Precision machining of advanced materials with waterjets

NASA Astrophysics Data System (ADS)

Liu, H. T.

2017-01-01

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

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.

Aspects of ultra-high-precision diamond machining of RSA 443 optical aluminium

NASA Astrophysics Data System (ADS)

Mkoko, Z.; Abou-El-Hossein, K.

2015-08-01

Optical aluminium alloys such as 6061-T6 are traditionally used in ultra-high precision manufacturing for making optical mirrors for aerospace and other applications. However, the optics industry has recently witnessed the development of more advanced optical aluminium grades that are capable of addressing some of the issues encountered when turning with single-point natural monocrystalline diamond cutters. The advent of rapidly solidified aluminium (RSA) grades has generally opened up new possibilities for ultra-high precision manufacturing of optical components. In this study, experiments were conducted with single-point diamond cutters on rapidly solidified aluminium RSA 443 material. The objective of this study is to observe the effects of depth of cut and feed rate at a fixed rotational speed on the tool wear rate and resulting surface roughness of diamond turned specimens. This is done to gain further understanding of the rate of wear on the diamond cutters versus the surface texture generated on the RSA 443 material. The diamond machining experiments yielded machined surfaces which are less reflective but with consistent surface roughness values. Cutting tools were observed for wear through scanning microscopy; relatively low wear pattern was evident on the diamond tool edge. The highest tool wear were obtained at higher depth of cut and increased feed rate.

Ontological modelling of knowledge management for human-machine integrated design of ultra-precision grinding machine

NASA Astrophysics Data System (ADS)

Hong, Haibo; Yin, Yuehong; Chen, Xing

2016-11-01

Despite the rapid development of computer science and information technology, an efficient human-machine integrated enterprise information system for designing complex mechatronic products is still not fully accomplished, partly because of the inharmonious communication among collaborators. Therefore, one challenge in human-machine integration is how to establish an appropriate knowledge management (KM) model to support integration and sharing of heterogeneous product knowledge. Aiming at the diversity of design knowledge, this article proposes an ontology-based model to reach an unambiguous and normative representation of knowledge. First, an ontology-based human-machine integrated design framework is described, then corresponding ontologies and sub-ontologies are established according to different purposes and scopes. Second, a similarity calculation-based ontology integration method composed of ontology mapping and ontology merging is introduced. The ontology searching-based knowledge sharing method is then developed. Finally, a case of human-machine integrated design of a large ultra-precision grinding machine is used to demonstrate the effectiveness of the method.

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture.

PubMed

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-07-22

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched.

Developing Ubiquitous Sensor Network Platform Using Internet of Things: Application in Precision Agriculture

PubMed Central

Ferrández-Pastor, Francisco Javier; García-Chamizo, Juan Manuel; Nieto-Hidalgo, Mario; Mora-Pascual, Jerónimo; Mora-Martínez, José

2016-01-01

The application of Information Technologies into Precision Agriculture methods has clear benefits. Precision Agriculture optimises production efficiency, increases quality, minimises environmental impact and reduces the use of resources (energy, water); however, there are different barriers that have delayed its wide development. Some of these main barriers are expensive equipment, the difficulty to operate and maintain and the standard for sensor networks are still under development. Nowadays, new technological development in embedded devices (hardware and communication protocols), the evolution of Internet technologies (Internet of Things) and ubiquitous computing (Ubiquitous Sensor Networks) allow developing less expensive systems, easier to control, install and maintain, using standard protocols with low-power consumption. This work develops and test a low-cost sensor/actuator network platform, based in Internet of Things, integrating machine-to-machine and human-machine-interface protocols. Edge computing uses this multi-protocol approach to develop control processes on Precision Agriculture scenarios. A greenhouse with hydroponic crop production was developed and tested using Ubiquitous Sensor Network monitoring and edge control on Internet of Things paradigm. The experimental results showed that the Internet technologies and Smart Object Communication Patterns can be combined to encourage development of Precision Agriculture. They demonstrated added benefits (cost, energy, smart developing, acceptance by agricultural specialists) when a project is launched. PMID:27455265

The laser micro-machining system for diamond anvil cell experiments and general precision machining applications at the High Pressure Collaborative Access Team

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

Hrubiak, Rostislav; Sinogeikin, Stanislav; Rod, Eric

We have designed and constructed a new system for micro-machining parts and sample assemblies used for diamond anvil cells and general user operations at the High Pressure Collaborative Access Team, sector 16 of the Advanced Photon Source. The new micro-machining system uses a pulsed laser of 400 ps pulse duration, ablating various materials without thermal melting, thus leaving a clean edge. With optics designed for a tight focus, the system can machine holes any size larger than 3 μm in diameter. Unlike a standard electrical discharge machining drill, the new laser system allows micro-machining of non-conductive materials such as: amorphousmore » boron and silicon carbide gaskets, diamond, oxides, and other materials including organic materials such as polyimide films (i.e., Kapton). An important feature of the new system is the use of gas-tight or gas-flow environmental chambers which allow the laser micro-machining to be done in a controlled (e.g., inert gas) atmosphere to prevent oxidation and other chemical reactions in air sensitive materials. The gas-tight workpiece enclosure is also useful for machining materials with known health risks (e.g., beryllium). Specialized control software with a graphical interface enables micro-machining of custom 2D and 3D shapes. The laser-machining system was designed in a Class 1 laser enclosure, i.e., it includes laser safety interlocks and computer controls and allows for routine operation. Though initially designed mainly for machining of the diamond anvil cell gaskets, the laser-machining system has since found many other micro-machining applications, several of which are presented here.« less

Smart Cutting Tools and Smart Machining: Development Approaches, and Their Implementation and Application Perspectives

NASA Astrophysics Data System (ADS)

Cheng, Kai; Niu, Zhi-Chao; Wang, Robin C.; Rakowski, Richard; Bateman, Richard

2017-09-01

Smart machining has tremendous potential and is becoming one of new generation high value precision manufacturing technologies in line with the advance of Industry 4.0 concepts. This paper presents some innovative design concepts and, in particular, the development of four types of smart cutting tools, including a force-based smart cutting tool, a temperature-based internally-cooled cutting tool, a fast tool servo (FTS) and smart collets for ultraprecision and micro manufacturing purposes. Implementation and application perspectives of these smart cutting tools are explored and discussed particularly for smart machining against a number of industrial application requirements. They are contamination-free machining, machining of tool-wear-prone Si-based infra-red devices and medical applications, high speed micro milling and micro drilling, etc. Furthermore, implementation techniques are presented focusing on: (a) plug-and-produce design principle and the associated smart control algorithms, (b) piezoelectric film and surface acoustic wave transducers to measure cutting forces in process, (c) critical cutting temperature control in real-time machining, (d) in-process calibration through machining trials, (e) FE-based design and analysis of smart cutting tools, and (f) application exemplars on adaptive smart machining.

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

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

Application and machining of Zerodur for optical purposes

NASA Astrophysics Data System (ADS)

Reisert, Norbert

1991-03-01

'Zerodur' is a glass ceramic made by SCHOTT GLASWERKE, exhibiting special physical properties, while also being optimally suited for a variety of applications. Thermal expansion of 'Zerodur' is zero over a large temperature range and temperature variations, thus, have no bearing on the geometry of workpieces, which makes 'Zerodur' ideally suited for the use as mirror substrate blanks for astronomical telescopes, x-ray telescopes, or even for chips production, where maximum precision is a prime requirement. The temperature-independent base blocks of ring laser gyroscopes, as well as range spacers in laser resonators are likewise made of 'Zerodur'. 'Zerodur' can be machined like glass, but unlike with many optical glasses the warming generated upon cementing and polishing does not cause any deformations of tension at the surface. The paper aims to provide a general view of the most essential properties of 'Zerodur', its major fields of application, the manufacture and the machining in the forma of grinding and polishing.

a Precise, Low-Cost Rtk Gnss System for Uav Applications

NASA Astrophysics Data System (ADS)

Stempfhuber, W.; Buchholz, M.

