molecule with an accuracy of 10 nm and revealed the build-up of a deterministic ensemble interference pattern from single molecules that arrived stochastically at the detector. In addition to providing this particularly clear demonstration of wave-particle duality, our approach could also be used to study larger molecules and explore the boundary between quantum and classical physics.

48 CFR 252.211-7003 - Item unique identification and valuation.

Code of Federal Regulations, 2014 CFR

2014-10-01

... reader or interrogator, used to retrieve data encoded on machine-readable media. Concatenated unique item... identifier. Item means a single hardware article or a single unit formed by a grouping of subassemblies... manufactured under identical conditions. Machine-readable means an automatic identification technology media...

Learning Simple Machines through Cross-Age Collaborations

ERIC Educational Resources Information Center

Lancor, Rachael; Schiebel, Amy

2008-01-01

In this project, introductory college physics students (noneducation majors) were asked to teach simple machines to a class of second graders. This nontraditional activity proved to be a successful way to encourage college students to think critically about physics and how it applied to their everyday lives. The noneducation majors benefited by…

Support vector machines classifiers of physical activities in preschoolers

USDA-ARS?s Scientific Manuscript database

The goal of this study is to develop, test, and compare multinomial logistic regression (MLR) and support vector machines (SVM) in classifying preschool-aged children physical activity data acquired from an accelerometer. In this study, 69 children aged 3-5 years old were asked to participate in a s...

Research in Chinese-English Machine Translation. Final Report.

ERIC Educational Resources Information Center

Wang, William S-Y.; And Others

This report documents results of a two-year effort toward the study and investigation of the design of a prototype system for Chinese-English machine translation in the general area of physics. Previous work in Chinese-English machine translation is reviewed. Grammatical considerations in machine translation are discussed and detailed aspects of…

Comparisons between designs for single-sided linear electric motors: Homopolar synchronous and induction

NASA Astrophysics Data System (ADS)

Nondahl, T. A.; Richter, E.

1980-09-01

A design study of two types of single sided (with a passive rail) linear electric machine designs, namely homopolar linear synchronous machines (LSM's) and linear induction machines (LIM's), is described. It is assumed the machines provide tractive effort for several types of light rail vehicles and locomotives. These vehicles are wheel supported and require tractive powers ranging from 200 kW to 3735 kW and top speeds ranging from 112 km/hr to 400 km/hr. All designs are made according to specified magnetic and thermal criteria. The LSM advantages are a higher power factor, much greater restoring forces for track misalignments, and less track heating. The LIM advantages are no need to synchronize the excitation frequency precisely to vehicle speed, simpler machine construction, and a more easily anchored track structure. The relative weights of the two machine types vary with excitation frequency and speed; low frequencies and low speeds favor the LSM.

Simulation model of a single-stage lithium bromide-water absorption cooling unit

NASA Technical Reports Server (NTRS)

Miao, D.

1978-01-01

A computer model of a LiBr-H2O single-stage absorption machine was developed. The model, utilizing a given set of design data such as water-flow rates and inlet or outlet temperatures of these flow rates but without knowing the interior characteristics of the machine (heat transfer rates and surface areas), can be used to predict or simulate off-design performance. Results from 130 off-design cases for a given commercial machine agree with the published data within 2 percent.

Characterizing Slow Slip Applying Machine Learning

NASA Astrophysics Data System (ADS)

Hulbert, C.; Rouet-Leduc, B.; Bolton, D. C.; Ren, C. X.; Marone, C.; Johnson, P. A.

2017-12-01

Over the last two decades it has become apparent from strain and GPS measurements, that slow slip on earthquake faults is a widespread phenomenon. Slow slip is also inferred from small amplitude seismic signals known as tremor and low frequency earthquakes (LFE's) and has been reproduced in laboratory studies, providing useful physical insight into the frictional properties associated with the behavior. From such laboratory studies we ask whether we can obtain quantitative information regarding the physics of friction from only the recorded continuous acoustical data originating from the fault zone. We show that by applying machine learning to the acoustical signal, we can infer upcoming slow slip failure initiation as well as the slip termination, and that we can also infer the magnitudes by a second machine learning procedure based on predicted inter-event times. We speculate that by applying this or other machine learning approaches to continuous seismic data, new information regarding the physics of faulting could be obtained.

Dropout Prediction in E-Learning Courses through the Combination of Machine Learning Techniques

ERIC Educational Resources Information Center

Lykourentzou, Ioanna; Giannoukos, Ioannis; Nikolopoulos, Vassilis; Mpardis, George; Loumos, Vassili

2009-01-01

In this paper, a dropout prediction method for e-learning courses, based on three popular machine learning techniques and detailed student data, is proposed. The machine learning techniques used are feed-forward neural networks, support vector machines and probabilistic ensemble simplified fuzzy ARTMAP. Since a single technique may fail to…

Machined electrostatic sector for mass spectrometer

NASA Technical Reports Server (NTRS)

Sinha, Mahadeva P. (Inventor)

2001-01-01

An electrostatic sector device for a mass spectrometer is formed from a single piece of machinable ceramic. The machined ceramic is coated with a nickel coating, and a notch is etched in the nickel coating to form two separated portions. The sector can be covered by a cover formed from a separate piece of machined ceramic.

The Single Needle Lockstitch Machine. [Constructing Darts.] Module 3.

ERIC Educational Resources Information Center

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

This module on constructing darts, one in a series on the single needle lockstitch sewing machine for student self-study, contains two sections. Each section includes the following parts: an introduction, directions, an objective, learning activities, student information, student self-check, check-out activities, and an instructor's final…

The Single Needle Lockstitch Machine. [Setting Zippers.] Module 8.

ERIC Educational Resources Information Center

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

This module on setting zippers, one in a series on the single needle lockstitch sewing machine for student self-study, contains five sections. Each section includes the following parts: an introduction, directions, an objective, learning activities, student information, student self-check, check-out activities, and an instructor's final checklist.…

Ringo: Interactive Graph Analytics on Big-Memory Machines

PubMed Central

Perez, Yonathan; Sosič, Rok; Banerjee, Arijit; Puttagunta, Rohan; Raison, Martin; Shah, Pararth; Leskovec, Jure

2016-01-01

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads. PMID:27081215

Characteristics and Machining Performance of TiN and TiAlN Coatings on a Milling Cutter

NASA Astrophysics Data System (ADS)

Sarwar, Mohammed; Haider, Julfikar

2011-01-01

Titanium Nitride (TiN) coating deposited by Physical Vapour Deposition (PVD) or Chemical Vapour Deposition (CVD) techniques on cutting tools (single point or multipoint) has contributed towards the improvement of tool life, productivity and product quality [1]. Addition of Al in TiN coating (e.g., TiAlN or AlTiN) has further improved the coating properties required for machining applications [2, 3]. This work presents a comparative investigation on TiN and TiAlN coatings deposited on to a Powder Metallurgy High Speed Steel (PM HSS) milling cutter used for machining bimetal (M42+D6A) steel strips. PVD (Arc evaporation) technique was used to deposit the coatings after carefully preparing the cutting edges of the milling cutter. Microstructure, chemical composition, hardness and adhesion of the coatings have been characterised using different techniques. The incorporation of Al into TiN coating results in an improvement in hardness, wear resistance and cutting performance. Examination of the worn flank in the coated cutting edges revealed that abrasive and adhesive wear are the predominant failure mechanisms. Tool designers, coating suppliers and manufacturing engineers could benefit from the information provided.

Ringo: Interactive Graph Analytics on Big-Memory Machines.

PubMed

Perez, Yonathan; Sosič, Rok; Banerjee, Arijit; Puttagunta, Rohan; Raison, Martin; Shah, Pararth; Leskovec, Jure

2015-01-01

We present Ringo, a system for analysis of large graphs. Graphs provide a way to represent and analyze systems of interacting objects (people, proteins, webpages) with edges between the objects denoting interactions (friendships, physical interactions, links). Mining graphs provides valuable insights about individual objects as well as the relationships among them. In building Ringo, we take advantage of the fact that machines with large memory and many cores are widely available and also relatively affordable. This allows us to build an easy-to-use interactive high-performance graph analytics system. Graphs also need to be built from input data, which often resides in the form of relational tables. Thus, Ringo provides rich functionality for manipulating raw input data tables into various kinds of graphs. Furthermore, Ringo also provides over 200 graph analytics functions that can then be applied to constructed graphs. We show that a single big-memory machine provides a very attractive platform for performing analytics on all but the largest graphs as it offers excellent performance and ease of use as compared to alternative approaches. With Ringo, we also demonstrate how to integrate graph analytics with an iterative process of trial-and-error data exploration and rapid experimentation, common in data mining workloads.

The Four Lives of a Nuclear Accelerator

NASA Astrophysics Data System (ADS)

Wiescher, Michael

2017-06-01

Electrostatic accelerators have emerged as a major tool in research and industry in the second half of the twentieth century. In particular in low energy nuclear physics they have been essential for addressing a number of critical research questions from nuclear structure to nuclear astrophysics. This article describes this development on the example of a single machine which has been used for nearly sixty years at the forefront of scientific research in nuclear physics. The article summarizes the concept of electrostatic accelerators and outlines how this accelerator developed from a bare support function to an independent research tool that has been utilized in different research environments and institutions and now looks forward to a new life as part of the experiment CASPAR at the 4,850" level of the Sanford Underground Research Facility.

Reverse engineering physical models employing a sensor integration between 3D stereo detection and contact digitization

NASA Astrophysics Data System (ADS)

Chen, Liang-Chia; Lin, Grier C. I.

1997-12-01

A vision-drive automatic digitization process for free-form surface reconstruction has been developed, with a coordinate measurement machine (CMM) equipped with a touch-triggered probe and a CCD camera, in reverse engineering physical models. The process integrates 3D stereo detection, data filtering, Delaunay triangulation, adaptive surface digitization into a single process of surface reconstruction. By using this innovative approach, surface reconstruction can be implemented automatically and accurately. Least-squares B- spline surface models with the controlled accuracy of digitization can be generated for further application in product design and manufacturing processes. One industrial application indicates that this approach is feasible, and the processing time required in reverse engineering process can be significantly reduced up to more than 85%.

On the role of exchange of power and information signals in control and stability of the human-robot interaction

NASA Technical Reports Server (NTRS)

Kazerooni, H.

1991-01-01

A human's ability to perform physical tasks is limited, not only by his intelligence, but by his physical strength. If, in an appropriate environment, a machine's mechanical power is closely integrated with a human arm's mechanical power under the control of the human intellect, the resulting system will be superior to a loosely integrated combination of a human and a fully automated robot. Therefore, we must develop a fundamental solution to the problem of 'extending' human mechanical power. The work presented here defines 'extenders' as a class of robot manipulators worn by humans to increase human mechanical strength, while the wearer's intellect remains the central control system for manipulating the extender. The human, in physical contact with the extender, exchanges power and information signals with the extender. The aim is to determine the fundamental building blocks of an intelligent controller, a controller which allows interaction between humans and a broad class of computer-controlled machines via simultaneous exchange of both power and information signals. The prevalent trend in automation has been to physically separate the human from the machine so the human must always send information signals via an intermediary device (e.g., joystick, pushbutton, light switch). Extenders, however are perfect examples of self-powered machines that are built and controlled for the optimal exchange of power and information signals with humans. The human wearing the extender is in physical contact with the machine, so power transfer is unavoidable and information signals from the human help to control the machine. Commands are transferred to the extender via the contact forces and the EMG signals between the wearer and the extender. The extender augments human motor ability without accepting any explicit commands: it accepts the EMG signals and the contact force between the person's arm and the extender, and the extender 'translates' them into a desired position. In this unique configuration, mechanical power transfer between the human and the extender occurs because the human is pushing against the extender. The extender transfers to the human's hand, in feedback fashion, a scaled-down version of the actual external load which the extender is manipulating. This natural feedback force on the human's hand allows him to 'feel' a modified version of the external forces on the extender. The information signals from the human (e.g., EMG signals) to the computer reflect human cognitive ability, and the power transfer between the human and the machine (e.g., physical interaction) reflects human physical ability. Thus the information transfer to the machine augments cognitive ability, and the power transfer augments motor ability. These two actions are coupled through the human cognitive/motor dynamic behavior. The goal is to derive the control rules for a class of computer-controlled machines that augment human physical and cognitive abilities in certain manipulative tasks.

77 FR 38777 - 36(b)(1) Arms Sales Notification

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-29

... Systems (SINCGARS), 200 M2 Chrysler Mount Machine Guns, 400 7.62MM M240 Machine Guns, 12,049,842... Exportable Single Channel Ground and Airborne Radio Systems (SINCGARS), 200 M2 Chrysler Mount Machine Guns, and 400 7.62MM M240 Machine Guns. The possible sale also includes 12,049,842 Ammunition Rounds...

Machined versus roughened immediately loaded and finally restored single implants inserted flapless: Preliminary 6-month data from a split- mouth randomised controlled trial.

PubMed

Cannizzaro, Gioacchino; Felice, Pietro; Loi, Ignazio; Viola, Paolo; Ferri, Vittorio; Leone, Michele; Lazzarini, Matteo; Trullenque-Eriksson, Anna; Esposito, Marco

To compare the outcome of immediately loaded single implants with a machined or a roughened surface. Fifty patients had two implant sites randomly allocated to receive flaplessplaced single Syra implants (Sweden & Martina), one with a machined and one with a roughened surface (sand-blasted with zirconia powder and acid etched), according to a split-mouth design. To be loaded immediately, implants had to be inserted with a torque superior to 50 Ncm. Implants were restored with definitive crowns in direct occlusal contact within 48 h. Patients were followed for 6 months after loading. Outcome measures were prosthetic and implant failures and complications. Two machined implants and four roughened implants were not loaded immediately. Six months after loading no dropout occurred. One implant loaded late, which had a rough implant surface, failed 20 days after loading (P (McNemar test) = 0.625; difference in proportions = -0.04; 95% CI: -0.15 to 0.07). Three crowns had to be remade on machined implants and four on roughened implants (P (McNemar test) = 1.000; difference in proportions = -0.02; 95% CI: -0.12 to 0.08). Three machined and five roughened implants experienced complications (P (McNemar test) = 0.625; difference in proportions = -0.04; 95% CI: -0.15 to 0.07). There were no statistically significant differences between groups for crown and implant losses as well as complications. Up to 6 months after loading both machined and roughened flapless-placed and immediately loaded single implants provided good and similar results, however, longer follow-ups are needed to evaluate the long-term prognosis of implants with different surfaces.

A Wavelet Support Vector Machine Combination Model for Singapore Tourist Arrival to Malaysia

NASA Astrophysics Data System (ADS)

Rafidah, A.; Shabri, Ani; Nurulhuda, A.; Suhaila, Y.

2017-08-01

In this study, wavelet support vector machine model (WSVM) is proposed and applied for monthly data Singapore tourist time series prediction. The WSVM model is combination between wavelet analysis and support vector machine (SVM). In this study, we have two parts, first part we compare between the kernel function and second part we compare between the developed models with single model, SVM. The result showed that kernel function linear better than RBF while WSVM outperform with single model SVM to forecast monthly Singapore tourist arrival to Malaysia.

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.

Single product lot-sizing on unrelated parallel machines with non-decreasing processing times

NASA Astrophysics Data System (ADS)

Eremeev, A.; Kovalyov, M.; Kuznetsov, P.

2018-01-01

We consider a problem in which at least a given quantity of a single product has to be partitioned into lots, and lots have to be assigned to unrelated parallel machines for processing. In one version of the problem, the maximum machine completion time should be minimized, in another version of the problem, the sum of machine completion times is to be minimized. Machine-dependent lower and upper bounds on the lot size are given. The product is either assumed to be continuously divisible or discrete. The processing time of each machine is defined by an increasing function of the lot volume, given as an oracle. Setup times and costs are assumed to be negligibly small, and therefore, they are not considered. We derive optimal polynomial time algorithms for several special cases of the problem. An NP-hard case is shown to admit a fully polynomial time approximation scheme. An application of the problem in energy efficient processors scheduling is considered.

Currency crisis indication by using ensembles of support vector machine classifiers

NASA Astrophysics Data System (ADS)

Ramli, Nor Azuana; Ismail, Mohd Tahir; Wooi, Hooy Chee

2014-07-01

There are many methods that had been experimented in the analysis of currency crisis. However, not all methods could provide accurate indications. This paper introduces an ensemble of classifiers by using Support Vector Machine that's never been applied in analyses involving currency crisis before with the aim of increasing the indication accuracy. The proposed ensemble classifiers' performances are measured using percentage of accuracy, root mean squared error (RMSE), area under the Receiver Operating Characteristics (ROC) curve and Type II error. The performances of an ensemble of Support Vector Machine classifiers are compared with the single Support Vector Machine classifier and both of classifiers are tested on the data set from 27 countries with 12 macroeconomic indicators for each country. From our analyses, the results show that the ensemble of Support Vector Machine classifiers outperforms single Support Vector Machine classifier on the problem involving indicating a currency crisis in terms of a range of standard measures for comparing the performance of classifiers.

Man-systems integration and the man-machine interface

NASA Technical Reports Server (NTRS)

Hale, Joseph P.

1990-01-01

Viewgraphs on man-systems integration and the man-machine interface are presented. Man-systems integration applies the systems' approach to the integration of the user and the machine to form an effective, symbiotic Man-Machine System (MMS). A MMS is a combination of one or more human beings and one or more physical components that are integrated through the common purpose of achieving some objective. The human operator interacts with the system through the Man-Machine Interface (MMI).

A new self-regulated self-excited single-phase induction generator using a squirrel cage three-phase induction machine

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

Fukami, Tadashi; Imamura, Michinori; Kaburaki, Yuichi

1995-12-31

A new single-phase capacitor self-excited induction generator with self-regulating feature is presented. The new generator consists of a squirrel cage three-phase induction machine and three capacitors connected in series and parallel with a single phase load. The voltage regulation of this generator is very small due to the effect of the three capacitors. Moreover, since a Y-connected stator winding is employed, the waveform of the output voltage becomes sinusoidal. In this paper the system configuration and the operating principle of the new generator are explained, and the basic characteristics are also investigated by means of a simple analysis and experimentsmore » with a laboratory machine.« less

Single instruction computer architecture and its application in image processing

NASA Astrophysics Data System (ADS)

Laplante, Phillip A.

1992-03-01

A single processing computer system using only half-adder circuits is described. In addition, it is shown that only a single hard-wired instruction is needed in the control unit to obtain a complete instruction set for this general purpose computer. Such a system has several advantages. First it is intrinsically a RISC machine--in fact the 'ultimate RISC' machine. Second, because only a single type of logic element is employed the entire computer system can be easily realized on a single, highly integrated chip. Finally, due to the homogeneous nature of the computer's logic elements, the computer has possible implementations as an optical or chemical machine. This in turn suggests possible paradigms for neural computing and artificial intelligence. After showing how we can implement a full-adder, min, max and other operations using the half-adder, we use an array of such full-adders to implement the dilation operation for two black and white images. Next we implement the erosion operation of two black and white images using a relative complement function and the properties of erosion and dilation. This approach was inspired by papers by van der Poel in which a single instruction is used to furnish a complete set of general purpose instructions and by Bohm- Jacopini where it is shown that any problem can be solved using a Turing machine with one entry and one exit.

Study of a variable mass Atwood's machine using a smartphone

NASA Astrophysics Data System (ADS)

Lopez, Dany; Caprile, Isidora; Corvacho, Fernando; Reyes, Orfa

2018-03-01

The Atwood machine was invented in 1784 by George Atwood and this system has been widely studied both theoretically and experimentally over the years. Nowadays, it is commonplace that many experimental physics courses include both Atwood's machine and variable mass to introduce more complex concepts in physics. To study the dynamics of the masses that compose the variable Atwood's machine, laboratories typically use a smart pulley. Now, the first work that introduced a smartphone as data acquisition equipment to study the acceleration in the Atwood's machine was the one by M. Monteiro et al. Since then, there has been no further information available on the usage of smartphones in variable mass systems. This prompted us to do a study of this kind of system by means of data obtained with a smartphone and to show the practicality of using smartphones in complex experimental situations.

CEPC-SPPC accelerator status towards CDR

NASA Astrophysics Data System (ADS)

Gao, J.

2017-12-01

In this paper we will give an introduction to the Circular Electron Positron Collider (CEPC). The scientific background, physics goal, the collider design requirements and the conceptual design principle of the CEPC are described. On the CEPC accelerator, the optimization of parameter designs for the CEPC with different energies, machine lengths, single ring and crab-waist collision partial double ring, advanced partial double ring and fully partial double ring options, etc. have been discussed systematically, and compared. The CEPC accelerator baseline and alternative designs have been proposed based on the luminosity potential in relation with the design goals. The CEPC sub-systems, such as the collider main ring, booster, electron positron injector, etc. have also been introduced. The detector and the MAchine-Detector Interface (MDI) design have been briefly mentioned. Finally, the optimization design of the Super Proton-Proton Collider (SppC), its energy and luminosity potentials, in the same tunnel of the CEPC are also discussed. The CEPC-SppC Progress Report (2015-2016) has been published.

Simple Machines. Physical Science in Action[TM]. Schlessinger Science Library. [Videotape].

ERIC Educational Resources Information Center

2000

In today's world, kids are aware that there are machines all around them. What they may not realize is that the function of all machines is to make work easier in some way. Simple Machines uses engaging visuals and colorful graphics to explain the concept of work and how humans use certain basic tools to help get work done. Students will learn…

All about Simple Machines. Physical Science for Children[TM]. Schlessinger Science Library. [Videotape].

ERIC Educational Resources Information Center

2000

All kids know the word "work." But they probably don't understand that work happens whenever a force is used to move something--whether it's lifting a heavy object or playing on a see-saw. All About Simple Machines introduces kids to the concepts of forces, work and how machines are used to make work easier. Six simple machines are…

ARINC 818 express for high-speed avionics video and power over coax

NASA Astrophysics Data System (ADS)

Keller, Tim; Alexander, Jon

2012-06-01

CoaXPress is a new standard for high-speed video over coax cabling developed for the machine vision industry. CoaXPress includes both a physical layer and a video protocol. The physical layer has desirable features for aerospace and defense applications: it allows 3Gbps (up to 6Gbps) communication, includes 21Mbps return path allowing for bidirectional communication, and provides up to 13W of power, all over a single coax connection. ARINC 818, titled "Avionics Digital Video Bus" is a protocol standard developed specifically for high speed, mission critical aerospace video systems. ARINC 818 is being widely adopted for new military and commercial display and sensor applications. The ARINC 818 protocol combined with the CoaXPress physical layer provide desirable characteristics for many aerospace systems. This paper presents the results of a technology demonstration program to marry the physical layer from CoaXPress with the ARINC 818 protocol. ARINC 818 is a protocol, not a physical layer. Typically, ARINC 818 is implemented over fiber or copper for speeds of 1 to 2Gbps, but beyond 2Gbps, it has been implemented exclusively over fiber optic links. In many rugged applications, a copper interface is still desired, by implementing ARINC 818 over the CoaXPress physical layer, it provides a path to 3 and 6 Gbps copper interfaces for ARINC 818. Results of the successful technology demonstration dubbed ARINC 818 Express are presented showing 3Gbps communication while powering a remote module over a single coax cable. The paper concludes with suggested next steps for bring this technology to production readiness.

Power training using pneumatic machines vs. plate-loaded machines to improve muscle power in older adults.

PubMed

Balachandran, Anoop T; Gandia, Kristine; Jacobs, Kevin A; Streiner, David L; Eltoukhy, Moataz; Signorile, Joseph F

2017-11-01

Power training has been shown to be more effective than conventional resistance training for improving physical function in older adults; however, most trials have used pneumatic machines during training. Considering that the general public typically has access to plate-loaded machines, the effectiveness and safety of power training using plate-loaded machines compared to pneumatic machines is an important consideration. The purpose of this investigation was to compare the effects of high-velocity training using pneumatic machines (Pn) versus standard plate-loaded machines (PL). Independently-living older adults, 60years or older were randomized into two groups: pneumatic machine (Pn, n=19) and plate-loaded machine (PL, n=17). After 12weeks of high-velocity training twice per week, groups were analyzed using an intention-to-treat approach. Primary outcomes were lower body power measured using a linear transducer and upper body power using medicine ball throw. Secondary outcomes included lower and upper body muscle muscle strength, the Physical Performance Battery (PPB), gallon jug test, the timed up-and-go test, and self-reported function using the Patient Reported Outcomes Measurement Information System (PROMIS) and an online video questionnaire. Outcome assessors were blinded to group membership. Lower body power significantly improved in both groups (Pn: 19%, PL: 31%), with no significant difference between the groups (Cohen's d=0.4, 95% CI (-1.1, 0.3)). Upper body power significantly improved only in the PL group, but showed no significant difference between the groups (Pn: 3%, PL: 6%). For balance, there was a significant difference between the groups favoring the Pn group (d=0.7, 95% CI (0.1, 1.4)); however, there were no statistically significant differences between groups for PPB, gallon jug transfer, muscle muscle strength, timed up-and-go or self-reported function. No serious adverse events were reported in either of the groups. Pneumatic and plate-loaded machines were effective in improving lower body power and physical function in older adults. The results suggest that power training can be safely and effectively performed by older adults using either pneumatic or plate-loaded machines. Copyright © 2017 Elsevier Inc. All rights reserved.

Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning

PubMed Central

Garcia-Gancedo, Luis; Van der Drift, Anniek; Powell, Adam; Hamy, Valentin; Keller, Thomas; Yang, Guang-Zhong

2017-01-01

In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits. PMID:28906437

Developing Fine-Grained Actigraphies for Rheumatoid Arthritis Patients from a Single Accelerometer Using Machine Learning.

PubMed

Andreu-Perez, Javier; Garcia-Gancedo, Luis; McKinnell, Jonathan; Van der Drift, Anniek; Powell, Adam; Hamy, Valentin; Keller, Thomas; Yang, Guang-Zhong

2017-09-14

In addition to routine clinical examination, unobtrusive and physical monitoring of Rheumatoid Arthritis (RA) patients provides an important source of information to enable understanding the impact of the disease on quality of life. Besides an increase in sedentary behaviour, pain in RA can negatively impact simple physical activities such as getting out of bed and standing up from a chair. The objective of this work is to develop a method that can generate fine-grained actigraphies to capture the impact of the disease on the daily activities of patients. A processing methodology is presented to automatically tag activity accelerometer data from a cohort of moderate-to-severe RA patients. A study of procesing methods based on machine learning and deep learning is provided. Thirty subjects, 10 RA patients and 20 healthy control subjects, were recruited in the study. A single tri-axial accelerometer was attached to the position of the fifth lumbar vertebra (L5) of each subject with a tag prediction granularity of 3 s. The proposed method is capable of handling unbalanced datasets from tagged data while accounting for long-duration activities such as sitting and lying, as well as short transitions such as sit-to-stand or lying-to-sit. The methodology also includes a novel mechanism for automatically applying a threshold to predictions by their confidence levels, in addition to a logical filter to correct for infeasible sequences of activities. Performance tests showed that the method was able to achieve around 95% accuracy and 81% F-score. The produced actigraphies can be helpful to generate objective RA disease-specific markers of patient mobility in-between clinical site visits.

Single molecule thermodynamics in biological motors.

PubMed

Taniguchi, Yuichi; Karagiannis, Peter; Nishiyama, Masayoshi; Ishii, Yoshiharu; Yanagida, Toshio

2007-04-01

Biological molecular machines use thermal activation energy to carry out various functions. The process of thermal activation has the stochastic nature of output events that can be described according to the laws of thermodynamics. Recently developed single molecule detection techniques have allowed each distinct enzymatic event of single biological machines to be characterized providing clues to the underlying thermodynamics. In this study, the thermodynamic properties in the stepping movement of a biological molecular motor have been examined. A single molecule detection technique was used to measure the stepping movements at various loads and temperatures and a range of thermodynamic parameters associated with the production of each forward and backward step including free energy, enthalpy, entropy and characteristic distance were obtained. The results show that an asymmetry in entropy is a primary factor that controls the direction in which the motor will step. The investigation on single molecule thermodynamics has the potential to reveal dynamic properties underlying the mechanisms of how biological molecular machines work.

Development of a rapid and sensitive one-step reverse transcription-nested polymerase chain reaction in a single tube using the droplet-polymerase chain reaction machine.

PubMed

Yamaguchi, Akemi; Matsuda, Kazuyuki; Sueki, Akane; Taira, Chiaki; Uehara, Masayuki; Saito, Yasunori; Honda, Takayuki

2015-08-25

Reverse transcription (RT)-nested polymerase chain reaction (PCR) is a time-consuming procedure because it has several handling steps and is associated with the risk of cross-contamination during each step. Therefore, a rapid and sensitive one-step RT-nested PCR was developed that could be performed in a single tube using a droplet-PCR machine. The K562 BCR-ABL mRNA-positive cell line as well as bone marrow aspirates from 5 patients with chronic myelogenous leukemia (CML) and 5 controls without CML were used. We evaluated one-step RT-nested PCR using the droplet-PCR machine. One-step RT-nested PCR performed in a single tube using the droplet-PCR machine enabled the detection of BCR-ABL mRNA within 40min, which was 10(3)-fold superior to conventional RT nested PCR using three steps in separate tubes. The sensitivity of the one-step RT-nested PCR was 0.001%, with sample reactivity comparable to that of the conventional assay. One-step RT-nested PCR was developed using the droplet-PCR machine, which enabled all reactions to be performed in a single tube accurately and rapidly and with high sensitivity. This one-step RT-nested PCR may be applicable to a wide spectrum of genetic tests in clinical laboratories. Copyright © 2015 Elsevier B.V. All rights reserved.

General Theory of the Double Fed Synchronous Machine. Ph.D. Thesis - Swiss Technological Univ., 1950

NASA Technical Reports Server (NTRS)

El-Magrabi, M. G.

1982-01-01

Motor and generator operation of a double-fed synchronous machine were studied and physically and mathematically treated. Experiments with different connections, voltages, etc. were carried out. It was concluded that a certain degree of asymmetry is necessary for the best utilization of the machine.

Optical alignment of electrodes on electrical discharge machines

NASA Technical Reports Server (NTRS)

Boissevain, A. G.; Nelson, B. W.

1972-01-01

Shadowgraph system projects magnified image on screen so that alignment of small electrodes mounted on electrical discharge machines can be corrected and verified. Technique may be adapted to other machine tool equipment where physical contact cannot be made during inspection and access to tool limits conventional runout checking procedures.

14 CFR 382.3 - What do the terms in this rule mean?

Code of Federal Regulations, 2014 CFR

2014-01-01

... devices and medications. Automated airport kiosk means a self-service transaction machine that a carrier... machine means a continuous positive airway pressure machine. Department or DOT means the United States..., emotional or mental illness, and specific learning disabilities. The term physical or mental impairment...

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

Liphardt, Jan

In April 1953, Watson and Crick largely defined the program of 20th century biology: obtaining the blueprint of life encoded in the DNA. Fifty years later, in 2003, the sequencing of the human genome was completed. Like any major scientific breakthrough, the sequencing of the human genome raised many more questions than it answered. I'll brief you on some of the big open problems in cell and developmental biology, and I'll explain why approaches, tools, and ideas from the physical sciences are currently reshaping biological research. Super-resolution light microscopies are revealing the intricate spatial organization of cells, single-molecule methods showmore » how molecular machines function, and new probes are clarifying the role of mechanical forces in cell and tissue function. At the same time, Physics stands to gain beautiful new problems in soft condensed matter, quantum mechanics, and non-equilibrium thermodynamics.« less

Physics 30 Program Machine-Scorable Open-Ended Questions: Unit 2: Electric and Magnetic Forces. Diploma Examinations Program.

ERIC Educational Resources Information Center

Alberta Dept. of Education, Edmonton.

This document outlines the use of machine-scorable open-ended questions for the evaluation of Physics 30 in Alberta. Contents include: (1) an introduction to the questions; (2) sample instruction sheet; (3) fifteen sample items; (4) item information including the key, difficulty, and source of each item; (5) solutions to items having multiple…

PHYSICAL MODEL FOR RECOGNITION TUNNELING

PubMed Central

Krstić, Predrag; Ashcroft, Brian; Lindsay, Stuart

2015-01-01

Recognition tunneling (RT) identifies target molecules trapped between tunneling electrodes functionalized with recognition molecules that serve as specific chemical linkages between the metal electrodes and the trapped target molecule. Possible applications include single molecule DNA and protein sequencing. This paper addresses several fundamental aspects of RT by multiscale theory, applying both all-atom and coarse-grained DNA models: (1) We show that the magnitude of the observed currents are consistent with the results of non-equilibrium Green's function calculations carried out on a solvated all-atom model. (2) Brownian fluctuations in hydrogen bond-lengths lead to current spikes that are similar to what is observed experimentally. (3) The frequency characteristics of these fluctuations can be used to identify the trapped molecules with a machine-learning algorithm, giving a theoretical underpinning to this new method of identifying single molecule signals. PMID:25650375

Relative optical navigation around small bodies via Extreme Learning Machine

NASA Astrophysics Data System (ADS)

Law, Andrew M.

To perform close proximity operations under a low-gravity environment, relative and absolute positions are vital information to the maneuver. Hence navigation is inseparably integrated in space travel. Extreme Learning Machine (ELM) is presented as an optical navigation method around small celestial bodies. Optical Navigation uses visual observation instruments such as a camera to acquire useful data and determine spacecraft position. The required input data for operation is merely a single image strip and a nadir image. ELM is a machine learning Single Layer feed-Forward Network (SLFN), a type of neural network (NN). The algorithm is developed on the predicate that input weights and biases can be randomly assigned and does not require back-propagation. The learned model is the output layer weights which are used to calculate a prediction. Together, Extreme Learning Machine Optical Navigation (ELM OpNav) utilizes optical images and ELM algorithm to train the machine to navigate around a target body. In this thesis the asteroid, Vesta, is the designated celestial body. The trained ELMs estimate the position of the spacecraft during operation with a single data set. The results show the approach is promising and potentially suitable for on-board navigation.

Machine learning for many-body physics: The case of the Anderson impurity model

DOE PAGES

Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole; ...

2014-10-31

We applied machine learning methods in order to find the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Furthermore, different methods of parametrizing the Green's function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. Our results indicate that a machine learning approach to dynamical mean-field theory may be feasible.

Machine learning for many-body physics: The case of the Anderson impurity model

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

Arsenault, Louis-François; Lopez-Bezanilla, Alejandro; von Lilienfeld, O. Anatole

We applied machine learning methods in order to find the Green's function of the Anderson impurity model, a basic model system of quantum many-body condensed-matter physics. Furthermore, different methods of parametrizing the Green's function are investigated; a representation in terms of Legendre polynomials is found to be superior due to its limited number of coefficients and its applicability to state of the art methods of solution. The dependence of the errors on the size of the training set is determined. Our results indicate that a machine learning approach to dynamical mean-field theory may be feasible.

The universal numbers. From Biology to Physics.

PubMed

Marchal, Bruno

2015-12-01

I will explain how the mathematicians have discovered the universal numbers, or abstract computer, and I will explain some abstract biology, mainly self-reproduction and embryogenesis. Then I will explain how and why, and in which sense, some of those numbers can dream and why their dreams can glue together and must, when we assume computationalism in cognitive science, generate a phenomenological physics, as part of a larger phenomenological theology (in the sense of the greek theologians). The title should have been "From Biology to Physics, through the Phenomenological Theology of the Universal Numbers", if that was not too long for a title. The theology will consist mainly, like in some (neo)platonist greek-indian-chinese tradition, in the truth about numbers' relative relations, with each others, and with themselves. The main difference between Aristotle and Plato is that Aristotle (especially in its common and modern christian interpretation) makes reality WYSIWYG (What you see is what you get: reality is what we observe, measure, i.e. the natural material physical science) where for Plato and the (rational) mystics, what we see might be only the shadow or the border of something else, which might be non physical (mathematical, arithmetical, theological, …). Since Gödel, we know that Truth, even just the Arithmetical Truth, is vastly bigger than what the machine can rationally justify. Yet, with Church's thesis, and the mechanizability of the diagonalizations involved, machines can apprehend this and can justify their limitations, and get some sense of what might be true beyond what they can prove or justify rationally. Indeed, the incompleteness phenomenon introduces a gap between what is provable by some machine and what is true about that machine, and, as Gödel saw already in 1931, the existence of that gap is accessible to the machine itself, once it is has enough provability abilities. Incompleteness separates truth and provable, and machines can justify this in some way. More importantly incompleteness entails the distinction between many intensional variants of provability. For example, the absence of reflexion (beweisbar(⌜A⌝) → A with beweisbar being Gödel's provability predicate) makes it impossible for the machine's provability to obey the axioms usually taken for a theory of knowledge. The most important consequence of this in the machine's possible phenomenology is that it provides sense, indeed arithmetical sense, to intensional variants of provability, like the logics of provability-and-truth, which at the propositional level can be mirrored by the logic of provable-and-true statements (beweisbar(⌜A⌝) ∧ A). It is incompleteness which makes this logic different from the logic of provability. Other variants, like provable-and-consistent, or provable-and-consistent-and-true, appears in the same way, and inherits the incompleteness splitting, unlike beweisbar(⌜A⌝) ∧ A. I will recall thought experience which motivates the use of those intensional variants to associate a knower and an observer in some canonical way to the machines or the numbers. We will in this way get an abstract and phenomenological theology of a machine M through the true logics of their true self-referential abilities (even if not provable, or knowable, by the machine itself), in those different intensional senses. Cognitive science and theoretical physics motivate the study of those logics with the arithmetical interpretation of the atomic sentences restricted to the "verifiable" (Σ1) sentences, which is the way to study the theology of the computationalist machine. This provides a logic of the observable, as expected by the Universal Dovetailer Argument, which will be recalled briefly, and which can lead to a comparison of the machine's logic of physics with the empirical logic of the physicists (like quantum logic). This leads also to a series of open problems. Copyright © 2015 Elsevier Ltd. All rights reserved.

Tool simplifies machining of pipe ends for precision welding

NASA Technical Reports Server (NTRS)

Matus, S. T.

1969-01-01

Single tool prepares a pipe end for precision welding by simultaneously performing internal machining, end facing, and bevel cutting to specification standards. The machining operation requires only one milling adjustment, can be performed quickly, and produces the high quality pipe-end configurations required to ensure precision-welded joints.

Abstract quantum computing machines and quantum computational logics

NASA Astrophysics Data System (ADS)

Chiara, Maria Luisa Dalla; Giuntini, Roberto; Sergioli, Giuseppe; Leporini, Roberto

2016-06-01

Classical and quantum parallelism are deeply different, although it is sometimes claimed that quantum Turing machines are nothing but special examples of classical probabilistic machines. We introduce the concepts of deterministic state machine, classical probabilistic state machine and quantum state machine. On this basis, we discuss the question: To what extent can quantum state machines be simulated by classical probabilistic state machines? Each state machine is devoted to a single task determined by its program. Real computers, however, behave differently, being able to solve different kinds of problems. This capacity can be modeled, in the quantum case, by the mathematical notion of abstract quantum computing machine, whose different programs determine different quantum state machines. The computations of abstract quantum computing machines can be linguistically described by the formulas of a particular form of quantum logic, termed quantum computational logic.

Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data.

PubMed

Janik, M; Bossew, P; Kurihara, O

2018-07-15

Machine learning is a class of statistical techniques which has proven to be a powerful tool for modelling the behaviour of complex systems, in which response quantities depend on assumed controls or predictors in a complicated way. In this paper, as our first purpose, we propose the application of machine learning to reconstruct incomplete or irregularly sampled data of time series indoor radon ( 222 Rn). The physical assumption underlying the modelling is that Rn concentration in the air is controlled by environmental variables such as air temperature and pressure. The algorithms "learn" from complete sections of multivariate series, derive a dependence model and apply it to sections where the controls are available, but not the response (Rn), and in this way complete the Rn series. Three machine learning techniques are applied in this study, namely random forest, its extension called the gradient boosting machine and deep learning. For a comparison, we apply the classical multiple regression in a generalized linear model version. Performance of the models is evaluated through different metrics. The performance of the gradient boosting machine is found to be superior to that of the other techniques. By applying learning machines, we show, as our second purpose, that missing data or periods of Rn series data can be reconstructed and resampled on a regular grid reasonably, if data of appropriate physical controls are available. The techniques also identify to which degree the assumed controls contribute to imputing missing Rn values. Our third purpose, though no less important from the viewpoint of physics, is identifying to which degree physical, in this case environmental variables, are relevant as Rn predictors, or in other words, which predictors explain most of the temporal variability of Rn. We show that variables which contribute most to the Rn series reconstruction, are temperature, relative humidity and day of the year. The first two are physical predictors, while "day of the year" is a statistical proxy or surrogate for missing or unknown predictors. Copyright © 2018 Elsevier B.V. All rights reserved.

Design and Development of an Engineering Prototype Compact X-Ray Scanner (FMS 5000)

DTIC Science & Technology

1989-03-31

machined by "wire-EDM" (electro discharge machining ). Three different slice thicknesses can be selected from the scan menu. The set of slice thicknesses...circuit. This type of circuit is used whenever more than ten kilowatts of power are needed by a machine . For example, lathes and milling machines in a... machine shop usually use this type of input power. A three- phase circuit delivers power more efficiently than a single-phase circuit because three

Computational physics in RISC environments

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

Rhoades, C.E. Jr.

The new high performance Reduced Instruction Set Computers (RISC) promise near Cray-level performance at near personal-computer prices. This paper explores the performance, conversion and compatibility issues associated with developing, testing and using our traditional, large-scale simulation models in the RISC environments exemplified by the IBM RS6000 and MISP R3000 machines. The questions of operating systems (CTSS versus UNIX), compilers (Fortran, C, pointers) and data are addressed in detail. Overall, it is concluded that the RISC environments are practical for a very wide range of computational physic activities. Indeed, all but the very largest two- and three-dimensional codes will work quitemore » well, particularly in a single user environment. Easily projected hardware-performance increases will revolutionize the field of computational physics. The way we do research will change profoundly in the next few years. There is, however, nothing more difficult to plan, nor more dangerous to manage than the creation of this new world.« less

Computational physics in RISC environments. Revision 1

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

Rhoades, C.E. Jr.