2011-09-01

High accuracy with real-time positioning of moving objects has been considered a standard task of engineering geodesy for 10 to 15 years. An absolute positioning accuracy of 1-3 cm is generally possible worldwide and is further used in many areas of machine guidance (machine control and guidance), and farming (precision farming) as well as for various special applications (e.g. railway trolley, mining, etc.). The cost of the measuring instruments required for the use of geodetic L1/L2 receivers with a local reference station amounts to approximately USD 30,000 to 50,000. Therefore, dual frequency RTK GNSS receivers are not used in the mass market. Affordable GPS/GNSS modules have already reached the mass market in various areas such as mobile phones, car navigation, the leisure industry, etc. Kinematic real-time positioning applications with centimetre or decimetre levels could also evolve into a mass product. In order for this to happen, the costs for such systems must lie between USD 1,000 to 2,000. What exactly low-cost means is determined by the precise specifications of the given individual application. Several university studies in geodesy focus on the approach of high-accuracy positioning by means of single frequency receivers for static applications [e.g. GLABSCH et. al. 2009, SCHWIEGER and GLÄSER 2005, ALKAN 2010, REALINI et. al. 2010, KORTH and HOFMANN 2011]. Although intelligent approaches have been developed that compute a trajectory in the post-processing mode [REALINI et. al., 2010], at present, there are only a very few GNSS Low-Cost Systems that enable real-time processing. This approach to precise position determination by means of the computation of static raw data with single frequency receivers is currently being explored in a research project at the Beuth Hochschule für Technik Berlin - and is being further developed for kinematic applications. The project is embedded in the European Social Fund. It is a follow-up project in the area of

Analysis of the application of poly-nanocrystalline diamond tools for ultra precision machining of steel with ultrasonic assistance

NASA Astrophysics Data System (ADS)

Doetz, M.; Dambon, O.; Klocke, F.; Bulla, B.; Schottka, K.; Robertson, D. J.

2017-10-01

Ultra-precision diamond turning enables the manufacturing of parts with mirror-like surfaces and highest form accuracies out of non-ferrous, a few crystalline and plastic materials. Furthermore, an ultrasonic assistance has the ability to push these boundaries and enables the machining of materials like steel, which is not possible in a conventional way due to the excessive tool wear caused by the affinity of carbon to iron. Usually monocrystalline diamonds tools are applied due to their unsurpassed cutting edge properties. New cutting tool material developments have shown that it is possible to produce tools made of nano-polycrystalline diamonds with cutting edges equivalent to monocrystalline diamonds. In nano-polycrystalline diamonds ultra-fine grains of a few tens of nanometers are firmly and directly bonded together creating an unisotropic structure. The properties of this material are described to be isotropic, harder and tougher than those of the monocrystalline diamonds, which are unisotropic. This publication will present machining results from the newest investigations of the process potential of this new polycrystalline cutting material. In order to provide a baseline with which to characterize the cutting material cutting experiments on different conventional machinable materials like Cooper or Aluminum are performed. The results provide information on the roughness and the topography of the surface focusing on the comparison to the results while machining with monocrystalline diamond. Furthermore, the cutting material is tested in machining steel with ultrasonic assistance with a focus on tool life time and surface roughness. An outlook on the machinability of other materials will be given.

Precision injection molding of freeform optics

NASA Astrophysics Data System (ADS)

Fang, Fengzhou; Zhang, Nan; Zhang, Xiaodong

2016-08-01

Precision injection molding is the most efficient mass production technology for manufacturing plastic optics. Applications of plastic optics in field of imaging, illumination, and concentration demonstrate a variety of complex surface forms, developing from conventional plano and spherical surfaces to aspheric and freeform surfaces. It requires high optical quality with high form accuracy and lower residual stresses, which challenges both optical tool inserts machining and precision injection molding process. The present paper reviews recent progress in mold tool machining and precision injection molding, with more emphasis on precision injection molding. The challenges and future development trend are also discussed.

Mesoplasticity approach to studies of the cutting mechanism in ultra-precision machining

NASA Astrophysics Data System (ADS)

Lee, Rongbin W. B.; Wang, Hao; To, Suet; Cheung, Chi Fai; Chan, Chang Yuen

2014-03-01

There have been various theoretical attempts by researchers worldwide to link up different scales of plasticity studies from the nano-, micro- and macro-scale of observation, based on molecular dynamics, crystal plasticity and continuum mechanics. Very few attempts, however, have been reported in ultra-precision machining studies. A mesoplasticity approach advocated by Lee and Yang is adopted by the authors and is successfully applied to studies of the micro-cutting mechanisms in ultra-precision machining. Traditionally, the shear angle in metal cutting, as well as the cutting force variation, can only be determined from cutting tests. In the pioneering work of the authors, the use of mesoplasticity theory enables prediction of the fluctuation of the shear angle and micro-cutting force, shear band formation, chip morphology in diamond turning and size effect in nano-indentation. These findings are verified by experiments. The mesoplasticity formulation opens up a new direction of studies to enable how the plastic behaviour of materials and their constitutive representations in deformation processing, such as machining can be predicted, assessed and deduced from the basic properties of the materials measurable at the microscale.

Machining approach of freeform optics on infrared materials via ultra-precision turning.

PubMed

Li, Zexiao; Fang, Fengzhou; Chen, Jinjin; Zhang, Xiaodong

2017-02-06

Optical freeform surfaces are of great advantage in excellent optical performance and integrated alignment features. It has wide applications in illumination, imaging and non-imaging, etc. Machining freeform surfaces on infrared (IR) materials with ultra-precision finish is difficult due to its brittle nature. Fast tool servo (FTS) assisted diamond turning is a powerful technique for the realization of freeform optics on brittle materials due to its features of high spindle speed and high cutting speed. However it has difficulties with large slope angles and large rise-and-falls in the sagittal direction. In order to overcome this defect, the balance of the machining quality on the freeform surface and the brittle nature in IR materials should be realized. This paper presents the design of a near-rotational freeform surface (NRFS) with a low non-rotational degree (NRD) to constraint the variation of traditional freeform optics to solve this issue. In NRFS, the separation of the surface results in a rotational part and a residual part denoted as a non-rotational surface (NRS). Machining NRFS on germanium is operated by FTS diamond turning. Characteristics of the surface indicate that the optical finish of the freeform surface has been achieved. The modulation transfer function (MTF) of the freeform optics shows a good agreement to the design expectation. Images of the final optical system confirm that the fabricating strategy is of high efficiency and high quality. Challenges and prospects are discussed to provide guidance of future work.

Apparatus for correcting precision errors in slide straightness in machine tools

DOEpatents

Robinson, Samuel C.; Gerth, Howard L.

1981-01-01

The present invention is directed to a mechanism by which small deviations in slideway straightness and roll of a precision machining apparatus may be compensated for. The mechanism of the present invention comprises a fixture support disposed between the slideway carriage and the tool or workpiece fixture and provided with a hinge-like coupling between the carriage and the fixture support so as to allow for the minute and precise displacement of the fixture support in a direction normal to the direction of the slide path so as to readily compensate for slight deviations in the straightness and roll of the slide path.

Apparatus for correcting precision errors in slide straigntness in machine tools

DOEpatents

Robinson, S.C.; Gerth, H.L.

The present invention is directed to a mechanism by which small deviations in slideway straightness and roll of a precision machining apparatus may be compensated for. The mechanism of the present invention comprises a fixture support disposed between the slideway carriage and the tool or workpiece fixture and provided with a hinge-like coupling between the carriage and the fixture support so as to allow for the minute and precise displacement of the fixture support in a direction normal to the direction of the slide path soa as to readily compensate for slight deviations in the straightness and roll of the slide path.

Protein function in precision medicine: deep understanding with machine learning.

PubMed

Rost, Burkhard; Radivojac, Predrag; Bromberg, Yana

2016-08-01

Precision medicine and personalized health efforts propose leveraging complex molecular, medical and family history, along with other types of personal data toward better life. We argue that this ambitious objective will require advanced and specialized machine learning solutions. Simply skimming some low-hanging results off the data wealth might have limited potential. Instead, we need to better understand all parts of the system to define medically relevant causes and effects: how do particular sequence variants affect particular proteins and pathways? How do these effects, in turn, cause the health or disease-related phenotype? Toward this end, deeper understanding will not simply diffuse from deeper machine learning, but from more explicit focus on understanding protein function, context-specific protein interaction networks, and impact of variation on both. © 2016 Federation of European Biochemical Societies.

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

NASA Astrophysics Data System (ADS)

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

2017-06-01

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

Research on the tool holder mode in high speed machining

NASA Astrophysics Data System (ADS)

Zhenyu, Zhao; Yongquan, Zhou; Houming, Zhou; Xiaomei, Xu; Haibin, Xiao

2018-03-01

High speed machining technology can improve the processing efficiency and precision, but also reduce the processing cost. Therefore, the technology is widely regarded in the industry. With the extensive application of high-speed machining technology, high-speed tool system has higher and higher requirements on the tool chuck. At present, in high speed precision machining, several new kinds of clip heads are as long as there are heat shrinkage tool-holder, high-precision spring chuck, hydraulic tool-holder, and the three-rib deformation chuck. Among them, the heat shrinkage tool-holder has the advantages of high precision, high clamping force, high bending rigidity and dynamic balance, etc., which are widely used. Therefore, it is of great significance to research the new requirements of the machining tool system. In order to adapt to the requirement of high speed machining precision machining technology, this paper expounds the common tool holder technology of high precision machining, and proposes how to select correctly tool clamping system in practice. The characteristics and existing problems are analyzed in the tool clamping system.

Theoretical and experimental research on machine tool servo system for ultra-precision position compensation on CNC lathe

NASA Astrophysics Data System (ADS)

Ma, Zhichao; Hu, Leilei; Zhao, Hongwei; Wu, Boda; Peng, Zhenxing; Zhou, Xiaoqin; Zhang, Hongguo; Zhu, Shuai; Xing, Lifeng; Hu, Huang

2010-08-01

The theories and techniques for improving machining accuracy via position control of diamond tool's tip and raising resolution of cutting depth on precise CNC lathes have been extremely focused on. A new piezo-driven ultra-precision machine tool servo system is designed and tested to improve manufacturing accuracy of workpiece. The mathematical model of machine tool servo system is established and the finite element analysis is carried out on parallel plate flexure hinges. The output position of diamond tool's tip driven by the machine tool servo system is tested via a contact capacitive displacement sensor. Proportional, integral, derivative (PID) feedback is also implemented to accommodate and compensate dynamical change owing cutting forces as well as the inherent non-linearity factors of the piezoelectric stack during cutting process. By closed loop feedback controlling strategy, the tracking error is limited to 0.8 μm. Experimental results have shown the proposed machine tool servo system could provide a tool positioning resolution of 12 nm, which is much accurate than the inherent CNC resolution magnitude. The stepped shaft of aluminum specimen with a step increment of cutting depth of 1 μm is tested, and the obtained contour illustrates the displacement command output from controller is accurately and real-time reflected on the machined part.

The advancement of the high precision stress polishing

NASA Astrophysics Data System (ADS)

Li, Chaoqiang; Lei, Baiping; Han, Yu

2016-10-01

The stress polishing is a kind of large-diameter aspheric machining technology with high efficiency. This paper focuses on the principle, application in the processing of large aspheric mirror, and the domestic and foreign research status of stress polishing, aimed at the problem of insufficient precision of mirror surface deformation calculated by some traditional theories and the problem that the output precision and stability of the support device in stress polishing cannot meet the requirements. The improvement methods from these three aspects are put forward, the characterization method of mirror's elastic deformation in stress polishing, the deformation theory of influence function and the calculation of correction force, the design of actuator's mechanical structure. These improve the precision of stress polishing and provide theoretical basis for the further application of stress polishing in large-diameter aspheric machining.

Liquid-Assisted Femtosecond Laser Precision-Machining of Silica.

PubMed

Cao, Xiao-Wen; Chen, Qi-Dai; Fan, Hua; Zhang, Lei; Juodkazis, Saulius; Sun, Hong-Bo

2018-04-28

We report a systematical study on the liquid assisted femtosecond laser machining of quartz plate in water and under different etching solutions. The ablation features in liquid showed a better structuring quality and improved resolution with 1/3~1/2 smaller features as compared with those made in air. It has been demonstrated that laser induced periodic structures are present to a lesser extent when laser processed in water solutions. The redistribution of oxygen revealed a strong surface modification, which is related to the etching selectivity of laser irradiated regions. Laser ablation in KOH and HF solution showed very different morphology, which relates to the evolution of laser induced plasma on the formation of micro/nano-features in liquid. This work extends laser precision fabrication of hard materials. The mechanism of strong absorption in the regions with permittivity (epsilon) near zero is discussed.

Long-range nanopositioning and nanomeasuring machine for application to micro- and nanotechnology

NASA Astrophysics Data System (ADS)

Jäger, Gerd; Hausotte, Tino; Büchner, Hans-Joachim; Manske, Eberhard; Schmidt, Ingomar; Mastylo, Rostyslav

2006-03-01

The paper describes the operation of a high-precision long range three-dimensional nanopositioning and nanomeasuring machine (NPM-Machine). The NPM-Machine has been developed by the Institute of Process Measurement and Sensor Technology of the Technische Universität Ilmenau. The machine was successfully tested and continually improved in the last few years. The machines are operating successfully in several German and foreign research institutes including the Physikalisch-Technische Bundesanstalt (PTB). Three plane mirror miniature interferometers are installed into the NPM-machine having a resolution of less than 0,1 nm over the entire positioning and measuring range of 25 mm x 25 mm x 5 mm. An Abbe offset-free design of the three miniature plane mirror interferometers and applying a new concept for compensating systematic errors resulting from mechanical guide systems provide extraordinary accuracy with an expanded uncertainty of only 5 - 10 nm. The integration of several, optical and tactile probe systems and nanotools makes the NPM-Machine suitable for various tasks, such as large-area scanning probe microscopy, mask and wafer inspection, nanostructuring, biotechnology and genetic engineering as well as measuring mechanical precision workpieces, precision treatment and for engineering new material. Various developed probe systems have been integrated into the NPM-Machine. The measurement results of a focus sensor, metrological AFM, white light sensor, tactile stylus probe and of a 3D-micro-touch-probe are presented. Single beam-, double beam- and triple beam interferometers built in the NPM-Machine for six degrees of freedom measurements are described.

A real-time surface inspection system for precision steel balls based on machine vision

NASA Astrophysics Data System (ADS)

Chen, Yi-Ji; Tsai, Jhy-Cherng; Hsu, Ya-Chen

2016-07-01

Precision steel balls are one of the most fundament components for motion and power transmission parts and they are widely used in industrial machinery and the automotive industry. As precision balls are crucial for the quality of these products, there is an urgent need to develop a fast and robust system for inspecting defects of precision steel balls. In this paper, a real-time system for inspecting surface defects of precision steel balls is developed based on machine vision. The developed system integrates a dual-lighting system, an unfolding mechanism and inspection algorithms for real-time signal processing and defect detection. The developed system is tested under feeding speeds of 4 pcs s-1 with a detection rate of 99.94% and an error rate of 0.10%. The minimum detectable surface flaw area is 0.01 mm2, which meets the requirement for inspecting ISO grade 100 precision steel balls.