The new high performance Reduced Instruction Set Computers (RISC) promise near Cray-level performance at near personal-computer prices. This paper explores the performance, conversion and compatibility issues associated with developing, testing and using our traditional, large-scale simulation models in the RISC environments exemplified by the IBM RS6000 and MISP R3000 machines. The questions of operating systems (CTSS versus UNIX), compilers (Fortran, C, pointers) and data are addressed in detail. Overall, it is concluded that the RISC environments are practical for a very wide range of computational physic activities. Indeed, all but the very largest two- and three-dimensional codes will work quitemore » well, particularly in a single user environment. Easily projected hardware-performance increases will revolutionize the field of computational physics. The way we do research will change profoundly in the next few years. There is, however, nothing more difficult to plan, nor more dangerous to manage than the creation of this new world.« less

Machining Specific Fourier Power Spectrum Profiles into Plastics for High Energy Density Physics Experiments [Machining Specific Fourier Power Spectrum Profiles into Plastics for HEDP Experiments

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

Schmidt, Derek William; Cardenas, Tana; Doss, Forrest W.

In this paper, the High Energy Density Physics program at Los Alamos National Laboratory (LANL) has had a multiyear campaign to verify the predictive capability of the interface evolution of shock propagation through different profiles machined into the face of a plastic package with an iodine-doped plastic center region. These experiments varied the machined surface from a simple sine wave to a double sine wave and finally to a multitude of different profiles with power spectrum ranges and shapes to verify LANL’s simulation capability. The MultiMode-A profiles had a band-pass flat region of the power spectrum, while the MultiMode-B profilemore » had two band-pass flat regions. Another profile of interest was the 1-Peak profile, a band-pass concept with a spike to one side of the power spectrum. All these profiles were machined in flat and tilted orientations of 30 and 60 deg. Tailor-made machining profiles, supplied by experimental physicists, were compared to actual machined surfaces, and Fourier power spectra were compared to see the reproducibility of the machining process over the frequency ranges that physicists require.« less

Machining Specific Fourier Power Spectrum Profiles into Plastics for High Energy Density Physics Experiments [Machining Specific Fourier Power Spectrum Profiles into Plastics for HEDP Experiments

DOE PAGES

Schmidt, Derek William; Cardenas, Tana; Doss, Forrest W.; ...

2018-01-15

In this paper, the High Energy Density Physics program at Los Alamos National Laboratory (LANL) has had a multiyear campaign to verify the predictive capability of the interface evolution of shock propagation through different profiles machined into the face of a plastic package with an iodine-doped plastic center region. These experiments varied the machined surface from a simple sine wave to a double sine wave and finally to a multitude of different profiles with power spectrum ranges and shapes to verify LANL’s simulation capability. The MultiMode-A profiles had a band-pass flat region of the power spectrum, while the MultiMode-B profilemore » had two band-pass flat regions. Another profile of interest was the 1-Peak profile, a band-pass concept with a spike to one side of the power spectrum. All these profiles were machined in flat and tilted orientations of 30 and 60 deg. Tailor-made machining profiles, supplied by experimental physicists, were compared to actual machined surfaces, and Fourier power spectra were compared to see the reproducibility of the machining process over the frequency ranges that physicists require.« less

Needs of ergonomic design at control units in production industries.

PubMed

Levchuk, I; Schäfer, A; Lang, K-H; Gebhardt, Hj; Klussmann, A

2012-01-01

During the last decades, an increasing use of innovative technologies in manufacturing areas was monitored. A huge amount of physical workload was replaced by the change from conventional machine tools to computer-controlled units. CNC systems spread in current production processes. Because of this, machine operators today mostly have an observational function. This caused increasing of static work (e.g., standing, sitting) and cognitive demands (e.g., process observation). Machine operators have a high responsibility, because mistakes may lead to human injuries as well as to product losses - and in consequence may lead to high monetary losses (for the company) as well. Being usable often means for a CNC machine being efficient. An intuitive usability and an ergonomic organization of CNC workplaces can be an essential basis to reduce the risk of failures in operation as well as physical complaints (e.g. pain or diseases because of bad body posture during work). In contrast to conventional machines, CNC machines are equipped both with hardware and software. An intuitive and clear-sighted operating of CNC systems is a requirement for quick learning of new systems. Within this study, a survey was carried out among trainees learning the operation of CNC machines.

Investigation of laser ablation of CVD diamond film

NASA Astrophysics Data System (ADS)

Chao, Choung-Lii; Chou, W. C.; Ma, Kung-Jen; Chen, Ta-Tung; Liu, Y. M.; Kuo, Y. S.; Chen, Ying-Tung

2005-04-01

Diamond, having many advanced physical and mechanical properties, is one of the most important materials used in the mechanical, telecommunication and optoelectronic industry. However, high hardness value and extreme brittleness have made diamond extremely difficult to be machined by conventional mechanical grinding and polishing. In the present study, the microwave CVD method was employed to produce epitaxial diamond films on silicon single crystal. Laser ablation experiments were then conducted on the obtained diamond films. The underlying material removal mechanisms, microstructure of the machined surface and related machining conditions were also investigated. It was found that during the laser ablation, peaks of the diamond grains were removed mainly by the photo-thermal effects introduced by excimer laser. The diamond structures of the protruded diamond grains were transformed by the laser photonic energy into graphite, amorphous diamond and amorphous carbon which were removed by the subsequent laser shots. As the protruding peaks gradually removed from the surface the removal rate decreased. Surface roughness (Ra) was improved from above 1μm to around 0.1μm in few minutes time in this study. However, a scanning technique would be required if a large area was to be polished by laser and, as a consequence, it could be very time consuming.

Contemporary machine learning: techniques for practitioners in the physical sciences

NASA Astrophysics Data System (ADS)

Spears, Brian

2017-10-01

Machine learning is the science of using computers to find relationships in data without explicitly knowing or programming those relationships in advance. Often without realizing it, we employ machine learning every day as we use our phones or drive our cars. Over the last few years, machine learning has found increasingly broad application in the physical sciences. This most often involves building a model relationship between a dependent, measurable output and an associated set of controllable, but complicated, independent inputs. The methods are applicable both to experimental observations and to databases of simulated output from large, detailed numerical simulations. In this tutorial, we will present an overview of current tools and techniques in machine learning - a jumping-off point for researchers interested in using machine learning to advance their work. We will discuss supervised learning techniques for modeling complicated functions, beginning with familiar regression schemes, then advancing to more sophisticated decision trees, modern neural networks, and deep learning methods. Next, we will cover unsupervised learning and techniques for reducing the dimensionality of input spaces and for clustering data. We'll show example applications from both magnetic and inertial confinement fusion. Along the way, we will describe methods for practitioners to help ensure that their models generalize from their training data to as-yet-unseen test data. We will finally point out some limitations to modern machine learning and speculate on some ways that practitioners from the physical sciences may be particularly suited to help. This work was performed by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344.

Learn about Physical Science: Simple Machines. [CD-ROM].

ERIC Educational Resources Information Center

2000

This CD-ROM, designed for students in grades K-2, explores the world of simple machines. It allows students to delve into the mechanical world and learn the ways in which simple machines make work easier. Animated demonstrations are provided of the lever, pulley, wheel, screw, wedge, and inclined plane. Activities include practical matching and…

Fun with Physics in the Elementary School.

ERIC Educational Resources Information Center

Ediger, Marlow

Primary grade pupils can become fascinated with simple machines. This paper suggests that teachers have simple machines in the classroom for a unit of study. It proposes some guidelines to create a unit of study for six simple machines that include the fulcrum, inclined plane, pulley, wheel and axle, wedge, and screw. Friction, gravity, force, and…

Simple Machine Junk Cars

ERIC Educational Resources Information Center

Herald, Christine

2010-01-01

During the month of May, the author's eighth-grade physical science students study the six simple machines through hands-on activities, reading assignments, videos, and notes. At the end of the month, they can easily identify the six types of simple machine: inclined plane, wheel and axle, pulley, screw, wedge, and lever. To conclude this unit,…

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach.

PubMed

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-06-19

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification.

Machine-Aided Indexing of Technical Literature

ERIC Educational Resources Information Center

Klingbiel, Paul H.

1973-01-01

To index at the Defense Documentation Center (DDC), an automated system must choose single words or phrases rapidly and economically. Automation of DDC's indexing has been machine-aided from its inception. A machine-aided indexing system is described that indexes one million words of text per hour of CPU time. (22 references) (Author/SJ)

THE ROLE OF REVIEW MATERIAL IN CONTINUOUS PROGRAMMING WITH TEACHING MACHINES.

ERIC Educational Resources Information Center

FERSTER, C.B.

STUDENTS WERE PRESENTED 61 LESSONS BY MEANS OF SEMIAUTOMATIC TEACHING MACHINES. LESSONS WERE ARRANGED SO THAT EACH PARTICIPATING STUDENT STUDIED PART OF THE COURSE MATERIAL WITH A SINGLE REPETITION AND PART WITHOUT REPETITION. DATA WERE OBTAINED FROM TWO TESTS SHOWING TEACHING-MACHINE RESULTS AND ONE FINAL COURSE EXAMINATION. NO SIGNIFICANT…

Impact of the HEALTHY study on vending machine offerings in middle schools

USDA-ARS?s Scientific Manuscript database

The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminat...

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

None

This factsheet describes a project that developed and demonstrated a new manufacturing-informed design framework that utilizes advanced multi-scale, physics-based process modeling to dramatically improve manufacturing productivity and quality in machining operations while reducing the cost of machined components.

Biomachining - A new approach for micromachining of metals

NASA Astrophysics Data System (ADS)

Vigneshwaran, S. C. Sakthi; Ramakrishnan, R.; Arun Prakash, C.; Sashank, C.

2018-04-01

Machining is the process of removal of material from workpiece. Machining can be done by physical, chemical or biological methods. Though physical and chemical methods have been widely used in machining process, they have their own disadvantages such as development of heat affected zone and usage of hazardous chemicals. Biomachining is the machining process in which bacteria is used to remove material from the metal parts. Chemolithotrophic bacteria such as Acidothiobacillus ferroxidans has been used in biomachining of metals like copper, iron etc. These bacteria are used because of their property of catalyzing the oxidation of inorganic substances. Biomachining is a suitable process for micromachining of metals. This paper reviews the biomachining process and various mechanisms involved in biomachining. This paper also briefs about various parameters/factors to be considered in biomachining and also the effect of those parameters on metal removal rate.

Study of Man-Machine Communications Systems for Disabled Persons (The Handicapped). Volume V. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

Instructions are given for teaching severely physically and/or neurologically handicapped students to use the 14-key Cybertype man-machine communications system, an electric writing machine with a simplified keyboard to enable persons with limited motor ability or coordination to communicate in written form. Explained are the various possible…

The compound Atwood machine problem

NASA Astrophysics Data System (ADS)

Lopes Coelho, R.

2017-05-01

The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an Atwood machine.

Galaxy Classification using Machine Learning

NASA Astrophysics Data System (ADS)

Fowler, Lucas; Schawinski, Kevin; Brandt, Ben-Elias; widmer, Nicole

2017-01-01

We present our current research into the use of machine learning to classify galaxy imaging data with various convolutional neural network configurations in TensorFlow. We are investigating how five-band Sloan Digital Sky Survey imaging data can be used to train on physical properties such as redshift, star formation rate, mass and morphology. We also investigate the performance of artificially redshifted images in recovering physical properties as image quality degrades.

Assessment of genetic and nongenetic interactions for the prediction of depressive symptomatology: an analysis of the Wisconsin Longitudinal Study using machine learning algorithms.

PubMed

Roetker, Nicholas S; Page, C David; Yonker, James A; Chang, Vicky; Roan, Carol L; Herd, Pamela; Hauser, Taissa S; Hauser, Robert M; Atwood, Craig S

2013-10-01

We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors-13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors-18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic-environmental-sociobehavioral interactions in depressive symptoms.

Performance of a plasma fluid code on the Intel parallel computers

NASA Technical Reports Server (NTRS)

Lynch, V. E.; Carreras, B. A.; Drake, J. B.; Leboeuf, J. N.; Liewer, P.

1992-01-01

One approach to improving the real-time efficiency of plasma turbulence calculations is to use a parallel algorithm. A parallel algorithm for plasma turbulence calculations was tested on the Intel iPSC/860 hypercube and the Touchtone Delta machine. Using the 128 processors of the Intel iPSC/860 hypercube, a factor of 5 improvement over a single-processor CRAY-2 is obtained. For the Touchtone Delta machine, the corresponding improvement factor is 16. For plasma edge turbulence calculations, an extrapolation of the present results to the Intel (sigma) machine gives an improvement factor close to 64 over the single-processor CRAY-2.

Boosted Regression Trees Outperforms Support Vector Machines in Predicting (Regional) Yields of Winter Wheat from Single and Cumulated Dekadal Spot-VGT Derived Normalized Difference Vegetation Indices

NASA Astrophysics Data System (ADS)

Stas, Michiel; Dong, Qinghan; Heremans, Stien; Zhang, Beier; Van Orshoven, Jos

2016-08-01

This paper compares two machine learning techniques to predict regional winter wheat yields. The models, based on Boosted Regression Trees (BRT) and Support Vector Machines (SVM), are constructed of Normalized Difference Vegetation Indices (NDVI) derived from low resolution SPOT VEGETATION satellite imagery. Three types of NDVI-related predictors were used: Single NDVI, Incremental NDVI and Targeted NDVI. BRT and SVM were first used to select features with high relevance for predicting the yield. Although the exact selections differed between the prefectures, certain periods with high influence scores for multiple prefectures could be identified. The same period of high influence stretching from March to June was detected by both machine learning methods. After feature selection, BRT and SVM models were applied to the subset of selected features for actual yield forecasting. Whereas both machine learning methods returned very low prediction errors, BRT seems to slightly but consistently outperform SVM.

A machine-learning approach for computation of fractional flow reserve from coronary computed tomography.

PubMed

Itu, Lucian; Rapaka, Saikiran; Passerini, Tiziano; Georgescu, Bogdan; Schwemmer, Chris; Schoebinger, Max; Flohr, Thomas; Sharma, Puneet; Comaniciu, Dorin

2016-07-01

Fractional flow reserve (FFR) is a functional index quantifying the severity of coronary artery lesions and is clinically obtained using an invasive, catheter-based measurement. Recently, physics-based models have shown great promise in being able to noninvasively estimate FFR from patient-specific anatomical information, e.g., obtained from computed tomography scans of the heart and the coronary arteries. However, these models have high computational demand, limiting their clinical adoption. In this paper, we present a machine-learning-based model for predicting FFR as an alternative to physics-based approaches. The model is trained on a large database of synthetically generated coronary anatomies, where the target values are computed using the physics-based model. The trained model predicts FFR at each point along the centerline of the coronary tree, and its performance was assessed by comparing the predictions against physics-based computations and against invasively measured FFR for 87 patients and 125 lesions in total. Correlation between machine-learning and physics-based predictions was excellent (0.9994, P < 0.001), and no systematic bias was found in Bland-Altman analysis: mean difference was -0.00081 ± 0.0039. Invasive FFR ≤ 0.80 was found in 38 lesions out of 125 and was predicted by the machine-learning algorithm with a sensitivity of 81.6%, a specificity of 83.9%, and an accuracy of 83.2%. The correlation was 0.729 (P < 0.001). Compared with the physics-based computation, average execution time was reduced by more than 80 times, leading to near real-time assessment of FFR. Average execution time went down from 196.3 ± 78.5 s for the CFD model to ∼2.4 ± 0.44 s for the machine-learning model on a workstation with 3.4-GHz Intel i7 8-core processor. Copyright © 2016 the American Physiological Society.

40 CFR 63.3960 - By what date must I conduct performance tests and other initial compliance demonstrations?

Code of Federal Regulations, 2011 CFR

2011-07-01

... performance test of one representative magnet wire coating machine for each group of identical or very similar... you complete the performance test of a representative magnet wire coating machine. The requirements in... operations, you may, with approval, conduct a performance test of a single magnet wire coating machine that...

Impact of the HEALTHY Study on Vending Machine Offerings in Middle Schools

ERIC Educational Resources Information Center

Hartstein, Jill; Cullen, Karen W.; Virus, Amy; El Ghormli, Laure; Volpe, Stella L.; Staten, Myrlene A.; Bridgman, Jessica C.; Stadler, Diane D.; Gillis, Bonnie; McCormick, Sarah B.; Mobley, Connie C.

2011-01-01

Purpose/Objectives: The purpose of this study is to report the impact of the three-year middle school-based HEALTHY study on intervention school vending machine offerings. There were two goals for the vending machines: serve only dessert/snack foods with 200 kilocalories or less per single serving package, and eliminate 100% fruit juice and…

Machine Tests Optical Fibers In Flexure

NASA Technical Reports Server (NTRS)

Darejeh, Hadi; Thomas, Henry; Delcher, Ray

1993-01-01

Machine repeatedly flexes single optical fiber or cable or bundle of optical fibers at low temperature. Liquid nitrogen surrounds specimen as it is bent back and forth by motion of piston. Machine inexpensive to build and operate. Tests under repeatable conditions so candidate fibers, cables, and bundles evaluated for general robustness before subjected to expensive shock and vibration tests.

Scheduling Jobs and a Variable Maintenance on a Single Machine with Common Due-Date Assignment

PubMed Central

Wan, Long

2014-01-01

We investigate a common due-date assignment scheduling problem with a variable maintenance on a single machine. The goal is to minimize the total earliness, tardiness, and due-date cost. We derive some properties on an optimal solution for our problem. For a special case with identical jobs we propose an optimal polynomial time algorithm followed by a numerical example. PMID:25147861

Comparison of measured electron energy spectra for six matched, radiotherapy accelerators.

PubMed

McLaughlin, David J; Hogstrom, Kenneth R; Neck, Daniel W; Gibbons, John P

2018-05-01

This study compares energy spectra of the multiple electron beams of individual radiotherapy machines, as well as the sets of spectra across multiple matched machines. Also, energy spectrum metrics are compared with central-axis percent depth-dose (PDD) metrics. A lightweight, permanent magnet spectrometer was used to measure energy spectra for seven electron beams (7-20 MeV) on six matched Elekta Infinity accelerators with the MLCi2 treatment head. PDD measurements in the distal falloff region provided R 50 and R 80-20 metrics in Plastic Water ® , which correlated with energy spectrum metrics, peak mean energy (PME) and full-width at half maximum (FWHM). Visual inspection of energy spectra and their metrics showed whether beams on single machines were properly tuned, i.e., FWHM is expected to increase and peak height decrease monotonically with increased PME. Also, PME spacings are expected to be approximately equal for 7-13 MeV beams (0.5-cm R 90 spacing) and for 13-16 MeV beams (1.0-cm R 90 spacing). Most machines failed these expectations, presumably due to tolerances for initial beam matching (0.05 cm in R 90 ; 0.10 cm in R 80-20 ) and ongoing quality assurance (0.2 cm in R 50 ). Also, comparison of energy spectra or metrics for a single beam energy (six machines) showed outlying spectra. These variations in energy spectra provided ample data spread for correlating PME and FWHM with PDD metrics. Least-squares fits showed that R 50 and R 80-20 varied linearly and supralinearly with PME, respectively; however, both suggested a secondary dependence on FWHM. Hence, PME and FWHM could serve as surrogates for R 50 and R 80-20 for beam tuning by the accelerator engineer, possibly being more sensitive (e.g., 0.1 cm in R 80-20 corresponded to 2.0 MeV in FWHM). Results of this study suggest a lightweight, permanent magnet spectrometer could be a useful beam-tuning instrument for the accelerator engineer to (a) match electron beams prior to beam commissioning, (b) tune electron beams for the duration of their clinical use, and (c) provide estimates of PDD metrics following machine maintenance. However, a real-time version of the spectrometer is needed to be practical. © 2018 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

On the Safety of Machine Learning: Cyber-Physical Systems, Decision Sciences, and Data Products.

PubMed

Varshney, Kush R; Alemzadeh, Homa

2017-09-01

Machine learning algorithms increasingly influence our decisions and interact with us in all parts of our daily lives. Therefore, just as we consider the safety of power plants, highways, and a variety of other engineered socio-technical systems, we must also take into account the safety of systems involving machine learning. Heretofore, the definition of safety has not been formalized in a machine learning context. In this article, we do so by defining machine learning safety in terms of risk, epistemic uncertainty, and the harm incurred by unwanted outcomes. We then use this definition to examine safety in all sorts of applications in cyber-physical systems, decision sciences, and data products. We find that the foundational principle of modern statistical machine learning, empirical risk minimization, is not always a sufficient objective. We discuss how four different categories of strategies for achieving safety in engineering, including inherently safe design, safety reserves, safe fail, and procedural safeguards can be mapped to a machine learning context. We then discuss example techniques that can be adopted in each category, such as considering interpretability and causality of predictive models, objective functions beyond expected prediction accuracy, human involvement for labeling difficult or rare examples, and user experience design of software and open data.

Surfactant/detergent titration analysis method and apparatus for machine working fluids, surfactant-containing wastewater and the like

DOEpatents

Smith, Douglas D.; Hiller, John M.

1998-01-01

The present invention is an improved method and related apparatus for quantitatively analyzing machine working fluids and other aqueous compositions such as wastewater which contain various mixtures of cationic, neutral, and/or anionic surfactants, soluble soaps, and the like. The method utilizes a single-phase, non-aqueous, reactive titration composition containing water insoluble bismuth nitrate dissolved in glycerol for the titration reactant. The chemical reaction of the bismuth ion and glycerol with the surfactant in the test solutions results in formation of micelles, changes in micelle size, and the formation of insoluble bismuth soaps. These soaps are quantified by physical and chemical changes in the aqueous test solution. Both classical potentiometric analysis and turbidity measurements have been used as sensing techniques to determine the quantity of surfactant present in test solutions. This method is amenable to the analysis of various types of new, in-use, dirty or decomposed surfactants and detergents. It is a quick and efficient method utilizing a single-phase reaction without needing a separate extraction from the aqueous solution. It is adaptable to automated control with simple and reliable sensing methods. The method is applicable to a variety of compositions with concentrations from about 1% to about 10% weight. It is also applicable to the analysis of waste water containing surfactants with appropriate pre-treatments for concentration.

Surfactant/detergent titration analysis method and apparatus for machine working fluids, surfactant-containing wastewater and the like

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

Smith, D.D.; Hiller, J.M.

1998-02-24

The present invention is an improved method and related apparatus for quantitatively analyzing machine working fluids and other aqueous compositions such as wastewater which contain various mixtures of cationic, neutral, and/or anionic surfactants, soluble soaps, and the like. The method utilizes a single-phase, non-aqueous, reactive titration composition containing water insoluble bismuth nitrate dissolved in glycerol for the titration reactant. The chemical reaction of the bismuth ion and glycerol with the surfactant in the test solutions results in formation of micelles, changes in micelle size, and the formation of insoluble bismuth soaps. These soaps are quantified by physical and chemical changesmore » in the aqueous test solution. Both classical potentiometric analysis and turbidity measurements have been used as sensing techniques to determine the quantity of surfactant present in test solutions. This method is amenable to the analysis of various types of new, in-use, dirty or decomposed surfactants and detergents. It is a quick and efficient method utilizing a single-phase reaction without needing a separate extraction from the aqueous solution. It is adaptable to automated control with simple and reliable sensing methods. The method is applicable to a variety of compositions with concentrations from about 1% to about 10% weight. It is also applicable to the analysis of waste water containing surfactants with appropriate pre-treatments for concentration. 1 fig.« less

Surfactant/detergent titration analysis method and apparatus for machine working fluids, surfactant-containing wastewater and the like

DOEpatents

Smith, D.D.; Hiller, J.M.

1998-02-24

The present invention is an improved method and related apparatus for quantitatively analyzing machine working fluids and other aqueous compositions such as wastewater which contain various mixtures of cationic, neutral, and/or anionic surfactants, soluble soaps, and the like. The method utilizes a single-phase, non-aqueous, reactive titration composition containing water insoluble bismuth nitrate dissolved in glycerol for the titration reactant. The chemical reaction of the bismuth ion and glycerol with the surfactant in the test solutions results in formation of micelles, changes in micelle size, and the formation of insoluble bismuth soaps. These soaps are quantified by physical and chemical changes in the aqueous test solution. Both classical potentiometric analysis and turbidity measurements have been used as sensing techniques to determine the quantity of surfactant present in test solutions. This method is amenable to the analysis of various types of new, in-use, dirty or decomposed surfactants and detergents. It is a quick and efficient method utilizing a single-phase reaction without needing a separate extraction from the aqueous solution. It is adaptable to automated control with simple and reliable sensing methods. The method is applicable to a variety of compositions with concentrations from about 1% to about 10% weight. It is also applicable to the analysis of waste water containing surfactants with appropriate pre-treatments for concentration. 1 fig.

TEACHING PHYSICS: Atwood's machine: experiments in an accelerating frame

NASA Astrophysics Data System (ADS)

Teck Chee, Chia; Hong, Chia Yee

1999-03-01

Experiments in an accelerating frame are often difficult to perform, but simple computer software allows sufficiently rapid and accurate measurements to be made on an arrangement of weights and pulleys known as Atwood's machine.

A Novel Extreme Learning Machine Classification Model for e-Nose Application Based on the Multiple Kernel Approach

PubMed Central

Jian, Yulin; Huang, Daoyu; Yan, Jia; Lu, Kun; Huang, Ying; Wen, Tailai; Zeng, Tanyue; Zhong, Shijie; Xie, Qilong

2017-01-01

A novel classification model, named the quantum-behaved particle swarm optimization (QPSO)-based weighted multiple kernel extreme learning machine (QWMK-ELM), is proposed in this paper. Experimental validation is carried out with two different electronic nose (e-nose) datasets. Being different from the existing multiple kernel extreme learning machine (MK-ELM) algorithms, the combination coefficients of base kernels are regarded as external parameters of single-hidden layer feedforward neural networks (SLFNs). The combination coefficients of base kernels, the model parameters of each base kernel, and the regularization parameter are optimized by QPSO simultaneously before implementing the kernel extreme learning machine (KELM) with the composite kernel function. Four types of common single kernel functions (Gaussian kernel, polynomial kernel, sigmoid kernel, and wavelet kernel) are utilized to constitute different composite kernel functions. Moreover, the method is also compared with other existing classification methods: extreme learning machine (ELM), kernel extreme learning machine (KELM), k-nearest neighbors (KNN), support vector machine (SVM), multi-layer perceptron (MLP), radical basis function neural network (RBFNN), and probabilistic neural network (PNN). The results have demonstrated that the proposed QWMK-ELM outperforms the aforementioned methods, not only in precision, but also in efficiency for gas classification. PMID:28629202

A microcomputer network for control of a continuous mining machine

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

Schiffbauer, W.H.

1993-12-31

This report details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines and installed on a continuous mining machine. The network consists of microcomputers that are connected together via a single twisted-pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers, in conjunction with the appropriate sensors, provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and control the continuous miningmore » machine. Because of the network`s generic structure, it can be installed on most mining machines.« less

The upgraded Large Plasma Device, a machine for studying frontier basic plasma physics.

PubMed

Gekelman, W; Pribyl, P; Lucky, Z; Drandell, M; Leneman, D; Maggs, J; Vincena, S; Van Compernolle, B; Tripathi, S K P; Morales, G; Carter, T A; Wang, Y; DeHaas, T

2016-02-01

In 1991 a manuscript describing an instrument for studying magnetized plasmas was published in this journal. The Large Plasma Device (LAPD) was upgraded in 2001 and has become a national user facility for the study of basic plasma physics. The upgrade as well as diagnostics introduced since then has significantly changed the capabilities of the device. All references to the machine still quote the original RSI paper, which at this time is not appropriate. In this work, the properties of the updated LAPD are presented. The strategy of the machine construction, the available diagnostics, the parameters available for experiments, as well as illustrations of several experiments are presented here.

Machine learning with quantum relative entropy

NASA Astrophysics Data System (ADS)

Tsuda, Koji

2009-12-01

Density matrices are a central tool in quantum physics, but it is also used in machine learning. A positive definite matrix called kernel matrix is used to represent the similarities between examples. Positive definiteness assures that the examples are embedded in an Euclidean space. When a positive definite matrix is learned from data, one has to design an update rule that maintains the positive definiteness. Our update rule, called matrix exponentiated gradient update, is motivated by the quantum relative entropy. Notably, the relative entropy is an instance of Bregman divergences, which are asymmetric distance measures specifying theoretical properties of machine learning algorithms. Using the calculus commonly used in quantum physics, we prove an upperbound of the generalization error of online learning.

TEACHING PHYSICS: A computer-based revitalization of Atwood's machine

NASA Astrophysics Data System (ADS)

Trumper, Ricardo; Gelbman, Moshe

2000-09-01

Atwood's machine is used in a microcomputer-based experiment to demonstrate Newton's second law with considerable precision. The friction force on the masses and the moment of inertia of the pulley can also be estimated.

Progress Toward Fabrication of Machined Metal Shells for the First Double-Shell Implosions at the National Ignition Facility

DOE PAGES

Cardenas, Tana; Schmidt, Derek W.; Loomis, Eric N.; ...

2018-01-25

The double-shell platform fielded at the National Ignition Facility requires developments in new machining techniques and robotic assembly stations to meet the experimental specifications. Current double-shell target designs use a dense high-Z inner shell, a foam cushion, and a low-Z outer shell. The design requires that the inner shell be gas filled using a fill tube. This tube impacts the entire machining and assembly design. Other intermediate physics designs have to be fielded to answer physics questions and advance the technology to be able to fabricate the full point design in the near future. One of these intermediate designs ismore » a mid-Z imaging design. The methods of designing, fabricating, and characterizing each of the major components of an imaging double shell are discussed with an emphasis on the fabrication of the machined outer metal shell.« less

Progress Toward Fabrication of Machined Metal Shells for the First Double-Shell Implosions at the National Ignition Facility

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

Cardenas, Tana; Schmidt, Derek W.; Loomis, Eric N.

The double-shell platform fielded at the National Ignition Facility requires developments in new machining techniques and robotic assembly stations to meet the experimental specifications. Current double-shell target designs use a dense high-Z inner shell, a foam cushion, and a low-Z outer shell. The design requires that the inner shell be gas filled using a fill tube. This tube impacts the entire machining and assembly design. Other intermediate physics designs have to be fielded to answer physics questions and advance the technology to be able to fabricate the full point design in the near future. One of these intermediate designs ismore » a mid-Z imaging design. The methods of designing, fabricating, and characterizing each of the major components of an imaging double shell are discussed with an emphasis on the fabrication of the machined outer metal shell.« less

Machine learning of network metrics in ATLAS Distributed Data Management

NASA Astrophysics Data System (ADS)

Lassnig, Mario; Toler, Wesley; Vamosi, Ralf; Bogado, Joaquin; ATLAS Collaboration

2017-10-01

The increasing volume of physics data poses a critical challenge to the ATLAS experiment. In anticipation of high luminosity physics, automation of everyday data management tasks has become necessary. Previously many of these tasks required human decision-making and operation. Recent advances in hardware and software have made it possible to entrust more complicated duties to automated systems using models trained by machine learning algorithms. In this contribution we show results from one of our ongoing automation efforts that focuses on network metrics. First, we describe our machine learning framework built atop the ATLAS Analytics Platform. This framework can automatically extract and aggregate data, train models with various machine learning algorithms, and eventually score the resulting models and parameters. Second, we use these models to forecast metrics relevant for networkaware job scheduling and data brokering. We show the characteristics of the data and evaluate the forecasting accuracy of our models.

Single bus star connected reluctance drive and method

DOEpatents

Fahimi, Babak; Shamsi, Pourya

2016-05-10

A system and methods for operating a switched reluctance machine includes a controller, an inverter connected to the controller and to the switched reluctance machine, a hysteresis control connected to the controller and to the inverter, a set of sensors connected to the switched reluctance machine and to the controller, the switched reluctance machine further including a set of phases the controller further comprising a processor and a memory connected to the processor, wherein the processor programmed to execute a control process and a generation process.

Revisiting the Central Dogma One Molecule at a Time

PubMed Central

Bustamante, Carlos; Cheng, Wei; Meija, Yara

2011-01-01

The faithful relay and timely expression of genetic information depend on specialized molecular machines, many of which function as nucleic acid translocases. The emergence over the last decade of single-molecule fluorescence detection and manipulation techniques with nm and Å resolution, and their application to the study of nucleic acid translocases are painting an increasingly sharp picture of the inner workings of these machines, the dynamics and coordination of their moving parts, their thermodynamic efficiency, and the nature of their transient intermediates. Here we present an overview of the main results arrived at by the application of single-molecule methods to the study of the main machines of the central dogma. PMID:21335233

Learning to predict chemical reactions.

PubMed

Kayala, Matthew A; Azencott, Chloé-Agathe; Chen, Jonathan H; Baldi, Pierre

2011-09-26

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles, respectively, are not high throughput, are not generalizable or scalable, and lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry data set consisting of 1630 full multistep reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top-ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of nonproductive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal ( http://cdb.ics.uci.edu) under the Toolkits section.

Learning to Predict Chemical Reactions

PubMed Central

Kayala, Matthew A.; Azencott, Chloé-Agathe; Chen, Jonathan H.

2011-01-01

Being able to predict the course of arbitrary chemical reactions is essential to the theory and applications of organic chemistry. Approaches to the reaction prediction problems can be organized around three poles corresponding to: (1) physical laws; (2) rule-based expert systems; and (3) inductive machine learning. Previous approaches at these poles respectively are not high-throughput, are not generalizable or scalable, or lack sufficient data and structure to be implemented. We propose a new approach to reaction prediction utilizing elements from each pole. Using a physically inspired conceptualization, we describe single mechanistic reactions as interactions between coarse approximations of molecular orbitals (MOs) and use topological and physicochemical attributes as descriptors. Using an existing rule-based system (Reaction Explorer), we derive a restricted chemistry dataset consisting of 1630 full multi-step reactions with 2358 distinct starting materials and intermediates, associated with 2989 productive mechanistic steps and 6.14 million unproductive mechanistic steps. And from machine learning, we pose identifying productive mechanistic steps as a statistical ranking, information retrieval, problem: given a set of reactants and a description of conditions, learn a ranking model over potential filled-to-unfilled MO interactions such that the top ranked mechanistic steps yield the major products. The machine learning implementation follows a two-stage approach, in which we first train atom level reactivity filters to prune 94.00% of non-productive reactions with a 0.01% error rate. Then, we train an ensemble of ranking models on pairs of interacting MOs to learn a relative productivity function over mechanistic steps in a given system. Without the use of explicit transformation patterns, the ensemble perfectly ranks the productive mechanism at the top 89.05% of the time, rising to 99.86% of the time when the top four are considered. Furthermore, the system is generalizable, making reasonable predictions over reactants and conditions which the rule-based expert does not handle. A web interface to the machine learning based mechanistic reaction predictor is accessible through our chemoinformatics portal (http://cdb.ics.uci.edu) under the Toolkits section. PMID:21819139

Machine learning vortices at the Kosterlitz-Thouless transition

NASA Astrophysics Data System (ADS)

Beach, Matthew J. S.; Golubeva, Anna; Melko, Roger G.

2018-01-01

Efficient and automated classification of phases from minimally processed data is one goal of machine learning in condensed-matter and statistical physics. Supervised algorithms trained on raw samples of microstates can successfully detect conventional phase transitions via learning a bulk feature such as an order parameter. In this paper, we investigate whether neural networks can learn to classify phases based on topological defects. We address this question on the two-dimensional classical XY model which exhibits a Kosterlitz-Thouless transition. We find significant feature engineering of the raw spin states is required to convincingly claim that features of the vortex configurations are responsible for learning the transition temperature. We further show a single-layer network does not correctly classify the phases of the XY model, while a convolutional network easily performs classification by learning the global magnetization. Finally, we design a deep network capable of learning vortices without feature engineering. We demonstrate the detection of vortices does not necessarily result in the best classification accuracy, especially for lattices of less than approximately 1000 spins. For larger systems, it remains a difficult task to learn vortices.

The Virtual Xenbase: transitioning an online bioinformatics resource to a private cloud

PubMed Central

Karimi, Kamran; Vize, Peter D.

2014-01-01

As a model organism database, Xenbase has been providing informatics and genomic data on Xenopus (Silurana) tropicalis and Xenopus laevis frogs for more than a decade. The Xenbase database contains curated, as well as community-contributed and automatically harvested literature, gene and genomic data. A GBrowse genome browser, a BLAST+ server and stock center support are available on the site. When this resource was first built, all software services and components in Xenbase ran on a single physical server, with inherent reliability, scalability and inter-dependence issues. Recent advances in networking and virtualization techniques allowed us to move Xenbase to a virtual environment, and more specifically to a private cloud. To do so we decoupled the different software services and components, such that each would run on a different virtual machine. In the process, we also upgraded many of the components. The resulting system is faster and more reliable. System maintenance is easier, as individual virtual machines can now be updated, backed up and changed independently. We are also experiencing more effective resource allocation and utilization. Database URL: www.xenbase.org PMID:25380782

Physical mechanism of ultrasonic machining

NASA Astrophysics Data System (ADS)

Isaev, A.; Grechishnikov, V.; Kozochkin, M.; Pivkin, P.; Petuhov, Y.; Romanov, V.

2016-04-01

In this paper, the main aspects of ultrasonic machining of constructional materials are considered. Influence of coolant on surface parameters is studied. Results of experiments on ultrasonic lathe cutting with application of tangential vibrations and with use of coolant are considered.

Biomimetic machine vision system.

PubMed

Harman, William M; Barrett, Steven F; Wright, Cameron H G; Wilcox, Michael

2005-01-01

Real-time application of digital imaging for use in machine vision systems has proven to be prohibitive when used within control systems that employ low-power single processors without compromising the scope of vision or resolution of captured images. Development of a real-time machine analog vision system is the focus of research taking place at the University of Wyoming. This new vision system is based upon the biological vision system of the common house fly. Development of a single sensor is accomplished, representing a single facet of the fly's eye. This new sensor is then incorporated into an array of sensors capable of detecting objects and tracking motion in 2-D space. This system "preprocesses" incoming image data resulting in minimal data processing to determine the location of a target object. Due to the nature of the sensors in the array, hyperacuity is achieved thereby eliminating resolutions issues found in digital vision systems. In this paper, we will discuss the biological traits of the fly eye and the specific traits that led to the development of this machine vision system. We will also discuss the process of developing an analog based sensor that mimics the characteristics of interest in the biological vision system. This paper will conclude with a discussion of how an array of these sensors can be applied toward solving real-world machine vision issues.

Assessment of Genetic and Nongenetic Interactions for the Prediction of Depressive Symptomatology: An Analysis of the Wisconsin Longitudinal Study Using Machine Learning Algorithms

PubMed Central

Roetker, Nicholas S.; Yonker, James A.; Chang, Vicky; Roan, Carol L.; Herd, Pamela; Hauser, Taissa S.; Hauser, Robert M.

2013-01-01

Objectives. We examined depression within a multidimensional framework consisting of genetic, environmental, and sociobehavioral factors and, using machine learning algorithms, explored interactions among these factors that might better explain the etiology of depressive symptoms. Methods. We measured current depressive symptoms using the Center for Epidemiologic Studies Depression Scale (n = 6378 participants in the Wisconsin Longitudinal Study). Genetic factors were 78 single nucleotide polymorphisms (SNPs); environmental factors—13 stressful life events (SLEs), plus a composite proportion of SLEs index; and sociobehavioral factors—18 personality, intelligence, and other health or behavioral measures. We performed traditional SNP associations via logistic regression likelihood ratio testing and explored interactions with support vector machines and Bayesian networks. Results. After correction for multiple testing, we found no significant single genotypic associations with depressive symptoms. Machine learning algorithms showed no evidence of interactions. Naïve Bayes produced the best models in both subsets and included only environmental and sociobehavioral factors. Conclusions. We found no single or interactive associations with genetic factors and depressive symptoms. Various environmental and sociobehavioral factors were more predictive of depressive symptoms, yet their impacts were independent of one another. A genome-wide analysis of genetic alterations using machine learning methodologies will provide a framework for identifying genetic–environmental–sociobehavioral interactions in depressive symptoms. PMID:23927508

The machine body metaphor: From science and technology to physical education and sport, in France (1825-1935).

PubMed

Gleyse, J

2013-12-01

The long history of the conception of physical exercise in France may be viewed as a function of a series of changes in understanding the body. Scientific concepts were used to present the body in official texts by authors specializing in the subject, or to describe them, as did Michel Foucault, as epistemic changes. A departure occurred during the 19th century that is clearly demonstrated in the writings of Gustave Adolphe Hirn. This breakthrough concerned the idea of considering the organism as an energy-generating machine. This metaphor was employed in describing the body during physical exercise from the 17th to the 19th centuries, when the body was thought of as mechanical. Such metaphors were used by the most relevant figures writing at the end of the 19th century in the rationale that is examined in this paper. It shows how Hirn, Marey, Lagrange, Demenij, Hebert, and Tissié saw the body and how they employed machine metaphors when referring to it. These machine metaphors are analyzed from the time of their scientific and technological origins up to their current use in physical and sports education. This analysis will contribute to the understanding of how a scientific metaphor comes to be in common use and may lead to particular exercise practices. © 2012 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Microcomputer network for control of a continuous mining machine. Information circular/1993

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

Schiffbauer, W.H.

1993-01-01

The paper details a microcomputer-based control and monitoring network that was developed in-house by the U.S. Bureau of Mines, and installed on a Joy 14 continuous mining machine. The network consists of microcomputers that are connected together via a single twisted pair cable. Each microcomputer was developed to provide a particular function in the control process. Machine-mounted microcomputers in conjunction with the appropriate sensors provide closed-loop control of the machine, navigation, and environmental monitoring. Off-the-machine microcomputers provide remote control of the machine, sensor status, and a connection to the network so that external computers can access network data and controlmore » the continuous mining machine. Although the network was installed on a Joy 14 continuous mining machine, its use extends beyond it. Its generic structure lends itself to installation onto most mining machine types.« less

How much information is in a jet?

NASA Astrophysics Data System (ADS)

Datta, Kaustuv; Larkoski, Andrew

2017-06-01

Machine learning techniques are increasingly being applied toward data analyses at the Large Hadron Collider, especially with applications for discrimination of jets with different originating particles. Previous studies of the power of machine learning to jet physics have typically employed image recognition, natural language processing, or other algorithms that have been extensively developed in computer science. While these studies have demonstrated impressive discrimination power, often exceeding that of widely-used observables, they have been formulated in a non-constructive manner and it is not clear what additional information the machines are learning. In this paper, we study machine learning for jet physics constructively, expressing all of the information in a jet onto sets of observables that completely and minimally span N-body phase space. For concreteness, we study the application of machine learning for discrimination of boosted, hadronic decays of Z bosons from jets initiated by QCD processes. Our results demonstrate that the information in a jet that is useful for discrimination power of QCD jets from Z bosons is saturated by only considering observables that are sensitive to 4-body (8 dimensional) phase space.

Self-Rupturing Hermetic Valve

NASA Technical Reports Server (NTRS)

Tucker, Curtis E., Jr.; Sherrit, Stewart

2011-01-01

For commercial, military, and aerospace applications, low-cost, small, reliable, and lightweight gas and liquid hermetically sealed valves with post initiation on/off capability are highly desirable for pressurized systems. Applications include remote fire suppression, single-use system-pressurization systems, spacecraft propellant systems, and in situ instruments. Current pyrotechnic- activated rupture disk hermetic valves were designed for physically larger systems and are heavy and integrate poorly with portable equipment, aircraft, and small spacecraft and instrument systems. Additionally, current pyrotechnically activated systems impart high g-force shock loads to surrounding components and structures, which increase the risk of damage and can require additional mitigation. The disclosed mechanism addresses the need for producing a hermetically sealed micro-isolation valve for low and high pressure for commercial, aerospace, and spacecraft applications. High-precision electrical discharge machining (EDM) parts allow for the machining of mated parts with gaps less than a thousandth of an inch. These high-precision parts are used to support against pressure and extrusion, a thin hermetically welded diaphragm. This diaphragm ruptures from a pressure differential when the support is removed and/or when the plunger is forced against the diaphragm. With the addition of conventional seals to the plunger and a two-way actuator, a derivative of this design would allow nonhermetic use as an on/off or metering valve after the initial rupturing of the hermetic sealing disk. In addition, in a single-use hermetically sealed isolation valve, the valve can be activated without the use of potential leak-inducing valve body penetrations. One implementation of this technology is a high-pressure, high-flow-rate rupture valve that is self-rupturing, which is advantageous for high-pressure applications such as gas isolation valves. Once initiated, this technology is self-energizing and requires low force compared to current pyrotechnic-based burst disk hermetic valves. This is a novel design for producing a single-use, self-rupturing, hermetically sealed valve for isolation of pressurized gas and/or liquids. This design can also be applied for single-use disposable valves for chemical instruments. A welded foil diaphragm is fully supported by two mated surfaces that are machined to micron accuracies using EDM. To open the valve, one of the surfaces is moved relative to the other to (a) remove the support creating an unsupported diaphragm that ruptures due to over pressure, and/or (b) produce tension in the diaphragm and rupture it.

Using Machine Learning as a fast emulator of physical processes within the Met Office's Unified Model

NASA Astrophysics Data System (ADS)

Prudden, R.; Arribas, A.; Tomlinson, J.; Robinson, N.

2017-12-01

The Unified Model is a numerical model of the atmosphere used at the UK Met Office (and numerous partner organisations including Korean Meteorological Agency, Australian Bureau of Meteorology and US Air Force) for both weather and climate applications.Especifically, dynamical models such as the Unified Model are now a central part of weather forecasting. Starting from basic physical laws, these models make it possible to predict events such as storms before they have even begun to form. The Unified Model can be simply described as having two components: one component solves the navier-stokes equations (usually referred to as the "dynamics"); the other solves relevant sub-grid physical processes (usually referred to as the "physics"). Running weather forecasts requires substantial computing resources - for example, the UK Met Office operates the largest operational High Performance Computer in Europe - and the cost of a typical simulation is spent roughly 50% in the "dynamics" and 50% in the "physics". Therefore there is a high incentive to reduce cost of weather forecasts and Machine Learning is a possible option because, once a machine learning model has been trained, it is often much faster to run than a full simulation. This is the motivation for a technique called model emulation, the idea being to build a fast statistical model which closely approximates a far more expensive simulation. In this paper we discuss the use of Machine Learning as an emulator to replace the "physics" component of the Unified Model. Various approaches and options will be presented and the implications for further model development, operational running of forecasting systems, development of data assimilation schemes, and development of ensemble prediction techniques will be discussed.

Identification of Interesting Objects in Large Spectral Surveys Using Highly Parallelized Machine Learning

NASA Astrophysics Data System (ADS)

Škoda, Petr; Palička, Andrej; Koza, Jakub; Shakurova, Ksenia

2017-06-01

The current archives of LAMOST multi-object spectrograph contain millions of fully reduced spectra, from which the automatic pipelines have produced catalogues of many parameters of individual objects, including their approximate spectral classification. This is, however, mostly based on the global shape of the whole spectrum and on integral properties of spectra in given bandpasses, namely presence and equivalent width of prominent spectral lines, while for identification of some interesting object types (e.g. Be stars or quasars) the detailed shape of only a few lines is crucial. Here the machine learning is bringing a new methodology capable of improving the reliability of classification of such objects even in boundary cases. We present results of Spark-based semi-supervised machine learning of LAMOST spectra attempting to automatically identify the single and double-peak emission of Hα line typical for Be and B[e] stars. The labelled sample was obtained from archive of 2m Perek telescope at Ondřejov observatory. A simple physical model of spectrograph resolution was used in domain adaptation to LAMOST training domain. The resulting list of candidates contains dozens of Be stars (some are likely yet unknown), but also a bunch of interesting objects resembling spectra of quasars and even blazars, as well as many instrumental artefacts. The verification of a nature of interesting candidates benefited considerably from cross-matching and visualisation in the Virtual Observatory environment.

Prediction of the far field noise from wind energy farms

NASA Technical Reports Server (NTRS)

Shepherd, K. P.; Hubbard, H. H.

1986-01-01

The basic physical factors involved in making predictions of wind turbine noise and an approach which allows for differences in the machines, the wind energy farm configurations and propagation conditions are reviewed. Example calculations to illustrate the sensitivity of the radiated noise to such variables as machine size, spacing and numbers, and such atmosphere variables as absorption and wind direction are presented. It is found that calculated far field distances to particular sound level contours are greater for lower values of atmospheric absorption, for a larger total number of machines, for additional rows of machines and for more powerful machines. At short and intermediate distances, higher sound pressure levels are calculated for closer machine spacings, for more powerful machines, for longer row lengths and for closer row spacings.

Development of Knitted Warm Garments from Speciality Jute Yarns

NASA Astrophysics Data System (ADS)

Roy, Alok Nath

2013-09-01

Jute-polyester blended core and textured polyester multifilament cover spun-wrapped yarn was produced using existing jute spinning machines. The spun-wrapped yarn so produced show a reduction in hairiness up to 86.1 %, improvement in specific work of rupture up to 9.8 % and specific flexural rigidity up to 23.6 % over ordinary jute-polyester blended yarn. The knitted swatch produced out of these spun-wrapped yarn using seven gauge and nine gauge needle in both single jersey and double jersey knitting machines showed very good dimensional stability even after three washing. The two-ply and three-ply yarn produced from single spun-wrapped yarn can be easily used in knitting machines and also in hand-knitting for the production of sweaters. The thermal insulation value of the sweaters produced with jute-polyester blended spun-wrapped yarn is comparable with thermal insulation value of sweaters made from 100 % acrylic and 100 % wool. However, the hand-knitted sweaters showed higher thermal insulation value than the machine-knitted sweaters due to less packing of yarn in hand knitted structure as compared to machine knitting.

Reprographics Career Ladder AFSC 703X0.

DTIC Science & Technology

1981-07-01

LINEUP AND REGISTER TABLES 39 BINDING MACHINES 36 FLOURESCENT LAMPS 36 WET PROCESS PLATEMAKERS 36 ELECTRIC STAPLERS 32 MANUAL PAPER CUTTERS 32...ELECTROSTATIC COPIERS/PLATEMAKERS 78% PAPER CUTTERS 57% ELECTRIC STAPLERS 47% BINDING MACHINES 42% SINGLE HEAD DRILLS 37% PADDING RACKS 31% PLATEMAKING...HEAD DRILLS 78% MANUAL PAPER CUTTERS 71% STATION COLLATORS 51% BINDING MACHiNES 46% ELECTRIC STAPLERS 46% PLATEMAKING CAMERAS 44% SADDLE STITCHERS 42

TELNET under Single-Connection TCP Specification

DTIC Science & Technology

1976-02-02

Manager User Oriented Systems International Business Machines Corp. K54-282, Monterey and Cottle Roads San Jose, CA 95193 Dr. Leonard Y. Liu...Manager Computer Science International Business Machines Corp. K51-282, Monterey and Cottle Roads San Jose, CA 95193 Mr. Harry Reinstein... International Business Machines Corp. 1501 California Avenue Palo Alto, Ca 94303 Illinois, University of Mr. John D. Day University of Illinois Center for

An application of eddy current damping effect on single point diamond turning of titanium alloys

NASA Astrophysics Data System (ADS)

Yip, W. S.; To, S.

2017-11-01

Titanium alloys Ti6Al4V (TC4) have been popularly applied in many industries. They have superior material properties including an excellent strength-to-weight ratio and corrosion resistance. However, they are regarded as difficult to cut materials; serious tool wear, a high level of cutting vibration and low surface integrity are always involved in machining processes especially in ultra-precision machining (UPM). In this paper, a novel hybrid machining technology using an eddy current damping effect is firstly introduced in UPM to suppress machining vibration and improve the machining performance of titanium alloys. A magnetic field was superimposed on samples during single point diamond turning (SPDT) by exposing the samples in between two permanent magnets. When the titanium alloys were rotated within a magnetic field in the SPDT, an eddy current was generated through a stationary magnetic field inside the titanium alloys. An eddy current generated its own magnetic field with the opposite direction of the external magnetic field leading a repulsive force, compensating for the machining vibration induced by the turning process. The experimental results showed a remarkable improvement in cutting force variation, a significant reduction in adhesive tool wear and an extreme long chip formation in comparison to normal SPDT of titanium alloys, suggesting the enhancement of the machinability of titanium alloys using an eddy current damping effect. An eddy current damping effect was firstly introduced in the area of UPM to deliver the results of outstanding machining performance.

Conceptual design of a pulsed-power accelerator optimized for megajoule-class 1-TPa dynamic-material-physics experiments

DOE PAGES

Stygar, William A.; Reisman, David B.; Stoltzfus, Brian S.; ...

2016-07-07

In this study, we have developed a conceptual design of a next-generation pulsed-power accelerator that is optmized for driving megajoule-class dynamic-material-physics experiments at pressures as high as 1 TPa. The design is based on an accelerator architecture that is founded on three concepts: single-stage electrical-pulse compression, impedance matching, and transit-time-isolated drive circuits. Since much of the accelerator is water insulated, we refer to this machine as Neptune. The prime power source of Neptune consists of 600 independent impedance-matched Marx generators. As much as 0.8 MJ and 20 MA can be delivered in a 300-ns pulse to a 16-mΩ physics load;more » hence Neptune is a megajoule-class 20-MA arbitrary waveform generator. Neptune will allow the international scientific community to conduct dynamic equation-of-state, phase-transition, mechanical-property, and other material-physics experiments with a wide variety of well-defined drive-pressure time histories. Because Neptune can deliver on the order of a megajoule to a load, such experiments can be conducted on centimeter-scale samples at terapascal pressures with time histories as long as 1 μs.« less

Soft electroactive actuators and hard ratchet-wheels enable unidirectional locomotion of hybrid machine

NASA Astrophysics Data System (ADS)

Sun, Wenjie; Liu, Fan; Ma, Ziqi; Li, Chenghai; Zhou, Jinxiong

2017-01-01

Combining synergistically the muscle-like actuation of soft materials and load-carrying and locomotive capability of hard mechanical components results in hybrid soft machines that can exhibit specific functions. Here, we describe the design, fabrication, modeling and experiment of a hybrid soft machine enabled by marrying unidirectionally actuated dielectric elastomer (DE) membrane-spring system and ratchet wheels. Subjected to an applied voltage 8.2 kV at ramping velocity 820 V/s, the hybrid machine prototype exhibits monotonic uniaxial locomotion with an averaged velocity 0.5mm/s. The underlying physics and working mechanisms of the soft machine are verified and elucidated by finite element simulation.

Design Comparison of Inner and Outer Rotor of Permanent Magnet Flux Switching Machine for Electric Bicycle Application

NASA Astrophysics Data System (ADS)

Jusoh, L. I.; Sulaiman, E.; Bahrim, F. S.; Kumar, R.

2017-08-01

Recent advancements have led to the development of flux switching machines (FSMs) with flux sources within the stators. The advantage of being a single-piece machine with a robust rotor structure makes FSM an excellent choice for speed applications. There are three categories of FSM, namely, the permanent magnet (PM) FSM, the field excitation (FE) FSM, and the hybrid excitation (HE) FSM. The PMFSM and the FEFSM have their respective PM and field excitation coil (FEC) as their key flux sources. Meanwhile, as the name suggests, the HEFSM has a combination of PM and FECs as the flux sources. The PMFSM is a simple and cheap machine, and it has the ability to control variable flux, which would be suitable for an electric bicycle. Thus, this paper will present a design comparison between an inner rotor and an outer rotor for a single-phase permanent magnet flux switching machine with 8S-10P, designed specifically for an electric bicycle. The performance of this machine was validated using the 2D- FEA. As conclusion, the outer-rotor has much higher torque approximately at 54.2% of an innerrotor PMFSM. From the comprehensive analysis of both designs it can be conclude that output performance is lower than the SRM and IPMSM design machine. But, it shows that the possibility to increase the design performance by using “deterministic optimization method”.

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.

Quantum Computing: Solving Complex Problems

ScienceCinema

DiVincenzo, David

2018-05-22

One of the motivating ideas of quantum computation was that there could be a new kind of machine that would solve hard problems in quantum mechanics. There has been significant progress towards the experimental realization of these machines (which I will review), but there are still many questions about how such a machine could solve computational problems of interest in quantum physics. New categorizations of the complexity of computational problems have now been invented to describe quantum simulation. The bad news is that some of these problems are believed to be intractable even on a quantum computer, falling into a quantum analog of the NP class. The good news is that there are many other new classifications of tractability that may apply to several situations of physical interest.

CP Violation and the Future of Flavor Physics

NASA Astrophysics Data System (ADS)

Kiesling, Christian

2009-12-01

With the nearing completion of the first-generation experiments at asymmetric e+e- colliders running at the Υ(4S) resonance ("B-Factories") a new era of high luminosity machines is at the horizon. We report here on the plans at KEK in Japan to upgrade the KEKB machine ("SuperKEKB") with the goal of achieving an instantaneous luminosity exceeding 8×1035 cm-2 s-1, which is almost two orders of magnitude higher than KEKB. Together with the machine, the Belle detector will be upgraded as well ("Belle-II"), with significant improvements to increase its background tolerance as well as improving its physics performance. The new generation of experiments is scheduled to take first data in the year 2013.

Parabolic single-crystal diamond compound refractive lenses for coherent x-ray imaging (Conference Presentation)

NASA Astrophysics Data System (ADS)

Terentyev, Sergey; Blank, Vladimir D.; Polyakov, Sergey; Zholudev, Sergey; Snigirev, Anatoly A.; Polikarpov, Maxim; Kolodziej, Tomasz; Qian, Jun; Zhou, Hua; Shvyd'ko, Yuri V.

2016-09-01

We demonstrate parabolic single-crystal diamond compound refractive lenses [1] designed for coherent x-ray imaging resilient to extreme thermal and radiation loading expected from next generation light sources. To ensure the preservation of coherence and resilience, the lenses are manufactured from the highest-quality single-crystalline synthetic diamond material grown by a high-pressure high-temperature technique. Picosecond laser milling is applied to machine lenses to parabolic shapes with a 1-micron precision and surface roughness. A compound refractive lens comprised of six lenses with a radius of curvature R=200 microns at the vertex of the parabola and a geometrical aperture A=900 microns focuses 10 keV x-ray photons from an undulator source at the Advanced Photon Source facility to a focal spot size of 10x40 microns^2 with a gain factor of 100. [1] S. Terentyev, V. Blank, S. Polyakov, S. Zholudev, A. Snigirev, M. Polikarpov, T. Kolodziej, J. Qian, H. Zhou, and Yu. Shvyd'ko Applied Physics Letters 107, 111108 (2015); doi: 10.1063/1.4931357

Single wheel testers, single track testers, and instrumented tractors

USDA-ARS?s Scientific Manuscript database

Single wheel testers and single track testers are used for determining tractive performance characteristics of tires and tracks. Instrumented tractors are useful in determining the tractive performance of tractors. These machines are also used for determining soil-tire and soil-track interactions,...

Development and validation of methods for man-made machine interface evaluation. [for shuttles and shuttle payloads

NASA Technical Reports Server (NTRS)

Malone, T. B.; Micocci, A.

1975-01-01

The alternate methods of conducting a man-machine interface evaluation are classified as static and dynamic, and are evaluated. A dynamic evaluation tool is presented to provide for a determination of the effectiveness of the man-machine interface in terms of the sequence of operations (task and task sequences) and in terms of the physical characteristics of the interface. This dynamic checklist approach is recommended for shuttle and shuttle payload man-machine interface evaluations based on reduced preparation time, reduced data, and increased sensitivity of critical problems.

The desktop muon detector: A simple, physics-motivated machine- and electronics-shop project for university students

NASA Astrophysics Data System (ADS)

Axani, S. N.; Conrad, J. M.; Kirby, C.

2017-12-01

This paper describes the construction of a desktop muon detector, an undergraduate-level physics project that develops machine-shop and electronics-shop technical skills. The desktop muon detector is a self-contained apparatus that employs a plastic scintillator as the detection medium and a silicon photomultiplier for light collection. This detector can be battery powered and is used in conjunction with the provided software. The total cost per detector is approximately 100. We describe physics experiments we have performed, and then suggest several other interesting measurements that are possible, with one or more desktop muon detectors.

Visualization and characterization of individual type III protein secretion machines in live bacteria

PubMed Central

Lara-Tejero, María; Bewersdorf, Jörg; Galán, Jorge E.

2017-01-01

Type III protein secretion machines have evolved to deliver bacterially encoded effector proteins into eukaryotic cells. Although electron microscopy has provided a detailed view of these machines in isolation or fixed samples, little is known about their organization in live bacteria. Here we report the visualization and characterization of the Salmonella type III secretion machine in live bacteria by 2D and 3D single-molecule switching superresolution microscopy. This approach provided access to transient components of this machine, which previously could not be analyzed. We determined the subcellular distribution of individual machines, the stoichiometry of the different components of this machine in situ, and the spatial distribution of the substrates of this machine before secretion. Furthermore, by visualizing this machine in Salmonella mutants we obtained major insights into the machine’s assembly. This study bridges a major resolution gap in the visualization of this nanomachine and may serve as a paradigm for the examination of other bacterially encoded molecular machines. PMID:28533372

Phenomenology tools on cloud infrastructures using OpenStack

NASA Astrophysics Data System (ADS)

Campos, I.; Fernández-del-Castillo, E.; Heinemeyer, S.; Lopez-Garcia, A.; Pahlen, F.; Borges, G.

2013-04-01

We present a new environment for computations in particle physics phenomenology employing recent developments in cloud computing. On this environment users can create and manage "virtual" machines on which the phenomenology codes/tools can be deployed easily in an automated way. We analyze the performance of this environment based on "virtual" machines versus the utilization of physical hardware. In this way we provide a qualitative result for the influence of the host operating system on the performance of a representative set of applications for phenomenology calculations.

The world problem: on the computability of the topology of 4-manifolds

NASA Technical Reports Server (NTRS)

vanMeter, J. R.

2005-01-01

Topological classification of the 4-manifolds bridges computation theory and physics. A proof of the undecidability of the homeomorphy problem for 4-manifolds is outlined here in a clarifying way. It is shown that an arbitrary Turing machine with an arbitrary input can be encoded into the topology of a 4-manifold, such that the 4-manifold is homeomorphic to a certain other 4-manifold if and only if the corresponding Turing machine halts on the associated input. Physical implications are briefly discussed.

PARTICLE PHYSICS: CERN Gives Higgs Hunters Extra Month to Collect Data.

PubMed

Morton, O

2000-09-22

After 11 years of banging electrons and positrons together at higher energies than any other machine in the world, CERN, the European laboratory for particle physics, had decided to shut down the Large Electron-Positron collider (LEP) and install a new machine, the Large Hadron Collider (LHC), in its 27-kilometer tunnel. In 2005, the LHC will start bashing protons together at even higher energies. But tantalizing hints of a long-sought fundamental particle have forced CERN managers to grant LEP a month's reprieve.

Surface and subsurface cracks characteristics of single crystal SiC wafer in surface machining

NASA Astrophysics Data System (ADS)

Qiusheng, Y.; Senkai, C.; Jisheng, P.

2015-03-01

Different machining processes were used in the single crystal SiC wafer machining. SEM was used to observe the surface morphology and a cross-sectional cleavages microscopy method was used for subsurface cracks detection. Surface and subsurface cracks characteristics of single crystal SiC wafer in abrasive machining were analysed. The results show that the surface and subsurface cracks system of single crystal SiC wafer in abrasive machining including radial crack, lateral crack and the median crack. In lapping process, material removal is dominated by brittle removal. Lots of chipping pits were found on the lapping surface. With the particle size becomes smaller, the surface roughness and subsurface crack depth decreases. When the particle size was changed to 1.5µm, the surface roughness Ra was reduced to 24.0nm and the maximum subsurface crack was 1.2µm. The efficiency of grinding is higher than lapping. Plastic removal can be achieved by changing the process parameters. Material removal was mostly in brittle fracture when grinding with 325# diamond wheel. Plow scratches and chipping pits were found on the ground surface. The surface roughness Ra was 17.7nm and maximum subsurface crack depth was 5.8 µm. When grinding with 8000# diamond wheel, the material removal was in plastic flow. Plastic scratches were found on the surface. A smooth surface of roughness Ra 2.5nm without any subsurface cracks was obtained. Atomic scale removal was possible in cluster magnetorheological finishing with diamond abrasive size of 0.5 µm. A super smooth surface eventually obtained with a roughness of Ra 0.4nm without any subsurface crack.

Advances in molecular dynamics simulation of ultra-precision machining of hard and brittle materials

NASA Astrophysics Data System (ADS)

Guo, Xiaoguang; Li, Qiang; Liu, Tao; Kang, Renke; Jin, Zhuji; Guo, Dongming

2017-03-01

Hard and brittle materials, such as silicon, SiC, and optical glasses, are widely used in aerospace, military, integrated circuit, and other fields because of their excellent physical and chemical properties. However, these materials display poor machinability because of their hard and brittle properties. Damages such as surface micro-crack and subsurface damage often occur during machining of hard and brittle materials. Ultra-precision machining is widely used in processing hard and brittle materials to obtain nanoscale machining quality. However, the theoretical mechanism underlying this method remains unclear. This paper provides a review of present research on the molecular dynamics simulation of ultra-precision machining of hard and brittle materials. The future trends in this field are also discussed.

Comparison between extreme learning machine and wavelet neural networks in data classification

NASA Astrophysics Data System (ADS)

Yahia, Siwar; Said, Salwa; Jemai, Olfa; Zaied, Mourad; Ben Amar, Chokri

2017-03-01

Extreme learning Machine is a well known learning algorithm in the field of machine learning. It's about a feed forward neural network with a single-hidden layer. It is an extremely fast learning algorithm with good generalization performance. In this paper, we aim to compare the Extreme learning Machine with wavelet neural networks, which is a very used algorithm. We have used six benchmark data sets to evaluate each technique. These datasets Including Wisconsin Breast Cancer, Glass Identification, Ionosphere, Pima Indians Diabetes, Wine Recognition and Iris Plant. Experimental results have shown that both extreme learning machine and wavelet neural networks have reached good results.

A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine.

PubMed

Lee, Haerin; Jung, Moonki; Lee, Ki-Kwang; Lee, Sang Hun

2017-02-06

In this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human-machine-environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines.

Single crystal diamond lapping procedure

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

Grayson, R.A.

A facility capable of resharpening quality cutting edges on single crystal diamond cutting tools was needed as the demand in precision machining of special optical surfaces became a common occurrence here at Lawrence Livermore National Laboratory. A specially constructed lapping machine using an air bearing spindle was built to achieve the required edge quality. The basic design for this lap was taken out of a technical report by W.L. Duke and R.T. Lovell of Oak Ridge Y-12 Plant Union Carbide Corp. We have also purchased two commercially built lapping machines recommended to us by Mr. Cory A. Knottenbelt, formerly ofmore » R.C.A. Diamond Lapping Facility, in Indianapolis, Indiana, now doing state-of-the-art polishing and relapping at LLNL facilities.« less

Flotation machine and process for removing impurities from coals

DOEpatents

Szymocha, K.; Ignasiak, B.; Pawlak, W.; Kulik, C.; Lebowitz, H.E.

1995-12-05

The present invention is directed to a type of flotation machine that combines three separate operations in a single unit. The flotation machine is a hydraulic separator that is capable of reducing the pyrite and other mineral matter content of a coal. When the hydraulic separator is used with a flotation system, the pyrite and certain other mineral particles that may have been entrained by hydrodynamic forces associated with conventional flotation machines and/or by the attachment forces associated with the formation of microagglomerates are washed and separated from the coal. 4 figs.

Flotation machine and process for removing impurities from coals

DOEpatents

Szymocha, Kazimierz; Ignasiak, Boleslaw; Pawlak, Wanda; Kulik, Conrad; Lebowitz, Howard E.

1995-01-01

The present invention is directed to a type of flotation machine that combines three separate operations in a single unit. The flotation machine is a hydraulic separator that is capable of reducing the pyrite and other mineral matter content of a coal. When the hydraulic separator is used with a flotation system, the pyrite and certain other minerals particles that may have been entrained by hydrodynamic forces associated with conventional flotation machines and/or by the attachment forces associated with the formation of microagglomerates are washed and separated from the coal.

Flotation machine and process for removing impurities from coals

DOEpatents

Szymocha, K.; Ignasiak, B.; Pawlak, W.; Kulik, C.; Lebowitz, H.E.

1997-02-11

The present invention is directed to a type of flotation machine that combines three separate operations in a single unit. The flotation machine is a hydraulic separator that is capable of reducing the pyrite and other mineral matter content of a coal. When the hydraulic separator is used with a flotation system, the pyrite and certain other minerals particles that may have been entrained by hydrodynamic forces associated with conventional flotation machines and/or by the attachment forces associated with the formation of microagglomerates are washed and separated from the coal. 4 figs.

Flotation machine and process for removing impurities from coals

DOEpatents

Szymocha, Kazimierz; Ignasiak, Boleslaw; Pawlak, Wanda; Kulik, Conrad; Lebowitz, Howard E.

1997-01-01

The present invention is directed to a type of flotation machine that combines three separate operations in a single unit. The flotation machine is a hydraulic separator that is capable of reducing the pyrite and other mineral matter content of a coal. When the hydraulic separator is used with a flotation system, the pyrite and certain other minerals particles that may have been entrained by hydrodynamic forces associated with conventional flotation machines and/or by the attachment forces associated with the formation of microagglomerates are washed and separated from the coal.

Acoustic emission from single point machining: Part 2, Signal changes with tool wear

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

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-01-01

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

Simulation and Experimental Study on Surface Formation Mechanism in Machining of SiCp/Al Composites

NASA Astrophysics Data System (ADS)

Du, Jinguang; Zhang, Haizhen; He, Wenbin; Ma, Jun; Ming, Wuyi; Cao, Yang

2018-03-01

To intuitively reveal the surface formation mechanism in machining of SiCp/Al composites, in this paper the removal mode of reinforced particle and aluminum matrix, and their influence on surface formation mechanism were analyzed by single diamond grit cutting simulation and single diamond grit scratch experiment. Simulation and experiment results show that when the depth of cut is small, the scratched surface of the workpiece is relatively smooth; however, there are also irregular pits on the machined surface. When increasing the depth of cut, there are many obvious laminar structures on the scratched surface, and the surface appearance becomes coarser. When the cutting speed is small, the squeezing action of abrasive grit on SiC particles plays a dominant role in the extrusion of SiC particles. When increasing the cutting speed, SiC particles also occur broken or fractured; but the machined surface becomes smooth. When machining SiCp/Al composites, the SiC may happen in such removal ways, such as fracture, debonding, broken, sheared, pulled into and pulled out, etc. By means of reasonably developing micro cutting finite element simulation model of SiCp/Al composites could be used to analyze the surface formation process and particle removal way in different machining conditions.

Man/Machine Interaction Dynamics And Performance (MMIDAP) capability

NASA Technical Reports Server (NTRS)

Frisch, Harold P.

1991-01-01

The creation of an ability to study interaction dynamics between a machine and its human operator can be approached from a myriad of directions. The Man/Machine Interaction Dynamics and Performance (MMIDAP) project seeks to create an ability to study the consequences of machine design alternatives relative to the performance of both machine and operator. The class of machines to which this study is directed includes those that require the intelligent physical exertions of a human operator. While Goddard's Flight Telerobotic's program was expected to be a major user, basic engineering design and biomedical applications reach far beyond telerobotics. Ongoing efforts are outlined of the GSFC and its University and small business collaborators to integrate both human performance and musculoskeletal data bases with analysis capabilities necessary to enable the study of dynamic actions, reactions, and performance of coupled machine/operator systems.

30 CFR 18.80 - Approval of machines assembled with certified or explosion-proof components.

Code of Federal Regulations, 2014 CFR

2014-07-01

... approved machines will be accepted in lieu of certified components. (b) A single layout drawing (see Figure... approval. A design of the approval plate will accompany the notification of approval. (Refer to §§ 18.10...

A STUDY OF DISLOCATION STRUCTURE OF SUBBOUNDARIES IN MOLYBDENUM SINGLE CRYSTALS,

DTIC Science & Technology

MOLYBDENUM, *DISLOCATIONS), GRAIN STRUCTURES(METALLURGY), SINGLE CRYSTALS, ZONE MELTING, ELECTRON BEAM MELTING, GRAIN BOUNDARIES, MATHEMATICAL ANALYSIS, ETCHED CRYSTALS, ETCHING, ELECTROEROSIVE MACHINING, CHINA

Design and fabrication of complete dentures using CAD/CAM technology

PubMed Central

Han, Weili; Li, Yanfeng; Zhang, Yue; lv, Yuan; Zhang, Ying; Hu, Ping; Liu, Huanyue; Ma, Zheng; Shen, Yi

2017-01-01

Abstract The aim of the study was to test the feasibility of using commercially available computer-aided design and computer-aided manufacturing (CAD/CAM) technology including 3Shape Dental System 2013 trial version, WIELAND V2.0.049 and WIELAND ZENOTEC T1 milling machine to design and fabricate complete dentures. The modeling process of full denture available in the trial version of 3Shape Dental System 2013 was used to design virtual complete dentures on the basis of 3-dimensional (3D) digital edentulous models generated from the physical models. The virtual complete dentures designed were exported to CAM software of WIELAND V2.0.049. A WIELAND ZENOTEC T1 milling machine controlled by the CAM software was used to fabricate physical dentitions and baseplates by milling acrylic resin composite plates. The physical dentitions were bonded to the corresponding baseplates to form the maxillary and mandibular complete dentures. Virtual complete dentures were successfully designed using the software through several steps including generation of 3D digital edentulous models, model analysis, arrangement of artificial teeth, trimming relief area, and occlusal adjustment. Physical dentitions and baseplates were successfully fabricated according to the designed virtual complete dentures using milling machine controlled by a CAM software. Bonding physical dentitions to the corresponding baseplates generated the final physical complete dentures. Our study demonstrated that complete dentures could be successfully designed and fabricated by using CAD/CAM. PMID:28072686

Experiments on PIM in Support of the Development of IVA Technology for Radiography at AWE

NASA Astrophysics Data System (ADS)

Clough, Stephen G.; Thomas, Kenneth J.; Williamson, Mark C.; Phillips, Martin J.; Smith, Ian D.; Bailey, Vernon L.; Kishi, Hiroshi J.; Maenchen, John E.; Johnson, David L.

2002-12-01

The PIM machine has been designed and constructed at AWE as part of a program to investigate IVA technology for radiographic applications. PIM, as originally constructed, was a prospective single module of a 14 MV, 100 kA, ten module machine. The design of such a machine is a primary goal of the program as several are required to provide multi-axis radiography in a new Hydrodynamics Research Facility (HRF). Another goal is to design lower voltage machines (ranging from 1 to 5 MV) utilizing PIM style components. The original PIM machine consisted of a single inductive cavity pulsed by a 10 ohm water dielectric Blumlein pulse forming line (PFL) charged by a Marx generator. These components successfully achieved their design voltages and data on the prepulse was obtained showing it to be worse than expected. This information provided a basis for design work on the 14 MV HRF IVA, carried out by Titan-PSD, resulting in a proposal for a prepulse switch, a prototype of which should be installed on PIM by the end of this year. The original single, coaxial switch used to initiate the Blumlein has been replaced by a prototype laser triggered switching arrangement, also designed by Titan-PSD, which it was desired to test prior to its eventual use in the HRF. Despite problems with the laser, which will necessitate further experiments, it was determined that laser triggering with low jitter was occurring. A split oil co-ax feed has now been used to install a second cavity, in parallel with the first, on the PIM Blumlein. This two cavity configuration provides a prototype for future radiographic machines operating at up to 3 MV and a test facility for diode research.

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.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

PubMed

Newton, Richard; Wernisch, Lorenz

2017-08-11

Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping. Previously we developed an empirical Bayesian model for the classification of serotypes from a molecular serotyping array. With only few samples available, a model driven approach was the only option. In the meanwhile, several thousand samples have been made available to us, providing an opportunity to investigate serotype classification by machine learning methods, which could complement the Bayesian model. We compare the performance of the original Bayesian model with two machine learning algorithms: Gradient Boosting Machines and Random Forests. We present our results as an example of a generic strategy whereby a preliminary probabilistic model is complemented or replaced by a machine learning classifier once enough data are available. Despite the availability of thousands of serotyping arrays, a problem encountered when applying machine learning methods is the lack of training data containing mixtures of serotypes; due to the large number of possible combinations. Most of the available training data comprises samples with only a single serotype. To overcome the lack of training data we implemented an iterative analysis, creating artificial training data of serotype mixtures by combining raw data from single serotype arrays. With the enhanced training set the machine learning algorithms out perform the original Bayesian model. However, for serotypes currently lacking sufficient training data the best performing implementation was a combination of the results of the Bayesian Model and the Gradient Boosting Machine. As well as being an effective method for classifying biological data, machine learning can also be used as an efficient method for revealing subtle biological insights, which we illustrate with an example.

(Machine) learning to do more with less

NASA Astrophysics Data System (ADS)

Cohen, Timothy; Freytsis, Marat; Ostdiek, Bryan

2018-02-01

Determining the best method for training a machine learning algorithm is critical to maximizing its ability to classify data. In this paper, we compare the standard "fully supervised" approach (which relies on knowledge of event-by-event truth-level labels) with a recent proposal that instead utilizes class ratios as the only discriminating information provided during training. This so-called "weakly supervised" technique has access to less information than the fully supervised method and yet is still able to yield impressive discriminating power. In addition, weak supervision seems particularly well suited to particle physics since quantum mechanics is incompatible with the notion of mapping an individual event onto any single Feynman diagram. We examine the technique in detail — both analytically and numerically — with a focus on the robustness to issues of mischaracterizing the training samples. Weakly supervised networks turn out to be remarkably insensitive to a class of systematic mismodeling. Furthermore, we demonstrate that the event level outputs for weakly versus fully supervised networks are probing different kinematics, even though the numerical quality metrics are essentially identical. This implies that it should be possible to improve the overall classification ability by combining the output from the two types of networks. For concreteness, we apply this technology to a signature of beyond the Standard Model physics to demonstrate that all these impressive features continue to hold in a scenario of relevance to the LHC. Example code is provided on GitHub.

Machine Learning and Deep Learning Models to Predict Runoff Water Quantity and Quality

NASA Astrophysics Data System (ADS)

Bradford, S. A.; Liang, J.; Li, W.; Murata, T.; Simunek, J.

2017-12-01

Contaminants can be rapidly transported at the soil surface by runoff to surface water bodies. Physically-based models, which are based on the mathematical description of main hydrological processes, are key tools for predicting surface water impairment. Along with physically-based models, data-driven models are becoming increasingly popular for describing the behavior of hydrological and water resources systems since these models can be used to complement or even replace physically based-models. In this presentation we propose a new data-driven model as an alternative to a physically-based overland flow and transport model. First, we have developed a physically-based numerical model to simulate overland flow and contaminant transport (the HYDRUS-1D overland flow module). A large number of numerical simulations were carried out to develop a database containing information about the impact of various input parameters (weather patterns, surface topography, vegetation, soil conditions, contaminants, and best management practices) on runoff water quantity and quality outputs. This database was used to train data-driven models. Three different methods (Neural Networks, Support Vector Machines, and Recurrence Neural Networks) were explored to prepare input- output functional relations. Results demonstrate the ability and limitations of machine learning and deep learning models to predict runoff water quantity and quality.

Finite machines, mental procedures, and modern physics.

PubMed

Lupacchini, Rossella

2007-01-01

A Turing machine provides a mathematical definition of the natural process of calculating. It rests on trust that a procedure of reason can be reproduced mechanically. Turing's analysis of the concept of mechanical procedure in terms of a finite machine convinced Gödel of the validity of the Church thesis. And yet, Gödel's later concern was that, insofar as Turing's work shows that "mental procedure cannot go beyond mechanical procedures", it would imply the same kind of limitation on human mind. He therefore deems Turing's argument to be inconclusive. The question then arises as to which extent a computing machine operating by finite means could provide an adequate model of human intelligence. It is argued that a rigorous answer to this question can be given by developing Turing's considerations on the nature of mental processes. For Turing such processes are the consequence of physical processes and he seems to be led to the conclusion that quantum mechanics could help to find a more comprehensive explanation of them.

Using virtual machine monitors to overcome the challenges of monitoring and managing virtualized cloud infrastructures

NASA Astrophysics Data System (ADS)

Bamiah, Mervat Adib; Brohi, Sarfraz Nawaz; Chuprat, Suriayati

2012-01-01

Virtualization is one of the hottest research topics nowadays. Several academic researchers and developers from IT industry are designing approaches for solving security and manageability issues of Virtual Machines (VMs) residing on virtualized cloud infrastructures. Moving the application from a physical to a virtual platform increases the efficiency, flexibility and reduces management cost as well as effort. Cloud computing is adopting the paradigm of virtualization, using this technique, memory, CPU and computational power is provided to clients' VMs by utilizing the underlying physical hardware. Beside these advantages there are few challenges faced by adopting virtualization such as management of VMs and network traffic, unexpected additional cost and resource allocation. Virtual Machine Monitor (VMM) or hypervisor is the tool used by cloud providers to manage the VMs on cloud. There are several heterogeneous hypervisors provided by various vendors that include VMware, Hyper-V, Xen and Kernel Virtual Machine (KVM). Considering the challenge of VM management, this paper describes several techniques to monitor and manage virtualized cloud infrastructures.

Design comparison of single phase outer and inner-rotor hybrid excitation flux switching motor for hybrid electric vehicles

NASA Astrophysics Data System (ADS)

Mazlan, Mohamed Mubin Aizat; Sulaiman, Erwan; Husin, Zhafir Aizat; Othman, Syed Muhammad Naufal Syed; Khan, Faisal

2015-05-01

In hybrid excitation machines (HEMs), there are two main flux sources which are permanent magnet (PM) and field excitation coil (FEC). These HEMs have better features when compared with the interior permanent magnet synchronous machines (IPMSM) used in conventional hybrid electric vehicles (HEVs). Since all flux sources including PM, FEC and armature coils are located on the stator core, the rotor becomes a single piece structure similar with switch reluctance machine (SRM). The combined flux generated by PM and FEC established more excitation fluxes that are required to produce much higher torque of the motor. In addition, variable DC FEC can control the flux capabilities of the motor, thus the machine can be applied for high-speed motor drive system. In this paper, the comparisons of single-phase 8S-4P outer and inner rotor hybrid excitation flux switching machine (HEFSM) are presented. Initially, design procedures of the HEFSM including parts drawing, materials and conditions setting, and properties setting are explained. Flux comparisons analysis is performed to investigate the flux capabilities at various current densities. Then the flux linkages of PM with DC FEC of various DC FEC current densities are examined. Finally torque performances are analyzed at various armature and FEC current densities for both designs. As a result, the outer-rotor HEFSM has higher flux linkage of PM with DC FEC and higher average torque of approximately 10% when compared with inner-rotor HEFSM.

Technical Note: Defining cyclotron-based clinical scanning proton machines in a FLUKA Monte Carlo system.

PubMed

Fiorini, Francesca; Schreuder, Niek; Van den Heuvel, Frank

2018-02-01

Cyclotron-based pencil beam scanning (PBS) proton machines represent nowadays the majority and most affordable choice for proton therapy facilities, however, their representation in Monte Carlo (MC) codes is more complex than passively scattered proton system- or synchrotron-based PBS machines. This is because degraders are used to decrease the energy from the cyclotron maximum energy to the desired energy, resulting in a unique spot size, divergence, and energy spread depending on the amount of degradation. This manuscript outlines a generalized methodology to characterize a cyclotron-based PBS machine in a general-purpose MC code. The code can then be used to generate clinically relevant plans starting from commercial TPS plans. The described beam is produced at the Provision Proton Therapy Center (Knoxville, TN, USA) using a cyclotron-based IBA Proteus Plus equipment. We characterized the Provision beam in the MC FLUKA using the experimental commissioning data. The code was then validated using experimental data in water phantoms for single pencil beams and larger irregular fields. Comparisons with RayStation TPS plans are also presented. Comparisons of experimental, simulated, and planned dose depositions in water plans show that same doses are calculated by both programs inside the target areas, while penumbrae differences are found at the field edges. These differences are lower for the MC, with a γ(3%-3 mm) index never below 95%. Extensive explanations on how MC codes can be adapted to simulate cyclotron-based scanning proton machines are given with the aim of using the MC as a TPS verification tool to check and improve clinical plans. For all the tested cases, we showed that dose differences with experimental data are lower for the MC than TPS, implying that the created FLUKA beam model is better able to describe the experimental beam. © 2017 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine.

Single Molecule Cluster Analysis Identifies Signature Dynamic Conformations along the Splicing Pathway

PubMed Central

Blanco, Mario R.; Martin, Joshua S.; Kahlscheuer, Matthew L.; Krishnan, Ramya; Abelson, John; Laederach, Alain; Walter, Nils G.