Analysis of precision and accuracy in a simple model of machine learning

NASA Astrophysics Data System (ADS)

Lee, Julian

2017-12-01

Machine learning is a procedure where a model for the world is constructed from a training set of examples. It is important that the model should capture relevant features of the training set, and at the same time make correct prediction for examples not included in the training set. I consider the polynomial regression, the simplest method of learning, and analyze the accuracy and precision for different levels of the model complexity.

Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture

NASA Astrophysics Data System (ADS)

Elarab, Manal; Ticlavilca, Andres M.; Torres-Rua, Alfonso F.; Maslova, Inga; McKee, Mac

2015-12-01

Precision agriculture requires high-resolution information to enable greater precision in the management of inputs to production. Actionable information about crop and field status must be acquired at high spatial resolution and at a temporal frequency appropriate for timely responses. In this study, high spatial resolution imagery was obtained through the use of a small, unmanned aerial system called AggieAirTM. Simultaneously with the AggieAir flights, intensive ground sampling for plant chlorophyll was conducted at precisely determined locations. This study reports the application of a relevance vector machine coupled with cross validation and backward elimination to a dataset composed of reflectance from high-resolution multi-spectral imagery (VIS-NIR), thermal infrared imagery, and vegetative indices, in conjunction with in situ SPAD measurements from which chlorophyll concentrations were derived, to estimate chlorophyll concentration from remotely sensed data at 15-cm resolution. The results indicate that a relevance vector machine with a thin plate spline kernel type and kernel width of 5.4, having LAI, NDVI, thermal and red bands as the selected set of inputs, can be used to spatially estimate chlorophyll concentration with a root-mean-squared-error of 5.31 μg cm-2, efficiency of 0.76, and 9 relevance vectors.

Machine learning applications in genetics and genomics.

PubMed

Libbrecht, Maxwell W; Noble, William Stafford

2015-06-01

The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets. Here, we provide an overview of machine learning applications for the analysis of genome sequencing data sets, including the annotation of sequence elements and epigenetic, proteomic or metabolomic data. We present considerations and recurrent challenges in the application of supervised, semi-supervised and unsupervised machine learning methods, as well as of generative and discriminative modelling approaches. We provide general guidelines to assist in the selection of these machine learning methods and their practical application for the analysis of genetic and genomic data sets.

Applications of high power lasers. [using reflection holograms for machining and surface treatment

NASA Technical Reports Server (NTRS)

Angus, J. C.

1979-01-01

The use of computer generated, reflection holograms in conjunction with high power lasers for precision machining of metals and ceramics was investigated. The Reflection holograms which were developed and made to work at both optical wavelength (He-Ne, 6328 A) and infrared (CO2, 10.6) meet the primary practical requirement of ruggedness and are relatively economical and simple to fabricate. The technology is sufficiently advanced now so that reflection holography could indeed be used as a practical manufacturing device in certain applications requiring low power densities. However, the present holograms are energy inefficient and much of the laser power is lost in the zero order spot and higher diffraction orders. Improvements of laser machining over conventional methods are discussed and addition applications are listed. Possible uses in the electronics industry include drilling holes in printed circuit boards making soldered connections, and resistor trimming.

Successful fabrication of a convex platform PMMA cell-counting slide using a high-precision perpendicular dual-spindle CNC machine tool

NASA Astrophysics Data System (ADS)

Chen, Shun-Tong; Chang, Chih-Hsien

2013-12-01

This study presents a novel approach to the fabrication of a biomedical-mold for producing convex platform PMMA (poly-methyl-meth-acrylate) slides for counting cells. These slides allow for the microscopic examination of urine sediment cells. Manufacturing of such slides incorporates three important procedures: (1) the development of a tabletop high-precision dual-spindle CNC (computerized numerical control) machine tool; (2) the formation of a boron-doped polycrystalline composite diamond (BD-PCD) wheel-tool on the machine tool developed in procedure (1); and (3) the cutting of a multi-groove-biomedical-mold array using the formed diamond wheel-tool in situ on the developed machine. The machine incorporates a hybrid working platform providing wheel-tool thinning using spark erosion to cut, polish, and deburr microgrooves on NAK80 steel directly. With consideration given for the electrical conductive properties of BD-PCD, the diamond wheel-tool is thinned to a thickness of 5 µm by rotary wire electrical discharge machining. The thinned wheel-tool can grind microgrooves 10 µm wide. An embedded design, which inserts a close fitting precision core into the biomedical-mold to create step-difference (concave inward) of 50 µm in height between the core and the mold, is also proposed and realized. The perpendicular dual-spindles and precision rotary stage are features that allow for biomedical-mold machining without the necessity of uploading and repositioning materials until all tasks are completed. A PMMA biomedical-slide with a plurality of juxtaposed counting chambers is formed and its usefulness verified.

Using hyperspectral data in precision farming applications

USDA-ARS?s Scientific Manuscript database

Precision farming practices such as variable rate applications of fertilizer and agricultural chemicals require accurate field variability mapping. This chapter investigated the value of hyperspectral remote sensing in providing useful information for five applications of precision farming: (a) Soil...

Apparatus for precision micromachining with lasers

DOEpatents

Chang, Jim J.; Dragon, Ernest P.; Warner, Bruce E.

1998-01-01

A new material processing apparatus using a short-pulsed, high-repetition-rate visible laser for precision micromachining utilizes a near diffraction limited laser, a high-speed precision two-axis tilt-mirror for steering the laser beam, an optical system for either focusing or imaging the laser beam on the part, and a part holder that may consist of a cover plate and a back plate. The system is generally useful for precision drilling, cutting, milling and polishing of metals and ceramics, and has broad application in manufacturing precision components. Precision machining has been demonstrated through percussion drilling and trepanning using this system. With a 30 W copper vapor laser running at multi-kHz pulse repetition frequency, straight parallel holes with size varying from 500 microns to less than 25 microns and with aspect ratios up to 1:40 have been consistently drilled with good surface finish on a variety of metals. Micromilling and microdrilling on ceramics using a 250 W copper vapor laser have also been demonstrated with good results. Materialogroaphic sections of machined parts show little (submicron scale) recast layer and heat affected zone.

Apparatus for precision micromachining with lasers

DOEpatents

Chang, J.J.; Dragon, E.P.; Warner, B.E.

1998-04-28

A new material processing apparatus using a short-pulsed, high-repetition-rate visible laser for precision micromachining utilizes a near diffraction limited laser, a high-speed precision two-axis tilt-mirror for steering the laser beam, an optical system for either focusing or imaging the laser beam on the part, and a part holder that may consist of a cover plate and a back plate. The system is generally useful for precision drilling, cutting, milling and polishing of metals and ceramics, and has broad application in manufacturing precision components. Precision machining has been demonstrated through percussion drilling and trepanning using this system. With a 30 W copper vapor laser running at multi-kHz pulse repetition frequency, straight parallel holes with size varying from 500 microns to less than 25 microns and with aspect ratios up to 1:40 have been consistently drilled with good surface finish on a variety of metals. Micromilling and microdrilling on ceramics using a 250 W copper vapor laser have also been demonstrated with good results. Materialographic sections of machined parts show little (submicron scale) recast layer and heat affected zone. 1 fig.

Drilling High Precision Holes in Ti6Al4V Using Rotary Ultrasonic Machining and Uncertainties Underlying Cutting Force, Tool Wear, and Production Inaccuracies.