2016-01-01

The spliceosome is the dynamic RNA-protein machine responsible for faithfully splicing introns from precursor messenger RNAs (pre-mRNAs). Many of the dynamic processes required for the proper assembly, catalytic activation, and disassembly of the spliceosome as it acts on its pre-mRNA substrate remain poorly understood, a challenge that persists for many biomolecular machines. Here, we developed a fluorescence-based Single Molecule Cluster Analysis (SiMCAn) tool to dissect the manifold conformational dynamics of a pre-mRNA through the splicing cycle. By clustering common dynamic behaviors derived from selectively blocked splicing reactions, SiMCAn was able to identify signature conformations and dynamic behaviors of multiple ATP-dependent intermediates. In addition, it identified a conformation adopted late in splicing by a 3′ splice site mutant, invoking a mechanism for substrate proofreading. SiMCAn presents a novel framework for interpreting complex single molecule behaviors that should prove widely useful for the comprehensive analysis of a plethora of dynamic cellular machines. PMID:26414013

Evolution of stacking fault tetrahedral and work hardening effect in copper single crystals

NASA Astrophysics Data System (ADS)

Liu, Hai Tao; Zhu, Xiu Fu; Sun, Ya Zhou; Xie, Wen Kun

2017-11-01

Stacking fault tetrahedral (SFT), generated in machining of copper single crystal as one type of subsurface defects, has significant influence on the performance of workpiece. In this study, molecular dynamics (MD) simulation is used to investigate the evolution of stacking fault tetrahedral in nano-cutting of copper single crystal. The result shows that SFT is nucleated at the intersection of differently oriented stacking fault (SF) planes and SFT evolves from the preform only containing incomplete surfaces into a solid defect. The evolution of SFT contains several stress fluctuations until the complete formation. Nano-indentation simulation is then employed on the machined workpiece from nano-cutting, through which the interaction between SFT and later-formed dislocations in subsurface is studied. In the meanwhile, force-depth curves obtained from nano-indentation on pristine and machined workpieces are compared to analyze the mechanical properties. By simulation of nano-cutting and nano-indentation, it is verified that SFT is a reason of the work hardening effect.

Single-molecule protein unfolding and translocation by an ATP-fueled proteolytic machine

PubMed Central

Aubin-Tam, Marie-Eve; Olivares, Adrian O.; Sauer, Robert T.; Baker, Tania A.; Lang, Matthew J.

2011-01-01

All cells employ ATP-powered proteases for protein-quality control and regulation. In the ClpXP protease, ClpX is a AAA+ machine that recognizes specific protein substrates, unfolds these molecules, and then translocates the denatured polypeptide through a central pore and into ClpP for degradation. Here, we use optical-trapping nanometry to probe the mechanics of enzymatic unfolding and translocation of single molecules of a multidomain substrate. Our experiments demonstrate the capacity of ClpXP and ClpX to perform mechanical work under load, reveal very fast and highly cooperative unfolding of individual substrate domains, suggest a translocation step size of 5–8 amino acids, and support a power-stroke model of denaturation in which successful enzyme-mediated unfolding of stable domains requires coincidence between mechanical pulling by the enzyme and a transient stochastic reduction in protein stability. We anticipate that single-molecule studies of the mechanical properties of other AAA+ proteolytic machines will reveal many shared features with ClpXP. PMID:21496645

Dynamic provisioning of local and remote compute resources with OpenStack

NASA Astrophysics Data System (ADS)

Giffels, M.; Hauth, T.; Polgart, F.; Quast, G.

2015-12-01

Modern high-energy physics experiments rely on the extensive usage of computing resources, both for the reconstruction of measured events as well as for Monte-Carlo simulation. The Institut fur Experimentelle Kernphysik (EKP) at KIT is participating in both the CMS and Belle experiments with computing and storage resources. In the upcoming years, these requirements are expected to increase due to growing amount of recorded data and the rise in complexity of the simulated events. It is therefore essential to increase the available computing capabilities by tapping into all resource pools. At the EKP institute, powerful desktop machines are available to users. Due to the multi-core nature of modern CPUs, vast amounts of CPU time are not utilized by common desktop usage patterns. Other important providers of compute capabilities are classical HPC data centers at universities or national research centers. Due to the shared nature of these installations, the standardized software stack required by HEP applications cannot be installed. A viable way to overcome this constraint and offer a standardized software environment in a transparent manner is the usage of virtualization technologies. The OpenStack project has become a widely adopted solution to virtualize hardware and offer additional services like storage and virtual machine management. This contribution will report on the incorporation of the institute's desktop machines into a private OpenStack Cloud. The additional compute resources provisioned via the virtual machines have been used for Monte-Carlo simulation and data analysis. Furthermore, a concept to integrate shared, remote HPC centers into regular HEP job workflows will be presented. In this approach, local and remote resources are merged to form a uniform, virtual compute cluster with a single point-of-entry for the user. Evaluations of the performance and stability of this setup and operational experiences will be discussed.

21 CFR 890.1850 - Diagnostic muscle stimulator.

Code of Federal Regulations, 2010 CFR

2010-04-01

...) MEDICAL DEVICES PHYSICAL MEDICINE DEVICES Physical Medicine Diagnostic Devices § 890.1850 Diagnostic... electromyograph machine to initiate muscle activity. It is intended for medical purposes, such as to diagnose...

Plasma Wall interaction in the IGNITOR machine

NASA Astrophysics Data System (ADS)

Ferro, C.

1998-11-01

One of the critical issues in ignited machines is the management of the heat and particle exhaust without degradation of the plasma quality (pollution and confinement time) and without damage of the material facing the plasma. The IGNITOR machine has been conceived as a ``limiter" device, i.e., with the plasma leaning nearly on the entire surface of the first wall. Peak heat loads can easily be maintained at values lower than 1.35 MW/m^2 even considering displacements of the plasma column^1. This ``limiter" choice is based on the operational performances of high density, high field machines which suggests that intrinsic physics processes in the edge of the plasma are effective in spreading heat loads and maintaining the plasma pollution at a low level. The possibility of these operating scenarios has been demonstrated recently by different machines both in limiter and divertor configurations. The basis for the different physical processes that are expected to influence the IGNITOR edge parameters ^2 are discussed and a comparison with the latest experimental results is given. ^1 C. Ferro, G. Franzoni, R. Zanino, ENEA Internal Report RT/ERG/FUS/94/14. ^2 C. Ferro, R. Zanino, J. Nucl. Mater. 543, 176 (1990).

An SVM-Based Classifier for Estimating the State of Various Rotating Components in Agro-Industrial Machinery with a Vibration Signal Acquired from a Single Point on the Machine Chassis

PubMed Central

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-01-01

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels. PMID:25372618

An SVM-based classifier for estimating the state of various rotating components in agro-industrial machinery with a vibration signal acquired from a single point on the machine chassis.

PubMed

Ruiz-Gonzalez, Ruben; Gomez-Gil, Jaime; Gomez-Gil, Francisco Javier; Martínez-Martínez, Víctor

2014-11-03

The goal of this article is to assess the feasibility of estimating the state of various rotating components in agro-industrial machinery by employing just one vibration signal acquired from a single point on the machine chassis. To do so, a Support Vector Machine (SVM)-based system is employed. Experimental tests evaluated this system by acquiring vibration data from a single point of an agricultural harvester, while varying several of its working conditions. The whole process included two major steps. Initially, the vibration data were preprocessed through twelve feature extraction algorithms, after which the Exhaustive Search method selected the most suitable features. Secondly, the SVM-based system accuracy was evaluated by using Leave-One-Out cross-validation, with the selected features as the input data. The results of this study provide evidence that (i) accurate estimation of the status of various rotating components in agro-industrial machinery is possible by processing the vibration signal acquired from a single point on the machine structure; (ii) the vibration signal can be acquired with a uniaxial accelerometer, the orientation of which does not significantly affect the classification accuracy; and, (iii) when using an SVM classifier, an 85% mean cross-validation accuracy can be reached, which only requires a maximum of seven features as its input, and no significant improvements are noted between the use of either nonlinear or linear kernels.

Jacks--A Study of Simple Machines.

ERIC Educational Resources Information Center

Parsons, Ralph

This vocational physics individualized student instructional module on jacks (simple machines used to lift heavy objects) contains student prerequisites and objectives, an introduction, and sections on the ratchet bumper jack, the hydraulic jack, the screw jack, and load limitations. Designed with a laboratory orientation, each section consists of…

Fundamental aspects of steady-state conversion of heat to work at the nanoscale

NASA Astrophysics Data System (ADS)

Benenti, Giuliano; Casati, Giulio; Saito, Keiji; Whitney, Robert S.

2017-06-01

In recent years, the study of heat to work conversion has been re-invigorated by nanotechnology. Steady-state devices do this conversion without any macroscopic moving parts, through steady-state flows of microscopic particles such as electrons, photons, phonons, etc. This review aims to introduce some of the theories used to describe these steady-state flows in a variety of mesoscopic or nanoscale systems. These theories are introduced in the context of idealized machines which convert heat into electrical power (heat-engines) or convert electrical power into a heat flow (refrigerators). In this sense, the machines could be categorized as thermoelectrics, although this should be understood to include photovoltaics when the heat source is the sun. As quantum mechanics is important for most such machines, they fall into the field of quantum thermodynamics. In many cases, the machines we consider have few degrees of freedom, however the reservoirs of heat and work that they interact with are assumed to be macroscopic. This review discusses different theories which can take into account different aspects of mesoscopic and nanoscale physics, such as coherent quantum transport, magnetic-field induced effects (including topological ones such as the quantum Hall effect), and single electron charging effects. It discusses the efficiency of thermoelectric conversion, and the thermoelectric figure of merit. More specifically, the theories presented are (i) linear response theory with or without magnetic fields, (ii) Landauer scattering theory in the linear response regime and far from equilibrium, (iii) Green-Kubo formula for strongly interacting systems within the linear response regime, (iv) rate equation analysis for small quantum machines with or without interaction effects, (v) stochastic thermodynamic for fluctuating small systems. In all cases, we place particular emphasis on the fundamental questions about the bounds on ideal machines. Can magnetic-fields change the bounds on power or efficiency? What is the relationship between quantum theories of transport and the laws of thermodynamics? Does quantum mechanics place fundamental bounds on heat to work conversion which are absent in the thermodynamics of classical systems?

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

Wang Yinan; Shi Handuo; Xiong Zhaoxi

We present a unified universal quantum cloning machine, which combines several different existing universal cloning machines together, including the asymmetric case. In this unified framework, the identical pure states are projected equally into each copy initially constituted by input and one half of the maximally entangled states. We show explicitly that the output states of those universal cloning machines are the same. One importance of this unified cloning machine is that the cloning procession is always the symmetric projection, which reduces dramatically the difficulties for implementation. Also, it is found that this unified cloning machine can be directly modified tomore » the general asymmetric case. Besides the global fidelity and the single-copy fidelity, we also present all possible arbitrary-copy fidelities.« less

Geologic Carbon Sequestration Leakage Detection: A Physics-Guided Machine Learning Approach

NASA Astrophysics Data System (ADS)

Lin, Y.; Harp, D. R.; Chen, B.; Pawar, R.

2017-12-01

One of the risks of large-scale geologic carbon sequestration is the potential migration of fluids out of the storage formations. Accurate and fast detection of this fluids migration is not only important but also challenging, due to the large subsurface uncertainty and complex governing physics. Traditional leakage detection and monitoring techniques rely on geophysical observations including pressure. However, the resulting accuracy of these methods is limited because of indirect information they provide requiring expert interpretation, therefore yielding in-accurate estimates of leakage rates and locations. In this work, we develop a novel machine-learning technique based on support vector regression to effectively and efficiently predict the leakage locations and leakage rates based on limited number of pressure observations. Compared to the conventional data-driven approaches, which can be usually seem as a "black box" procedure, we develop a physics-guided machine learning method to incorporate the governing physics into the learning procedure. To validate the performance of our proposed leakage detection method, we employ our method to both 2D and 3D synthetic subsurface models. Our novel CO2 leakage detection method has shown high detection accuracy in the example problems.

Hidden physics models: Machine learning of nonlinear partial differential equations

NASA Astrophysics Data System (ADS)

Raissi, Maziar; Karniadakis, George Em

2018-03-01

While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from small data. In particular, we introduce hidden physics models, which are essentially data-efficient learning machines capable of leveraging the underlying laws of physics, expressed by time dependent and nonlinear partial differential equations, to extract patterns from high-dimensional data generated from experiments. The proposed methodology may be applied to the problem of learning, system identification, or data-driven discovery of partial differential equations. Our framework relies on Gaussian processes, a powerful tool for probabilistic inference over functions, that enables us to strike a balance between model complexity and data fitting. The effectiveness of the proposed approach is demonstrated through a variety of canonical problems, spanning a number of scientific domains, including the Navier-Stokes, Schrödinger, Kuramoto-Sivashinsky, and time dependent linear fractional equations. The methodology provides a promising new direction for harnessing the long-standing developments of classical methods in applied mathematics and mathematical physics to design learning machines with the ability to operate in complex domains without requiring large quantities of data.

SUPAR: Smartphone as a ubiquitous physical activity recognizer for u-healthcare services.

PubMed

Fahim, Muhammad; Lee, Sungyoung; Yoon, Yongik

2014-01-01

Current generation smartphone can be seen as one of the most ubiquitous device for physical activity recognition. In this paper we proposed a physical activity recognizer to provide u-healthcare services in a cost effective manner by utilizing cloud computing infrastructure. Our model is comprised on embedded triaxial accelerometer of the smartphone to sense the body movements and a cloud server to store and process the sensory data for numerous kind of services. We compute the time and frequency domain features over the raw signals and evaluate different machine learning algorithms to identify an accurate activity recognition model for four kinds of physical activities (i.e., walking, running, cycling and hopping). During our experiments we found Support Vector Machine (SVM) algorithm outperforms for the aforementioned physical activities as compared to its counterparts. Furthermore, we also explain how smartphone application and cloud server communicate with each other.

Edge detection

NASA Astrophysics Data System (ADS)

Hildreth, E. C.

1985-09-01

For both biological systems and machines, vision begins with a large and unwieldly array of measurements of the amount of light reflected from surfaces in the environment. The goal of vision is to recover physical properties of objects in the scene such as the location of object boundaries and the structure, color and texture of object surfaces, from the two-dimensional image that is projected onto the eye or camera. This goal is not achieved in a single step: vision proceeds in stages, with each stage producing increasingly more useful descriptions of the image and then the scene. The first clues about the physical properties of the scene are provided by the changes of intensity in the image. The importance of intensity changes and edges in early visual processing has led to extensive research on their detection, description and use, both in computer and biological vision systems. This article reviews some of the theory that underlies the detection of edges, and the methods used to carry out this analysis.

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

DOE PAGES

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian; ...

2017-09-29

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

HEPCloud, a New Paradigm for HEP Facilities: CMS Amazon Web Services Investigation

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

Holzman, Burt; Bauerdick, Lothar A. T.; Bockelman, Brian

Historically, high energy physics computing has been performed on large purpose-built computing systems. These began as single-site compute facilities, but have evolved into the distributed computing grids used today. Recently, there has been an exponential increase in the capacity and capability of commercial clouds. Cloud resources are highly virtualized and intended to be able to be flexibly deployed for a variety of computing tasks. There is a growing interest among the cloud providers to demonstrate the capability to perform large-scale scientific computing. In this paper, we discuss results from the CMS experiment using the Fermilab HEPCloud facility, which utilized bothmore » local Fermilab resources and virtual machines in the Amazon Web Services Elastic Compute Cloud. We discuss the planning, technical challenges, and lessons learned involved in performing physics workflows on a large-scale set of virtualized resources. Additionally, we will discuss the economics and operational efficiencies when executing workflows both in the cloud and on dedicated resources.« less

The LET Procedure for Prosthetic Myocontrol: Towards Multi-DOF Control Using Single-DOF Activations.

PubMed

Nowak, Markus; Castellini, Claudio

2016-01-01

Simultaneous and proportional myocontrol of dexterous hand prostheses is to a large extent still an open problem. With the advent of commercially and clinically available multi-fingered hand prostheses there are now more independent degrees of freedom (DOFs) in prostheses than can be effectively controlled using surface electromyography (sEMG), the current standard human-machine interface for hand amputees. In particular, it is uncertain, whether several DOFs can be controlled simultaneously and proportionally by exclusively calibrating the intended activation of single DOFs. The problem is currently solved by training on all required combinations. However, as the number of available DOFs grows, this approach becomes overly long and poses a high cognitive burden on the subject. In this paper we present a novel approach to overcome this problem. Multi-DOF activations are artificially modelled from single-DOF ones using a simple linear combination of sEMG signals, which are then added to the training set. This procedure, which we named LET (Linearly Enhanced Training), provides an augmented data set to any machine-learning-based intent detection system. In two experiments involving intact subjects, one offline and one online, we trained a standard machine learning approach using the full data set containing single- and multi-DOF activations as well as using the LET-augmented data set in order to evaluate the performance of the LET procedure. The results indicate that the machine trained on the latter data set obtains worse results in the offline experiment compared to the full data set. However, the online implementation enables the user to perform multi-DOF tasks with almost the same precision as single-DOF tasks without the need of explicitly training multi-DOF activations. Moreover, the parameters involved in the system are statistically uniform across subjects.

The impact of the availability of school vending machines on eating behavior during lunch: the Youth Physical Activity and Nutrition Survey.

PubMed

Park, Sohyun; Sappenfield, William M; Huang, Youjie; Sherry, Bettylou; Bensyl, Diana M

2010-10-01

Childhood obesity is a major public health concern and is associated with substantial morbidities. Access to less-healthy foods might facilitate dietary behaviors that contribute to obesity. However, less-healthy foods are usually available in school vending machines. This cross-sectional study examined the prevalence of students buying snacks or beverages from school vending machines instead of buying school lunch and predictors of this behavior. Analyses were based on the 2003 Florida Youth Physical Activity and Nutrition Survey using a representative sample of 4,322 students in grades six through eight in 73 Florida public middle schools. Analyses included χ2 tests and logistic regression. The outcome measure was buying a snack or beverage from vending machines 2 or more days during the previous 5 days instead of buying lunch. The survey response rate was 72%. Eighteen percent of respondents reported purchasing a snack or beverage from a vending machine 2 or more days during the previous 5 school days instead of buying school lunch. Although healthier options were available, the most commonly purchased vending machine items were chips, pretzels/crackers, candy bars, soda, and sport drinks. More students chose snacks or beverages instead of lunch in schools where beverage vending machines were also available than did students in schools where beverage vending machines were unavailable: 19% and 7%, respectively (P≤0.05). The strongest risk factor for buying snacks or beverages from vending machines instead of buying school lunch was availability of beverage vending machines in schools (adjusted odds ratio=3.5; 95% confidence interval, 2.2 to 5.7). Other statistically significant risk factors were smoking, non-Hispanic black race/ethnicity, Hispanic ethnicity, and older age. Although healthier choices were available, the most common choices were the less-healthy foods. Schools should consider developing policies to reduce the availability of less-healthy choices in vending machines and to reduce access to beverage vending machines. Copyright © 2010 American Dietetic Association. Published by Elsevier Inc. All rights reserved.

Comparison and Analysis on Mechanical Property and Machinability about Polyetheretherketone and Carbon-Fibers Reinforced Polyetheretherketone

PubMed Central

Ji, Shijun; Sun, Changrui; Zhao, Ji; Liang, Fusheng

2015-01-01

The aim of this paper is to compare the mechanical property and machinability of Polyetheretherketone (PEEK) and 30 wt% carbon-fibers reinforced Polyetheretherketone (PEEK CF 30). The method of nano-indentation is used to investigate the microscopic mechanical property. The evolution of load with displacement, Young’s modulus curves and hardness curves are analyzed. The results illustrate that the load-displacement curves of PEEK present better uniformity, and the variation of Young’s modulus and hardness of PEEK both change smaller at the experimental depth. The machinability between PEEK and PEEK CF 30 are also compared by the method of single-point diamond turning (SPDT), and the peak-to-valley value (PV) and surface roughness (Ra) are obtained to evaluate machinability of the materials after machining. The machining results show that PEEK has smaller PV and Ra, which means PEEK has superior machinability. PMID:28793428

Comparison and Analysis on Mechanical Property and Machinability about Polyetheretherketone and Carbon-Fibers Reinforced Polyetheretherketone.

PubMed

Ji, Shijun; Sun, Changrui; Zhao, Ji; Liang, Fusheng

2015-07-07

The aim of this paper is to compare the mechanical property and machinability of Polyetheretherketone (PEEK) and 30 wt% carbon-fibers reinforced Polyetheretherketone (PEEK CF 30). The method of nano-indentation is used to investigate the microscopic mechanical property. The evolution of load with displacement, Young's modulus curves and hardness curves are analyzed. The results illustrate that the load-displacement curves of PEEK present better uniformity, and the variation of Young's modulus and hardness of PEEK both change smaller at the experimental depth. The machinability between PEEK and PEEK CF 30 are also compared by the method of single-point diamond turning (SPDT), and the peak-to-valley value (PV) and surface roughness (Ra) are obtained to evaluate machinability of the materials after machining. The machining results show that PEEK has smaller PV and Ra, which means PEEK has superior machinability.

Three independent one-dimensional margins for single-fraction frameless stereotactic radiosurgery brain cases using CBCT

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

Zhang, Qinghui; Chan, Maria F.; Burman, Chandra

2013-12-15

Purpose: Setting a proper margin is crucial for not only delivering the required radiation dose to a target volume, but also reducing the unnecessary radiation to the adjacent organs at risk. This study investigated the independent one-dimensional symmetric and asymmetric margins between the clinical target volume (CTV) and the planning target volume (PTV) for linac-based single-fraction frameless stereotactic radiosurgery (SRS).Methods: The authors assumed a Dirac delta function for the systematic error of a specific machine and a Gaussian function for the residual setup errors. Margin formulas were then derived in details to arrive at a suitable CTV-to-PTV margin for single-fractionmore » frameless SRS. Such a margin ensured that the CTV would receive the prescribed dose in 95% of the patients. To validate our margin formalism, the authors retrospectively analyzed nine patients who were previously treated with noncoplanar conformal beams. Cone-beam computed tomography (CBCT) was used in the patient setup. The isocenter shifts between the CBCT and linac were measured for a Varian Trilogy linear accelerator for three months. For each plan, the authors shifted the isocenter of the plan in each direction by ±3 mm simultaneously to simulate the worst setup scenario. Subsequently, the asymptotic behavior of the CTV V{sub 80%} for each patient was studied as the setup error approached the CTV-PTV margin.Results: The authors found that the proper margin for single-fraction frameless SRS cases with brain cancer was about 3 mm for the machine investigated in this study. The isocenter shifts between the CBCT and the linac remained almost constant over a period of three months for this specific machine. This confirmed our assumption that the machine systematic error distribution could be approximated as a delta function. This definition is especially relevant to a single-fraction treatment. The prescribed dose coverage for all the patients investigated was 96.1%± 5.5% with an extreme 3-mm setup error in all three directions simultaneously. It was found that the effect of the setup error on dose coverage was tumor location dependent. It mostly affected the tumors located in the posterior part of the brain, resulting in a minimum coverage of approximately 72%. This was entirely due to the unique geometry of the posterior head.Conclusions: Margin expansion formulas were derived for single-fraction frameless SRS such that the CTV would receive the prescribed dose in 95% of the patients treated for brain cancer. The margins defined in this study are machine-specific and account for nonzero mean systematic error. The margin for single-fraction SRS for a group of machines was also derived in this paper.« less

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.

Predicting the Performance of Chain Saw Machines Based on Shore Scleroscope Hardness

NASA Astrophysics Data System (ADS)

Tumac, Deniz

2014-03-01

Shore hardness has been used to estimate several physical and mechanical properties of rocks over the last few decades. However, the number of researches correlating Shore hardness with rock cutting performance is quite limited. Also, rather limited researches have been carried out on predicting the performance of chain saw machines. This study differs from the previous investigations in the way that Shore hardness values (SH1, SH2, and deformation coefficient) are used to determine the field performance of chain saw machines. The measured Shore hardness values are correlated with the physical and mechanical properties of natural stone samples, cutting parameters (normal force, cutting force, and specific energy) obtained from linear cutting tests in unrelieved cutting mode, and areal net cutting rate of chain saw machines. Two empirical models developed previously are improved for the prediction of the areal net cutting rate of chain saw machines. The first model is based on a revised chain saw penetration index, which uses SH1, machine weight, and useful arm cutting depth as predictors. The second model is based on the power consumed for only cutting the stone, arm thickness, and specific energy as a function of the deformation coefficient. While cutting force has a strong relationship with Shore hardness values, the normal force has a weak or moderate correlation. Uniaxial compressive strength, Cerchar abrasivity index, and density can also be predicted by Shore hardness values.

Predicting Solar Activity Using Machine-Learning Methods

NASA Astrophysics Data System (ADS)

Bobra, M.

2017-12-01

Of all the activity observed on the Sun, two of the most energetic events are flares and coronal mass ejections. However, we do not, as of yet, fully understand the physical mechanism that triggers solar eruptions. A machine-learning algorithm, which is favorable in cases where the amount of data is large, is one way to [1] empirically determine the signatures of this mechanism in solar image data and [2] use them to predict solar activity. In this talk, we discuss the application of various machine learning algorithms - specifically, a Support Vector Machine, a sparse linear regression (Lasso), and Convolutional Neural Network - to image data from the photosphere, chromosphere, transition region, and corona taken by instruments aboard the Solar Dynamics Observatory in order to predict solar activity on a variety of time scales. Such an approach may be useful since, at the present time, there are no physical models of flares available for real-time prediction. We discuss our results (Bobra and Couvidat, 2015; Bobra and Ilonidis, 2016; Jonas et al., 2017) as well as other attempts to predict flares using machine-learning (e.g. Ahmed et al., 2013; Nishizuka et al. 2017) and compare these results with the more traditional techniques used by the NOAA Space Weather Prediction Center (Crown, 2012). We also discuss some of the challenges in using machine-learning algorithms for space science applications.

Acoustic emission from single point machining: Part 2, Signal changes with tool wear. Revised

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

Heiple, C.R.; Carpenter, S.H.; Armentrout, D.L.

1989-12-31

Changes in acoustic emission signal characteristics with tool wear were monitored during single point machining of 4340 steel and Ti-6Al-4V heat treated to several strength levels, 606l-T6 aluminum, 304 stainless steel, 17-4PH stainless steel, 410 stainless steel, lead, and teflon. No signal characteristic changed in the same way with tool wear for all materials tested. A single change in a particular AE signal characteristic with tool wear valid for all materials probably does not exist. Nevertheless, changes in various signal characteristic with wear for a given material may be sufficient to be used to monitor tool wear.

14 CFR 382.3 - What do the terms in this rule mean?

Code of Federal Regulations, 2011 CFR

2011-01-01

... and places between which those flights are performed. CPAP machine means a continuous positive airway pressure machine. Department or DOT means the United States Department of Transportation. Direct threat... learning disabilities. The term physical or mental impairment includes, but is not limited to, such...

14 CFR 382.3 - What do the terms in this rule mean?

Code of Federal Regulations, 2012 CFR

2012-01-01

... and places between which those flights are performed. CPAP machine means a continuous positive airway pressure machine. Department or DOT means the United States Department of Transportation. Direct threat... learning disabilities. The term physical or mental impairment includes, but is not limited to, such...

14 CFR 382.3 - What do the terms in this rule mean?

Code of Federal Regulations, 2013 CFR

2013-01-01

... and places between which those flights are performed. CPAP machine means a continuous positive airway pressure machine. Department or DOT means the United States Department of Transportation. Direct threat... learning disabilities. The term physical or mental impairment includes, but is not limited to, such...

A machine learning approach to the accurate prediction of monitor units for a compact proton machine.

PubMed

Sun, Baozhou; Lam, Dao; Yang, Deshan; Grantham, Kevin; Zhang, Tiezhi; Mutic, Sasa; Zhao, Tianyu

2018-05-01

Clinical treatment planning systems for proton therapy currently do not calculate monitor units (MUs) in passive scatter proton therapy due to the complexity of the beam delivery systems. Physical phantom measurements are commonly employed to determine the field-specific output factors (OFs) but are often subject to limited machine time, measurement uncertainties and intensive labor. In this study, a machine learning-based approach was developed to predict output (cGy/MU) and derive MUs, incorporating the dependencies on gantry angle and field size for a single-room proton therapy system. The goal of this study was to develop a secondary check tool for OF measurements and eventually eliminate patient-specific OF measurements. The OFs of 1754 fields previously measured in a water phantom with calibrated ionization chambers and electrometers for patient-specific fields with various range and modulation width combinations for 23 options were included in this study. The training data sets for machine learning models in three different methods (Random Forest, XGBoost and Cubist) included 1431 (~81%) OFs. Ten-fold cross-validation was used to prevent "overfitting" and to validate each model. The remaining 323 (~19%) OFs were used to test the trained models. The difference between the measured and predicted values from machine learning models was analyzed. Model prediction accuracy was also compared with that of the semi-empirical model developed by Kooy (Phys. Med. Biol. 50, 2005). Additionally, gantry angle dependence of OFs was measured for three groups of options categorized on the selection of the second scatters. Field size dependence of OFs was investigated for the measurements with and without patient-specific apertures. All three machine learning methods showed higher accuracy than the semi-empirical model which shows considerably large discrepancy of up to 7.7% for the treatment fields with full range and full modulation width. The Cubist-based solution outperformed all other models (P < 0.001) with the mean absolute discrepancy of 0.62% and maximum discrepancy of 3.17% between the measured and predicted OFs. The OFs showed a small dependence on gantry angle for small and deep options while they were constant for large options. The OF decreased by 3%-4% as the field radius was reduced to 2.5 cm. Machine learning methods can be used to predict OF for double-scatter proton machines with greater prediction accuracy than the most popular semi-empirical prediction model. By incorporating the gantry angle dependence and field size dependence, the machine learning-based methods can be used for a sanity check of OF measurements and bears the potential to eliminate the time-consuming patient-specific OF measurements. © 2018 American Association of Physicists in Medicine.

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

Cernoch, Antonin; Soubusta, Jan; Celechovska, Lucie

We report on experimental implementation of the optimal universal asymmetric 1->2 quantum cloning machine for qubits encoded into polarization states of single photons. Our linear-optical machine performs asymmetric cloning by partially symmetrizing the input polarization state of signal photon and a blank copy idler photon prepared in a maximally mixed state. We show that the employed method of measurement of mean clone fidelities exhibits strong resilience to imperfect calibration of the relative efficiencies of single-photon detectors used in the experiment. Reliable characterization of the quantum cloner is thus possible even when precise detector calibration is difficult to achieve.

Low-cost autonomous perceptron neural network inspired by quantum computation

NASA Astrophysics Data System (ADS)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

Single machine scheduling with slack due dates assignment

NASA Astrophysics Data System (ADS)

Liu, Weiguo; Hu, Xiangpei; Wang, Xuyin

2017-04-01

This paper considers a single machine scheduling problem in which each job is assigned an individual due date based on a common flow allowance (i.e. all jobs have slack due date). The goal is to find a sequence for jobs, together with a due date assignment, that minimizes a non-regular criterion comprising the total weighted absolute lateness value and common flow allowance cost, where the weight is a position-dependent weight. In order to solve this problem, an ? time algorithm is proposed. Some extensions of the problem are also shown.

On The Computational Capabilities of Physical Systems. Part 2; Relationship With Conventional Computer Science

NASA Technical Reports Server (NTRS)

Wolpert, David H.; Koga, Dennis (Technical Monitor)

2000-01-01

In the first of this pair of papers, it was proven that there cannot be a physical computer to which one can properly pose any and all computational tasks concerning the physical universe. It was then further proven that no physical computer C can correctly carry out all computational tasks that can be posed to C. As a particular example, this result means that no physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly "processing information faster than the universe does". These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - "physical computation" - is needed to address the issues considered in these papers, which concern real physical computers. While this novel definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. This second paper of the pair presents a preliminary exploration of some of this mathematical structure. Analogues of Chomskian results concerning universal Turing Machines and the Halting theorem are derived, as are results concerning the (im)possibility of certain kinds of error-correcting codes. In addition, an analogue of algorithmic information complexity, "prediction complexity", is elaborated. A task-independent bound is derived on how much the prediction complexity of a computational task can differ for two different reference universal physical computers used to solve that task, a bound similar to the "encoding" bound governing how much the algorithm information complexity of a Turing machine calculation can differ for two reference universal Turing machines. Finally, it is proven that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

TRUFLO GONDOLA, USED WITH THE HUNTER 10 MOLDING MACHINE, OPERATES ...

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

TRUFLO GONDOLA, USED WITH THE HUNTER 10 MOLDING MACHINE, OPERATES THE SAME AS THE TWO LARGER TRUFLOS USED IN CONJUNCTION WITH THE TWO HUNTER 20S. EACH GONDOLA IS CONNECTED TO THE NEXT AND RIDES ON A SINGLE TRACK RAIL FROM MOLDING MACHINES THROUGH POURING AREAS CARRYING A MOLD AROUND TWICE BEFORE THE MOLD IS PUSHED OFF ONTO A VIBRATING SHAKEOUT CONVEYOR. - Southern Ductile Casting Company, Casting, 2217 Carolina Avenue, Bessemer, Jefferson County, AL

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.

Analysis of Adhesively Bonded Ceramics Using an Asymmetric Wedge Test

DTIC Science & Technology

2008-12-01

4 Figure 2. Average crack ...flexure specimen. The flaw, indicated by the white arrow, is a subsurface semi-elliptical crack induced by surface machining damage...strength-limiting orthogonal surface machining crack in an alumina flexure specimen coated with a single layer of film adhesive. The white arrow

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.

State machine analysis of sensor data from dynamic processes

DOEpatents

Cook, William R.; Brabson, John M.; Deland, Sharon M.

2003-12-23

A state machine model analyzes sensor data from dynamic processes at a facility to identify the actual processes that were performed at the facility during a period of interest for the purpose of remote facility inspection. An inspector can further input the expected operations into the state machine model and compare the expected, or declared, processes to the actual processes to identify undeclared processes at the facility. The state machine analysis enables the generation of knowledge about the state of the facility at all levels, from location of physical objects to complex operational concepts. Therefore, the state machine method and apparatus may benefit any agency or business with sensored facilities that stores or manipulates expensive, dangerous, or controlled materials or information.

Software Reviews.

ERIC Educational Resources Information Center

Science and Children, 1990

1990-01-01

Reviewed are seven computer software packages for IBM and/or Apple Computers. Included are "Windows on Science: Volume 1--Physical Science"; "Science Probe--Physical Science"; "Wildlife Adventures--Grizzly Bears"; "Science Skills--Development Programs"; "The Clean Machine"; "Rock Doctor";…

What is the machine learning?

NASA Astrophysics Data System (ADS)

Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

2018-03-01

Applications of machine learning tools to problems of physical interest are often criticized for producing sensitivity at the expense of transparency. To address this concern, we explore a data planing procedure for identifying combinations of variables—aided by physical intuition—that can discriminate signal from background. Weights are introduced to smooth away the features in a given variable(s). New networks are then trained on this modified data. Observed decreases in sensitivity diagnose the variable's discriminating power. Planing also allows the investigation of the linear versus nonlinear nature of the boundaries between signal and background. We demonstrate the efficacy of this approach using a toy example, followed by an application to an idealized heavy resonance scenario at the Large Hadron Collider. By unpacking the information being utilized by these algorithms, this method puts in context what it means for a machine to learn.

Molecular machines operating on the nanoscale: from classical to quantum

PubMed Central

2016-01-01

Summary The main physical features and operating principles of isothermal nanomachines in the microworld, common to both classical and quantum machines, are reviewed. Special attention is paid to the dual, constructive role of dissipation and thermal fluctuations, the fluctuation–dissipation theorem, heat losses and free energy transduction, thermodynamic efficiency, and thermodynamic efficiency at maximum power. Several basic models are considered and discussed to highlight generic physical features. This work examines some common fallacies that continue to plague the literature. In particular, the erroneous beliefs that one should minimize friction and lower the temperature for high performance of Brownian machines, and that the thermodynamic efficiency at maximum power cannot exceed one-half are discussed. The emerging topic of anomalous molecular motors operating subdiffusively but very efficiently in the viscoelastic environment of living cells is also discussed. PMID:27335728

The Virtual Xenbase: transitioning an online bioinformatics resource to a private cloud.

PubMed

Karimi, Kamran; Vize, Peter D

2014-01-01

As a model organism database, Xenbase has been providing informatics and genomic data on Xenopus (Silurana) tropicalis and Xenopus laevis frogs for more than a decade. The Xenbase database contains curated, as well as community-contributed and automatically harvested literature, gene and genomic data. A GBrowse genome browser, a BLAST+ server and stock center support are available on the site. When this resource was first built, all software services and components in Xenbase ran on a single physical server, with inherent reliability, scalability and inter-dependence issues. Recent advances in networking and virtualization techniques allowed us to move Xenbase to a virtual environment, and more specifically to a private cloud. To do so we decoupled the different software services and components, such that each would run on a different virtual machine. In the process, we also upgraded many of the components. The resulting system is faster and more reliable. System maintenance is easier, as individual virtual machines can now be updated, backed up and changed independently. We are also experiencing more effective resource allocation and utilization. Database URL: www.xenbase.org. © The Author(s) 2014. Published by Oxford University Press.

Cogging Torque Minimization in Transverse Flux Machines

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

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

2017-02-16

This paper presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method.more » A three-level DOE is performed to reach an optimum solution with minimum simulations. Finite element analysis is used to study the different effects. Two prototypes are being fabricated for experimental verification.« less

Investigation of a tubular dual-stator flux-switching permanent-magnet linear generator for free-piston energy converter

NASA Astrophysics Data System (ADS)

Sui, Yi; Zheng, Ping; Tong, Chengde; Yu, Bin; Zhu, Shaohong; Zhu, Jianguo

2015-05-01

This paper describes a tubular dual-stator flux-switching permanent-magnet (PM) linear generator for free-piston energy converter. The operating principle, topology, and design considerations of the machine are investigated. Combining the motion characteristic of free-piston Stirling engine, a tubular dual-stator PM linear generator is designed by finite element method. Some major structural parameters, such as the outer and inner radii of the mover, PM thickness, mover tooth width, tooth width of the outer and inner stators, etc., are optimized to improve the machine performances like thrust capability and power density. In comparison with conventional single-stator PM machines like moving-magnet linear machine and flux-switching linear machine, the proposed dual-stator flux-switching PM machine shows advantages in higher mass power density, higher volume power density, and lighter mover.

A tubular hybrid Halbach/axially-magnetized permanent-magnet linear machine

NASA Astrophysics Data System (ADS)

Sui, Yi; Liu, Yong; Cheng, Luming; Liu, Jiaqi; Zheng, Ping

2017-05-01

A single-phase tubular permanent-magnet linear machine (PMLM) with hybrid Halbach/axially-magnetized PM arrays is proposed for free-piston Stirling power generation system. Machine topology and operating principle are elaborately illustrated. With the sinusoidal speed characteristic of the free-piston Stirling engine considered, the proposed machine is designed and calculated by finite-element analysis (FEA). The main structural parameters, such as outer radius of the mover, radial length of both the axially-magnetized PMs and ferromagnetic poles, axial length of both the middle and end radially-magnetized PMs, etc., are optimized to improve both the force capability and power density. Compared with the conventional PMLMs, the proposed machine features high mass and volume power density, and has the advantages of simple control and low converter cost. The proposed machine topology is applicable to tubular PMLMs with any phases.

Single-trial dynamics of motor cortex and their applications to brain-machine interfaces

PubMed Central

Kao, Jonathan C.; Nuyujukian, Paul; Ryu, Stephen I.; Churchland, Mark M.; Cunningham, John P.; Shenoy, Krishna V.

2015-01-01

Increasing evidence suggests that neural population responses have their own internal drive, or dynamics, that describe how the neural population evolves through time. An important prediction of neural dynamical models is that previously observed neural activity is informative of noisy yet-to-be-observed activity on single-trials, and may thus have a denoising effect. To investigate this prediction, we built and characterized dynamical models of single-trial motor cortical activity. We find these models capture salient dynamical features of the neural population and are informative of future neural activity on single trials. To assess how neural dynamics may beneficially denoise single-trial neural activity, we incorporate neural dynamics into a brain–machine interface (BMI). In online experiments, we find that a neural dynamical BMI achieves substantially higher performance than its non-dynamical counterpart. These results provide evidence that neural dynamics beneficially inform the temporal evolution of neural activity on single trials and may directly impact the performance of BMIs. PMID:26220660

Advanced Machine Learning Emulators of Radiative Transfer Models

NASA Astrophysics Data System (ADS)

Camps-Valls, G.; Verrelst, J.; Martino, L.; Vicent, J.

2017-12-01

Physically-based model inversion methodologies are based on physical laws and established cause-effect relationships. A plethora of remote sensing applications rely on the physical inversion of a Radiative Transfer Model (RTM), which lead to physically meaningful bio-geo-physical parameter estimates. The process is however computationally expensive, needs expert knowledge for both the selection of the RTM, its parametrization and the the look-up table generation, as well as its inversion. Mimicking complex codes with statistical nonlinear machine learning algorithms has become the natural alternative very recently. Emulators are statistical constructs able to approximate the RTM, although at a fraction of the computational cost, providing an estimation of uncertainty, and estimations of the gradient or finite integral forms. We review the field and recent advances of emulation of RTMs with machine learning models. We posit Gaussian processes (GPs) as the proper framework to tackle the problem. Furthermore, we introduce an automatic methodology to construct emulators for costly RTMs. The Automatic Gaussian Process Emulator (AGAPE) methodology combines the interpolation capabilities of GPs with the accurate design of an acquisition function that favours sampling in low density regions and flatness of the interpolation function. We illustrate the good capabilities of our emulators in toy examples, leaf and canopy levels PROSPECT and PROSAIL RTMs, and for the construction of an optimal look-up-table for atmospheric correction based on MODTRAN5.

Exploring the Function Space of Deep-Learning Machines

NASA Astrophysics Data System (ADS)

Li, Bo; Saad, David

2018-06-01

The function space of deep-learning machines is investigated by studying growth in the entropy of functions of a given error with respect to a reference function, realized by a deep-learning machine. Using physics-inspired methods we study both sparsely and densely connected architectures to discover a layerwise convergence of candidate functions, marked by a corresponding reduction in entropy when approaching the reference function, gain insight into the importance of having a large number of layers, and observe phase transitions as the error increases.

Mi Quinto Libro de Maquinas Simples: El Plano Inclinado. Escuela Intermedia Grados 7, 8 y 9 (My Fifth Book of Simple Machines: The Inclined Plane. Intermediate School Grades 7, 8, and 9).

ERIC Educational Resources Information Center

Alvarado, Patricio R.; Montalvo, Luis

This is the fifth book in a five-book physical science series on simple machines. The books are designed for Spanish-speaking junior high school students. This volume explains the principles and some of the uses of inclined planes, as they appear in simple machines, by suggesting experiments and posing questions concerning drawings in the book…

Teach students Semiconductor Lasers according to their natural ability

NASA Astrophysics Data System (ADS)

Liu, Ken; Guo, Chu Cai; Zhang, Jian Fa

2017-08-01

Physics explain the world in strict rules. And with these rules, modern machines and electronic devices with exact operation manner have been developed. However, human beings exceed these machines with self-awareness. To treat these self-awareness students as machines to learn strict rules, or to teach these students according to their aptitude? We choose the latter, because the first kind of teaching would let students lose their individual thoughts and natural ability. In this paper we describe the individualized teaching of "semiconductor lasers".