PubMed

Chowdhury, M A K; Sharif Ullah, A M M; Anwar, Saqib

2017-09-12

Ti6Al4V alloys are difficult-to-cut materials that have extensive applications in the automotive and aerospace industry. A great deal of effort has been made to develop and improve the machining operations of Ti6Al4V alloys. This paper presents an experimental study that systematically analyzes the effects of the machining conditions (ultrasonic power, feed rate, spindle speed, and tool diameter) on the performance parameters (cutting force, tool wear, overcut error, and cylindricity error), while drilling high precision holes on the workpiece made of Ti6Al4V alloys using rotary ultrasonic machining (RUM). Numerical results were obtained by conducting experiments following the design of an experiment procedure. The effects of the machining conditions on each performance parameter have been determined by constructing a set of possibility distributions (i.e., trapezoidal fuzzy numbers) from the experimental data. A possibility distribution is a probability-distribution-neural representation of uncertainty, and is effective in quantifying the uncertainty underlying physical quantities when there is a limited number of data points which is the case here. Lastly, the optimal machining conditions have been identified using these possibility distributions.

Time-optimized laser micro machining by using a new high dynamic and high precision galvo scanner

NASA Astrophysics Data System (ADS)

Jaeggi, Beat; Neuenschwander, Beat; Zimmermann, Markus; Zecherle, Markus; Boeckler, Ernst W.

2016-03-01

High accuracy, quality and throughput are key factors in laser micro machining. To obtain these goals the ablation process, the machining strategy and the scanning device have to be optimized. The precision is influenced by the accuracy of the galvo scanner and can further be enhanced by synchronizing the movement of the mirrors with the laser pulse train. To maintain a high machining quality i.e. minimum surface roughness, the pulse-to-pulse distance has also to be optimized. Highest ablation efficiency is obtained by choosing the proper laser peak fluence together with highest specific removal rate. The throughput can now be enhanced by simultaneously increasing the average power, the repetition rate as well as the scanning speed to preserve the fluence and the pulse-to-pulse distance. Therefore a high scanning speed is of essential importance. To guarantee the required excellent accuracy even at high scanning speeds a new interferometry based encoder technology was used, that provides a high quality signal for closed-loop control of the galvo scanner position. Low inertia encoder design enables a very dynamic scanner system, which can be driven to very high line speeds by a specially adapted control solution. We will present results with marking speeds up to 25 m/s using a f = 100 mm objective obtained with a new scanning system and scanner tuning maintaining a precision of about 5 μm. Further it will be shown that, especially for short line lengths, the machining time can be minimized by choosing the proper speed which has not to be the maximum one.

Workshop on Fielded Applications of Machine Learning

DTIC Science & Technology

1994-05-11

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

High precision applications of the global positioning system

NASA Technical Reports Server (NTRS)

Lichten, Stephen M.

1991-01-01

The Global Positioning System (GPS) is a constellation of U.S. defense navigation satellites which can be used for military and civilian positioning applications. A wide variety of GPS scientific applications were identified and precise positioning capabilities with GPS were already demonstrated with data available from the present partial satellite constellation. Expected applications include: measurements of Earth crustal motion, particularly in seismically active regions; measurements of the Earth's rotation rate and pole orientation; high-precision Earth orbiter tracking; surveying; measurements of media propagation delays for calibration of deep space radiometric data in support of NASA planetary missions; determination of precise ground station coordinates; and precise time transfer worldwide.

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Machine learning and data mining advance predictive big data analysis in precision animal agriculture.

PubMed

Morota, Gota; Ventura, Ricardo V; Silva, Fabyano F; Koyama, Masanori; Fernando, Samodha C

2018-04-14

Precision animal agriculture is poised to rise to prominence in the livestock enterprise in the domains of management, production, welfare, sustainability, health surveillance, and environmental footprint. Considerable progress has been made in the use of tools to routinely monitor and collect information from animals and farms in a less laborious manner than before. These efforts have enabled the animal sciences to embark on information technology-driven discoveries to improve animal agriculture. However, the growing amount and complexity of data generated by fully automated, high-throughput data recording or phenotyping platforms, including digital images, sensor and sound data, unmanned systems, and information obtained from real-time noninvasive computer vision, pose challenges to the successful implementation of precision animal agriculture. The emerging fields of machine learning and data mining are expected to be instrumental in helping meet the daunting challenges facing global agriculture. Yet, their impact and potential in "big data" analysis have not been adequately appreciated in the animal science community, where this recognition has remained only fragmentary. To address such knowledge gaps, this article outlines a framework for machine learning and data mining and offers a glimpse into how they can be applied to solve pressing problems in animal sciences.

New machining method of high precision infrared window part

NASA Astrophysics Data System (ADS)

Yang, Haicheng; Su, Ying; Xu, Zengqi; Guo, Rui; Li, Wenting; Zhang, Feng; Liu, Xuanmin

2016-10-01

Most of the spherical shell of the photoelectric multifunctional instrument was designed as multi optical channel mode to adapt to the different band of the sensor, there were mainly TV, laser and infrared channels. Without affecting the optical diameter, wind resistance and pneumatic performance of the optical system, the overall layout of the spherical shell was optimized to save space and reduce weight. Most of the shape of the optical windows were special-shaped, each optical window directly participated in the high resolution imaging of the corresponding sensor system, and the optical axis parallelism of each sensor needed to meet the accuracy requirement of 0.05mrad.Therefore precision machining of optical window parts quality will directly affect the photoelectric system's pointing accuracy and interchangeability. Processing and testing of the TV and laser window had been very mature, while because of the special nature of the material, transparent and high refractive rate, infrared window parts had the problems of imaging quality and the control of the minimum focal length and second level parallel in the processing. Based on years of practical experience, this paper was focused on how to control the shape and parallel difference precision of infrared window parts in the processing. Single pass rate was increased from 40% to more than 95%, the processing efficiency was significantly enhanced, an effective solution to the bottleneck problem in the actual processing, which effectively solve the bottlenecks in research and production.

Precise on-machine extraction of the surface normal vector using an eddy current sensor array

NASA Astrophysics Data System (ADS)

Wang, Yongqing; Lian, Meng; Liu, Haibo; Ying, Yangwei; Sheng, Xianjun

2016-11-01

To satisfy the requirements of on-machine measurement of the surface normal during complex surface manufacturing, a highly robust normal vector extraction method using an Eddy current (EC) displacement sensor array is developed, the output of which is almost unaffected by surface brightness, machining coolant and environmental noise. A precise normal vector extraction model based on a triangular-distributed EC sensor array is first established. Calibration of the effects of object surface inclination and coupling interference on measurement results, and the relative position of EC sensors, is involved. A novel apparatus employing three EC sensors and a force transducer was designed, which can be easily integrated into the computer numerical control (CNC) machine tool spindle and/or robot terminal execution. Finally, to test the validity and practicability of the proposed method, typical experiments were conducted with specified testing pieces using the developed approach and system, such as an inclined plane and cylindrical and spherical surfaces.

Machine Learning in Radiology: Applications Beyond Image Interpretation.

PubMed

Lakhani, Paras; Prater, Adam B; Hutson, R Kent; Andriole, Kathy P; Dreyer, Keith J; Morey, Jose; Prevedello, Luciano M; Clark, Toshi J; Geis, J Raymond; Itri, Jason N; Hawkins, C Matthew

2018-02-01

Much attention has been given to machine learning and its perceived impact in radiology, particularly in light of recent success with image classification in international competitions. However, machine learning is likely to impact radiology outside of image interpretation long before a fully functional "machine radiologist" is implemented in practice. Here, we describe an overview of machine learning, its application to radiology and other domains, and many cases of use that do not involve image interpretation. We hope that better understanding of these potential applications will help radiology practices prepare for the future and realize performance improvement and efficiency gains. Copyright © 2017 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Applications of Machine Learning and Rule Induction,

DTIC Science & Technology

1995-02-15

An important area of application for machine learning is in automating the acquisition of knowledge bases required for expert systems. In this paper...we review the major paradigms for machine learning , including neural networks, instance-based methods, genetic learning, rule induction, and analytic

An iterative learning control method with application for CNC machine tools

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

Kim, D.I.; Kim, S.