Towards a generalized energy prediction model for machine tools

PubMed Central

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H.; Dornfeld, David A.; Helu, Moneer; Rachuri, Sudarsan

2017-01-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process. PMID:28652687

Towards a generalized energy prediction model for machine tools.

PubMed

Bhinge, Raunak; Park, Jinkyoo; Law, Kincho H; Dornfeld, David A; Helu, Moneer; Rachuri, Sudarsan

2017-04-01

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based, energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian Process (GP) Regression, a non-parametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed to machine any part using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

Turning the LHC ring into a new physics search machine

NASA Astrophysics Data System (ADS)

Orava, Risto

2017-03-01

The LHC Collider Ring is proposed to be turned into an ultimate automatic search engine for new physics in four consecutive phases: (1) Searches for heavy particles produced in Central Exclusive Process (CEP): pp → p + X + p based on the existing Beam Loss Monitoring (BLM) system of the LHC; (2) Feasibility study of using the LHC Ring as a gravitation wave antenna; (3) Extensions to the current BLM system to facilitate precise registration of the selected CEP proton exit points from the LHC beam vacuum chamber; (4) Integration of the BLM based event tagging system together with the trigger/data acquisition systems of the LHC experiments to facilitate an on-line automatic search machine for the physics of tomorrow.

Application of machine learning techniques to analyse the effects of physical exercise in ventricular fibrillation.

PubMed

Caravaca, Juan; Soria-Olivas, Emilio; Bataller, Manuel; Serrano, Antonio J; Such-Miquel, Luis; Vila-Francés, Joan; Guerrero, Juan F

2014-02-01

This work presents the application of machine learning techniques to analyse the influence of physical exercise in the physiological properties of the heart, during ventricular fibrillation. To this end, different kinds of classifiers (linear and neural models) are used to classify between trained and sedentary rabbit hearts. The use of those classifiers in combination with a wrapper feature selection algorithm allows to extract knowledge about the most relevant features in the problem. The obtained results show that neural models outperform linear classifiers (better performance indices and a better dimensionality reduction). The most relevant features to describe the benefits of physical exercise are those related to myocardial heterogeneity, mean activation rate and activation complexity. © 2013 Published by Elsevier Ltd.

Mobile Landing Platform with Core Capability Set (MLP w/CCS): Combined Initial Operational Test and Evaluation and Live Fire Test and Evaluation Report

DTIC Science & Technology

2015-07-01

annex.   iii Self-defense testing was limited to structural test firing from each machine gun mount and an ammunition resupply drill. Robust self...provided in the classified annex. Self-   8 defense testing was limited to structural test firing from each machine gun mount and a single...Caliber Machine Gun Mount Structural Test Fire November 2014 San Diego, Offshore Ship Weapons Range Operating Independently       9 Section Three

Reproducing an Early-20th-Century Wave Machine

ERIC Educational Resources Information Center

Daffron, John A.; Greenslade, Thomas B., Jr.

2016-01-01

Physics students often have problems understanding waves. Over the years numerous mechanical devices have been devised to show the propagation of both transverse and longitudinal waves (Ref. 1). In this article an updated version of an early-20th-century transverse wave machine is discussed. The original, Fig. 1, is at Creighton University in…

Cybernetic anthropomorphic machine systems

NASA Technical Reports Server (NTRS)

Gray, W. E.

1974-01-01

Functional descriptions are provided for a number of cybernetic man machine systems that augment the capacity of normal human beings in the areas of strength, reach or physical size, and environmental interaction, and that are also applicable to aiding the neurologically handicapped. Teleoperators, computer control, exoskeletal devices, quadruped vehicles, space maintenance systems, and communications equipment are considered.

Financial heat machine

NASA Astrophysics Data System (ADS)

Khrennikov, Andrei

2005-05-01

We consider dynamics of financial markets as dynamics of expectations and discuss such a dynamics from the point of view of phenomenological thermodynamics. We describe a financial Carnot cycle and the financial analog of a heat machine. We see, that while in physics a perpetuum mobile is absolutely impossible, in economics such mobile may exist under some conditions.

A SYSTEMS APPROACH UTILIZING GENERAL-PURPOSE AND SPECIAL-PURPOSE TEACHING MACHINES.

ERIC Educational Resources Information Center

SILVERN, LEONARD C.

IN ORDER TO IMPROVE THE EMPLOYEE TRAINING-EVALUATION METHOD, TEACHING MACHINES AND PERFORMANCE AIDS MUST BE PHYSICALLY AND OPERATIONALLY INTEGRATED INTO THE SYSTEM, THUS RETURNING TRAINING TO THE ACTUAL JOB ENVIRONMENT. GIVEN THESE CONDITIONS, TRAINING CAN BE MEASURED, CALIBRATED, AND CONTROLLED WITH RESPECT TO ACTUAL JOB PERFORMANCE STANDARDS AND…

AMS 4.0: consensus prediction of post-translational modifications in protein sequences.

PubMed

Plewczynski, Dariusz; Basu, Subhadip; Saha, Indrajit

2012-08-01

We present here the 2011 update of the AutoMotif Service (AMS 4.0) that predicts the wide selection of 88 different types of the single amino acid post-translational modifications (PTM) in protein sequences. The selection of experimentally confirmed modifications is acquired from the latest UniProt and Phospho.ELM databases for training. The sequence vicinity of each modified residue is represented using amino acids physico-chemical features encoded using high quality indices (HQI) obtaining by automatic clustering of known indices extracted from AAindex database. For each type of the numerical representation, the method builds the ensemble of Multi-Layer Perceptron (MLP) pattern classifiers, each optimising different objectives during the training (for example the recall, precision or area under the ROC curve (AUC)). The consensus is built using brainstorming technology, which combines multi-objective instances of machine learning algorithm, and the data fusion of different training objects representations, in order to boost the overall prediction accuracy of conserved short sequence motifs. The performance of AMS 4.0 is compared with the accuracy of previous versions, which were constructed using single machine learning methods (artificial neural networks, support vector machine). Our software improves the average AUC score of the earlier version by close to 7 % as calculated on the test datasets of all 88 PTM types. Moreover, for the selected most-difficult sequence motifs types it is able to improve the prediction performance by almost 32 %, when compared with previously used single machine learning methods. Summarising, the brainstorming consensus meta-learning methodology on the average boosts the AUC score up to around 89 %, averaged over all 88 PTM types. Detailed results for single machine learning methods and the consensus methodology are also provided, together with the comparison to previously published methods and state-of-the-art software tools. The source code and precompiled binaries of brainstorming tool are available at http://code.google.com/p/automotifserver/ under Apache 2.0 licensing.

Comparative study of state-of-the-art myoelectric controllers for multigrasp prosthetic hands.

PubMed

Segil, Jacob L; Controzzi, Marco; Weir, Richard F ff; Cipriani, Christian

2014-01-01

A myoelectric controller should provide an intuitive and effective human-machine interface that deciphers user intent in real-time and is robust enough to operate in daily life. Many myoelectric control architectures have been developed, including pattern recognition systems, finite state machines, and more recently, postural control schemes. Here, we present a comparative study of two types of finite state machines and a postural control scheme using both virtual and physical assessment procedures with seven nondisabled subjects. The Southampton Hand Assessment Procedure (SHAP) was used in order to compare the effectiveness of the controllers during activities of daily living using a multigrasp artificial hand. Also, a virtual hand posture matching task was used to compare the controllers when reproducing six target postures. The performance when using the postural control scheme was significantly better (p < 0.05) than the finite state machines during the physical assessment when comparing within-subject averages using the SHAP percent difference metric. The virtual assessment results described significantly greater completion rates (97% and 99%) for the finite state machines, but the movement time tended to be faster (2.7 s) for the postural control scheme. Our results substantiate that postural control schemes rival other state-of-the-art myoelectric controllers.

A 3D Human-Machine Integrated Design and Analysis Framework for Squat Exercises with a Smith Machine

PubMed Central

Lee, Haerin; Jung, Moonki; Lee, Ki-Kwang; Lee, Sang Hun

2017-01-01

In this paper, we propose a three-dimensional design and evaluation framework and process based on a probabilistic-based motion synthesis algorithm and biomechanical analysis system for the design of the Smith machine and squat training programs. Moreover, we implemented a prototype system to validate the proposed framework. The framework consists of an integrated human–machine–environment model as well as a squat motion synthesis system and biomechanical analysis system. In the design and evaluation process, we created an integrated model in which interactions between a human body and machine or the ground are modeled as joints with constraints at contact points. Next, we generated Smith squat motion using the motion synthesis program based on a Gaussian process regression algorithm with a set of given values for independent variables. Then, using the biomechanical analysis system, we simulated joint moments and muscle activities from the input of the integrated model and squat motion. We validated the model and algorithm through physical experiments measuring the electromyography (EMG) signals, ground forces, and squat motions as well as through a biomechanical simulation of muscle forces. The proposed approach enables the incorporation of biomechanics in the design process and reduces the need for physical experiments and prototypes in the development of training programs and new Smith machines. PMID:28178184

Multi-parameter monitoring of electrical machines using integrated fibre Bragg gratings

NASA Astrophysics Data System (ADS)

Fabian, Matthias; Hind, David; Gerada, Chris; Sun, Tong; Grattan, Kenneth T. V.

2017-04-01

In this paper a sensor system for multi-parameter electrical machine condition monitoring is reported. The proposed FBG-based system allows for the simultaneous monitoring of machine vibration, rotor speed and position, torque, spinning direction, temperature distribution along the stator windings and on the rotor surface as well as the stator wave frequency. This all-optical sensing solution reduces the component count of conventional sensor systems, i.e., all 48 sensing elements are contained within the machine operated by a single sensing interrogation unit. In this work, the sensing system has been successfully integrated into and tested on a permanent magnet motor prototype.

Learning dominance relations in combinatorial search problems

NASA Technical Reports Server (NTRS)

Yu, Chee-Fen; Wah, Benjamin W.

1988-01-01

Dominance relations commonly are used to prune unnecessary nodes in search graphs, but they are problem-dependent and cannot be derived by a general procedure. The authors identify machine learning of dominance relations and the applicable learning mechanisms. A study of learning dominance relations using learning by experimentation is described. This system has been able to learn dominance relations for the 0/1-knapsack problem, an inventory problem, the reliability-by-replication problem, the two-machine flow shop problem, a number of single-machine scheduling problems, and a two-machine scheduling problem. It is considered that the same methodology can be extended to learn dominance relations in general.

A Framework for Final Drive Simultaneous Failure Diagnosis Based on Fuzzy Entropy and Sparse Bayesian Extreme Learning Machine

PubMed Central

Ye, Qing; Pan, Hao; Liu, Changhua

2015-01-01

This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F 1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach. PMID:25722717

MPIRUN: A Portable Loader for Multidisciplinary and Multi-Zonal Applications

NASA Technical Reports Server (NTRS)

Fineberg, Samuel A.; Woodrow, Thomas S. (Technical Monitor)

1994-01-01

Multidisciplinary and multi-zonal applications are an important class of applications in the area of Computational Aerosciences. In these codes, two or more distinct parallel programs or copies of a single program are utilized to model a single problem. To support such applications, it is common to use a programming model where a program is divided into several single program multiple data stream (SPMD) applications, each of which solves the equations for a single physical discipline or grid zone. These SPMD applications are then bound together to form a single multidisciplinary or multi-zonal program in which the constituent parts communicate via point-to-point message passing routines. One method for implementing the message passing portion of these codes is with the new Message Passing Interface (MPI) standard. Unfortunately, this standard only specifies the message passing portion of an application, but does not specify any portable mechanisms for loading an application. MPIRUN was developed to provide a portable means for loading MPI programs, and was specifically targeted at multidisciplinary and multi-zonal applications. Programs using MPIRUN for loading and MPI for message passing are then portable between all machines supported by MPIRUN. MPIRUN is currently implemented for the Intel iPSC/860, TMC CM5, IBM SP-1 and SP-2, Intel Paragon, and workstation clusters. Further, MPIRUN is designed to be simple enough to port easily to any system supporting MPI.

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.

Active Gaming: Is "Virtual" Reality Right for Your Physical Education Program?

ERIC Educational Resources Information Center

Hansen, Lisa; Sanders, Stephen W.

2012-01-01

Active gaming is growing in popularity and the idea of increasing children's physical activity by using technology is largely accepted by physical educators. Teachers nationwide have been providing active gaming equipment such as virtual bikes, rhythmic dance machines, virtual sporting games, martial arts simulators, balance boards, and other…

Strategies for single-point diamond machining a large format germanium blazed immersion grating

NASA Astrophysics Data System (ADS)

Montesanti, R. C.; Little, S. L.; Kuzmenko, P. J.; Bixler, J. V.; Jackson, J. L.; Lown, J. G.; Priest, R. E.; Yoxall, B. E.

2016-07-01

A large format germanium immersion grating was flycut with a single-point diamond tool on the Precision Engineering Research Lathe (PERL) at the Lawrence Livermore National Laboratory (LLNL) in November - December 2015. The grating, referred to as 002u, has an area of 59 mm x 67 mm (along-groove and cross-groove directions), line pitch of 88 line/mm, and blaze angle of 32 degree. Based on total groove length, the 002u grating is five times larger than the previous largest grating (ZnSe) cut on PERL, and forty-five times larger than the previous largest germanium grating cut on PERL. The key risks associated with cutting the 002u grating were tool wear and keeping the PERL machine running uninterrupted in a stable machining environment. This paper presents the strategies employed to mitigate these risks, introduces pre-machining of the as-etched grating substrate to produce a smooth, flat, damage-free surface into which the grooves are cut, and reports on trade-offs that drove decisions and experimental results.

Scheduling with non-decreasing deterioration jobs and variable maintenance activities on a single machine

NASA Astrophysics Data System (ADS)

Zhang, Xingong; Yin, Yunqiang; Wu, Chin-Chia

2017-01-01

There is a situation found in many manufacturing systems, such as steel rolling mills, fire fighting or single-server cycle-queues, where a job that is processed later consumes more time than that same job when processed earlier. The research finds that machine maintenance can improve the worsening of processing conditions. After maintenance activity, the machine will be restored. The maintenance duration is a positive and non-decreasing differentiable convex function of the total processing times of the jobs between maintenance activities. Motivated by this observation, the makespan and the total completion time minimization problems in the scheduling of jobs with non-decreasing rates of job processing time on a single machine are considered in this article. It is shown that both the makespan and the total completion time minimization problems are NP-hard in the strong sense when the number of maintenance activities is arbitrary, while the makespan minimization problem is NP-hard in the ordinary sense when the number of maintenance activities is fixed. If the deterioration rates of the jobs are identical and the maintenance duration is a linear function of the total processing times of the jobs between maintenance activities, then this article shows that the group balance principle is satisfied for the makespan minimization problem. Furthermore, two polynomial-time algorithms are presented for solving the makespan problem and the total completion time problem under identical deterioration rates, respectively.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process.

PubMed

Deng, Bo; Shi, Yaoyao; Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-31

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

PubMed Central

Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-01

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

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

Belley, M; Schmidt, M; Knutson, N

Purpose: Physics second-checks for external beam radiation therapy are performed, in-part, to verify that the machine parameters in the Record-and-Verify (R&V) system that will ultimately be sent to the LINAC exactly match the values initially calculated by the Treatment Planning System (TPS). While performing the second-check, a large portion of the physicists’ time is spent navigating and arranging display windows to locate and compare the relevant numerical values (MLC position, collimator rotation, field size, MU, etc.). Here, we describe the development of a software tool that guides the physicist by aggregating and succinctly displaying machine parameter data relevant to themore » physics second-check process. Methods: A data retrieval software tool was developed using Python to aggregate data and generate a list of machine parameters that are commonly verified during the physics second-check process. This software tool imported values from (i) the TPS RT Plan DICOM file and (ii) the MOSAIQ (R&V) Structured Query Language (SQL) database. The machine parameters aggregated for this study included: MLC positions, X&Y jaw positions, collimator rotation, gantry rotation, MU, dose rate, wedges and accessories, cumulative dose, energy, machine name, couch angle, and more. Results: A GUI interface was developed to generate a side-by-side display of the aggregated machine parameter values for each field, and presented to the physicist for direct visual comparison. This software tool was tested for 3D conformal, static IMRT, sliding window IMRT, and VMAT treatment plans. Conclusion: This software tool facilitated the data collection process needed in order for the physicist to conduct a second-check, thus yielding an optimized second-check workflow that was both more user friendly and time-efficient. Utilizing this software tool, the physicist was able to spend less time searching through the TPS PDF plan document and the R&V system and focus the second-check efforts on assessing the patient-specific plan-quality.« less

Machine Learning to Improve Energy Expenditure Estimation in Children With Disabilities: A Pilot Study in Duchenne Muscular Dystrophy.

PubMed

Pande, Amit; Mohapatra, Prasant; Nicorici, Alina; Han, Jay J

2016-07-19

Children with physical impairments are at a greater risk for obesity and decreased physical activity. A better understanding of physical activity pattern and energy expenditure (EE) would lead to a more targeted approach to intervention. This study focuses on studying the use of machine-learning algorithms for EE estimation in children with disabilities. A pilot study was conducted on children with Duchenne muscular dystrophy (DMD) to identify important factors for determining EE and develop a novel algorithm to accurately estimate EE from wearable sensor-collected data. There were 7 boys with DMD, 6 healthy control boys, and 22 control adults recruited. Data were collected using smartphone accelerometer and chest-worn heart rate sensors. The gold standard EE values were obtained from the COSMED K4b2 portable cardiopulmonary metabolic unit worn by boys (aged 6-10 years) with DMD and controls. Data from this sensor setup were collected simultaneously during a series of concurrent activities. Linear regression and nonlinear machine-learning-based approaches were used to analyze the relationship between accelerometer and heart rate readings and COSMED values. Existing calorimetry equations using linear regression and nonlinear machine-learning-based models, developed for healthy adults and young children, give low correlation to actual EE values in children with disabilities (14%-40%). The proposed model for boys with DMD uses ensemble machine learning techniques and gives a 91% correlation with actual measured EE values (root mean square error of 0.017). Our results confirm that the methods developed to determine EE using accelerometer and heart rate sensor values in normal adults are not appropriate for children with disabilities and should not be used. A much more accurate model is obtained using machine-learning-based nonlinear regression specifically developed for this target population. ©Amit Pande, Prasant Mohapatra, Alina Nicorici, Jay J Han. Originally published in JMIR Rehabilitation and Assistive Technology (http://rehab.jmir.org), 19.07.2016.

Harry Mergler with His Modified Differential Analyzer

NASA Image and Video Library

1951-06-21

Harry Mergler stands at the control board of a differential analyzer in the new Instrument Research Laboratory at the National Advisory Committee for Aeronautics (NACA) Lewis Flight Propulsion Laboratory. The differential analyzer was a multi-variable analog computation machine devised in 1931 by Massachusetts Institute of Technology researcher and future NACA Committee member Vannevar Bush. The mechanical device could solve computations up to the sixth order, but had to be rewired before each new computation. Mergler modified Bush’s differential analyzer in the late 1940s to calculate droplet trajectories for Lewis’ icing research program. In four days Mergler’s machine could calculate what previously required weeks. NACA Lewis built the Instrument Research Laboratory in 1950 and 1951 to house the large analog computer equipment. The two-story structure also provided offices for the Mechanical Computational Analysis, and Flow Physics sections of the Physics Division. The division had previously operated from the lab’s hangar because of its icing research and flight operations activities. Mergler joined the Instrument Research Section of the Physics Division in 1948 after earning an undergraduate degree in Physics from the Case Institute of Technology. Mergler’s focus was on the synthesis of analog computers with the machine tools used to create compressor and turbine blades for jet engines.

Using Phun to Study ``Perpetual Motion'' Machines

NASA Astrophysics Data System (ADS)

Koreš, Jaroslav

2012-05-01

The concept of "perpetual motion" has a long history. The Indian astronomer and mathematician Bhaskara II (12th century) was the first person to describe a perpetual motion (PM) machine. An example of a 13th- century PM machine is shown in Fig. 1. Although the law of conservation of energy clearly implies the impossibility of PM construction, over the centuries numerous proposals for PM have been made, involving ever more elements of modern science in their construction. It is possible to test a variety of PM machines in the classroom using a program called Phun2 or its commercial version Algodoo.3 The programs are designed to simulate physical processes and we can easily simulate mechanical machines using them. They provide an intuitive graphical environment controlled with a mouse; a programming language is not needed. This paper describes simulations of four different (supposed) PM machines.4

Parameter optimization of electrochemical machining process using black hole algorithm

NASA Astrophysics Data System (ADS)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

Advanced machining processes are significant as higher accuracy in machined component is required in the manufacturing industries. Parameter optimization of machining processes gives optimum control to achieve the desired goals. In this paper, electrochemical machining (ECM) process is considered to evaluate the performance of the considered process using black hole algorithm (BHA). BHA considers the fundamental idea of a black hole theory and it has less operating parameters to tune. The two performance parameters, material removal rate (MRR) and overcut (OC) are considered separately to get optimum machining parameter settings using BHA. The variations of process parameters with respect to the performance parameters are reported for better and effective understanding of the considered process using single objective at a time. The results obtained using BHA are found better while compared with results of other metaheuristic algorithms, such as, genetic algorithm (GA), artificial bee colony (ABC) and bio-geography based optimization (BBO) attempted by previous researchers.

Alumina-zirconia machinable abutments for implant-supported single-tooth anterior crowns.

PubMed

Sadoun, M; Perelmuter, S

1997-01-01

Innovative materials and application techniques are constantly being developed in the ongoing search for improved restorations. This article describes a new material and the fabrication process of aesthetic machinable ceramic anterior implant abutments. The ceramic material utilized is a mixture of alumina (aluminum oxide) and ceria (cerium oxide) with partially stabilized zirconia (zirconium oxide). The initial core material is a cylinder with a 9-mm diameter and a 15-mm height, obtained by ceramic injection and presintering processes. The resultant alumina-zirconia core is porous and readily machinable. It is secured to the analog, and its design is customized by machining the abutment to suit the particular clinical circumstances. The machining is followed by glass infiltration, and the crown is finalized. The learning objective of this article is to gain a basic knowledge of the fabrication and clinical application of the custom machinable abutments.

Predicting competency in automated machine use in an acquired brain injury population using neuropsychological measures.

PubMed

Crowe, Simon F; Mahony, Kate; Jackson, Martin

2004-08-01

The purpose of the current study was to explore whether performance on standardised neuropsychological measures could predict functional ability with automated machines and services among people with an acquired brain injury (ABI). Participants were 45 individuals who met the criteria for mild, moderate or severe ABI and 15 control participants matched on demographic variables including age- and education. Each participant was required to complete a battery of neuropsychological tests, as well as performing three automated service delivery tasks: a transport automated ticketing machine, an automated teller machine (ATM) and an automated telephone service. The results showed consistently high relationship between the neuropsychological measures, both as single predictors and in combination, and level of competency with the automated machines. Automated machines are part of a relatively new phenomena in service delivery and offer an ecologically valid functional measure of performance that represents a true indication of functional disability.

Math Machines: Using Actuators in Physics Classes

NASA Astrophysics Data System (ADS)

Thomas, Frederick J.; Chaney, Robert A.; Gruesbeck, Marta

2018-01-01

Probeware (sensors combined with data-analysis software) is a well-established part of physics education. In engineering and technology, sensors are frequently paired with actuators—motors, heaters, buzzers, valves, color displays, medical dosing systems, and other devices that are activated by electrical signals to produce intentional physical change. This article describes how a 20-year project aimed at better integration of the STEM disciplines (science, technology, engineering and mathematics) uses brief actuator activities in physics instruction. Math Machines "actionware" includes software and hardware that convert virtually any free-form, time-dependent algebraic function into the dynamic actions of a stepper motor, servo motor, or RGB (red, green, blue) color mixer. With wheels and a platform, the stepper motor becomes LACI, a programmable vehicle. Adding a low-power laser module turns the servo motor into a programmable Pointer. Adding a gear and platform can transform the Pointer into an earthquake simulator.

Mutual information, neural networks and the renormalization group

NASA Astrophysics Data System (ADS)

Koch-Janusz, Maciej; Ringel, Zohar

2018-06-01

Physical systems differing in their microscopic details often display strikingly similar behaviour when probed at macroscopic scales. Those universal properties, largely determining their physical characteristics, are revealed by the powerful renormalization group (RG) procedure, which systematically retains `slow' degrees of freedom and integrates out the rest. However, the important degrees of freedom may be difficult to identify. Here we demonstrate a machine-learning algorithm capable of identifying the relevant degrees of freedom and executing RG steps iteratively without any prior knowledge about the system. We introduce an artificial neural network based on a model-independent, information-theoretic characterization of a real-space RG procedure, which performs this task. We apply the algorithm to classical statistical physics problems in one and two dimensions. We demonstrate RG flow and extract the Ising critical exponent. Our results demonstrate that machine-learning techniques can extract abstract physical concepts and consequently become an integral part of theory- and model-building.

An innovative multimodal virtual platform for communication with devices in a natural way

NASA Astrophysics Data System (ADS)

Kinkar, Chhayarani R.; Golash, Richa; Upadhyay, Akhilesh R.

2012-03-01

As technology grows people are diverted and are more interested in communicating with machine or computer naturally. This will make machine more compact and portable by avoiding remote, keyboard etc. also it will help them to live in an environment free from electromagnetic waves. This thought has made 'recognition of natural modality in human computer interaction' a most appealing and promising research field. Simultaneously it has been observed that using single mode of interaction limit the complete utilization of commands as well as data flow. In this paper a multimodal platform, where out of many natural modalities like eye gaze, speech, voice, face etc. human gestures are combined with human voice is proposed which will minimize the mean square error. This will loosen the strict environment needed for accurate and robust interaction while using single mode. Gesture complement Speech, gestures are ideal for direct object manipulation and natural language is used for descriptive tasks. Human computer interaction basically requires two broad sections recognition and interpretation. Recognition and interpretation of natural modality in complex binary instruction is a tough task as it integrate real world to virtual environment. The main idea of the paper is to develop a efficient model for data fusion coming from heterogeneous sensors, camera and microphone. Through this paper we have analyzed that the efficiency is increased if heterogeneous data (image & voice) is combined at feature level using artificial intelligence. The long term goal of this paper is to design a robust system for physically not able or having less technical knowledge.

Single phase space laundry development

NASA Technical Reports Server (NTRS)

Colombo, Gerald V.; Putnam, David F.; Lunsford, Teddie D.; Streech, Neil D.; Wheeler, Richard R., Jr.; Reimers, Harold

1993-01-01

This paper describes a newly designed, 2.7 Kg (6 pound) capacity, laundry machine called the Single Phase Laundry (SPSL). The machine was designed to wash and dry crew clothing in a micro-gravity environment. A prototype unit was fabricated for NASA-JSC under a Small Business Innovated Research (SBIR) contract extending from September 1990 to January 1993. The unit employs liquid jet agitation, microwave vacuum drying, and air jet tumbling, which was perfected by KC-135 zero-g flight testing. Operation is completely automated except for loading and unloading clothes. The unit uses about 20 percent less power than a conventional household appliance.

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.

Heuristic methods for the single machine scheduling problem with different ready times and a common due date

NASA Astrophysics Data System (ADS)

Birgin, Ernesto G.; Ronconi, Débora P.

2012-10-01

The single machine scheduling problem with a common due date and non-identical ready times for the jobs is examined in this work. Performance is measured by the minimization of the weighted sum of earliness and tardiness penalties of the jobs. Since this problem is NP-hard, the application of constructive heuristics that exploit specific characteristics of the problem to improve their performance is investigated. The proposed approaches are examined through a computational comparative study on a set of 280 benchmark test problems with up to 1000 jobs.

Analysis of the Laser Drilling Process for the Combination with a Single-Lip Deep Hole Drilling Process with Small Diameters

NASA Astrophysics Data System (ADS)

Biermann, Dirk; Heilmann, Markus

Due to the tendency of downsizing of components, also the industrial relevance of bore holes with small diameters and high length-to-diameter ratios rises with the growing requirements on parts. In these applications, the combination of laser pre-drilling and single-lip deep hole drilling can shorten the process chain in machining components with non-planar surfaces, or can reduce tool wear in machining case-hardened materials. In this research, the combination of these processes was realized and investigated for the very first time.

Process of making cryogenically cooled high thermal performance crystal optics

DOEpatents

Kuzay, Tuncer M.

1992-01-01

A method for constructing a cooled optic wherein one or more cavities are milled, drilled or formed using casting or ultrasound laser machining techniques in a single crystal base and filled with porous material having high thermal conductivity at cryogenic temperatures. A non-machined strain-free single crystal can be bonded to the base to produce superior optics. During operation of the cooled optic, N.sub.2 is pumped through the porous material at a sub-cooled cryogenic inlet temperature and with sufficient system pressure to prevent the fluid bulk temperature from reaching saturation.

Process of making cryogenically cooled high thermal performance crystal optics

DOEpatents

Kuzay, T.M.

1992-06-23

A method is disclosed for constructing a cooled optic wherein one or more cavities are milled, drilled or formed using casting or ultrasound laser machining techniques in a single crystal base and filled with porous material having high thermal conductivity at cryogenic temperatures. A non-machined strain-free single crystal can be bonded to the base to produce superior optics. During operation of the cooled optic, N[sub 2] is pumped through the porous material at a sub-cooled cryogenic inlet temperature and with sufficient system pressure to prevent the fluid bulk temperature from reaching saturation. 7 figs.

Dynamics of multirate sampled data control systems. [for space shuttle boost vehicle

NASA Technical Reports Server (NTRS)

Naylor, J. R.; Hynes, R. J.; Molnar, D. O.

1974-01-01

The effect was investigated of the synthesis approach (single or multirate) on the machine requirements for a digital control system for the space shuttle boost vehicle. The study encompassed four major work areas: synthesis approach trades, machine requirements trades, design analysis requirements and multirate adaptive control techniques. The primary results are two multirate autopilot designs for the low Q and maximum Q flight conditions that exhibits equal or better performance than the analog and single rate system designs. Also, a preferred technique for analyzing and synthesizing multirate digital control systems is included.

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

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.

Perspex machine: V. Compilation of C programs

NASA Astrophysics Data System (ADS)

Spanner, Matthew P.; Anderson, James A. D. W.

2006-01-01

The perspex machine arose from the unification of the Turing machine with projective geometry. The original, constructive proof used four special, perspective transformations to implement the Turing machine in projective geometry. These four transformations are now generalised and applied in a compiler, implemented in Pop11, that converts a subset of the C programming language into perspexes. This is interesting both from a geometrical and a computational point of view. Geometrically, it is interesting that program source can be converted automatically to a sequence of perspective transformations and conditional jumps, though we find that the product of homogeneous transformations with normalisation can be non-associative. Computationally, it is interesting that program source can be compiled for a Reduced Instruction Set Computer (RISC), the perspex machine, that is a Single Instruction, Zero Exception (SIZE) computer.

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

Husain, Tausif; Hasan, Iftekhar; Sozer, Yilmaz

This paper presents the design considerations in cogging torque minimization in two types of transverse flux machines. The machines have a double stator-single rotor configuration with flux concentrating ferrite magnets. One of the machines has pole windings across each leg of an E-Core stator. Another machine has quasi-U-shaped stator cores and a ring winding. The flux in the stator back iron is transverse in both machines. Different methods of cogging torque minimization are investigated. Key methods of cogging torque minimization are identified and used as design variables for optimization using a design of experiments (DOE) based on the Taguchi method.more » A three-level DOE is performed to reach an optimum solution with minimum simulations. Finite element analysis is used to study the different effects. Two prototypes are being fabricated for experimental verification.« less

Solving the Cauchy-Riemann equations on parallel computers

NASA Technical Reports Server (NTRS)

Fatoohi, Raad A.; Grosch, Chester E.

1987-01-01

Discussed is the implementation of a single algorithm on three parallel-vector computers. The algorithm is a relaxation scheme for the solution of the Cauchy-Riemann equations; a set of coupled first order partial differential equations. The computers were chosen so as to encompass a variety of architectures. They are: the MPP, and SIMD machine with 16K bit serial processors; FLEX/32, an MIMD machine with 20 processors; and CRAY/2, an MIMD machine with four vector processors. The machine architectures are briefly described. The implementation of the algorithm is discussed in relation to these architectures and measures of the performance on each machine are given. Simple performance models are used to describe the performance. These models highlight the bottlenecks and limiting factors for this algorithm on these architectures. Conclusions are presented.

Chip breaking system for automated machine tool

DOEpatents

Arehart, Theodore A.; Carey, Donald O.

1987-01-01

The invention is a rotary selectively directional valve assembly for use in an automated turret lathe for directing a stream of high pressure liquid machining coolant to the interface of a machine tool and workpiece for breaking up ribbon-shaped chips during the formation thereof so as to inhibit scratching or other marring of the machined surfaces by these ribbon-shaped chips. The valve assembly is provided by a manifold arrangement having a plurality of circumferentially spaced apart ports each coupled to a machine tool. The manifold is rotatable with the turret when the turret is positioned for alignment of a machine tool in a machining relationship with the workpiece. The manifold is connected to a non-rotational header having a single passageway therethrough which conveys the high pressure coolant to only the port in the manifold which is in registry with the tool disposed in a working relationship with the workpiece. To position the machine tools the turret is rotated and one of the tools is placed in a material-removing relationship of the workpiece. The passageway in the header and one of the ports in the manifold arrangement are then automatically aligned to supply the machining coolant to the machine tool workpiece interface for breaking up of the chips as well as cooling the tool and workpiece during the machining operation.

Study of Man-Machine Communications Systems for Disabled Persons (The Handicapped). Volume VII. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

Teaching instructions, lesson plans, and exercises are provided for severely physically and/or neurologically handicapped persons learning to use the Cybertype electric writing machine with a tongue-body keyboard. The keyboard, which has eight double-throw toggle switches and a three-position state-selector switch, is designed to be used by…

The Compound Atwood Machine Problem

ERIC Educational Resources Information Center

Coelho, R. Lopes

2017-01-01

The present paper accounts for progress in physics teaching in the sense that a problem, which has been closed to students for being too difficult, is gained for the high school curriculum. This problem is the compound Atwood machine with three bodies. Its introduction into high school classes is based on a recent study on the weighing of an…

An integer batch scheduling model considering learning, forgetting, and deterioration effects for a single machine to minimize total inventory holding cost

NASA Astrophysics Data System (ADS)

Yusriski, R.; Sukoyo; Samadhi, T. M. A. A.; Halim, A. H.

2018-03-01

This research deals with a single machine batch scheduling model considering the influenced of learning, forgetting, and machine deterioration effects. The objective of the model is to minimize total inventory holding cost, and the decision variables are the number of batches (N), batch sizes (Q[i], i = 1, 2, .., N) and the sequence of processing the resulting batches. The parts to be processed are received at the right time and the right quantities, and all completed parts must be delivered at a common due date. We propose a heuristic procedure based on the Lagrange method to solve the problem. The effectiveness of the procedure is evaluated by comparing the resulting solution to the optimal solution obtained from the enumeration procedure using the integer composition technique and shows that the average effectiveness is 94%.

Micro-optical fabrication by ultraprecision diamond machining and precision molding

NASA Astrophysics Data System (ADS)

Li, Hui; Li, Likai; Naples, Neil J.; Roblee, Jeffrey W.; Yi, Allen Y.

2017-06-01

Ultraprecision diamond machining and high volume molding for affordable high precision high performance optical elements are becoming a viable process in optical industry for low cost high quality microoptical component manufacturing. In this process, first high precision microoptical molds are fabricated using ultraprecision single point diamond machining followed by high volume production methods such as compression or injection molding. In the last two decades, there have been steady improvements in ultraprecision machine design and performance, particularly with the introduction of both slow tool and fast tool servo. Today optical molds, including freeform surfaces and microlens arrays, are routinely diamond machined to final finish without post machining polishing. For consumers, compression molding or injection molding provide efficient and high quality optics at extremely low cost. In this paper, first ultraprecision machine design and machining processes such as slow tool and fast too servo are described then both compression molding and injection molding of polymer optics are discussed. To implement precision optical manufacturing by molding, numerical modeling can be included in the future as a critical part of the manufacturing process to ensure high product quality.

A VLSI chip set for real time vector quantization of image sequences

NASA Technical Reports Server (NTRS)

Baker, Richard L.

1989-01-01

The architecture and implementation of a VLSI chip set that vector quantizes (VQ) image sequences in real time is described. The chip set forms a programmable Single-Instruction, Multiple-Data (SIMD) machine which can implement various vector quantization encoding structures. Its VQ codebook may contain unlimited number of codevectors, N, having dimension up to K = 64. Under a weighted least squared error criterion, the engine locates at video rates the best code vector in full-searched or large tree searched VQ codebooks. The ability to manipulate tree structured codebooks, coupled with parallelism and pipelining, permits searches in as short as O (log N) cycles. A full codebook search results in O(N) performance, compared to O(KN) for a Single-Instruction, Single-Data (SISD) machine. With this VLSI chip set, an entire video code can be built on a single board that permits realtime experimentation with very large codebooks.

Energy landscapes for machine learning

NASA Astrophysics Data System (ADS)

Ballard, Andrew J.; Das, Ritankar; Martiniani, Stefano; Mehta, Dhagash; Sagun, Levent; Stevenson, Jacob D.; Wales, David J.

Machine learning techniques are being increasingly used as flexible non-linear fitting and prediction tools in the physical sciences. Fitting functions that exhibit multiple solutions as local minima can be analysed in terms of the corresponding machine learning landscape. Methods to explore and visualise molecular potential energy landscapes can be applied to these machine learning landscapes to gain new insight into the solution space involved in training and the nature of the corresponding predictions. In particular, we can define quantities analogous to molecular structure, thermodynamics, and kinetics, and relate these emergent properties to the structure of the underlying landscape. This Perspective aims to describe these analogies with examples from recent applications, and suggest avenues for new interdisciplinary research.

Physical dimensions, torsional performance, bending properties, and metallurgical characteristics of rotary endodontic instruments. VI. Canal Master drills.

PubMed

Luebke, N H; Brantley, W A; Sabri, Z I; Luebke, F L; Lausten, L L

1995-05-01

A laboratory study was performed on machine-driven Canal Master drills to determine their physical dimensions, torsional performance, bending properties, and metallurgical characteristics in fracture. Physical dimensions were determined for each of the available sizes (#50 to #100) of Canal Master drills from the manufacturer that distributes these instruments in the United States. Samples were also tested in clockwise torsion using a Maillefer memocouple. Bending properties of cantilever specimens were measured with a Tinius Olsen stiffness tester. Bending fatigue testing was performed on a unique laboratory apparatus. Scanning electron microscope examination confirmed visual observations that the stainless steel Canal Master drills exhibited ductile torsional fracture. This study is part of a continuing investigation to establish standards for all machine-driven rotary endodontic instruments.

Toward a Theory of Conversational Style: The Machine-Gun Question.

ERIC Educational Resources Information Center

Tannen, Deborah

This paper, part of a larger study, focuses on a single linguistic device, the "machine-gun question," which was used by three of six participants in a Thanksgiving dinner conversation. This conversational device is characteristic of a style that seems to grow out of the need to have others approve of one's wants. It is a style…

Resource Letter AFHEP-1: Accelerators for the Future of High-Energy Physics

NASA Astrophysics Data System (ADS)

Barletta, William A.

2012-02-01

This Resource Letter provides a guide to literature concerning the development of accelerators for the future of high-energy physics. Research articles, books, and Internet resources are cited for the following topics: motivation for future accelerators, present accelerators for high-energy physics, possible future machine, and laboratory and collaboration websites.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Review of EuCARD project on accelerator infrastructure in Europe

NASA Astrophysics Data System (ADS)

Romaniuk, Ryszard S.

2013-01-01

The aim of big infrastructural and research programs (like pan-European Framework Programs) and individual projects realized inside these programs in Europe is to structure the European Research Area - ERA in this way as to be competitive with the leaders of the world. One of this projects in EuCARD (European Coordination of Accelerator Research and Development) with the aim to structure and modernize accelerator, (including accelerators for big free electron laser machines) research infrastructure. This article presents the periodic development of EuCARD which took place between the annual meeting, April 2012 in Warsaw and SC meeting in Uppsala, December 2012. The background of all these efforts are achievements of the LHC machine and associated detectors in the race for new physics. The LHC machine works in the regime of p-p, Pb-p, Pb-Pb (protons and lead ions). Recently, a discovery by the LHC of Higgs like boson, has started vivid debates on the further potential of this machine and the future. The periodic EuCARD conference, workshop and meetings concern building of the research infrastructure, including in this advanced photonic and electronic systems for servicing large high energy physics experiments. There are debated a few basic groups of such systems like: measurement - control networks of large geometrical extent, multichannel systems for large amounts of metrological data acquisition, precision photonic networks of reference time, frequency and phase distribution. The aim of the discussion is not only summarize the current status but make plans and prepare practically to building new infrastructures. Accelerator science and technology is one of a key enablers of the developments in the particle physic, photon physics and also applications in medicine and industry. Accelerator technology is intensely developed in all developed nations and regions of the world. The EuCARD project contains a lot of subjects related directly and indirectly to photon physics and photonics, as well as optoelectronics, electronics and integration of these with large research infrastructure.

Diamond Smoothing Tools

NASA Technical Reports Server (NTRS)

Voronov, Oleg

2007-01-01

Diamond smoothing tools have been proposed for use in conjunction with diamond cutting tools that are used in many finish-machining operations. Diamond machining (including finishing) is often used, for example, in fabrication of precise metal mirrors. A diamond smoothing tool according to the proposal would have a smooth spherical surface. For a given finish machining operation, the smoothing tool would be mounted next to the cutting tool. The smoothing tool would slide on the machined surface left behind by the cutting tool, plastically deforming the surface material and thereby reducing the roughness of the surface, closing microcracks and otherwise generally reducing or eliminating microscopic surface and subsurface defects, and increasing the microhardness of the surface layer. It has been estimated that if smoothing tools of this type were used in conjunction with cutting tools on sufficiently precise lathes, it would be possible to reduce the roughness of machined surfaces to as little as 3 nm. A tool according to the proposal would consist of a smoothing insert in a metal holder. The smoothing insert would be made from a diamond/metal functionally graded composite rod preform, which, in turn, would be made by sintering together a bulk single-crystal or polycrystalline diamond, a diamond powder, and a metallic alloy at high pressure. To form the spherical smoothing tip, the diamond end of the preform would be subjected to flat grinding, conical grinding, spherical grinding using diamond wheels, and finally spherical polishing and/or buffing using diamond powders. If the diamond were a single crystal, then it would be crystallographically oriented, relative to the machining motion, to minimize its wear and maximize its hardness. Spherically polished diamonds could also be useful for purposes other than smoothing in finish machining: They would likely also be suitable for use as heat-resistant, wear-resistant, unlubricated sliding-fit bearing inserts.