1996-01-01

A proportional, integral, and derivative (PID) type iterative learning controller is proposed for precise tracking control of industrial robots and computer numerical controller (CNC) machine tools performing repetitive tasks. The convergence of the output error by the proposed learning controller is guaranteed under a certain condition even when the system parameters are not known exactly and unknown external disturbances exist. As the proposed learning controller is repeatedly applied to the industrial robot or the CNC machine tool with the path-dependent repetitive task, the distance difference between the desired path and the actual tracked or machined path, which is one ofmore » the most significant factors in the evaluation of control performance, is progressively reduced. The experimental results demonstrate that the proposed learning controller can improve machining accuracy when the CNC machine tool performs repetitive machining tasks.« less

Application of target costing in machining

NASA Astrophysics Data System (ADS)

Gopalakrishnan, Bhaskaran; Kokatnur, Ameet; Gupta, Deepak P.

2004-11-01

In today's intensely competitive and highly volatile business environment, consistent development of low cost and high quality products meeting the functionality requirements is a key to a company's survival. Companies continuously strive to reduce the costs while still producing quality products to stay ahead in the competition. Many companies have turned to target costing to achieve this objective. Target costing is a structured approach to determine the cost at which a proposed product, meeting the quality and functionality requirements, must be produced in order to generate the desired profits. It subtracts the desired profit margin from the company's selling price to establish the manufacturing cost of the product. Extensive literature review revealed that companies in automotive, electronic and process industries have reaped the benefits of target costing. However target costing approach has not been applied in the machining industry, but other techniques based on Geometric Programming, Goal Programming, and Lagrange Multiplier have been proposed for application in this industry. These models follow a forward approach, by first selecting a set of machining parameters, and then determining the machining cost. Hence in this study we have developed an algorithm to apply the concepts of target costing, which is a backward approach that selects the machining parameters based on the required machining costs, and is therefore more suitable for practical applications in process improvement and cost reduction. A target costing model was developed for turning operation and was successfully validated using practical data.

The research on construction and application of machining process knowledge base

NASA Astrophysics Data System (ADS)

Zhao, Tan; Qiao, Lihong; Qie, Yifan; Guo, Kai

2018-03-01

In order to realize the application of knowledge in machining process design, from the perspective of knowledge in the application of computer aided process planning(CAPP), a hierarchical structure of knowledge classification is established according to the characteristics of mechanical engineering field. The expression of machining process knowledge is structured by means of production rules and the object-oriented methods. Three kinds of knowledge base models are constructed according to the representation of machining process knowledge. In this paper, the definition and classification of machining process knowledge, knowledge model, and the application flow of the process design based on the knowledge base are given, and the main steps of the design decision of the machine tool are carried out as an application by using the knowledge base.

Application of machine learning methods in bioinformatics

NASA Astrophysics Data System (ADS)

Yang, Haoyu; An, Zheng; Zhou, Haotian; Hou, Yawen

2018-05-01

Faced with the development of bioinformatics, high-throughput genomic technology have enabled biology to enter the era of big data. [1] Bioinformatics is an interdisciplinary, including the acquisition, management, analysis, interpretation and application of biological information, etc. It derives from the Human Genome Project. The field of machine learning, which aims to develop computer algorithms that improve with experience, holds promise to enable computers to assist humans in the analysis of large, complex data sets.[2]. This paper analyzes and compares various algorithms of machine learning and their applications in bioinformatics.

Hardware accuracy counters for application precision and quality feedback

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

de Paula Rosa Piga, Leonardo; Majumdar, Abhinandan; Paul, Indrani

Methods, devices, and systems for capturing an accuracy of an instruction executing on a processor. An instruction may be executed on the processor, and the accuracy of the instruction may be captured using a hardware counter circuit. The accuracy of the instruction may be captured by analyzing bits of at least one value of the instruction to determine a minimum or maximum precision datatype for representing the field, and determining whether to adjust a value of the hardware counter circuit accordingly. The representation may be output to a debugger or logfile for use by a developer, or may be outputmore » to a runtime or virtual machine to automatically adjust instruction precision or gating of portions of the processor datapath.« less

The precision measurement and assembly for miniature parts based on double machine vision systems

NASA Astrophysics Data System (ADS)

Wang, X. D.; Zhang, L. F.; Xin, M. Z.; Qu, Y. Q.; Luo, Y.; Ma, T. M.; Chen, L.

2015-02-01

In the process of miniature parts' assembly, the structural features on the bottom or side of the parts often need to be aligned and positioned. The general assembly equipment integrated with one vertical downward machine vision system cannot satisfy the requirement. A precision automatic assembly equipment was developed with double machine vision systems integrated. In the system, a horizontal vision system is employed to measure the position of the feature structure at the parts' side view, which cannot be seen with the vertical one. The position measured by horizontal camera is converted to the vertical vision system with the calibration information. By careful calibration, the parts' alignment and positioning in the assembly process can be guaranteed. The developed assembly equipment has the characteristics of easy implementation, modularization and high cost performance. The handling of the miniature parts and assembly procedure were briefly introduced. The calibration procedure was given and the assembly error was analyzed for compensation.

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.

Software architecture for time-constrained machine vision applications

NASA Astrophysics Data System (ADS)

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

2013-01-01

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

Design and Optimization of Ultrasonic Vibration Mechanism using PZT for Precision Laser Machining

NASA Astrophysics Data System (ADS)

Kim, Woo-Jin; Lu, Fei; Cho, Sung-Hak; Park, Jong-Kweon; Lee, Moon G.

As the aged population grows around the world, many medical instruments and devices have been developed recently. Among the devices, a drug delivery stent is a medical device which requires precision machining. Conventional drug delivery stent has problems of residual polymer and decoating because the drug is coated on the surface of stent with the polymer. If the drug is impregnated in the micro sized holes on the surface, the problems can be overcome because there is no need to use the polymer anymore. Micro sized holes are generally fabricated by laser machining; however, the fabricated holes do not have a high aspect ratio or a good surface finish. To overcome these problems, we propose a vibration-assisted machining mechanism with PZT (Piezoelectric Transducers) for the fabrication of micro sized holes. If the mechanism vibrates the eyepiece of the laser machining head, the laser spot on the workpiece will vibrate vertically because objective lens in the eyepiece shakes by the mechanism's vibration. According to the former researches, the vibrating frequency over 20 kHz and amplitude over 500 nm are preferable. The vibration mechanism has cylindrical guide, hollowed PZT and supports. In the cylinder, the eyepiece is mounted. The cylindrical guide has upper and low plates and side wall. The shape of plates and side wall are designed to have high resonating frequency and large amplitude of motion. The PZT is also selected to have high actuating force and high speed of motion. The support has symmetrical and rigid configuration. The mechanism secures linear motion of the eyepiece. This research includes sensitivity analysis and design of ultrasonic vibration mechanism. As a result of design, the requirements of high frequency and large amplitude are achieved.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

NASA Astrophysics Data System (ADS)

Kesava Reddy, Chirra; Manzoor Hussain, M.; Satyanarayana, S.; Krishna, M. V. S. Murali

2018-04-01

Emerging technology needs advanced machined parts with high strength and temperature resistance, high fatigue life at low production cost with good surface quality to fit into various industrial applications. Electro discharge machine is one of the extensively used machines to manufacture advanced machined parts which cannot be machined by other traditional machine with high precision and accuracy. Machining of DIN 17350-1.2080 (High Carbon High Chromium steel), using electro discharge machining has been discussed in this paper. In the present investigation an effort is made to use permanent magnet at various positions near the spark zone to improve surface quality of the machined surface. Taguchi methodology is used to obtain optimal choice for each machining parameter such as peak current, pulse duration, gap voltage and Servo reference voltage etc. Process parameters have significant influence on machining characteristics and surface finish. Improvement in surface finish is observed when process parameters are set at optimum condition under the influence of magnetic field at various positions.