Testing of the Support Vector Machine for Binary-Class Classification

NASA Technical Reports Server (NTRS)

Scholten, Matthew

2011-01-01

The Support Vector Machine is a powerful algorithm, useful in classifying data in to species. The Support Vector Machines implemented in this research were used as classifiers for the final stage in a Multistage Autonomous Target Recognition system. A single kernel SVM known as SVMlight, and a modified version known as a Support Vector Machine with K-Means Clustering were used. These SVM algorithms were tested as classifiers under varying conditions. Image noise levels varied, and the orientation of the targets changed. The classifiers were then optimized to demonstrate their maximum potential as classifiers. Results demonstrate the reliability of SMV as a method for classification. From trial to trial, SVM produces consistent results

Dynamic modelling and parameter estimation of a hydraulic robot manipulator using a multi-objective genetic algorithm

NASA Astrophysics Data System (ADS)

Montazeri, A.; West, C.; Monk, S. D.; Taylor, C. J.

2017-04-01

This paper concerns the problem of dynamic modelling and parameter estimation for a seven degree of freedom hydraulic manipulator. The laboratory example is a dual-manipulator mobile robotic platform used for research into nuclear decommissioning. In contrast to earlier control model-orientated research using the same machine, the paper develops a nonlinear, mechanistic simulation model that can subsequently be used to investigate physically meaningful disturbances. The second contribution is to optimise the parameters of the new model, i.e. to determine reliable estimates of the physical parameters of a complex robotic arm which are not known in advance. To address the nonlinear and non-convex nature of the problem, the research relies on the multi-objectivisation of an output error single-performance index. The developed algorithm utilises a multi-objective genetic algorithm (GA) in order to find a proper solution. The performance of the model and the GA is evaluated using both simulated (i.e. with a known set of 'true' parameters) and experimental data. Both simulation and experimental results show that multi-objectivisation has improved convergence of the estimated parameters compared to the single-objective output error problem formulation. This is achieved by integrating the validation phase inside the algorithm implicitly and exploiting the inherent structure of the multi-objective GA for this specific system identification problem.

No time machine construction in open 2+1 gravity with timelike total energy-momentum

NASA Astrophysics Data System (ADS)

Tiglio, Manuel H.

1998-09-01

It is shown that in (2+1)-dimensional gravity an open spacetime with timelike sources and total energy momentum cannot have a stable compactly generated Cauchy horizon. This constitutes a proof of a version of Kabat's conjecture and shows, in particular, that not only a Gott time machine cannot be formed from processes such as the decay of a single cosmic string as has been shown by Carroll et al., but that, in a precise sense, a time machine cannot be constructed at all.

Control coil arrangement for a rotating machine rotor

DOEpatents

Shah, Manoj R.; Lewandowsk, Chad R.

2001-07-31

A rotating machine (e.g., a turbine, motor or generator) is provided wherein a fixed solenoid or other coil configuration is disposed adjacent to one or both ends of the active portion of the machine rotor for producing an axially directed flux in the active portion so as to provide planar axial control at single or multiple locations for rotor balance, levitation, centering, torque and thrust action. Permanent magnets can be used to produce an axial bias magnetic field. The rotor can include magnetic disks disposed in opposed, facing relation to the coil configuration.

Volumetric Verification of Multiaxis Machine Tool Using Laser Tracker

PubMed Central

Aguilar, Juan José

2014-01-01

This paper aims to present a method of volumetric verification in machine tools with linear and rotary axes using a laser tracker. Beyond a method for a particular machine, it presents a methodology that can be used in any machine type. Along this paper, the schema and kinematic model of a machine with three axes of movement, two linear and one rotational axes, including the measurement system and the nominal rotation matrix of the rotational axis are presented. Using this, the machine tool volumetric error is obtained and nonlinear optimization techniques are employed to improve the accuracy of the machine tool. The verification provides a mathematical, not physical, compensation, in less time than other methods of verification by means of the indirect measurement of geometric errors of the machine from the linear and rotary axes. This paper presents an extensive study about the appropriateness and drawbacks of the regression function employed depending on the types of movement of the axes of any machine. In the same way, strengths and weaknesses of measurement methods and optimization techniques depending on the space available to place the measurement system are presented. These studies provide the most appropriate strategies to verify each machine tool taking into consideration its configuration and its available work space. PMID:25202744

Molecular machines open cell membranes

NASA Astrophysics Data System (ADS)

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G.; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B.; Robinson, Jacob T.; Wang, Gufeng; Pal, Robert; Tour, James M.

2017-08-01

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

Status and Roadmap of CernVM

NASA Astrophysics Data System (ADS)

Berzano, D.; Blomer, J.; Buncic, P.; Charalampidis, I.; Ganis, G.; Meusel, R.

2015-12-01

Cloud resources nowadays contribute an essential share of resources for computing in high-energy physics. Such resources can be either provided by private or public IaaS clouds (e.g. OpenStack, Amazon EC2, Google Compute Engine) or by volunteers computers (e.g. LHC@Home 2.0). In any case, experiments need to prepare a virtual machine image that provides the execution environment for the physics application at hand. The CernVM virtual machine since version 3 is a minimal and versatile virtual machine image capable of booting different operating systems. The virtual machine image is less than 20 megabyte in size. The actual operating system is delivered on demand by the CernVM File System. CernVM 3 has matured from a prototype to a production environment. It is used, for instance, to run LHC applications in the cloud, to tune event generators using a network of volunteer computers, and as a container for the historic Scientific Linux 5 and Scientific Linux 4 based software environments in the course of long-term data preservation efforts of the ALICE, CMS, and ALEPH experiments. We present experience and lessons learned from the use of CernVM at scale. We also provide an outlook on the upcoming developments. These developments include adding support for Scientific Linux 7, the use of container virtualization, such as provided by Docker, and the streamlining of virtual machine contextualization towards the cloud-init industry standard.

Molecular machines open cell membranes.

PubMed

García-López, Víctor; Chen, Fang; Nilewski, Lizanne G; Duret, Guillaume; Aliyan, Amir; Kolomeisky, Anatoly B; Robinson, Jacob T; Wang, Gufeng; Pal, Robert; Tour, James M

2017-08-30

Beyond the more common chemical delivery strategies, several physical techniques are used to open the lipid bilayers of cellular membranes. These include using electric and magnetic fields, temperature, ultrasound or light to introduce compounds into cells, to release molecular species from cells or to selectively induce programmed cell death (apoptosis) or uncontrolled cell death (necrosis). More recently, molecular motors and switches that can change their conformation in a controlled manner in response to external stimuli have been used to produce mechanical actions on tissue for biomedical applications. Here we show that molecular machines can drill through cellular bilayers using their molecular-scale actuation, specifically nanomechanical action. Upon physical adsorption of the molecular motors onto lipid bilayers and subsequent activation of the motors using ultraviolet light, holes are drilled in the cell membranes. We designed molecular motors and complementary experimental protocols that use nanomechanical action to induce the diffusion of chemical species out of synthetic vesicles, to enhance the diffusion of traceable molecular machines into and within live cells, to induce necrosis and to introduce chemical species into live cells. We also show that, by using molecular machines that bear short peptide addends, nanomechanical action can selectively target specific cell-surface recognition sites. Beyond the in vitro applications demonstrated here, we expect that molecular machines could also be used in vivo, especially as their design progresses to allow two-photon, near-infrared and radio-frequency activation.

More physics in the laundromat

NASA Astrophysics Data System (ADS)

Denny, Mark

2010-12-01

The physics of a washing machine spin cycle is extended to include the spin-up and spin-down phases. We show that, for realistic parameters, an adiabatic approximation applies, and thus the familiar forced, damped harmonic oscillator analysis can be applied to these phases.

An Inexpensive Method to use an Ocean Optics Spectrometer for Telescopic Spectroscopy

NASA Astrophysics Data System (ADS)

Joel, Berger; Sugerman, B. E. K.

2012-01-01

We present a relatively-inexpensive method for using an Ocean Optics spectrometer for telescopic spectroscopy. The Ocean Optics spectrometer is a highly-sensitive, affordable and versatile fiber-optic spectrometer that can be used in a variety of physics and astronomy classes and labs. With about $275 and a small amount of machining, this spectrometer can be easily adapted for any telescope that accepts 2" eyepieces. We provide the equipment list, machining specs, and calibration process, as well as sample stellar spectra. This work was supported by the Department of Physics and Astronomy and the Office of the Provost of Goucher College.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

PubMed Central

Memarian, Negar; Torre, Jared B.; Haltom, Kate E.; Stanton, Annette L.

2017-01-01

Abstract Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. PMID:28992270

MTR WING, TRA604. FIRST FLOOR PLAN. ENTRY LOBBY, MACHINE SHOP, ...

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

MTR WING, TRA-604. FIRST FLOOR PLAN. ENTRY LOBBY, MACHINE SHOP, INSTRUMENT SHOP, COUNTING ROOM, HEALTH PHYSICS LAB, LABS AND OFFICES, STORAGE, SHIPPING AND RECEIVING. BLAW-KNOX 3150-4-2, 7/1950. INL INDEX NO. 053-604-00-099-100008, REV. 7. - Idaho National Engineering Laboratory, Test Reactor Area, Materials & Engineering Test Reactors, Scoville, Butte County, ID

Ammunition Loading and Firing Test Pretest Physical Conditioning of Female Soldier Participants

DTIC Science & Technology

1978-10-01

appear to be a significant improvement considering that Cooper’s values are based upon women running it, shorts and tennis shoes as opposed to the Ss who...machine. of the other, facing machine between handles. 2. Grasp lift handles. 2. Squat down, bending at knees and hips, and 3. "Pin" elbows to your side

Study of Man-Machine Communications Systems for Disabled Persons (The Handicapped). Volume VI. Final Report.

ERIC Educational Resources Information Center

Kafafian, Haig

The instruction manual contains lessons for teaching severely physically and/or neurologically handicapped students to use the seven-key Cybertype electric writing machine. Unlike the 14-key keyboard, which requires bilateral coordination in arms, legs, or other parts of the body, the seven-key keyboard requires the use of only one part of the…

Black holes: theory and observations (Scientific session of the Physical Sciences Division of the Russian Academy of Sciences, 23 December 2015)

NASA Astrophysics Data System (ADS)

2016-07-01

A scientific session of the Physical Sciences Division of the Russian Academy of Sciences (RAS), "Black holes: theory and observations," was held in the conference hall of the Lebedev Physical Institute, RAS, on 23 December 2015. The papers collected in this issue were written based on talks given at the session: (1) I D Novikov (Lebedev Physical Institute, Russian Academy of Sciences, Astro Space Center, Moscow; The Niels Bohr International Academy, The Niels Bohr Institute, Copenhagen; National Research Center 'Kurchatov Institute', Moscow) "Black holes, wormholes, and time machines"; (2) A M Cherepashchuk (Lomonosov Moscow State University, Sternberg Astronomical Institute, Moscow) "Observing stellar-mass and supermassive black holes"; (3) N S Kardashev (Lebedev Physical Institute, Russian Academy of Sciences, Astro Space Center, Moscow) "Millimetron space project: a tool for researching black holes and wormholes." Papers written on the basis of oral presentations 1, 2 are published below. • Observing stellar mass and supermassive black holes, A M Cherepashchuk Physics-Uspekhi, 2016, Volume 59, Number 7, Pages 702-712 • Black holes, wormholes, and time machines, I D Novikov Physics-Uspekhi, 2016, Volume 59, Number 7, Pages 713-715

Performance analysis of single stage libr-water absorption machine operated by waste thermal energy of internal combustion engine: Case study

NASA Astrophysics Data System (ADS)

Sharif, Hafiz Zafar; Leman, A. M.; Muthuraman, S.; Salleh, Mohd Najib Mohd; Zakaria, Supaat

2017-09-01

Combined heating, cooling, and power is also known as Tri-generation. Tri-generation system can provide power, hot water, space heating and air -conditioning from single source of energy. The objective of this study is to propose a method to evaluate the characteristic and performance of a single stage lithium bromide-water (LiBr-H2O) absorption machine operated with waste thermal energy of internal combustion engine which is integral part of trigeneration system. Correlations for computer sensitivity analysis are developed in data fit software for (P-T-X), (H-T-X), saturated liquid (water), saturated vapor, saturation pressure and crystallization temperature curve of LiBr-H2O Solution. Number of equations were developed with data fit software and exported into excel work sheet for the evaluation of number of parameter concerned with the performance of vapor absorption machine such as co-efficient of performance, concentration of solution, mass flow rate, size of heat exchangers of the unit in relation to the generator, condenser, absorber and evaporator temperatures. Size of vapor absorption machine within its crystallization limits for cooling and heating by waste energy recovered from exhaust gas, and jacket water of internal combustion engine also presented in this study to save the time and cost for the facilities managers who are interested to utilize the waste thermal energy of their buildings or premises for heating and air conditioning applications.

Application of machine learning on brain cancer multiclass classification

NASA Astrophysics Data System (ADS)

Panca, V.; Rustam, Z.

2017-07-01

Classification of brain cancer is a problem of multiclass classification. One approach to solve this problem is by first transforming it into several binary problems. The microarray gene expression dataset has the two main characteristics of medical data: extremely many features (genes) and only a few number of samples. The application of machine learning on microarray gene expression dataset mainly consists of two steps: feature selection and classification. In this paper, the features are selected using a method based on support vector machine recursive feature elimination (SVM-RFE) principle which is improved to solve multiclass classification, called multiple multiclass SVM-RFE. Instead of using only the selected features on a single classifier, this method combines the result of multiple classifiers. The features are divided into subsets and SVM-RFE is used on each subset. Then, the selected features on each subset are put on separate classifiers. This method enhances the feature selection ability of each single SVM-RFE. Twin support vector machine (TWSVM) is used as the method of the classifier to reduce computational complexity. While ordinary SVM finds single optimum hyperplane, the main objective Twin SVM is to find two non-parallel optimum hyperplanes. The experiment on the brain cancer microarray gene expression dataset shows this method could classify 71,4% of the overall test data correctly, using 100 and 1000 genes selected from multiple multiclass SVM-RFE feature selection method. Furthermore, the per class results show that this method could classify data of normal and MD class with 100% accuracy.

Network challenges for cyber physical systems with tiny wireless devices: a case study on reliable pipeline condition monitoring.

PubMed

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Khan, Muhammad Farhan; Naeem, Muhammad; Anpalagan, Alagan

2015-03-25

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed.

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System

PubMed Central

Beruvides, Gerardo

2017-01-01

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors’ knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions. PMID:28906450

Network Challenges for Cyber Physical Systems with Tiny Wireless Devices: A Case Study on Reliable Pipeline Condition Monitoring

PubMed Central

Ali, Salman; Qaisar, Saad Bin; Saeed, Husnain; Farhan Khan, Muhammad; Naeem, Muhammad; Anpalagan, Alagan

2015-01-01

The synergy of computational and physical network components leading to the Internet of Things, Data and Services has been made feasible by the use of Cyber Physical Systems (CPSs). CPS engineering promises to impact system condition monitoring for a diverse range of fields from healthcare, manufacturing, and transportation to aerospace and warfare. CPS for environment monitoring applications completely transforms human-to-human, human-to-machine and machine-to-machine interactions with the use of Internet Cloud. A recent trend is to gain assistance from mergers between virtual networking and physical actuation to reliably perform all conventional and complex sensing and communication tasks. Oil and gas pipeline monitoring provides a novel example of the benefits of CPS, providing a reliable remote monitoring platform to leverage environment, strategic and economic benefits. In this paper, we evaluate the applications and technical requirements for seamlessly integrating CPS with sensor network plane from a reliability perspective and review the strategies for communicating information between remote monitoring sites and the widely deployed sensor nodes. Related challenges and issues in network architecture design and relevant protocols are also provided with classification. This is supported by a case study on implementing reliable monitoring of oil and gas pipeline installations. Network parameters like node-discovery, node-mobility, data security, link connectivity, data aggregation, information knowledge discovery and quality of service provisioning have been reviewed. PMID:25815444

Obstacle Recognition Based on Machine Learning for On-Chip LiDAR Sensors in a Cyber-Physical System.

PubMed

Castaño, Fernando; Beruvides, Gerardo; Haber, Rodolfo E; Artuñedo, Antonio

2017-09-14

Collision avoidance is an important feature in advanced driver-assistance systems, aimed at providing correct, timely and reliable warnings before an imminent collision (with objects, vehicles, pedestrians, etc.). The obstacle recognition library is designed and implemented to address the design and evaluation of obstacle detection in a transportation cyber-physical system. The library is integrated into a co-simulation framework that is supported on the interaction between SCANeR software and Matlab/Simulink. From the best of the authors' knowledge, two main contributions are reported in this paper. Firstly, the modelling and simulation of virtual on-chip light detection and ranging sensors in a cyber-physical system, for traffic scenarios, is presented. The cyber-physical system is designed and implemented in SCANeR. Secondly, three specific artificial intelligence-based methods for obstacle recognition libraries are also designed and applied using a sensory information database provided by SCANeR. The computational library has three methods for obstacle detection: a multi-layer perceptron neural network, a self-organization map and a support vector machine. Finally, a comparison among these methods under different weather conditions is presented, with very promising results in terms of accuracy. The best results are achieved using the multi-layer perceptron in sunny and foggy conditions, the support vector machine in rainy conditions and the self-organized map in snowy conditions.

MOD-2 wind turbine farm stability study

NASA Technical Reports Server (NTRS)

Hinrichsen, E. N.

1980-01-01

The dynamics of single and multiple 2.5 ME, Boeing MOD-2 wind turbine generators (WTGs) connected to utility power systems were investigated. The analysis was based on digital simulation. Both time response and frequency response methods were used. The dynamics of this type of WTG are characterized by two torsional modes, a low frequency 'shaft' mode below 1 Hz and an 'electrical' mode at 3-5 Hz. High turbine inertia and low torsional stiffness between turbine and generator are inherent features. Turbine control is based on electrical power, not turbine speed as in conventional utility turbine generators. Multi-machine dynamics differ very little from single machine dynamics.

An algorithm for a single machine scheduling problem with sequence dependent setup times and scheduling windows

NASA Technical Reports Server (NTRS)

Moore, J. E.

1975-01-01

An enumeration algorithm is presented for solving a scheduling problem similar to the single machine job shop problem with sequence dependent setup times. The scheduling problem differs from the job shop problem in two ways. First, its objective is to select an optimum subset of the available tasks to be performed during a fixed period of time. Secondly, each task scheduled is constrained to occur within its particular scheduling window. The algorithm is currently being used to develop typical observational timelines for a telescope that will be operated in earth orbit. Computational times associated with timeline development are presented.

Single-machine common/slack due window assignment problems with linear decreasing processing times

NASA Astrophysics Data System (ADS)

Zhang, Xingong; Lin, Win-Chin; Wu, Wen-Hsiang; Wu, Chin-Chia

2017-08-01

This paper studies linear non-increasing processing times and the common/slack due window assignment problems on a single machine, where the actual processing time of a job is a linear non-increasing function of its starting time. The aim is to minimize the sum of the earliness cost, tardiness cost, due window location and due window size. Some optimality results are discussed for the common/slack due window assignment problems and two O(n log n) time algorithms are presented to solve the two problems. Finally, two examples are provided to illustrate the correctness of the corresponding algorithms.

Resource Sharing in Montana: A Study of Interlibrary Loan and Alternatives for a Montana Union Catalog.

ERIC Educational Resources Information Center

Matthews, Joseph R.

This study recommends a variety of actions to create and maintain a Montana union catalog (MONCAT) for more effective usage of in-state resources and library funds. Specifically, it advocates (1) merger of existing COM, machine readable bibliographic records, and OCLC tapes into a single microform catalog; (2) acceptance of only machine readable…

Estimation of Teacher Practices Based on Text Transcripts of Teacher Speech Using a Support Vector Machine Algorithm

ERIC Educational Resources Information Center

Araya, Roberto; Plana, Francisco; Dartnell, Pablo; Soto-Andrade, Jorge; Luci, Gina; Salinas, Elena; Araya, Marylen

2012-01-01

Teacher practice is normally assessed by observers who watch classes or videos of classes. Here, we analyse an alternative strategy that uses text transcripts and a support vector machine classifier. For each one of the 710 videos of mathematics classes from the 2005 Chilean National Teacher Assessment Programme, a single 4-minute slice was…

Universal Tool Grinder Operator Instructor's Guide. Part of Single-Tool Skills Program Machine Industries Occupations.

ERIC Educational Resources Information Center

New York State Education Dept., Albany. Div. of Curriculum Development.

The document is an instructor's guide for a course on universal tool grinder operation. The course is designed to train people in making complicated machine setups and precision in the grinding operations and, although intended primarily for adult learners, it can be adapted for high school use. The guide is divided into three parts: (1) the…

Physically absorbable reagents-collectors in elementary flotation

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

S.A. Kondrat'ev; I.G. Bochkarev

2007-09-15

Based on the reviewed researches held at the Institute of Mining, Siberian Branch, Russian Academy of Sciences, the effect of physically absorbable reagents-collectors on formation of a flotation complex and its stability in turbulent pulp flows in flotation machines of basic types is considered. The basic requirements for physically absorbable reagents-collectors at different flotation stages are established.

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.

An element search ant colony technique for solving virtual machine placement problem

NASA Astrophysics Data System (ADS)

Srija, J.; Rani John, Rose; Kanaga, Grace Mary, Dr.

2017-09-01

The data centres in the cloud environment play a key role in providing infrastructure for ubiquitous computing, pervasive computing, mobile computing etc. This computing technique tries to utilize the available resources in order to provide services. Hence maintaining the resource utilization without wastage of power consumption has become a challenging task for the researchers. In this paper we propose the direct guidance ant colony system for effective mapping of virtual machines to the physical machine with maximal resource utilization and minimal power consumption. The proposed algorithm has been compared with the existing ant colony approach which is involved in solving virtual machine placement problem and thus the proposed algorithm proves to provide better result than the existing technique.

Estimation of in-situ bioremediation system cost using a hybrid Extreme Learning Machine (ELM)-particle swarm optimization approach

NASA Astrophysics Data System (ADS)

Yadav, Basant; Ch, Sudheer; Mathur, Shashi; Adamowski, Jan

2016-12-01

In-situ bioremediation is the most common groundwater remediation procedure used for treating organically contaminated sites. A simulation-optimization approach, which incorporates a simulation model for groundwaterflow and transport processes within an optimization program, could help engineers in designing a remediation system that best satisfies management objectives as well as regulatory constraints. In-situ bioremediation is a highly complex, non-linear process and the modelling of such a complex system requires significant computational exertion. Soft computing techniques have a flexible mathematical structure which can generalize complex nonlinear processes. In in-situ bioremediation management, a physically-based model is used for the simulation and the simulated data is utilized by the optimization model to optimize the remediation cost. The recalling of simulator to satisfy the constraints is an extremely tedious and time consuming process and thus there is need for a simulator which can reduce the computational burden. This study presents a simulation-optimization approach to achieve an accurate and cost effective in-situ bioremediation system design for groundwater contaminated with BTEX (Benzene, Toluene, Ethylbenzene, and Xylenes) compounds. In this study, the Extreme Learning Machine (ELM) is used as a proxy simulator to replace BIOPLUME III for the simulation. The selection of ELM is done by a comparative analysis with Artificial Neural Network (ANN) and Support Vector Machine (SVM) as they were successfully used in previous studies of in-situ bioremediation system design. Further, a single-objective optimization problem is solved by a coupled Extreme Learning Machine (ELM)-Particle Swarm Optimization (PSO) technique to achieve the minimum cost for the in-situ bioremediation system design. The results indicate that ELM is a faster and more accurate proxy simulator than ANN and SVM. The total cost obtained by the ELM-PSO approach is held to a minimum while successfully satisfying all the regulatory constraints of the contaminated site.

The Photon Shell Game and the Quantum von Neumann Architecture with Superconducting Circuits

NASA Astrophysics Data System (ADS)

Mariantoni, Matteo

2012-02-01

Superconducting quantum circuits have made significant advances over the past decade, allowing more complex and integrated circuits that perform with good fidelity. We have recently implemented a machine comprising seven quantum channels, with three superconducting resonators, two phase qubits, and two zeroing registers. I will explain the design and operation of this machine, first showing how a single microwave photon | 1 > can be prepared in one resonator and coherently transferred between the three resonators. I will also show how more exotic states such as double photon states | 2 > and superposition states | 0 >+ | 1 > can be shuffled among the resonators as well [1]. I will then demonstrate how this machine can be used as the quantum-mechanical analog of the von Neumann computer architecture, which for a classical computer comprises a central processing unit and a memory holding both instructions and data. The quantum version comprises a quantum central processing unit (quCPU) that exchanges data with a quantum random-access memory (quRAM) integrated on one chip, with instructions stored on a classical computer. I will also present a proof-of-concept demonstration of a code that involves all seven quantum elements: (1), Preparing an entangled state in the quCPU, (2), writing it to the quRAM, (3), preparing a second state in the quCPU, (4), zeroing it, and, (5), reading out the first state stored in the quRAM [2]. Finally, I will demonstrate that the quantum von Neumann machine provides one unit cell of a two-dimensional qubit-resonator array that can be used for surface code quantum computing. This will allow the realization of a scalable, fault-tolerant quantum processor with the most forgiving error rates to date. [4pt] [1] M. Mariantoni et al., Nature Physics 7, 287-293 (2011.)[0pt] [2] M. Mariantoni et al., Science 334, 61-65 (2011).

Machine learnt bond order potential to model metal-organic (Co-C) heterostructures.

PubMed

Narayanan, Badri; Chan, Henry; Kinaci, Alper; Sen, Fatih G; Gray, Stephen K; Chan, Maria K Y; Sankaranarayanan, Subramanian K R S

2017-11-30

A fundamental understanding of the inter-relationships between structure, morphology, atomic scale dynamics, chemistry, and physical properties of mixed metallic-covalent systems is essential to design novel functional materials for applications in flexible nano-electronics, energy storage and catalysis. To achieve such knowledge, it is imperative to develop robust and computationally efficient atomistic models that describe atomic interactions accurately within a single framework. Here, we present a unified Tersoff-Brenner type bond order potential (BOP) for a Co-C system, trained against lattice parameters, cohesive energies, equation of state, and elastic constants of different crystalline phases of cobalt as well as orthorhombic Co 2 C derived from density functional theory (DFT) calculations. The independent BOP parameters are determined using a combination of supervised machine learning (genetic algorithms) and local minimization via the simplex method. Our newly developed BOP accurately describes the structural, thermodynamic, mechanical, and surface properties of both the elemental components as well as the carbide phases, in excellent accordance with DFT calculations and experiments. Using our machine-learnt BOP potential, we performed large-scale molecular dynamics simulations to investigate the effect of metal/carbon concentration on the structure and mechanical properties of porous architectures obtained via self-assembly of cobalt nanoparticles and fullerene molecules. Such porous structures have implications in flexible electronics, where materials with high electrical conductivity and low elastic stiffness are desired. Using unsupervised machine learning (clustering), we identify the pore structure, pore-distribution, and metallic conduction pathways in self-assembled structures at different C/Co ratios. We find that as the C/Co ratio increases, the connectivity between the Co nanoparticles becomes limited, likely resulting in low electrical conductivity; on the other hand, such C-rich hybrid structures are highly flexible (i.e., low stiffness). The BOP model developed in this work is a valuable tool to investigate atomic scale processes, structure-property relationships, and temperature/pressure response of Co-C systems, as well as design organic-inorganic hybrid structures with a desired set of properties.

Precision replenishable grinding tool and manufacturing process

DOEpatents

Makowiecki, D.M.; Kerns, J.A.; Blaedel, K.L.; Colella, N.J.; Davis, P.J.; Juntz, R.S.

1998-06-09

A reusable grinding tool consisting of a replaceable single layer of abrasive particles intimately bonded to a precisely configured tool substrate, and a process for manufacturing the grinding tool are disclosed. The tool substrate may be ceramic or metal and the abrasive particles are preferably diamond, but may be cubic boron nitride. The manufacturing process involves: coating a configured tool substrate with layers of metals, such as titanium, copper and titanium, by physical vapor deposition (PVD); applying the abrasive particles to the coated surface by a slurry technique; and brazing the abrasive particles to the tool substrate by alloying the metal layers. The precision control of the composition and thickness of the metal layers enables the bonding of a single layer or several layers of micron size abrasive particles to the tool surface. By the incorporation of an easily dissolved metal layer in the composition such allows the removal and replacement of the abrasive particles, thereby providing a process for replenishing a precisely machined grinding tool with fine abrasive particles, thus greatly reducing costs as compared to replacing expensive grinding tools. 11 figs.

Precision replenishable grinding tool and manufacturing process

DOEpatents

Makowiecki, Daniel M.; Kerns, John A.; Blaedel, Kenneth L.; Colella, Nicholas J.; Davis, Pete J.; Juntz, Robert S.

1998-01-01

A reusable grinding tool consisting of a replaceable single layer of abrasive particles intimately bonded to a precisely configured tool substrate, and a process for manufacturing the grinding tool. The tool substrate may be ceramic or metal and the abrasive particles are preferably diamond, but may be cubic boron nitride. The manufacturing process involves: coating a configured tool substrate with layers of metals, such as titanium, copper and titanium, by physical vapor deposition (PVD); applying the abrasive particles to the coated surface by a slurry technique; and brazing the abrasive particles to the tool substrate by alloying the metal layers. The precision control of the composition and thickness of the metal layers enables the bonding of a single layer or several layers of micron size abrasive particles to the tool surface. By the incorporation of an easily dissolved metal layer in the composition such allows the removal and replacement of the abrasive particles, thereby providing a process for replenishing a precisely machined grinding tool with fine abrasive particles, thus greatly reducing costs as compared to replacing expensive grinding tools.

Fabrication of an infrared Shack-Hartmann sensor by combining high-speed single-point diamond milling and precision compression molding processes.

PubMed

Zhang, Lin; Zhou, Wenchen; Naples, Neil J; Yi, Allen Y

2018-05-01

A novel fabrication method by combining high-speed single-point diamond milling and precision compression molding processes for fabrication of discontinuous freeform microlens arrays was proposed. Compared with slow tool servo diamond broaching, high-speed single-point diamond milling was selected for its flexibility in the fabrication of true 3D optical surfaces with discontinuous features. The advantage of single-point diamond milling is that the surface features can be constructed sequentially by spacing the axes of a virtual spindle at arbitrary positions based on the combination of rotational and translational motions of both the high-speed spindle and linear slides. By employing this method, each micro-lenslet was regarded as a microstructure cell by passing the axis of the virtual spindle through the vertex of each cell. An optimization arithmetic based on minimum-area fabrication was introduced to the machining process to further increase the machining efficiency. After the mold insert was machined, it was employed to replicate the microlens array onto chalcogenide glass. In the ensuing optical measurement, the self-built Shack-Hartmann wavefront sensor was proven to be accurate in detecting an infrared wavefront by both experiments and numerical simulation. The combined results showed that precision compression molding of chalcogenide glasses could be an economic and precision optical fabrication technology for high-volume production of infrared optics.

A Web-Based Development Environment for Collaborative Data Analysis

NASA Astrophysics Data System (ADS)

Erdmann, M.; Fischer, R.; Glaser, C.; Klingebiel, D.; Komm, M.; Müller, G.; Rieger, M.; Steggemann, J.; Urban, M.; Winchen, T.

2014-06-01

Visual Physics Analysis (VISPA) is a web-based development environment addressing high energy and astroparticle physics. It covers the entire analysis spectrum from the design and validation phase to the execution of analyses and the visualization of results. VISPA provides a graphical steering of the analysis flow, which consists of self-written, re-usable Python and C++ modules for more demanding tasks. All common operating systems are supported since a standard internet browser is the only software requirement for users. Even access via mobile and touch-compatible devices is possible. In this contribution, we present the most recent developments of our web application concerning technical, state-of-the-art approaches as well as practical experiences. One of the key features is the use of workspaces, i.e. user-configurable connections to remote machines supplying resources and local file access. Thereby, workspaces enable the management of data, computing resources (e.g. remote clusters or computing grids), and additional software either centralized or individually. We further report on the results of an application with more than 100 third-year students using VISPA for their regular particle physics exercises during the winter term 2012/13. Besides the ambition to support and simplify the development cycle of physics analyses, new use cases such as fast, location-independent status queries, the validation of results, and the ability to share analyses within worldwide collaborations with a single click become conceivable.

Strength and endurance training of an individual with left upper and lower limb amputations.

PubMed

Donachy, J E; Brannon, K D; Hughes, L S; Seahorn, J; Crutcher, T T; Christian, E L

2004-04-22

The purpose of this article is to describe the development of a strength and endurance training programme designed to prepare an individual with a left glenohumeral disarticulation and transtibial amputation for a bike trip across the USA. The subject was scheduled for training three times per week over a two-month period followed by two times per week for an additional two months. Training consisted of a resistance training circuit using variable resistance machines, cycling using a recumbent stationary bike, and core stability training using stability ball exercises. Changes in strength were assessed using 10 RM tests on the resistance machines and changes in peak VO(2) were monitored utilizing the Cosmed K4b pulmonary function tester. The subject demonstrated a 30.3% gain in peak VO(2). The subject's 10 RM for left single limb leg press increased 36.8% and gains of at least 7.7% were seen for all other muscle groups tested. The strength and endurance training programme adapted to compensate for this subject's limb losses was effective in increasing both strength and peak VO(2). Adapting exercise programmes to compensate for limb loss may allow individuals with amputations to participate in physically challenging activities that otherwise may not be available to them.

Engineering model for ultrafast laser microprocessing

NASA Astrophysics Data System (ADS)

Audouard, E.; Mottay, E.

2016-03-01

Ultrafast laser micro-machining relies on complex laser-matter interaction processes, leading to a virtually athermal laser ablation. The development of industrial ultrafast laser applications benefits from a better understanding of these processes. To this end, a number of sophisticated scientific models have been developed, providing valuable insights in the physics of the interaction. Yet, from an engineering point of view, they are often difficult to use, and require a number of adjustable parameters. We present a simple engineering model for ultrafast laser processing, applied in various real life applications: percussion drilling, line engraving, and non normal incidence trepanning. The model requires only two global parameters. Analytical results are derived for single pulse percussion drilling or simple pass engraving. Simple assumptions allow to predict the effect of non normal incident beams to obtain key parameters for trepanning drilling. The model is compared to experimental data on stainless steel with a wide range of laser characteristics (time duration, repetition rate, pulse energy) and machining conditions (sample or beam speed). Ablation depth and volume ablation rate are modeled for pulse durations from 100 fs to 1 ps. Trepanning time of 5.4 s with a conicity of 0.15° is obtained for a hole of 900 μm depth and 100 μm diameter.

Line-drawing algorithms for parallel machines

NASA Technical Reports Server (NTRS)

Pang, Alex T.

1990-01-01

The fact that conventional line-drawing algorithms, when applied directly on parallel machines, can lead to very inefficient codes is addressed. It is suggested that instead of modifying an existing algorithm for a parallel machine, a more efficient implementation can be produced by going back to the invariants in the definition. Popular line-drawing algorithms are compared with two alternatives; distance to a line (a point is on the line if sufficiently close to it) and intersection with a line (a point on the line if an intersection point). For massively parallel single-instruction-multiple-data (SIMD) machines (with thousands of processors and up), the alternatives provide viable line-drawing algorithms. Because of the pixel-per-processor mapping, their performance is independent of the line length and orientation.

27 CFR 479.41 - Single sale.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2014-04-01 2014-04-01 false Single sale. 479.41 Section 479.41 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES, AND CERTAIN...

27 CFR 479.41 - Single sale.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2010-04-01 2010-04-01 false Single sale. 479.41 Section 479.41 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES, AND CERTAIN...

27 CFR 479.41 - Single sale.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2013-04-01 2013-04-01 false Single sale. 479.41 Section 479.41 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES, AND CERTAIN...

Methods And Systms For Analyzing The Degradation And Failure Of Mechanical Systems

DOEpatents

Jarrell, Donald B.; Sisk, Daniel R.; Hatley, Darrel D.; Kirihara, Leslie J.; Peters, Timothy J.

2005-02-08

Methods and systems for identifying, understanding, and predicting the degradation and failure of mechanical systems are disclosed. The methods include measuring and quantifying stressors that are responsible for the activation of degradation mechanisms in the machine component of interest. The intensity of the stressor may be correlated with the rate of physical degradation according to some determinable function such that a derivative relationship exists between the machine performance, degradation, and the underlying stressor. The derivative relationship may be used to make diagnostic and prognostic calculations concerning the performance and projected life of the machine. These calculations may be performed in real time to allow the machine operator to quickly adjust the operational parameters of the machinery in order to help minimize or eliminate the effects of the degradation mechanism, thereby prolonging the life of the machine. Various systems implementing the methods are also disclosed.

High performance cutting of aircraft and turbine components

NASA Astrophysics Data System (ADS)

Krämer, A.; Lung, D.; Klocke, F.

2012-04-01

Titanium and nickel-based alloys belong to the group of difficult-to-cut materials. The machining of these high-temperature alloys is characterized by low productivity and low process stability as a result of their physical and mechanical properties. Major problems during the machining of these materials are low applicable cutting speeds due to excessive tool wear, long machining times, and thus high manufacturing costs, as well as the formation of ribbon and snarled chips. Under these conditions automation of the production process is limited. This paper deals with strategies to improve machinability of titanium and nickel-based alloys. Using the example of the nickel-based alloy Inconel 718 high performance cutting with advanced cutting materials, such as PCBN and cutting ceramics, is presented. Afterwards the influence of different cooling strategies, like high-pressure lubricoolant supply and cryogenic cooling, during machining of TiAl6V4 is shown.

Ergonomic risk factor identification for sewing machine operators through supervised occupational therapy fieldwork in Bangladesh: A case study.

PubMed

Habib, Md Monjurul

2015-01-01

Many sewing machine operators are working with high risk factors for musculoskeletal health in the garments industries in Bangladesh. To identify the physical risk factors among sewing machine operators in a Bangladeshi garments factory. Sewing machine operators (327, 83% female), were evaluated. The mean age of the participants was 25.25 years. Six ergonomic risk factors were determined using the Musculoskeletal Disorders risk assessment. Data collection included measurements of sewing machine table and chair heights; this data was combined with information from informal interviews. Significant ergonomic risk factors found included the combination of awkward postures of the neck and back, repetitive hand and arm movements, poor ergonomic workstations and prolonged working hours without adequate breaks; these risk factors resulted in musculoskeletal complaints, sick leave, and switching jobs. One aspect of improving worker health in garment factories includes addressing musculoskeletal risk factors through ergonomic interventions.

Development of a Physical Employment Testing Battery for Armor Soldiers: 19D Cavalry Scout and 19K M1 Armor Crewman

DTIC Science & Technology

2015-12-01

M2 .50 Caliber Machine Gun on the Abrams Tank While wearing a task specific uniform weighing approximately 49 lb, Soldiers lifted the M2 .50...12 Engage Targets with a Caliber .50 M2 Machine Gun X 13 Lay a 120mm Mortar – Emplace Base Plate X 14 Lay a 120mm Mortar...17 Mount M2 .50 Cal Machine Gun Receiver on an Abrams Tank X 18 Stow Ammunition on an Abrams Tank (Load 120mm MPAT Round to the Ready Rack

Development of a Physical Employment Testing Battery for Infantry Soldiers: 11B Infantryman and 11C Infantryman-Indirect Fire

DTIC Science & Technology

2015-12-01

43 1.9 Images of Move Under Direct Fire (Task 10) 44 1.10 Engage Targets with a .50 Caliber M2 Machine Gun (Task 12) 45 1.11 Image of Lay a...Caliber M2 Machine Gun While wearing a fighting load (approximately 83 lb) and working as a member of a two-person team, Soldiers lifted and carried the... M2 HB Machine Gun with tripod (153 lb) a distance of 10 m. Army Standard: Successful completion of the task 13. Emplace Base Plate (11C

Development of a Physical Employment Testing Battery for Infantry Soldiers: 11B Infantryman and 11C Infantryman - Indirect Fire

DTIC Science & Technology

2015-12-01

25mm barrel install (Task 5) and engage targets with an M2 machine gun (Task 12). During these tasks, the performance of one individual will affect...TOW Missile Launcher on BFV (Task 8) 43 1.9 Images of Move Under Direct Fire (Task 10) 44 1.10 Engage Targets with a .50 Caliber M2 Machine Gun ...Engage Targets with a .50 Caliber M2 Machine Gun While wearing a fighting load (approximately 83 lb) and working as a member of a two-person team

APOLLO: a quality assessment service for single and multiple protein models.

PubMed

Wang, Zheng; Eickholt, Jesse; Cheng, Jianlin

2011-06-15

We built a web server named APOLLO, which can evaluate the absolute global and local qualities of a single protein model using machine learning methods or the global and local qualities of a pool of models using a pair-wise comparison approach. Based on our evaluations on 107 CASP9 (Critical Assessment of Techniques for Protein Structure Prediction) targets, the predicted quality scores generated from our machine learning and pair-wise methods have an average per-target correlation of 0.671 and 0.917, respectively, with the true model quality scores. Based on our test on 92 CASP9 targets, our predicted absolute local qualities have an average difference of 2.60 Å with the actual distances to native structure. http://sysbio.rnet.missouri.edu/apollo/. Single and pair-wise global quality assessment software is also available at the site.

Effects of Machine Traffic on the Physical Properties of Ash-Cap Soils

Treesearch

Leonard R. Johnson; Debbie Page-Dumroese; Han-Sup Han

2007-01-01

With pressure and vibration on a soil, air spaces between soil particles can be reduced by displaced soil particles. Activity associated with heavy machine traffic increases the density of the soil and can also increase the resistance of the soil to penetration. This paper reviews research related to disturbance of forest soils with a primary focus on compaction in ash...

Another look at Atwood's machine

NASA Astrophysics Data System (ADS)

LoPresto, Michael C.

1999-02-01

Atwood's machine is a standard experimental apparatus that is likely to get pushed out of the laboratory portion of the general physics course due to the ever increasing use of microcomputers. To avoid this, I now use the apparatus for an experiment during the work and energy portion of the course which not only allows us to demonstrate those principles but also compare them with Newton's laws of motion.