Future Cyborgs: Human-Machine Interface for Virtual Reality Applications

DTIC Science & Technology

2007-04-01

FUTURE CYBORGS : HUMAN-MACHINE INTERFACE FOR VIRTUAL REALITY APPLICATIONS Robert R. Powell, Major, USAF April 2007 Blue Horizons...SUBTITLE Future Cyborgs : Human-Machine Interface for Virtual Reality Applications 5a. CONTRACT NUMBER 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER...Nicholas Negroponte, Being Digital (New York: Alfred A Knopf, Inc, 1995), 123. 23 Ibid. 24 Andy Clark, Natural-Born Cyborgs (New York: Oxford

Precision Oncology beyond Targeted Therapy: Combining Omics Data with Machine Learning Matches the Majority of Cancer Cells to Effective Therapeutics.

PubMed

Ding, Michael Q; Chen, Lujia; Cooper, Gregory F; Young, Jonathan D; Lu, Xinghua

2018-02-01

Precision oncology involves identifying drugs that will effectively treat a tumor and then prescribing an optimal clinical treatment regimen. However, most first-line chemotherapy drugs do not have biomarkers to guide their application. For molecularly targeted drugs, using the genomic status of a drug target as a therapeutic indicator has limitations. In this study, machine learning methods (e.g., deep learning) were used to identify informative features from genome-scale omics data and to train classifiers for predicting the effectiveness of drugs in cancer cell lines. The methodology introduced here can accurately predict the efficacy of drugs, regardless of whether they are molecularly targeted or nonspecific chemotherapy drugs. This approach, on a per-drug basis, can identify sensitive cancer cells with an average sensitivity of 0.82 and specificity of 0.82; on a per-cell line basis, it can identify effective drugs with an average sensitivity of 0.80 and specificity of 0.82. This report describes a data-driven precision medicine approach that is not only generalizable but also optimizes therapeutic efficacy. The framework detailed herein, when successfully translated to clinical environments, could significantly broaden the scope of precision oncology beyond targeted therapies, benefiting an expanded proportion of cancer patients. Mol Cancer Res; 16(2); 269-78. ©2017 AACR . ©2017 American Association for Cancer Research.

On-machine precision preparation and dressing of ball-headed diamond wheel for the grinding of fused silica

NASA Astrophysics Data System (ADS)

Chen, Mingjun; Li, Ziang; Yu, Bo; Peng, Hui; Fang, Zhen

2013-09-01

In the grinding of high quality fused silica parts with complex surface or structure using ball-headed metal bonded diamond wheel with small diameter, the existing dressing methods are not suitable to dress the ball-headed diamond wheel precisely due to that they are either on-line in process dressing which may causes collision problem or without consideration for the effects of the tool setting error and electrode wear. An on-machine precision preparation and dressing method is proposed for ball-headed diamond wheel based on electrical discharge machining. By using this method the cylindrical diamond wheel with small diameter is manufactured to hemispherical-headed form. The obtained ball-headed diamond wheel is dressed after several grinding passes to recover geometrical accuracy and sharpness which is lost due to the wheel wear. A tool setting method based on high precision optical system is presented to reduce the wheel center setting error and dimension error. The effect of electrode tool wear is investigated by electrical dressing experiments, and the electrode tool wear compensation model is established based on the experimental results which show that the value of wear ratio coefficient K' tends to be constant with the increasing of the feed length of electrode and the mean value of K' is 0.156. Grinding experiments of fused silica are carried out on a test bench to evaluate the performance of the preparation and dressing method. The experimental results show that the surface roughness of the finished workpiece is 0.03 μm. The effect of the grinding parameter and dressing frequency on the surface roughness is investigated based on the measurement results of the surface roughness. This research provides an on-machine preparation and dressing method for ball-headed metal bonded diamond wheel used in the grinding of fused silica, which provides a solution to the tool setting method and the effect of electrode tool wear.

Machine Learning for Biological Trajectory Classification Applications

NASA Technical Reports Server (NTRS)

Sbalzarini, Ivo F.; Theriot, Julie; Koumoutsakos, Petros

2002-01-01

Machine-learning techniques, including clustering algorithms, support vector machines and hidden Markov models, are applied to the task of classifying trajectories of moving keratocyte cells. The different algorithms axe compared to each other as well as to expert and non-expert test persons, using concepts from signal-detection theory. The algorithms performed very well as compared to humans, suggesting a robust tool for trajectory classification in biological applications.

Flexible Conformable Clamps for a Machining Cell with Applications to Turbine Blade Machining.

DTIC Science & Technology

1983-05-01

PERIOD COVERED * FLEXIBLE CONFORMABLE CLAMPS FOR A MACHINING CELL Interim WITH APPLICATIONS TO TURBINE BLADE MACHINING 6. PERFORMING ORG. REPORT NUMBER...7. AuTmbR(s) 6. CONTRACT OR GRANT NUMBER(a) Eiki Kurokawa 3. PERFORMING ORGANIZATION NAME AND ADDRESS 10. PROGRAM ELE%4NTPROJECT. TASK Carnegie-Mellon...University AREA a WORK UhIT NUMBERS The Robotics Institute Pittsburgh, PA. 15213 II. CONTROLLING OFFICE NAME AND ADDRESS 12. REPORT DATE May 1983. 13

Scale effects and a method for similarity evaluation in micro electrical discharge machining

NASA Astrophysics Data System (ADS)

Liu, Qingyu; Zhang, Qinhe; Wang, Kan; Zhu, Guang; Fu, Xiuzhuo; Zhang, Jianhua

2016-08-01

Electrical discharge machining(EDM) is a promising non-traditional micro machining technology that offers a vast array of applications in the manufacturing industry. However, scale effects occur when machining at the micro-scale, which can make it difficult to predict and optimize the machining performances of micro EDM. A new concept of "scale effects" in micro EDM is proposed, the scale effects can reveal the difference in machining performances between micro EDM and conventional macro EDM. Similarity theory is presented to evaluate the scale effects in micro EDM. Single factor experiments are conducted and the experimental results are analyzed by discussing the similarity difference and similarity precision. The results show that the output results of scale effects in micro EDM do not change linearly with discharge parameters. The values of similarity precision of machining time significantly increase when scaling-down the capacitance or open-circuit voltage. It is indicated that the lower the scale of the discharge parameter, the greater the deviation of non-geometrical similarity degree over geometrical similarity degree, which means that the micro EDM system with lower discharge energy experiences more scale effects. The largest similarity difference is 5.34 while the largest similarity precision can be as high as 114.03. It is suggested that the similarity precision is more effective in reflecting the scale effects and their fluctuation than similarity difference. Consequently, similarity theory is suitable for evaluating the scale effects in micro EDM. This proposed research offers engineering values for optimizing the machining parameters and improving the machining performances of micro EDM.