Remote Sensing as a Demonstration of Applied Physics.

ERIC Educational Resources Information Center

Colwell, Robert N.

1980-01-01

Provides information about the field of remote sensing, including discussions of geo-synchronous and sun-synchronous remote-sensing platforms, the actual physical processes and equipment involved in sensing, the analysis of images by humans and machines, and inexpensive, small scale methods, including aerial photography. (CS)

Stochastic scheduling on a repairable manufacturing system

NASA Astrophysics Data System (ADS)

Li, Wei; Cao, Jinhua

1995-08-01

In this paper, we consider some stochastic scheduling problems with a set of stochastic jobs on a manufacturing system with a single machine that is subject to multiple breakdowns and repairs. When the machine processing a job fails, the job processing must restart some time later when the machine is repaired. For this typical manufacturing system, we find the optimal policies that minimize the following objective functions: (1) the weighed sum of the completion times; (2) the weighed number of late jobs having constant due dates; (3) the weighted number of late jobs having random due dates exponentially distributed, which generalize some previous results.

The Design of the Automatic Control System of the Gripping-Belt Speed in Long-Rootstalk Traditional Chinese Herbal Harvester

NASA Astrophysics Data System (ADS)

Huang, Jinxia; Wang, Junfa; Yu, Yonghong

This article aims to design a kind of gripping-belt speed automatic tracking system of traditional Chinese herbal harvester by AT89C52 single-chip micro computer as a core combined with fuzzy PID control algorithm. The system can adjust the gripping-belt speed in accordance with the variation of the machine's operation, so there is a perfect matching between the machine operation speed and the gripping-belt speed. The harvesting performance of the machine can be improved greatly. System design includes hardware and software.

Principle and design of small-sized and high-definition x-ray machine

NASA Astrophysics Data System (ADS)

Zhao, Anqing

2010-10-01

The paper discusses the circuit design and working principles of VMOS PWM type 75KV10mA high frequency X-ray machine. The system mainly consists of silicon controlled rectifier, VMOS tube PWM type high-frequency and highvoltage inverter circuit, filament inverter circuit, high-voltage rectifier filter circuit and as X-ray tube. The working process can be carried out under the control of a single-chip microcomputer. Due to the small size and high resolution in imaging, the X-ray machine is mostly adopted for emergent medical diagnosis and specific circumstances where nondestructive tests are conducted.

Pointright: a system to redirect mouse and keyboard control among multiple machines

DOEpatents

Johanson, Bradley E [Palo Alto, CA; Winograd, Terry A [Stanford, CA; Hutchins, Gregory M [Mountain View, CA

2008-09-30

The present invention provides a software system, PointRight, that allows for smooth and effortless control of pointing and input devices among multiple displays. With PointRight, a single free-floating mouse and keyboard can be used to control multiple screens. When the cursor reaches the edge of a screen it seamlessly moves to the adjacent screen and keyboard control is simultaneously redirected to the appropriate machine. Laptops may also redirect their keyboard and pointing device, and multiple pointers are supported simultaneously. The system automatically reconfigures itself as displays go on, go off, or change the machine they display.

A parallel-machine scheduling problem with two competing agents

NASA Astrophysics Data System (ADS)

Lee, Wen-Chiung; Chung, Yu-Hsiang; Wang, Jen-Ya

2017-06-01

Scheduling with two competing agents has become popular in recent years. Most of the research has focused on single-machine problems. This article considers a parallel-machine problem, the objective of which is to minimize the total completion time of jobs from the first agent given that the maximum tardiness of jobs from the second agent cannot exceed an upper bound. The NP-hardness of this problem is also examined. A genetic algorithm equipped with local search is proposed to search for the near-optimal solution. Computational experiments are conducted to evaluate the proposed genetic algorithm.

Creating Turbulent Flow Realizations with Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

King, Ryan; Graf, Peter; Chertkov, Michael

2017-11-01

Generating valid inflow conditions is a crucial, yet computationally expensive, step in unsteady turbulent flow simulations. We demonstrate a new technique for rapid generation of turbulent inflow realizations that leverages recent advances in machine learning for image generation using a deep convolutional generative adversarial network (DCGAN). The DCGAN is an unsupervised machine learning technique consisting of two competing neural networks that are trained against each other using backpropagation. One network, the generator, tries to produce samples from the true distribution of states, while the discriminator tries to distinguish between true and synthetic samples. We present results from a fully-trained DCGAN that is able to rapidly draw random samples from the full distribution of possible inflow states without needing to solve the Navier-Stokes equations, eliminating the costly process of spinning up inflow turbulence. This suggests a new paradigm in physics informed machine learning where the turbulence physics can be encoded in either the discriminator or generator. Finally, we also propose additional applications such as feature identification and subgrid scale modeling.

Clustering the Orion B giant molecular cloud based on its molecular emission.

PubMed

Bron, Emeric; Daudon, Chloé; Pety, Jérôme; Levrier, François; Gerin, Maryvonne; Gratier, Pierre; Orkisz, Jan H; Guzman, Viviana; Bardeau, Sébastien; Goicoechea, Javier R; Liszt, Harvey; Öberg, Karin; Peretto, Nicolas; Sievers, Albrecht; Tremblin, Pascal

2018-02-01

Previous attempts at segmenting molecular line maps of molecular clouds have focused on using position-position-velocity data cubes of a single molecular line to separate the spatial components of the cloud. In contrast, wide field spectral imaging over a large spectral bandwidth in the (sub)mm domain now allows one to combine multiple molecular tracers to understand the different physical and chemical phases that constitute giant molecular clouds (GMCs). We aim at using multiple tracers (sensitive to different physical processes and conditions) to segment a molecular cloud into physically/chemically similar regions (rather than spatially connected components), thus disentangling the different physical/chemical phases present in the cloud. We use a machine learning clustering method, namely the Meanshift algorithm, to cluster pixels with similar molecular emission, ignoring spatial information. Clusters are defined around each maximum of the multidimensional Probability Density Function (PDF) of the line integrated intensities. Simple radiative transfer models were used to interpret the astrophysical information uncovered by the clustering analysis. A clustering analysis based only on the J = 1 - 0 lines of three isotopologues of CO proves suffcient to reveal distinct density/column density regimes ( n H ~ 100 cm -3 , ~ 500 cm -3 , and > 1000 cm -3 ), closely related to the usual definitions of diffuse, translucent and high-column-density regions. Adding two UV-sensitive tracers, the J = 1 - 0 line of HCO + and the N = 1 - 0 line of CN, allows us to distinguish two clearly distinct chemical regimes, characteristic of UV-illuminated and UV-shielded gas. The UV-illuminated regime shows overbright HCO + and CN emission, which we relate to a photochemical enrichment effect. We also find a tail of high CN/HCO + intensity ratio in UV-illuminated regions. Finer distinctions in density classes ( n H ~ 7 × 10 3 cm -3 ~ 4 × 10 4 cm -3 ) for the densest regions are also identified, likely related to the higher critical density of the CN and HCO + (1 - 0) lines. These distinctions are only possible because the high-density regions are spatially resolved. Molecules are versatile tracers of GMCs because their line intensities bear the signature of the physics and chemistry at play in the gas. The association of simultaneous multi-line, wide-field mapping and powerful machine learning methods such as the Meanshift clustering algorithm reveals how to decode the complex information available in these molecular tracers.

Nanonewton optical force trap employing anti-reflection coated, high-refractive-index titania microspheres

NASA Astrophysics Data System (ADS)

Jannasch, Anita; Demirörs, Ahmet F.; van Oostrum, Peter D. J.; van Blaaderen, Alfons; Schäffer, Erik

2012-07-01

Optical tweezers are exquisite position and force transducers and are widely used for high-resolution measurements in fields as varied as physics, biology and materials science. Typically, small dielectric particles are trapped in a tightly focused laser and are often used as handles for sensitive force measurements. Improvement to the technique has largely focused on improving the instrument and shaping the light beam, and there has been little work exploring the benefit of customizing the trapped object. Here, we describe how anti-reflection coated, high-refractive-index core-shell particles composed of titania enable single-beam optical trapping with an optical force greater than a nanonewton. The increased force range broadens the scope of feasible optical trapping experiments and will pave the way towards more efficient light-powered miniature machines, tools and applications.

A heterogeneous computing environment for simulating astrophysical fluid flows

NASA Technical Reports Server (NTRS)

Cazes, J.

1994-01-01

In the Concurrent Computing Laboratory in the Department of Physics and Astronomy at Louisiana State University we have constructed a heterogeneous computing environment that permits us to routinely simulate complicated three-dimensional fluid flows and to readily visualize the results of each simulation via three-dimensional animation sequences. An 8192-node MasPar MP-1 computer with 0.5 GBytes of RAM provides 250 MFlops of execution speed for our fluid flow simulations. Utilizing the parallel virtual machine (PVM) language, at periodic intervals data is automatically transferred from the MP-1 to a cluster of workstations where individual three-dimensional images are rendered for inclusion in a single animation sequence. Work is underway to replace executions on the MP-1 with simulations performed on the 512-node CM-5 at NCSA and to simultaneously gain access to more potent volume rendering workstations.

Machine Learning Classification of Heterogeneous Fields to Estimate Physical Responses

NASA Astrophysics Data System (ADS)

McKenna, S. A.; Akhriev, A.; Alzate, C.; Zhuk, S.

2017-12-01

The promise of machine learning to enhance physics-based simulation is examined here using the transient pressure response to a pumping well in a heterogeneous aquifer. 10,000 random fields of log10 hydraulic conductivity (K) are created and conditioned on a single K measurement at the pumping well. Each K-field is used as input to a forward simulation of drawdown (pressure decline). The differential equations governing groundwater flow to the well serve as a non-linear transform of the input K-field to an output drawdown field. The results are stored and the data set is split into training and testing sets for classification. A Euclidean distance measure between any two fields is calculated and the resulting distances between all pairs of fields define a similarity matrix. Similarity matrices are calculated for both input K-fields and the resulting drawdown fields at the end of the simulation. The similarity matrices are then used as input to spectral clustering to determine groupings of similar input and output fields. Additionally, the similarity matrix is used as input to multi-dimensional scaling to visualize the clustering of fields in lower dimensional spaces. We examine the ability to cluster both input K-fields and output drawdown fields separately with the goal of identifying K-fields that create similar drawdowns and, conversely, given a set of simulated drawdown fields, identify meaningful clusters of input K-fields. Feature extraction based on statistical parametric mapping provides insight into what features of the fields drive the classification results. The final goal is to successfully classify input K-fields into the correct output class, and also, given an output drawdown field, be able to infer the correct class of input field that created it.

Vegetation Monitoring with Gaussian Processes and Latent Force Models

NASA Astrophysics Data System (ADS)

Camps-Valls, Gustau; Svendsen, Daniel; Martino, Luca; Campos, Manuel; Luengo, David

2017-04-01

Monitoring vegetation by biophysical parameter retrieval from Earth observation data is a challenging problem, where machine learning is currently a key player. Neural networks, kernel methods, and Gaussian Process (GP) regression have excelled in parameter retrieval tasks at both local and global scales. GP regression is based on solid Bayesian statistics, yield efficient and accurate parameter estimates, and provides interesting advantages over competing machine learning approaches such as confidence intervals. However, GP models are hampered by lack of interpretability, that prevented the widespread adoption by a larger community. In this presentation we will summarize some of our latest developments to address this issue. We will review the main characteristics of GPs and their advantages in vegetation monitoring standard applications. Then, three advanced GP models will be introduced. First, we will derive sensitivity maps for the GP predictive function that allows us to obtain feature ranking from the model and to assess the influence of examples in the solution. Second, we will introduce a Joint GP (JGP) model that combines in situ measurements and simulated radiative transfer data in a single GP model. The JGP regression provides more sensible confidence intervals for the predictions, respects the physics of the underlying processes, and allows for transferability across time and space. Finally, a latent force model (LFM) for GP modeling that encodes ordinary differential equations to blend data-driven modeling and physical models of the system is presented. The LFM performs multi-output regression, adapts to the signal characteristics, is able to cope with missing data in the time series, and provides explicit latent functions that allow system analysis and evaluation. Empirical evidence of the performance of these models will be presented through illustrative examples.

Diamond Turning Of Infra-Red Components

NASA Astrophysics Data System (ADS)

Hodgson, B.; Lettington, A. H.; Stillwell, P. F. T. C.

1986-05-01

Single point diamond machining of infra-red optical components such as aluminium mirrors, germanium lenses and zinc sulphide domes is potentially the most cost effective method for their manufacture since components may be machined from the blanks to a high surface finish, requiring no subsequent polishing, in a few minutes. Machines for the production of flat surfaces are well established. Diamond turning lathes for curved surfaces however require a high capital investment which can be justified only for research purposes or high volume production. The present paper describes the development of a low cost production machine based on a Bryant Symons diamond turning lathe which is able to machine spherical components to the required form and finish. It employs two horizontal spindles one for the workpiece the other for the tool. The machined radius of curvature is set by the alignment of the axes and the radius of the tool motion, as in conventional generation. The diamond tool is always normal to the workpiece and does not need to be accurately profiled. There are two variants of this basic machine. For machining hemispherical domes the axes are at right angles while for lenses with positive or negative curvature these axes are adjustable. An aspherical machine is under development, based on the all mechanical spherical machine, but in which a ± 2 mm aspherecity may be imposed on the best fit sphere by moving the work spindle under numerical control.

Rotating magnetizations in electrical machines: Measurements and modeling

NASA Astrophysics Data System (ADS)

Thul, Andreas; Steentjes, Simon; Schauerte, Benedikt; Klimczyk, Piotr; Denke, Patrick; Hameyer, Kay

2018-05-01

This paper studies the magnetization process in electrical steel sheets for rotational magnetizations as they occur in the magnetic circuit of electrical machines. A four-pole rotational single sheet tester is used to generate the rotating magnetic flux inside the sample. A field-oriented control scheme is implemented to improve the control performance. The magnetization process of different non-oriented materials is analyzed and compared.

Simulation investigation of thermal phase transformation and residual stress in single pulse EDM of Ti-6Al-4V

NASA Astrophysics Data System (ADS)

Tang, Jiajing; Yang, Xiaodong

2018-04-01

The thermal phase transformation and residual stress are ineluctable in the electrical discharge machining (EDM) process, and they will greatly affect the working performances of the machined surface. This paper presents a simulation study on the thermal phase transformation and residual stress in single-pulse EDM of Ti-6Al-4V, which is the most popular titanium alloy in fields such as aircraft engine and some other leading industries. A multi-physics model including thermal, hydraulic, metallography and structural mechanics was developed. Based on the proposed model, the thickness and metallographic structure of the recast layer and heat affected layer (HAZ) were investigated. The distribution and characteristics of residual stress around the discharge crater were obtained. The recast layer and HAZ at the center of crater are found to be the thinnest, and their thicknesses gradually increase approaching the periphery of the crater. The recast layer undergoes a complete α‧ (martensitic) transformation, while the HAZ is mainly composed by the α  +  β  +  α‧ three-phase microstructure. Along the depth direction of crater, the Von Mises stress increases first and then decreases, reaching its maximal value near the interface of recast layer and HAZ. In the recast layer, both compressive stress component and tensile stress component are observed. ANOVA results showed that the influence of discharge current on maximal tensile stress is more significant than that of pulse duration, while the pulse duration has more significant influence on average thickness of the recast layer and the depth location of the maximal tensile stress. The works conducted in this study will help to evaluate the quality and integrity of EDMed surface, especially when the non-destructive testing is difficult to achieve.

27 CFR 479.41 - Single sale.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2012-04-01 2010-04-01 true Single sale. 479.41 Section 479.41 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES, AND CERTAIN OTHER FIREARMS...

27 CFR 479.41 - Single sale.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 27 Alcohol, Tobacco Products and Firearms 3 2011-04-01 2010-04-01 true Single sale. 479.41 Section 479.41 Alcohol, Tobacco Products, and Firearms BUREAU OF ALCOHOL, TOBACCO, FIREARMS, AND EXPLOSIVES, DEPARTMENT OF JUSTICE FIREARMS AND AMMUNITION MACHINE GUNS, DESTRUCTIVE DEVICES, AND CERTAIN OTHER FIREARMS...

Clustering Single-Cell Expression Data Using Random Forest Graphs.

PubMed

Pouyan, Maziyar Baran; Nourani, Mehrdad

2017-07-01

Complex tissues such as brain and bone marrow are made up of multiple cell types. As the study of biological tissue structure progresses, the role of cell-type-specific research becomes increasingly important. Novel sequencing technology such as single-cell cytometry provides researchers access to valuable biological data. Applying machine-learning techniques to these high-throughput datasets provides deep insights into the cellular landscape of the tissue where those cells are a part of. In this paper, we propose the use of random-forest-based single-cell profiling, a new machine-learning-based technique, to profile different cell types of intricate tissues using single-cell cytometry data. Our technique utilizes random forests to capture cell marker dependences and model the cellular populations using the cell network concept. This cellular network helps us discover what cell types are in the tissue. Our experimental results on public-domain datasets indicate promising performance and accuracy of our technique in extracting cell populations of complex tissues.

Using machine learning to identify structural breaks in single-group interrupted time series designs.

PubMed

Linden, Ariel; Yarnold, Paul R

2016-12-01

Single-group interrupted time series analysis (ITSA) is a popular evaluation methodology in which a single unit of observation is being studied, the outcome variable is serially ordered as a time series and the intervention is expected to 'interrupt' the level and/or trend of the time series, subsequent to its introduction. Given that the internal validity of the design rests on the premise that the interruption in the time series is associated with the introduction of the treatment, treatment effects may seem less plausible if a parallel trend already exists in the time series prior to the actual intervention. Thus, sensitivity analyses should focus on detecting structural breaks in the time series before the intervention. In this paper, we introduce a machine-learning algorithm called optimal discriminant analysis (ODA) as an approach to determine if structural breaks can be identified in years prior to the initiation of the intervention, using data from California's 1988 voter-initiated Proposition 99 to reduce smoking rates. The ODA analysis indicates that numerous structural breaks occurred prior to the actual initiation of Proposition 99 in 1989, including perfect structural breaks in 1983 and 1985, thereby casting doubt on the validity of treatment effects estimated for the actual intervention when using a single-group ITSA design. Given the widespread use of ITSA for evaluating observational data and the increasing use of machine-learning techniques in traditional research, we recommend that structural break sensitivity analysis is routinely incorporated in all research using the single-group ITSA design. © 2016 John Wiley & Sons, Ltd.

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines

PubMed Central

2014-01-01

Background It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. Results We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. Conclusion SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/. PMID:24776231

SMOQ: a tool for predicting the absolute residue-specific quality of a single protein model with support vector machines.

PubMed

Cao, Renzhi; Wang, Zheng; Wang, Yiheng; Cheng, Jianlin

2014-04-28

It is important to predict the quality of a protein structural model before its native structure is known. The method that can predict the absolute local quality of individual residues in a single protein model is rare, yet particularly needed for using, ranking and refining protein models. We developed a machine learning tool (SMOQ) that can predict the distance deviation of each residue in a single protein model. SMOQ uses support vector machines (SVM) with protein sequence and structural features (i.e. basic feature set), including amino acid sequence, secondary structures, solvent accessibilities, and residue-residue contacts to make predictions. We also trained a SVM model with two new additional features (profiles and SOV scores) on 20 CASP8 targets and found that including them can only improve the performance when real deviations between native and model are higher than 5Å. The SMOQ tool finally released uses the basic feature set trained on 85 CASP8 targets. Moreover, SMOQ implemented a way to convert predicted local quality scores into a global quality score. SMOQ was tested on the 84 CASP9 single-domain targets. The average difference between the residue-specific distance deviation predicted by our method and the actual distance deviation on the test data is 2.637Å. The global quality prediction accuracy of the tool is comparable to other good tools on the same benchmark. SMOQ is a useful tool for protein single model quality assessment. Its source code and executable are available at: http://sysbio.rnet.missouri.edu/multicom_toolbox/.

Effect of mesh-peel ply variation on mechanical properties of E-glas composite by infusion vacuum method

NASA Astrophysics Data System (ADS)

Abdurohman, K.; Siahaan, Mabe

2018-04-01

Composite materials made of glass fiber EW-135 with epoxy lycal resin with vacuum infusion method have been performed. The dried glass fiber is arranged in a mold then connected to a vacuum machine and a resin tube. Then, the vacuum machine is turned on and at the same time the resin is sucked and flowed into the mold. This paper reports on the effect of using mesh- peel ply singles on upper-side laminates called A and the effect of using double mesh-peel ply on upper and lower-side laminates call B with glass fiber arrangement is normal and ± 450 in vacuum infusion process. Followed by the manufacture of tensile test specimen and tested its tensile strength with universal test machine 100kN Tensilon RTF 2410, at room temperature with constant crosshead speed. From tensile test results using single and double layers showed that double mesh-peel ply can increase tensile strength 14% and Young modulus 17%.

[Extension of cardiac monitoring function by used of ordinary ECG machine].

PubMed

Chen, Zhencheng; Jiang, Yong; Ni, Lili; Wang, Hongyan

2002-06-01

This paper deals with a portable monitor system on liquid crystal display (LCD) based on this available ordinary ECG machine, which is low power and suitable for China's specific condition. Apart from developing the overall scheme of the system, this paper also has completed the design of the hardware and the software. The 80c196 single chip microcomputer is taken as the central microprocessor and real time electrocardiac single is data treated and analyzed in the system. With the performance of ordinary monitor, this machine also possesses the following functions: five types of arrhythmia analysis, alarm, freeze, and record of automatic pappering, convenient in carrying, with alternate-current (AC) or direct-current (DC) powered. The hardware circuit is simplified and the software structure is optimized in this paper. Multiple low power designs and LCD unit design are adopted and completed in it. Popular in usage, low in cost price, the portable monitor system will have a valuable influence on China's monitor system field.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning.

PubMed

Davatzikos, Christos

2016-10-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. Copyright © 2016. Published by Elsevier B.V.

Computational neuroanatomy using brain deformations: From brain parcellation to multivariate pattern analysis and machine learning

PubMed Central

Davatzikos, Christos

2017-01-01

The past 20 years have seen a mushrooming growth of the field of computational neuroanatomy. Much of this work has been enabled by the development and refinement of powerful, high-dimensional image warping methods, which have enabled detailed brain parcellation, voxel-based morphometric analyses, and multivariate pattern analyses using machine learning approaches. The evolution of these 3 types of analyses over the years has overcome many challenges. We present the evolution of our work in these 3 directions, which largely follows the evolution of this field. We discuss the progression from single-atlas, single-registration brain parcellation work to current ensemble-based parcellation; from relatively basic mass-univariate t-tests to optimized regional pattern analyses combining deformations and residuals; and from basic application of support vector machines to generative-discriminative formulations of multivariate pattern analyses, and to methods dealing with heterogeneity of neuroanatomical patterns. We conclude with discussion of some of the future directions and challenges. PMID:27514582

Physics with e{sup +}e{sup -} Linear Colliders

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

Barklow, Timothy L

2003-05-05

We describe the physics potential of e{sup +}e{sup -} linear colliders in this report. These machines are planned to operate in the first phase at a center-of-mass energy of 500 GeV, before being scaled up to about 1 TeV. In the second phase of the operation, a final energy of about 2 TeV is expected. The machines will allow us to perform precision tests of the heavy particles in the Standard Model, the top quark and the electroweak bosons. They are ideal facilities for exploring the properties of Higgs particles, in particular in the intermediate mass range. New vector bosonsmore » and novel matter particles in extended gauge theories can be searched for and studied thoroughly. The machines provide unique opportunities for the discovery of particles in supersymmetric extensions of the Standard Model, the spectrum of Higgs particles, the supersymmetric partners of the electroweak gauge and Higgs bosons, and of the matter particles. High precision analyses of their properties and interactions will allow for extrapolations to energy scales close to the Planck scale where gravity becomes significant. In alternative scenarios, like compositeness models, novel matter particles and interactions can be discovered and investigated in the energy range above the existing colliders up to the TeV scale. Whatever scenario is realized in Nature, the discovery potential of e{sup +}e{sup -} linear colliders and the high-precision with which the properties of particles and their interactions can be analyzed, define an exciting physics programme complementary to hadron machines.« less

Single Side Electrolytic In-Process Dressing (ELID) Grinding with Lapping Kinematics of Silicon Carbide

NASA Astrophysics Data System (ADS)

Khoshaim, Ahmed Bakr

The demand for Silicon Carbide ceramics (SiC) has increased significantly in the last decade due to its reliable physical and chemical properties. The silicon carbide is widely used for aerospace segments in addition to many uses in the industry. Sometimes, a single side grinding is preferable than conventional grinding, for it has the ability to produce flat ceramics. However, the manufacturing cost is still high because of the high tool wear and long machining time. Part of the solution is to use electrolytic in process dressing (ELID) to reduce the processing time. The study on ELID single side grinding of ceramics has never been attempted before. The study involves four variables with three levels each. One of the variables, which is the eccentricity, is being investigated for the first time on ceramics. A full factorial design, for both the surface roughness and material removal rate, guides to calculate mathematical models that can predict future results. Three grinding wheel mesh sizes are used. An investigation of the influence of different grain size on the results can then be evaluated. The kinematics of the process was studied based on eccentricity in order to optimize the pattern of the diamond grains. The experiment is performed with the assist of the proposed specialized ELID fluid, TRIM C270E.

Physical Analytics: An emerging field with real-world applications and impact

NASA Astrophysics Data System (ADS)

Hamann, Hendrik

2015-03-01

In the past most information on the internet has been originated by humans or computers. However with the emergence of cyber-physical systems, vast amount of data is now being created by sensors from devices, machines etc digitizing the physical world. While cyber-physical systems are subject to active research around the world, the vast amount of actual data generated from the physical world has attracted so far little attention from the engineering and physics community. In this presentation we use examples to highlight the opportunities in this new subject of ``Physical Analytics'' for highly inter-disciplinary research (including physics, engineering and computer science), which aims understanding real-world physical systems by leveraging cyber-physical technologies. More specifically, the convergence of the physical world with the digital domain allows applying physical principles to everyday problems in a much more effective and informed way than what was possible in the past. Very much like traditional applied physics and engineering has made enormous advances and changed our lives by making detailed measurements to understand the physics of an engineered device, we can now apply the same rigor and principles to understand large-scale physical systems. In the talk we first present a set of ``configurable'' enabling technologies for Physical Analytics including ultralow power sensing and communication technologies, physical big data management technologies, numerical modeling for physical systems, machine learning based physical model blending, and physical analytics based automation and control. Then we discuss in detail several concrete applications of Physical Analytics ranging from energy management in buildings and data centers, environmental sensing and controls, precision agriculture to renewable energy forecasting and management.

Math Machines: Using Actuators in Physics Classes

ERIC Educational Resources Information Center

Thomas, Frederick J.; Chaney, Robert A.; Gruesbeck, Marta

2018-01-01

Probeware (sensors combined with data-analysis software) is a well-established part of physics education. In engineering and technology, sensors are frequently paired with actuators--motors, heaters, buzzers, valves, color displays, medical dosing systems, and other devices that are activated by electrical signals to produce intentional physical…

Development of plasma chemical vaporization machining

NASA Astrophysics Data System (ADS)

Mori, Yuzo; Yamauchi, Kazuto; Yamamura, Kazuya; Sano, Yasuhisa

2000-12-01

Conventional machining processes, such as turning, grinding, or lapping are still applied for many materials including functional ones. But those processes are accompanied with the formation of a deformed layer, so that machined surfaces cannot perform their original functions. In order to avoid such points, plasma chemical vaporization machining (CVM) has been developed. Plasma CVM is a chemical machining method using neutral radicals, which are generated by the atmospheric pressure plasma. By using a rotary electrode for generation of plasma, a high density of neutral radicals was formed, and we succeeded in obtaining high removal rate of several microns to several hundred microns per minute for various functional materials such as fused silica, single crystal silicon, molybdenum, tungsten, silicon carbide, and diamond. Especially, a high removal rate equal to lapping in the mechanical machining of fused silica and silicon was realized. 1.4 nm (p-v) was obtained as a surface roughness in the case of machining a silicon wafer. The defect density of a silicon wafer surface polished by various machining method was evaluated by the surface photo voltage spectroscopy. As a result, the defect density of the surface machined by plasma CVM was under 1/100 in comparison with the surface machined by mechanical polishing and argon ion sputtering, and very low defect density which was equivalent to the chemical etched surface was realized. A numerically controlled CVM machine for x-ray mirror fabrication is detailed in the accompanying article in this issue.

P-23 Highlights 6/10/12: Cygnus Dual Beam Radiographic Facility Refurbishment completed at U1A tunnel in Nevada NNSS meeting Level 2 milestone

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

Deyoung, Anemarie; Smith, John R.

2012-05-03

A moratorium was placed on U.S. underground nuclear testing in 1992. In response, the Stockpile Stewardship Program was created to maintain readiness of the existing nuclear inventory through several efforts such as computer modeling, material analysis, and subcritical nuclear experiments (SCEs). As in the underground test era, the Nevada National Security Site (NNSS), formerly the Nevada Test Site, provides a safe and secure environment for SCEs by the nature of its isolated and secure facilities. A major tool for SCE diagnosis installed in the 05 drift laboratory is a high energy x-ray source used for time resolved imaging. This toolmore » consists of two identical sources (Cygnus 1 and Cygnus 2) and is called the Cygnus Dual Beam Radiographic Facility (Figs. 2-6). Each Cygnus machine has 5 major elements: Marx Generator, Pulse Forming Line (PFL), Coaxial Transmission Line (CTL), 3-cell Inductive Voltage Adder (IVA), and Rod Pinch Diode. Each machine is independently triggered and may be fired in separate tests (staggered mode), or in a single test where there is submicrosecond separation between the pulses (dual mode). Cygnus must operate as a single shot machine since on each pulse the diode electrodes are destroyed. The diode is vented to atmosphere, cleaned, and new electrodes are inserted for each shot. There is normally two shots per day on each machine. Since its installation in 2003, Cygnus has participated in: 4 Subcritical Experiments (Armando, Bacchus, Barolo A, and Barolo B), a 12 shot plutonium physics series (Thermos), and 2 plutonium step wedge calibration series (2005, 2011), resulting in well over 1000 shots. Currently the Facility is in preparation for 2 SCEs scheduled for this calendar year - Castor and Pollux. Cygnus has performed well during 8 years of operations at NNSS. Many improvements in operations and performance have been implemented during this time. Throughout its service at U1a, major maintenance and replacement of many hardware items were delayed due to programmatic requirements. It is anticipated that Cygnus will be in service at U1a for another 5 years. With this assumption, it was realized that significant resources and effort should be allotted to bring the hardware back to its original condition, or even to improve elements when appropriate. The Cygnus Refurbishment and Enhancement Project started in April, 2011 with the intent to encompass a major overhaul of Cygnus.« less

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

Qiusheng, Y., E-mail: qsyan@gdut.edu.cn; Senkai, C., E-mail: senkite@sina.com; Jisheng, P., E-mail: panjisheng@gdut.edu.cn

Different machining processes were used in the single crystal SiC wafer machining. SEM was used to observe the surface morphology and a cross-sectional cleavages microscopy method was used for subsurface cracks detection. Surface and subsurface cracks characteristics of single crystal SiC wafer in abrasive machining were analysed. The results show that the surface and subsurface cracks system of single crystal SiC wafer in abrasive machining including radial crack, lateral crack and the median crack. In lapping process, material removal is dominated by brittle removal. Lots of chipping pits were found on the lapping surface. With the particle size becomes smaller,more » the surface roughness and subsurface crack depth decreases. When the particle size was changed to 1.5µm, the surface roughness Ra was reduced to 24.0nm and the maximum subsurface crack was 1.2µm. The efficiency of grinding is higher than lapping. Plastic removal can be achieved by changing the process parameters. Material removal was mostly in brittle fracture when grinding with 325# diamond wheel. Plow scratches and chipping pits were found on the ground surface. The surface roughness Ra was 17.7nm and maximum subsurface crack depth was 5.8 µm. When grinding with 8000# diamond wheel, the material removal was in plastic flow. Plastic scratches were found on the surface. A smooth surface of roughness Ra 2.5nm without any subsurface cracks was obtained. Atomic scale removal was possible in cluster magnetorheological finishing with diamond abrasive size of 0.5 µm. A super smooth surface eventually obtained with a roughness of Ra 0.4nm without any subsurface crack.« less

Severe acute respiratory distress syndrome caused by unintentional sewing machine lubricant ingestion: A case report.

PubMed

Kishore, Sunil; Chandelia, Sudha; Patharia, Neha; Swarnim

2016-11-01

Sewing machine oil ingestion is rare but is possible due to its availability at home. Chemically, it belongs to hydrocarbon family which is toxic if aspirated, owing to their physical properties such as high volatility and low viscosity. On the contrary, sewing machine lubricant has high viscosity and low volatility which makes it aspiration less likely. The main danger of hydrocarbon ingestion is chemical pneumonitis which may be as severe as acute respiratory distress syndrome (ARDS). We report a case of a 5-year-old girl with accidental ingestion of sewing machine lubricant oil, who subsequently developed ARDS refractory to mechanical ventilation. There was much improvement with airway pressure release ventilation mode of ventilation, but the child succumbed to death due to pulmonary hemorrhage.

Severe acute respiratory distress syndrome caused by unintentional sewing machine lubricant ingestion: A case report

PubMed Central

Kishore, Sunil; Chandelia, Sudha; Patharia, Neha; Swarnim

2016-01-01

Sewing machine oil ingestion is rare but is possible due to its availability at home. Chemically, it belongs to hydrocarbon family which is toxic if aspirated, owing to their physical properties such as high volatility and low viscosity. On the contrary, sewing machine lubricant has high viscosity and low volatility which makes it aspiration less likely. The main danger of hydrocarbon ingestion is chemical pneumonitis which may be as severe as acute respiratory distress syndrome (ARDS). We report a case of a 5-year-old girl with accidental ingestion of sewing machine lubricant oil, who subsequently developed ARDS refractory to mechanical ventilation. There was much improvement with airway pressure release ventilation mode of ventilation, but the child succumbed to death due to pulmonary hemorrhage. PMID:27994384

A note on resource allocation scheduling with group technology and learning effects on a single machine

NASA Astrophysics Data System (ADS)

Lu, Yuan-Yuan; Wang, Ji-Bo; Ji, Ping; He, Hongyu

2017-09-01

In this article, single-machine group scheduling with learning effects and convex resource allocation is studied. The goal is to find the optimal job schedule, the optimal group schedule, and resource allocations of jobs and groups. For the problem of minimizing the makespan subject to limited resource availability, it is proved that the problem can be solved in polynomial time under the condition that the setup times of groups are independent. For the general setup times of groups, a heuristic algorithm and a branch-and-bound algorithm are proposed, respectively. Computational experiments show that the performance of the heuristic algorithm is fairly accurate in obtaining near-optimal solutions.

INTERIOR OVERVIEW OF CONTINUOUS CASTER WITH NO. 12 LADLE. MOLTEN ...

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

INTERIOR OVERVIEW OF CONTINUOUS CASTER WITH NO. 12 LADLE. MOLTEN STEEL IS POURED FROM LADLE THROUGH SHROUD TO TUNDISH. FROM TUNDISH STEEL ENTERS MOLD THROUGH SHROUD AND FORMATION OF SLAB SHELL BEGINS. AS SLAB PROGRESSES THROUGH CONTAINMENT SECTION IT IS COOLED WITH AIR MIST SPRAYS AND CONTINUES SOLIDIFICATION. UPON EXITING THE MACHINE THE SLABS ARE CUT TO DESIRED LENGTH AND IDENTIFIED. THE SLABS ARE STACKED, REMOVED FROM MACHINE AND PREPARED FOR SHIPMENT TO HOT STRIP MILL. CASTER HAS ABILITY TO PRODUCE SINGLE OR TWIN CASTS. SINGLE SLABS PRODUCED MAY BE UP TO 102 INCHES; DOUBLE SLABS UP TO 49 INCHES. - U.S. Steel, Fairfield Works, Continuous Caster, Fairfield, Jefferson County, AL

Physics Accomplishments and Future Prospects of the BES Experiments at the Beijing Electron-Positron Collider

NASA Astrophysics Data System (ADS)

Briere, Roy A.; Harris, Frederick A.; Mitchell, Ryan E.

2016-10-01

The cornerstone of the Chinese experimental particle physics program is a series of experiments performed in the τ-charm energy region. China began building e+e- colliders at the Institute for High Energy Physics in Beijing more than three decades ago. Beijing Electron Spectrometer (BES) is the common root name for the particle physics detectors operated at these machines. We summarize the development of the BES program and highlight the physics results across several topical areas.

Requirements for fault-tolerant factoring on an atom-optics quantum computer.

PubMed

Devitt, Simon J; Stephens, Ashley M; Munro, William J; Nemoto, Kae

2013-01-01

Quantum information processing and its associated technologies have reached a pivotal stage in their development, with many experiments having established the basic building blocks. Moving forward, the challenge is to scale up to larger machines capable of performing computational tasks not possible today. This raises questions that need to be urgently addressed, such as what resources these machines will consume and how large will they be. Here we estimate the resources required to execute Shor's factoring algorithm on an atom-optics quantum computer architecture. We determine the runtime and size of the computer as a function of the problem size and physical error rate. Our results suggest that once the physical error rate is low enough to allow quantum error correction, optimization to reduce resources and increase performance will come mostly from integrating algorithms and circuits within the error correction environment, rather than from improving the physical hardware.

Atomic physics research with second and third generation synchrotron light sources

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

Johnson, B.M.

1990-10-01

This contribution to these proceedings is intended to provide an introduction and overview for other contributions on atomic (and related) physics research at existing and planned synchrotron light sources. The emphasis will be on research accomplishments and future opportunities, but a comparison will be given of operating characteristics for first, second, and third generation machines. First generation light sources were built to do research with the primary electron and positron beams, rather than with the synchrotron radiation itself. Second generation machines were specifically designed to be dedicated synchrotron-radiation facilities, with an emphasis on the use of bending-magnet radiation. The newmore » third generation light sources are being designed to optimize radiation from insertion devices, such as undulators and wigglers. Each generation of synchrotron light source offers useful capabilities for forefront research in atomic physics and many other disciplines. 27 refs., 1 fig., 3 tabs.« less

Thermal machines beyond the weak coupling regime

NASA Astrophysics Data System (ADS)

Gallego, R.; Riera, A.; Eisert, J.

2014-12-01

How much work can be extracted from a heat bath using a thermal machine? The study of this question has a very long history in statistical physics in the weak-coupling limit, when applied to macroscopic systems. However, the assumption that thermal heat baths remain uncorrelated with associated physical systems is less reasonable on the nano-scale and in the quantum setting. In this work, we establish a framework of work extraction in the presence of quantum correlations. We show in a mathematically rigorous and quantitative fashion that quantum correlations and entanglement emerge as limitations to work extraction compared to what would be allowed by the second law of thermodynamics. At the heart of the approach are operations that capture the naturally non-equilibrium dynamics encountered when putting physical systems into contact with each other. We discuss various limits that relate to known results and put our work into the context of approaches to finite-time quantum thermodynamics.

Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.

PubMed

Memarian, Negar; Torre, Jared B; Haltom, Kate E; Stanton, Annette L; Lieberman, Matthew D

2017-09-01

Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience. © The Author (2017). Published by Oxford University Press.

Full-Physics Inverse Learning Machine for Satellite Remote Sensing Retrievals

NASA Astrophysics Data System (ADS)

Loyola, D. G.

2017-12-01

The satellite remote sensing retrievals are usually ill-posed inverse problems that are typically solved by finding a state vector that minimizes the residual between simulated data and real measurements. The classical inversion methods are very time-consuming as they require iterative calls to complex radiative-transfer forward models to simulate radiances and Jacobians, and subsequent inversion of relatively large matrices. In this work we present a novel and extremely fast algorithm for solving inverse problems called full-physics inverse learning machine (FP-ILM). The FP-ILM algorithm consists of a training phase in which machine learning techniques are used to derive an inversion operator based on synthetic data generated using a radiative transfer model (which expresses the "full-physics" component) and the smart sampling technique, and an operational phase in which the inversion operator is applied to real measurements. FP-ILM has been successfully applied to the retrieval of the SO2 plume height during volcanic eruptions and to the retrieval of ozone profile shapes from UV/VIS satellite sensors. Furthermore, FP-ILM will be used for the near-real-time processing of the upcoming generation of European Sentinel sensors with their unprecedented spectral and spatial resolution and associated large increases in the amount of data.

Engineering of the `PCAST machine`

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

Sinnis, J.; Brooks, A.; Brown, T.

The President`s Committee of Advisors on Science and Technology (PCAST) has suggested that a device with a mission of ignition and moderate burn time could address the physics of burning plasmas at a lesser cost than ITER with its more comprehensive physics and technology mission. The Department of Energy commissioned a study to explore this PCAST suggestion. This paper describes the results of the engineering portion of the study of this `PCAST Machine;` physics is covered in a companion paper authored by G.H. Neilson, et al; and the costs are covered in a companion paper by R.T. Simmons, et al.more » Both are published in the proceedings of this conference. The study was undertaken by a team under the direction of Bruce Montgomery that included representatives from MIT, PPPL, ORNL, LLNL, GA, Northrup-Grumman, and Stone and Webster. The performance requirements for the PCAST machine are to form and sustain a burning plasma for three helium accumulation times. The philosophy adopted for this design was to achieve the required performance at lower cost by decreasing the major radius to five meters, increasing the toroidal field to 7 tesla, and using stronger shaping. The major device parameters are given. 4 refs., 4 figs., 1 tab.« less

A journey from nuclear criticality methods to high energy density radflow experiments

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

Urbatsch, Todd James

Los Alamos National Laboratory is a nuclear weapons laboratory supporting our nation's defense. In support of this mission is a high energy-density physics program in which we design and execute experiments to study radiationhydrodynamics phenomena and improve the predictive capability of our largescale multi-physics software codes on our big-iron computers. The Radflow project’s main experimental effort now is to understand why we haven't been able to predict opacities on Sandia National Laboratory's Z-machine. We are modeling an increasing fraction of the Z-machine's dynamic hohlraum to find multi-physics explanations for the experimental results. Further, we are building an entirely different opacitymore » platform on Lawrence Livermore National Laboratory's National Ignition Facility (NIF), which is set to get results early 2017. Will the results match our predictions, match the Z-machine, or give us something entirely different? The new platform brings new challenges such as designing hohlraums and spectrometers. The speaker will recount his history, starting with one-dimensional Monte Carlo nuclear criticality methods in graduate school, radiative transfer methods research and software development for his first 16 years at LANL, and, now, radflow technology and experiments. Who knew that the real world was more than just radiation transport? Experiments aren't easy, but they sure are fun.« 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.

48 CFR 252.211-7003 - Item identification and valuation.

Code of Federal Regulations, 2010 CFR

2010-10-01

..., used to retrieve data encoded on machine-readable media. Concatenated unique item identifier means— (1... Defense Logistics Information System (DLIS) Commercial and Government Entity (CAGE) Code). Issuing agency... identifier. Item means a single hardware article or a single unit formed by a grouping of subassemblies...

Nano Mechanical Machining Using AFM Probe

NASA Astrophysics Data System (ADS)

Mostofa, Md. Golam

Complex miniaturized components with high form accuracy will play key roles in the future development of many products, as they provide portability, disposability, lower material consumption in production, low power consumption during operation, lower sample requirements for testing, and higher heat transfer due to their very high surface-to-volume ratio. Given the high market demand for such micro and nano featured components, different manufacturing methods have been developed for their fabrication. Some of the common technologies in micro/nano fabrication are photolithography, electron beam lithography, X-ray lithography and other semiconductor processing techniques. Although these methods are capable of fabricating micro/nano structures with a resolution of less than a few nanometers, some of the shortcomings associated with these methods, such as high production costs for customized products, limited material choices, necessitate the development of other fabricating techniques. Micro/nano mechanical machining, such an atomic force microscope (AFM) probe based nano fabrication, has, therefore, been used to overcome some the major restrictions of the traditional processes. This technique removes material from the workpiece by engaging micro/nano size cutting tool (i.e. AFM probe) and is applicable on a wider range of materials compared to the photolithographic process. In spite of the unique benefits of nano mechanical machining, there are also some challenges with this technique, since the scale is reduced, such as size effects, burr formations, chip adhesions, fragility of tools and tool wear. Moreover, AFM based machining does not have any rotational movement, which makes fabrication of 3D features more difficult. Thus, vibration-assisted machining is introduced into AFM probe based nano mechanical machining to overcome the limitations associated with the conventional AFM probe based scratching method. Vibration-assisted machining reduced the cutting forces and burr formations through intermittent cutting. Combining the AFM probe based machining with vibration-assisted machining enhanced nano mechanical machining processes by improving the accuracy, productivity and surface finishes. In this study, several scratching tests are performed with a single crystal diamond AFM probe to investigate the cutting characteristics and model the ploughing cutting forces. Calibration of the probe for lateral force measurements, which is essential, is also extended through the force balance method. Furthermore, vibration-assisted machining system is developed and applied to fabricate different materials to overcome some of the limitations of the AFM probe based single point nano mechanical machining. The novelty of this study includes the application of vibration-assisted AFM probe based nano scale machining to fabricate micro/nano scale features, calibration of an AFM by considering different factors, and the investigation of the nano scale material removal process from a different perspective.

An experimental investigation on orthogonal cutting of hybrid CFRP/Ti stacks

NASA Astrophysics Data System (ADS)

Xu, Jinyang; El Mansori, Mohamed

2016-10-01

Hybrid CFRP/Ti stack has been widely used in the modern aerospace industry owing to its superior mechanical/physical properties and excellent structural functions. Several applications require mechanical machining of these hybrid composite stacks in order to achieve dimensional accuracy and assembly performance. However, machining of such composite-to-metal alliance is usually an extremely challenging task in the manufacturing sectors due to the disparate natures of each stacked constituent and their respective poor machinability. Special issues may arise from the high force/heat generation, severe subsurface damage and rapid tool wear. To study the fundamental mechanisms controlling the bi-material machining, this paper presented an experimental study on orthogonal cutting of hybrid CFRP/Ti stack by using superior polycrystalline diamond (PCD) tipped tools. The utilized cutting parameters for hybrid CFRP/Ti machining were rigorously adopted through a compromise selection due to the disparate machinability behaviors of the CFRP laminate and Ti alloy. The key cutting responses in terms of cutting force generation, machined surface quality and tool wear mechanism were precisely addressed. The experimental results highlighted the involved five stages of CFRP/Ti cutting and the predominant crater wear and edge fracture failure governing the PCD cutting process.

Spinal Muscular Atrophy

MedlinePlus

... with symptoms and prevent complications. They may include machines to help with breathing, nutritional support, physical therapy, and medicines. NIH: National Institute of Neurological Disorders and Stroke

Learning Activity Package, Physical Science. LAP Numbers 8, 9, 10, and 11.

ERIC Educational Resources Information Center

Williams, G. J.

These four units of the Learning Activity Packages (LAPs) for individualized instruction in physical science cover nuclear reactions, alpha and beta particles, atomic radiation, medical use of nuclear energy, fission, fusion, simple machines, Newton's laws of motion, electricity, currents, electromagnetism, Oersted's experiment, sound, light,…

Applied Physics Modules: Notes, Instructions, Data Sheets, Tests, and Test Answer Keys.

ERIC Educational Resources Information Center

Southeast Community Coll., Lincoln, NE.

These user instructions and related materials are designed to accompany a series of twenty-three applied physics modules which have been developed for postsecondary students in electrical, electronics, machine tool, metals, manufacturing, automotive, diesel, architecture, and civil drafting occupational programs. The instructions include an…

How Things Work: Physics in the Copy Machine.

ERIC Educational Resources Information Center

Crane, H. Richard, Ed.

1984-01-01

Discusses the physics principles applied to the main steps of the photocopying process. Of particular interest (and at the heart of the process) are the ways in which electric charges, or particles carrying charges, are caused to transfer from one surface or medium to another at each stage. (JN)

Applied Physics Modules Selected for Manufacturing and Metal Technologies.

ERIC Educational Resources Information Center

Waring, Gene

Designed for individualized use in an applied physics course in postsecondary vocational-technical education, this series of eighteen learning modules is equivalent to the content of two quarters of a five-credit hour class in manufacturing engineering technology, machine tool and design technology, welding technology, and industrial plastics…

Learning Activity Package, Physical Science 92, LAPs 1-9.

ERIC Educational Resources Information Center

Williams, G. J.

This set of nine teacher-prepared Learning Activity Packages (LAPs) for individualized instruction in physical science covers the topics of scientific equipment and procedures; measure of time, length, area, and volume; water; oxygen and oxidation; atmospheric pressure; motion; machines; carbon; and light and sound. Each unit contains a rationale…

Quantum Entanglement in Neural Network States

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-04-01

Machine learning, one of today's most rapidly growing interdisciplinary fields, promises an unprecedented perspective for solving intricate quantum many-body problems. Understanding the physical aspects of the representative artificial neural-network states has recently become highly desirable in the applications of machine-learning techniques to quantum many-body physics. In this paper, we explore the data structures that encode the physical features in the network states by studying the quantum entanglement properties, with a focus on the restricted-Boltzmann-machine (RBM) architecture. We prove that the entanglement entropy of all short-range RBM states satisfies an area law for arbitrary dimensions and bipartition geometry. For long-range RBM states, we show by using an exact construction that such states could exhibit volume-law entanglement, implying a notable capability of RBM in representing quantum states with massive entanglement. Strikingly, the neural-network representation for these states is remarkably efficient, in the sense that the number of nonzero parameters scales only linearly with the system size. We further examine the entanglement properties of generic RBM states by randomly sampling the weight parameters of the RBM. We find that their averaged entanglement entropy obeys volume-law scaling, and the meantime strongly deviates from the Page entropy of the completely random pure states. We show that their entanglement spectrum has no universal part associated with random matrix theory and bears a Poisson-type level statistics. Using reinforcement learning, we demonstrate that RBM is capable of finding the ground state (with power-law entanglement) of a model Hamiltonian with a long-range interaction. In addition, we show, through a concrete example of the one-dimensional symmetry-protected topological cluster states, that the RBM representation may also be used as a tool to analytically compute the entanglement spectrum. Our results uncover the unparalleled power of artificial neural networks in representing quantum many-body states regardless of how much entanglement they possess, which paves a novel way to bridge computer-science-based machine-learning techniques to outstanding quantum condensed-matter physics problems.

In-line drivetrain and four wheel drive work machine using same

DOEpatents

Hoff, Brian

2008-08-05

A four wheel drive articulated mine loader is powered by a fuel cell and propelled by a single electric motor. The drivetrain has the first axle, second axle, and motor arranged in series on the work machine chassis. Torque is carried from the electric motor to the back differential via a pinion meshed with the ring gear of the back differential. A second pinion oriented in an opposite direction away from the ring gear is coupled to a drive shaft to transfer torque from the ring gear to the differential of the front axle. Thus, the ring gear of the back differential acts both to receive torque from the motor and to transfer torque to the forward axle. The in-line drive configuration includes a single electric motor and a single reduction gear to power the four wheel drive mine loader.

Use of Fiber Reinforced Plastics in the Marine Industry

DTIC Science & Technology

1990-09-06

surface should be molded or machined into the hull. 129 Design of Detais Marine Composites With single skin laminates, holes are normally drilled...SH), FIre and Toxicity Test Methods and Qualification Procedure for Composite Material Systems Used In Hull, Machinely and Structural Applications...date on the state of the marine composites industry and should for many years serve as an excellent reference and source book for designers and

Polished polymide substrate

DOEpatents

Farah, John; Sudarshanam, Venkatapuram S.

2003-05-13

Polymer substrates, in particular polyimide substrates, and polymer laminates for optical applications are described. Polyimide substrates are polished on one or both sides depending on their thickness, and single-layer or multi-layer waveguide structures are deposited on the polished polyimide substrates. Optical waveguide devices are machined by laser ablation using a combination of IR and UV lasers. A waveguide-fiber coupler with a laser-machined groove for retaining the fiber is also disclosed.

Implementation of a parallel unstructured Euler solver on the CM-5

NASA Technical Reports Server (NTRS)

Morano, Eric; Mavriplis, D. J.

1995-01-01

An efficient unstructured 3D Euler solver is parallelized on a Thinking Machine Corporation Connection Machine 5, distributed memory computer with vectoring capability. In this paper, the single instruction multiple data (SIMD) strategy is employed through the use of the CM Fortran language and the CMSSL scientific library. The performance of the CMSSL mesh partitioner is evaluated and the overall efficiency of the parallel flow solver is discussed.

Induction machine

DOEpatents

Owen, Whitney H.

1980-01-01

A polyphase rotary induction machine for use as a motor or generator utilizing a single rotor assembly having two series connected sets of rotor windings, a first stator winding disposed around the first rotor winding and means for controlling the current induced in one set of the rotor windings compared to the current induced in the other set of the rotor windings. The rotor windings may be wound rotor windings or squirrel cage windings.

Integration of virtualized worker nodes in standard batch systems

NASA Astrophysics Data System (ADS)

Büge, Volker; Hessling, Hermann; Kemp, Yves; Kunze, Marcel; Oberst, Oliver; Quast, Günter; Scheurer, Armin; Synge, Owen

2010-04-01

Current experiments in HEP only use a limited number of operating system flavours. Their software might only be validated on one single OS platform. Resource providers might have other operating systems of choice for the installation of the batch infrastructure. This is especially the case if a cluster is shared with other communities, or communities that have stricter security requirements. One solution would be to statically divide the cluster into separated sub-clusters. In such a scenario, no opportunistic distribution of the load can be achieved, resulting in a poor overall utilization efficiency. Another approach is to make the batch system aware of virtualization, and to provide each community with its favoured operating system in a virtual machine. Here, the scheduler has full flexibility, resulting in a better overall efficiency of the resources. In our contribution, we present a lightweight concept for the integration of virtual worker nodes into standard batch systems. The virtual machines are started on the worker nodes just before jobs are executed there. No meta-scheduling is introduced. We demonstrate two prototype implementations, one based on the Sun Grid Engine (SGE), the other using Maui/Torque as a batch system. Both solutions support local job as well as Grid job submission. The hypervisors currently used are Xen and KVM, a port to another system is easily envisageable. To better handle different virtual machines on the physical host, the management solution VmImageManager is developed. We will present first experience from running the two prototype implementations. In a last part, we will show the potential future use of this lightweight concept when integrated into high-level (i.e. Grid) work-flows.

Effect of air gap variation on the performance of single stator single rotor axial flux permanent magnet generator

NASA Astrophysics Data System (ADS)

Kasim, Muhammad; Irasari, Pudji; Hikmawan, M. Fathul; Widiyanto, Puji; Wirtayasa, Ketut

2017-02-01

The axial flux permanent magnet generator (AFPMG) has been widely used especially for electricity generation. The effect of the air gap variation on the characteristic and performances of single rotor - single stator AFPMG has been described in this paper. Effect of air gap length on the magnetic flux distribution, starting torque and MMF has been investigated. The two dimensional finite element magnetic method has been deployed to model and simulated the characteristics of the machine which is based on the Maxwell equation. The analysis has been done for two different air gap lengths which were 2 mm and 4 mm using 2D FEMM 4.2 software at no load condition. The increasing of air gap length reduces the air-gap flux density. For air gap 2 mm, the maximum value of the flux density was 1.04 T while 0.73 T occured for air gap 4 mm.. Based on the experiment result, the increasing air gap also reduced the starting torque of the machine with 39.2 Nm for air gap 2 mm and this value decreased into 34.2 Nm when the air gap increased to 4 mm. Meanwhile, the MMF that was generated by AFPMG decreased around 22% at 50 Hz due to the reduction of magnetic flux induced on stator windings. Overall, the research result showed that the variation of air gap has significant effect on the machine characteristics.

Rare etiological factor of maxillofacial injury: Case series seen and managed in a tertiary referral centre

PubMed Central

Braimah, Ramat Oyebunmi; Ibikunle, Adebayo Aremu; Taiwo, Abdurrazaq Olanrewaju

2016-01-01

Entanglement injury from local milling/grinding machine with a conveyor belt is a rare etiology of maxillofacial injuries. While there is abundant literature on industrial cause of trauma, entanglement injury as a mechanism has not been reported in the literature. We present two cases of maxillofacial injury secondary to entanglement of the loose apparel into the conveyor belt of the local grinding machine. The community should be aware of this rare cause of trauma, and adequate protection of children using these facilities should be enforced. One of such measure is to provide physical barriers to guard against these machines. PMID:27162440

Rare etiological factor of maxillofacial injury: Case series seen and managed in a tertiary referral centre.

PubMed

Braimah, Ramat Oyebunmi; Ibikunle, Adebayo Aremu; Taiwo, Abdurrazaq Olanrewaju

2016-01-01

Entanglement injury from local milling/grinding machine with a conveyor belt is a rare etiology of maxillofacial injuries. While there is abundant literature on industrial cause of trauma, entanglement injury as a mechanism has not been reported in the literature. We present two cases of maxillofacial injury secondary to entanglement of the loose apparel into the conveyor belt of the local grinding machine. The community should be aware of this rare cause of trauma, and adequate protection of children using these facilities should be enforced. One of such measure is to provide physical barriers to guard against these machines.

An imperialist competitive algorithm for virtual machine placement in cloud computing

NASA Astrophysics Data System (ADS)

Jamali, Shahram; Malektaji, Sepideh; Analoui, Morteza

2017-05-01

Cloud computing, the recently emerged revolution in IT industry, is empowered by virtualisation technology. In this paradigm, the user's applications run over some virtual machines (VMs). The process of selecting proper physical machines to host these virtual machines is called virtual machine placement. It plays an important role on resource utilisation and power efficiency of cloud computing environment. In this paper, we propose an imperialist competitive-based algorithm for the virtual machine placement problem called ICA-VMPLC. The base optimisation algorithm is chosen to be ICA because of its ease in neighbourhood movement, good convergence rate and suitable terminology. The proposed algorithm investigates search space in a unique manner to efficiently obtain optimal placement solution that simultaneously minimises power consumption and total resource wastage. Its final solution performance is compared with several existing methods such as grouping genetic and ant colony-based algorithms as well as bin packing heuristic. The simulation results show that the proposed method is superior to other tested algorithms in terms of power consumption, resource wastage, CPU usage efficiency and memory usage efficiency.

Chaotic behaviour of Zeeman machines at introductory course of mechanics

NASA Astrophysics Data System (ADS)

Nagy, Péter; Tasnádi, Péter

2016-05-01

Investigation of chaotic motions and cooperative systems offers a magnificent opportunity to involve modern physics into the basic course of mechanics taught to engineering students. In the present paper it will be demonstrated that Zeeman Machine can be a versatile and motivating tool for students to get introductory knowledge about chaotic motion via interactive simulations. It works in a relatively simple way and its properties can be understood very easily. Since the machine can be built easily and the simulation of its movement is also simple the experimental investigation and the theoretical description can be connected intuitively. Although Zeeman Machine is known mainly for its quasi-static and catastrophic behaviour, its dynamic properties are also of interest with its typical chaotic features. By means of a periodically driven Zeeman Machine a wide range of chaotic properties of the simple systems can be demonstrated such as bifurcation diagrams, chaotic attractors, transient chaos and so on. The main goal of this paper is the presentation of an interactive learning material for teaching the basic features of the chaotic systems through the investigation of the Zeeman Machine.

Managing virtual machines with Vac and Vcycle

NASA Astrophysics Data System (ADS)

McNab, A.; Love, P.; MacMahon, E.

2015-12-01

We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

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

Johnston, H; UT Southwestern Medical Center, Dallas, TX; Hilts, M

Purpose: To commission a multislice computed tomography (CT) scanner for fast and reliable readout of radiation therapy (RT) dose distributions using CT polymer gel dosimetry (PGD). Methods: Commissioning was performed for a 16-slice CT scanner using images acquired through a 1L cylinder filled with water. Additional images were collected using a single slice machine for comparison purposes. The variability in CT number associated with the anode heel effect was evaluated and used to define a new slice-by-slice background image subtraction technique. Image quality was assessed for the multislice system by comparing image noise and uniformity to that of the singlemore » slice machine. The consistency in CT number across slices acquired simultaneously using the multislice detector array was also evaluated. Finally, the variability in CT number due to increasing x-ray tube load was measured for the multislice scanner and compared to the tube load effects observed on the single slice machine. Results: Slice-by-slice background subtraction effectively removes the variability in CT number across images acquired simultaneously using the multislice scanner and is the recommended background subtraction method when using a multislice CT system. Image quality for the multislice machine was found to be comparable to that of the single slice scanner. Further study showed CT number was consistent across image slices acquired simultaneously using the multislice detector array for each detector configuration of the slice thickness examined. In addition, the multislice system was found to eliminate variations in CT number due to increasing x-ray tube load and reduce scanning time by a factor of 4 when compared to imaging a large volume using a single slice scanner. Conclusion: A multislice CT scanner has been commissioning for CT PGD, allowing images of an entire dose distribution to be acquired in a matter of minutes. Funding support provided by the Natural Sciences and Engineering Research Council of Canada (NSERC)« less

Prosthetic EMG control enhancement through the application of man-machine principles

NASA Technical Reports Server (NTRS)

Simcox, W. A.

1977-01-01

An area in medicine that appears suitable to man-machine principles is rehabilitation research, particularly when the motor aspects of the body are involved. If one considers the limb, whether functional or not, as the machine, the brain as the controller and the neuromuscular system as the man-machine interface, the human body is reduced to a man-machine system that can benefit from the principles behind such systems. The area of rehabilitation that this paper deals with is that of an arm amputee and his prosthetic device. Reducing this area to its man-machine basics, the problem becomes one of attaining natural multiaxis prosthetic control using Electromyographic activity (EMG) as the means of communication between man and prothesis. In order to use EMG as the communication channel it must be amplified and processed to yield a high information signal suitable for control. The most common processing scheme employed is termed Mean Value Processing. This technique for extracting the useful EMG signal consists of a differential to single ended conversion to the surface activity followed by a rectification and smoothing.

25. Paper ready for the calender presses. This picture shows ...

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

25. Paper ready for the calender presses. This picture shows the paper after it has been coated and dried, as shown on page 238, and it being rolled at the end of the coating-machine. It is now ready to be sent to the big presses which calender it (or iron it, as popular pariance would have it). The pictures on pages 238 and 239 show a continuous process over a single machine; but on account of the length of teh machine, the process is illustrated in sections. (p.239.) - Champion-International Paper Company, West bank of Spicket River at Canal Street, Lawrence, Essex County, MA

Data mining in bioinformatics using Weka.

PubMed

Frank, Eibe; Hall, Mark; Trigg, Len; Holmes, Geoffrey; Witten, Ian H

2004-10-12

The Weka machine learning workbench provides a general-purpose environment for automatic classification, regression, clustering and feature selection-common data mining problems in bioinformatics research. It contains an extensive collection of machine learning algorithms and data pre-processing methods complemented by graphical user interfaces for data exploration and the experimental comparison of different machine learning techniques on the same problem. Weka can process data given in the form of a single relational table. Its main objectives are to (a) assist users in extracting useful information from data and (b) enable them to easily identify a suitable algorithm for generating an accurate predictive model from it. http://www.cs.waikato.ac.nz/ml/weka.

Parallel processors and nonlinear structural dynamics algorithms and software

NASA Technical Reports Server (NTRS)

Belytschko, Ted; Gilbertsen, Noreen D.; Neal, Mark O.; Plaskacz, Edward J.

1989-01-01

The adaptation of a finite element program with explicit time integration to a massively parallel SIMD (single instruction multiple data) computer, the CONNECTION Machine is described. The adaptation required the development of a new algorithm, called the exchange algorithm, in which all nodal variables are allocated to the element with an exchange of nodal forces at each time step. The architectural and C* programming language features of the CONNECTION Machine are also summarized. Various alternate data structures and associated algorithms for nonlinear finite element analysis are discussed and compared. Results are presented which demonstrate that the CONNECTION Machine is capable of outperforming the CRAY XMP/14.

VIEW OF INTERIOR OF SOUTHERN DUCTILE CASTING COMPANY, CENTERVILLE FOUNDRY ...

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

VIEW OF INTERIOR OF SOUTHERN DUCTILE CASTING COMPANY, CENTERVILLE FOUNDRY SHOWING MOLD MAKING WITH PNEWMATIC JOLT SQUEEZE COPE AND DRAG MOLDING MACHINES THAT INDIVIDUALLY MADE EITHER A COPE OR DRAG AND A SMALL WHEELED MATCHPLATE JOLT-SQUEEZE MACHINE THAT COMPRESSED AN ENTIRE MOLD AT A SINGLE TIME USING A DOUBLE-SIDED PATTERN (MATCHPLATE). ALSO SHOWN ARE RAILED PALLET CAR CONVEYORS THAT CARRIED COMPLETED MOLDS FROM MOLDING MACHINES TO POURING AREAS WHERE WORKERS USED SMALL OVERHEAD CRANE TO LIFT JACKETS AND WEIGHTS ONTO THE MOLDS TO HOLD THEM TOGETHER WHILE POURING. - Southern Ductile Casting Company, Centerville Foundry, 101 Airport Road, Centreville, Bibb County, AL

Single-point diamond crushing of Zerodur with in-situ polishing and metrology on a diamond turning machine

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

Bryan, J.B.; Carter, D.L.

1985-04-01

Large, complicated, aspherical optical elements of glass are presently used in many astronomical devices, both on land and in space. Grazing-incident mirrors are envisioned for use in such missions as the proposed Advanced X-Ray Astrophysical Facility (AXAF), the Far Ultraviolet Spectroscopic Explorer (FUSE), and others. These elements are very expensive to fabricate because a great deal of time and labor are required to shape a glass blank. The fabrication of these mirrors can best be achieved by applying precision machining techniques and precision machines for figuring and finishing low-expansion glasses such as Zerodur.

Looking for scaling laws, or physics with nuts and shells

NASA Astrophysics Data System (ADS)

Sheets, H. David; Lauffenburger, James C.

1999-09-01

Scaling laws relating the volume of a class of objects to a characteristic dimension of the object appear commonly in physics, chemistry, and biology. In this laboratory exercise for an introductory physics course scaling laws are derived for machine nuts and clam shells. In addition to covering a standard problem in physics, determining volume of the object by measuring the buoyant force on it, the biologically interesting idea of scaling laws are incorporated into the same lab.

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.

Automated Design of Complex Dynamic Systems

PubMed Central

Hermans, Michiel; Schrauwen, Benjamin; Bienstman, Peter; Dambre, Joni

2014-01-01

Several fields of study are concerned with uniting the concept of computation with that of the design of physical systems. For example, a recent trend in robotics is to design robots in such a way that they require a minimal control effort. Another example is found in the domain of photonics, where recent efforts try to benefit directly from the complex nonlinear dynamics to achieve more efficient signal processing. The underlying goal of these and similar research efforts is to internalize a large part of the necessary computations within the physical system itself by exploiting its inherent non-linear dynamics. This, however, often requires the optimization of large numbers of system parameters, related to both the system's structure as well as its material properties. In addition, many of these parameters are subject to fabrication variability or to variations through time. In this paper we apply a machine learning algorithm to optimize physical dynamic systems. We show that such algorithms, which are normally applied on abstract computational entities, can be extended to the field of differential equations and used to optimize an associated set of parameters which determine their behavior. We show that machine learning training methodologies are highly useful in designing robust systems, and we provide a set of both simple and complex examples using models of physical dynamical systems. Interestingly, the derived optimization method is intimately related to direct collocation a method known in the field of optimal control. Our work suggests that the application domains of both machine learning and optimal control have a largely unexplored overlapping area which envelopes a novel design methodology of smart and highly complex physical systems. PMID:24497969

Materials and optimized designs for human-machine interfaces via epidermal electronics.

PubMed

Jeong, Jae-Woong; Yeo, Woon-Hong; Akhtar, Aadeel; Norton, James J S; Kwack, Young-Jin; Li, Shuo; Jung, Sung-Young; Su, Yewang; Lee, Woosik; Xia, Jing; Cheng, Huanyu; Huang, Yonggang; Choi, Woon-Seop; Bretl, Timothy; Rogers, John A

2013-12-17

Thin, soft, and elastic electronics with physical properties well matched to the epidermis can be conformally and robustly integrated with the skin. Materials and optimized designs for such devices are presented for surface electromyography (sEMG). The findings enable sEMG from wide ranging areas of the body. The measurements have quality sufficient for advanced forms of human-machine interface. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Robotic Telepresence: Perception, Performance, and User Experience

DTIC Science & Technology

2012-02-01

defined as “a human-computer-machine condition in which a user receives sufficient information about a remote, real-world site through a machine so...that the user feels physically present at the remote, real-world site ” (Aliberti and Bruen, 2006). Telepresence often includes capabilities for a more...outdoor route reconnaissance course (figures 4 and 5) was located at the Molnar MOUT (Military Operations in Urban Terrain) site in Fort Benning, GA. It

Mi Segundo Libro de Maquinas Simples: Las Palancas. Escuela Intermedia Grados 7, 8 y 9 (My Second Book of Simple Machines: Levers. Intermediate School Grades 7, 8, and 9).

ERIC Educational Resources Information Center

Alvarado, Patricio R.; Montalvo, Luis

This is the second book in a five-book physical science series on simple machines. The books are designed for Spanish-speaking junior high school students. By suggesting experiments and posing questions concerning drawings in the book which illustrate the scientific principles, this book explains the workings of three types of levers. Resistance…

Design and Development of an Automatic Tool Changer for an Articulated Robot Arm

NASA Astrophysics Data System (ADS)

Ambrosio, H.; Karamanoglu, M.

2014-07-01

In the creative industries, the length of time between the ideation stage and the making of physical objects is decreasing due to the use of CAD/CAM systems and adicitive manufacturing. Natural anisotropic materials, such as solid wood can also be transformed using CAD/CAM systems, but only with subtractive processes such as machining with CNC routers. Whilst some 3 axis CNC routing machines are affordable to buy and widely available, more flexible 5 axis routing machines still present themselves as a too big investment for small companies. Small refurbished articulated robots can be a cheaper alternative but they require a light end-effector. This paper presents a new lightweight tool changer that converts a small 3kg payload 6 DOF robot into a robot apprentice able to machine wood and similar soft materials.

Metrological Characterization of the Vickers Hardness Primary Standard Machine Established at CSIR-NPL

NASA Astrophysics Data System (ADS)

Titus, S. Seelakumar; Vikram; Girish; Jain, Sushil Kumar

2018-06-01

CSIR-National Physical Laboratory (CSIR-NPL) is the National Metrological Institute (NMI) of India, which has the mandate for the realization of SI units of measurements and dissemination of the same to the user organizations. CSIR-NPL has established a hardness standardizing machine for realizing the Vickers hardness scale as per ISO 6507-3 standard for providing national traceability in hardness measurement. Direct verification of the machine has been carried out by measuring the uncertainty in the generated force, the indenter geometry and the indentation measuring system. From these measurements, it is found that the machine exhibits a calibration and measurement capability (CMC) of ±1.5% for HV1-HV3 scales and ±1.0% for HV5-HV50 scales and ±0.8% for HV100 scale.

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.

A machine learning approach for the identification of key markers involved in brain development from single-cell transcriptomic data.

PubMed

Hu, Yongli; Hase, Takeshi; Li, Hui Peng; Prabhakar, Shyam; Kitano, Hiroaki; Ng, See Kiong; Ghosh, Samik; Wee, Lawrence Jin Kiat

2016-12-22

The ability to sequence the transcriptomes of single cells using single-cell RNA-seq sequencing technologies presents a shift in the scientific paradigm where scientists, now, are able to concurrently investigate the complex biology of a heterogeneous population of cells, one at a time. However, till date, there has not been a suitable computational methodology for the analysis of such intricate deluge of data, in particular techniques which will aid the identification of the unique transcriptomic profiles difference between the different cellular subtypes. In this paper, we describe the novel methodology for the analysis of single-cell RNA-seq data, obtained from neocortical cells and neural progenitor cells, using machine learning algorithms (Support Vector machine (SVM) and Random Forest (RF)). Thirty-eight key transcripts were identified, using the SVM-based recursive feature elimination (SVM-RFE) method of feature selection, to best differentiate developing neocortical cells from neural progenitor cells in the SVM and RF classifiers built. Also, these genes possessed a higher discriminative power (enhanced prediction accuracy) as compared commonly used statistical techniques or geneset-based approaches. Further downstream network reconstruction analysis was carried out to unravel hidden general regulatory networks where novel interactions could be further validated in web-lab experimentation and be useful candidates to be targeted for the treatment of neuronal developmental diseases. This novel approach reported for is able to identify transcripts, with reported neuronal involvement, which optimally differentiate neocortical cells and neural progenitor cells. It is believed to be extensible and applicable to other single-cell RNA-seq expression profiles like that of the study of the cancer progression and treatment within a highly heterogeneous tumour.

Wearable-Sensor-Based Classification Models of Faller Status in Older Adults.

PubMed

Howcroft, Jennifer; Lemaire, Edward D; Kofman, Jonathan

2016-01-01

Wearable sensors have potential for quantitative, gait-based, point-of-care fall risk assessment that can be easily and quickly implemented in clinical-care and older-adult living environments. This investigation generated models for wearable-sensor based fall-risk classification in older adults and identified the optimal sensor type, location, combination, and modelling method; for walking with and without a cognitive load task. A convenience sample of 100 older individuals (75.5 ± 6.7 years; 76 non-fallers, 24 fallers based on 6 month retrospective fall occurrence) walked 7.62 m under single-task and dual-task conditions while wearing pressure-sensing insoles and tri-axial accelerometers at the head, pelvis, and left and right shanks. Participants also completed the Activities-specific Balance Confidence scale, Community Health Activities Model Program for Seniors questionnaire, six minute walk test, and ranked their fear of falling. Fall risk classification models were assessed for all sensor combinations and three model types: multi-layer perceptron neural network, naïve Bayesian, and support vector machine. The best performing model was a multi-layer perceptron neural network with input parameters from pressure-sensing insoles and head, pelvis, and left shank accelerometers (accuracy = 84%, F1 score = 0.600, MCC score = 0.521). Head sensor-based models had the best performance of the single-sensor models for single-task gait assessment. Single-task gait assessment models outperformed models based on dual-task walking or clinical assessment data. Support vector machines and neural networks were the best modelling technique for fall risk classification. Fall risk classification models developed for point-of-care environments should be developed using support vector machines and neural networks, with a multi-sensor single-task gait assessment.

Sensitivity analysis of machine-learning models of hydrologic time series

NASA Astrophysics Data System (ADS)

O'Reilly, A. M.

2017-12-01

Sensitivity analysis traditionally has been applied to assessing model response to perturbations in model parameters, where the parameters are those model input variables adjusted during calibration. Unlike physics-based models where parameters represent real phenomena, the equivalent of parameters for machine-learning models are simply mathematical "knobs" that are automatically adjusted during training/testing/verification procedures. Thus the challenge of extracting knowledge of hydrologic system functionality from machine-learning models lies in their very nature, leading to the label "black box." Sensitivity analysis of the forcing-response behavior of machine-learning models, however, can provide understanding of how the physical phenomena represented by model inputs affect the physical phenomena represented by model outputs.As part of a previous study, hybrid spectral-decomposition artificial neural network (ANN) models were developed to simulate the observed behavior of hydrologic response contained in multidecadal datasets of lake water level, groundwater level, and spring flow. Model inputs used moving window averages (MWA) to represent various frequencies and frequency-band components of time series of rainfall and groundwater use. Using these forcing time series, the MWA-ANN models were trained to predict time series of lake water level, groundwater level, and spring flow at 51 sites in central Florida, USA. A time series of sensitivities for each MWA-ANN model was produced by perturbing forcing time-series and computing the change in response time-series per unit change in perturbation. Variations in forcing-response sensitivities are evident between types (lake, groundwater level, or spring), spatially (among sites of the same type), and temporally. Two generally common characteristics among sites are more uniform sensitivities to rainfall over time and notable increases in sensitivities to groundwater usage during significant drought periods.

Sensitivity of Support Vector Machine Predictions of Passive Microwave Brightness Temperature Over Snow-covered Terrain in High Mountain Asia

NASA Astrophysics Data System (ADS)

Ahmad, J. A.; Forman, B. A.

2017-12-01

High Mountain Asia (HMA) serves as a water supply source for over 1.3 billion people, primarily in south-east Asia. Most of this water originates as snow (or ice) that melts during the summer months and contributes to the run-off downstream. In spite of its critical role, there is still considerable uncertainty regarding the total amount of snow in HMA and its spatial and temporal variation. In this study, the NASA Land Information Systems (LIS) is used to model the hydrologic cycle over the Indus basin. In addition, the ability of support vector machines (SVM), a machine learning technique, to predict passive microwave brightness temperatures at a specific frequency and polarization as a function of LIS-derived land surface model output is explored in a sensitivity analysis. Multi-frequency, multi-polarization passive microwave brightness temperatures as measured by the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E) over the Indus basin are used as training targets during the SVM training process. Normalized sensitivity coefficients (NSC) are then computed to assess the sensitivity of a well-trained SVM to each LIS-derived state variable. Preliminary results conform with the known first-order physics. For example, input states directly linked to physical temperature like snow temperature, air temperature, and vegetation temperature have positive NSC's whereas input states that increase volume scattering such as snow water equivalent or snow density yield negative NSC's. Air temperature exhibits the largest sensitivity coefficients due to its inherent, high-frequency variability. Adherence of this machine learning algorithm to the first-order physics bodes well for its potential use in LIS as the observation operator within a radiance data assimilation system aimed at improving regional- and continental-scale snow estimates.

An Intelligent and Interactive Simulation and Tutoring Environment for Exploring and Learning Simple Machines

NASA Astrophysics Data System (ADS)

Myneni, Lakshman Sundeep

Students in middle school science classes have difficulty mastering physics concepts such as energy and work, taught in the context of simple machines. Moreover, students' naive conceptions of physics often remain unchanged after completing a science class. To address this problem, I developed an intelligent tutoring system, called the Virtual Physics System (ViPS), which coaches students through problem solving with one class of simple machines, pulley systems. The tutor uses a unique cognitive based approach to teaching simple machines, and includes innovations in three areas. (1) It employs a teaching strategy that focuses on highlighting links among concepts of the domain that are essential for conceptual understanding yet are seldom learned by students. (2) Concepts are taught through a combination of effective human tutoring techniques (e.g., hinting) and simulations. (3) For each student, the system identifies which misconceptions he or she has, from a common set of student misconceptions gathered from domain experts, and tailors tutoring to match the correct line of scientific reasoning regarding the misconceptions. ViPS was implemented as a platform on which students can design and simulate pulley system experiments, integrated with a constraint-based tutor that intervenes when students make errors during problem solving to teach them and to help them. ViPS has a web-based client-server architecture, and has been implemented using Java technologies. ViPS is different from existing physics simulations and tutoring systems due to several original features. (1). It is the first system to integrate a simulation based virtual experimentation platform with an intelligent tutoring component. (2) It uses a novel approach, based on Bayesian networks, to help students construct correct pulley systems for experimental simulation. (3) It identifies student misconceptions based on a novel decision tree applied to student pretest scores, and tailors tutoring to individual students based on detected misconceptions. ViPS has been evaluated through usability and usefulness experiments with undergraduate engineering students taking their first college-level engineering physics course and undergraduate pre-service teachers taking their first college-level physics course. These experiments demonstrated that ViPS is highly usable and effective. Students using ViPS reduced their misconceptions, and students conducting virtual experiments in ViPS learned more than students who conducted experiments with physical pulley systems. Interestingly, it was also found that college students exhibited many of the same misconceptions that have been identified in middle school students.

Sales of healthy snacks and beverages following the implementation of healthy vending standards in City of Philadelphia vending machines.

PubMed

Pharis, Meagan L; Colby, Lisa; Wagner, Amanda; Mallya, Giridhar

2018-02-01

We examined outcomes following the implementation of employer-wide vending standards, designed to increase healthy snack and beverage options, on the proportion of healthy v. less healthy sales, sales volume and revenue for snack and beverage vending machines. A single-arm evaluation of a policy utilizing monthly sales volume and revenue data provided by the contracted vendor during baseline, machine conversion and post-conversion time periods. Study time periods are full calendar years unless otherwise noted. Property owned or leased by the City of Philadelphia, USA. Approximately 250 vending machines over a 4-year period (2010-2013). At post-conversion, the proportion of sales attributable to healthy items was 40 % for snacks and 46 % for beverages. Healthy snack sales were 323 % higher (38·4 to 162·5 items sold per machine per month) and total snack sales were 17 % lower (486·8 to 402·1 items sold per machine per month). Healthy beverage sales were 33 % higher (68·2 to 90·6 items sold per machine per month) and there was no significant change in total beverage sales (213·2 to 209·6 items sold per machine per month). Revenue was 11 % lower for snacks ($US 468·30 to $US 415·70 per machine per month) and 21 % lower for beverages ($US 344·00 to $US 270·70 per machine per month). Sales of healthy vending items were significantly higher following the implementation of employer-wide vending standards for snack and beverage vending machines. Entities receiving revenue-based commission payments from vending machines should employ strategies to minimize potential revenue losses.

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).

Replication and Reconfiguration in a Distributed Mail Repository.

DTIC Science & Technology

1987-04-01

a single machine and sees no improvement in availability over the old repository. Further, the static allocation of users to particular machines means...Reconfiguration Good old Wateon! You are the one fixed point in a changing universel -Sir Arthur Conan Doyle How can I be sure In a world that’s constantly...automatic storage allocation , and the Argus debugger. Then I discuss the drawbacks involved in using Argus: deadlocks, the awkwardness of retrying actions

Robust Airborne Networking Extensions (RANGE)

DTIC Science & Technology

2008-02-01

IMUNES [13] project, which provides an entire network stack virtualization and topology control inside a single FreeBSD machine . The emulated topology...Multicast versus broadcast in a manet.” in ADHOC-NOW, 2004, pp. 14–27. [9] J. Mukherjee, R. Atwood , “ Rendezvous point relocation in protocol independent...computer with an Ethernet connection, or a Linux virtual machine on some other (e.g., Windows) operating system, should work. 2.1 Patching the source code

Machine learning strategies for systems with invariance properties

NASA Astrophysics Data System (ADS)

Ling, Julia; Jones, Reese; Templeton, Jeremy

2016-08-01

In many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds Averaged Navier Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high performance computing has led to a growing availability of high fidelity simulation data. These data open up the possibility of using machine learning algorithms, such as random forests or neural networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these empirical models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first method, a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance at significantly reduced computational training costs.

Machine learning strategies for systems with invariance properties

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

Ling, Julia; Jones, Reese E.; Templeton, Jeremy Alan

Here, in many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds-Averaged Navier-Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high-performance computing has led to a growing availability of high-fidelity simulation data, which open up the possibility of using machine learning algorithms, such as random forests or neuralmore » networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first , a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance with significantly reduced computational training costs.« less

Machine learning strategies for systems with invariance properties

DOE PAGES

Ling, Julia; Jones, Reese E.; Templeton, Jeremy Alan

2016-05-06

Here, in many scientific fields, empirical models are employed to facilitate computational simulations of engineering systems. For example, in fluid mechanics, empirical Reynolds stress closures enable computationally-efficient Reynolds-Averaged Navier-Stokes simulations. Likewise, in solid mechanics, constitutive relations between the stress and strain in a material are required in deformation analysis. Traditional methods for developing and tuning empirical models usually combine physical intuition with simple regression techniques on limited data sets. The rise of high-performance computing has led to a growing availability of high-fidelity simulation data, which open up the possibility of using machine learning algorithms, such as random forests or neuralmore » networks, to develop more accurate and general empirical models. A key question when using data-driven algorithms to develop these models is how domain knowledge should be incorporated into the machine learning process. This paper will specifically address physical systems that possess symmetry or invariance properties. Two different methods for teaching a machine learning model an invariance property are compared. In the first , a basis of invariant inputs is constructed, and the machine learning model is trained upon this basis, thereby embedding the invariance into the model. In the second method, the algorithm is trained on multiple transformations of the raw input data until the model learns invariance to that transformation. Results are discussed for two case studies: one in turbulence modeling and one in crystal elasticity. It is shown that in both cases embedding the invariance property into the input features yields higher performance with significantly reduced computational training costs.« less

Minimal universal quantum heat machine.

PubMed

Gelbwaser-Klimovsky, D; Alicki, R; Kurizki, G

2013-01-01

In traditional thermodynamics the Carnot cycle yields the ideal performance bound of heat engines and refrigerators. We propose and analyze a minimal model of a heat machine that can play a similar role in quantum regimes. The minimal model consists of a single two-level system with periodically modulated energy splitting that is permanently, weakly, coupled to two spectrally separated heat baths at different temperatures. The equation of motion allows us to compute the stationary power and heat currents in the machine consistent with the second law of thermodynamics. This dual-purpose machine can act as either an engine or a refrigerator (heat pump) depending on the modulation rate. In both modes of operation, the maximal Carnot efficiency is reached at zero power. We study the conditions for finite-time optimal performance for several variants of the model. Possible realizations of the model are discussed.