Application of high speed machining technology in aviation

NASA Astrophysics Data System (ADS)

Bałon, Paweł; Szostak, Janusz; Kiełbasa, Bartłomiej; Rejman, Edward; Smusz, Robert

2018-05-01

Aircraft structures are exposed to many loads during their working lifespan. Every particular action made during a flight is composed of a series of air movements which generate various aircraft loads. The most rigorous requirement which modern aircraft structures must fulfill is to maintain their high durability and reliability. This requirement involves taking many restrictions into account during the aircraft design process. The most important factor is the structure's overall mass, which has a crucial impact on both utility properties and cost-effectiveness. This makes aircraft one of the most complex results of modern technology. Additionally, there is currently an increasing utilization of high strength aluminum alloys, which requires the implementation of new manufacturing processes. High Speed Machining technology (HSM) is currently one of the most important machining technologies used in the aviation industry, especially in the machining of aluminium alloys. The primary difference between HSM and other milling techniques is the ability to select cutting parameters - depth of the cut layer, feed rate, and cutting speed in order to simultaneously ensure high quality, precision of the machined surface, and high machining efficiency, all of which shorten the manufacturing process of the integral components. In this paper, the authors explain the implementation of the HSM method in integral aircraft constructions. It presents the method of the airframe manufacturing method, and the final results. The HSM method is compared to the previous method where all subcomponents were manufactured by bending and forming processes, and then, they were joined by riveting.

Scattering effects of machined optical surfaces

NASA Astrophysics Data System (ADS)

Thompson, Anita Kotha

1998-09-01

Optical fabrication is one of the most labor-intensive industries in existence. Lensmakers use pitch to affix glass blanks to metal chucks that hold the glass as they grind it with tools that have not changed much in fifty years. Recent demands placed on traditional optical fabrication processes in terms of surface accuracy, smoothnesses, and cost effectiveness has resulted in the exploitation of precision machining technology to develop a new generation of computer numerically controlled (CNC) optical fabrication equipment. This new kind of precision machining process is called deterministic microgrinding. The most conspicuous feature of optical surfaces manufactured by the precision machining processes (such as single-point diamond turning or deterministic microgrinding) is the presence of residual cutting tool marks. These residual tool marks exhibit a highly structured topography of periodic azimuthal or radial deterministic marks in addition to random microroughness. These distinct topographic features give rise to surface scattering effects that can significantly degrade optical performance. In this dissertation project we investigate the scattering behavior of machined optical surfaces and their imaging characteristics. In particular, we will characterize the residual optical fabrication errors and relate the resulting scattering behavior to the tool and machine parameters in order to evaluate and improve the deterministic microgrinding process. Other desired information derived from the investigation of scattering behavior is the optical fabrication tolerances necessary to satisfy specific image quality requirements. Optical fabrication tolerances are a major cost driver for any precision optical manufacturing technology. The derivation and control of the optical fabrication tolerances necessary for different applications and operating wavelength regimes will play a unique and central role in establishing deterministic microgrinding as a preferred and a

Fabrication of high precision metallic freeform mirrors with magnetorheological finishing (MRF)

NASA Astrophysics Data System (ADS)

Beier, Matthias; Scheiding, Sebastian; Gebhardt, Andreas; Loose, Roman; Risse, Stefan; Eberhardt, Ramona; Tünnermann, Andreas

2013-09-01

The fabrication of complex shaped metal mirrors for optical imaging is a classical application area of diamond machining techniques. Aspherical and freeform shaped optical components up to several 100 mm in diameter can be manufactured with high precision in an acceptable amount of time. However, applications are naturally limited to the infrared spectral region due to scatter losses for shorter wavelengths as a result of the remaining periodic diamond turning structure. Achieving diffraction limited performance in the visible spectrum demands for the application of additional polishing steps. Magnetorheological Finishing (MRF) is a powerful tool to improve figure and finish of complex shaped optics at the same time in a single processing step. The application of MRF as a figuring tool for precise metal mirrors is a nontrivial task since the technology was primarily developed for figuring and finishing a variety of other optical materials, such as glasses or glass ceramics. In the presented work, MRF is used as a figuring tool for diamond turned aluminum lightweight mirrors with electroless nickel plating. It is applied as a direct follow-up process after diamond machining of the mirrors. A high precision measurement setup, composed of an interferometer and an advanced Computer Generated Hologram with additional alignment features, allows for precise metrology of the freeform shaped optics in short measuring cycles. Shape deviations less than 150 nm PV / 20 nm rms are achieved reliably for freeform mirrors with apertures of more than 300 mm. Characterization of removable and induced spatial frequencies is carried out by investigating the Power Spectral Density.

Application of Machine Learning Approaches for Protein-protein Interactions Prediction.

PubMed

Zhang, Mengying; Su, Qiang; Lu, Yi; Zhao, Manman; Niu, Bing

2017-01-01

Proteomics endeavors to study the structures, functions and interactions of proteins. Information of the protein-protein interactions (PPIs) helps to improve our knowledge of the functions and the 3D structures of proteins. Thus determining the PPIs is essential for the study of the proteomics. In this review, in order to study the application of machine learning in predicting PPI, some machine learning approaches such as support vector machine (SVM), artificial neural networks (ANNs) and random forest (RF) were selected, and the examples of its applications in PPIs were listed. SVM and RF are two commonly used methods. Nowadays, more researchers predict PPIs by combining more than two methods. This review presents the application of machine learning approaches in predicting PPI. Many examples of success in identification and prediction in the area of PPI prediction have been discussed, and the PPIs research is still in progress. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Laser Induced Damage of Potassium Dihydrogen Phosphate (KDP) Optical Crystal Machined by Water Dissolution Ultra-Precision Polishing Method

PubMed Central

Gao, Hang; Wang, Xu; Guo, Dongming; Liu, Ziyuan

2018-01-01

Laser induced damage threshold (LIDT) is an important optical indicator for nonlinear Potassium Dihydrogen Phosphate (KDP) crystal used in high power laser systems. In this study, KDP optical crystals are initially machined with single point diamond turning (SPDT), followed by water dissolution ultra-precision polishing (WDUP) and then tested with 355 nm nanosecond pulsed-lasers. Power spectral density (PSD) analysis shows that WDUP process eliminates the laser-detrimental spatial frequencies band of micro-waviness on SPDT machined surface and consequently decreases its modulation effect on the laser beams. The laser test results show that LIDT of WDUP machined crystal improves and its stability has a significant increase by 72.1% compared with that of SPDT. Moreover, a subsequent ultrasonic assisted solvent cleaning process is suggested to have a positive effect on the laser performance of machined KDP crystal. Damage crater investigation indicates that the damage morphologies exhibit highly thermal explosion features of melted cores and brittle fractures of periphery material, which can be described with the classic thermal explosion model. The comparison result demonstrates that damage mechanisms for SPDT and WDUP machined crystal are the same and WDUP process reveals the real bulk laser resistance of KDP optical crystal by removing the micro-waviness and subsurface damage on SPDT machined surface. This improvement of WDUP method makes the LIDT more accurate and will be beneficial to the laser performance of KDP crystal. PMID:29534032

Depth indicator and stop aid machining to precise tolerances

NASA Technical Reports Server (NTRS)

Laverty, J. L.

1966-01-01

Attachment for machine tools provides a visual indication of the depth of cut and a positive stop to prevent overcutting. This attachment is used with drill presses, vertical milling machines, and jig borers.

Novel Transverse Flux Machine for Vehicle Traction Applications: Preprint

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

Wan, Z.; Ahmed, A.; Husain, I.

2015-04-02

A novel transverse flux machine topology for electric vehicle traction applications using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to the Halbach array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite-element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from finite element analysis (FEA) show that the motor achievedmore » comparable torque density to conventional rare-earth permanent magnet (PM) machines. This machine is a viable candidate for direct-drive applications with low cost and high torque density.« less

A Novel Transverse Flux Machine for Vehicle Traction Applications

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

Wan, Zhao; Ahmed, Adeeb; Husain, Iqbal

2015-10-05

A novel transverse flux machine topology for electric vehicle traction application using ferrite magnets is presented in this paper. The proposed transverse flux topology utilizes novel magnet arrangements in the rotor that are similar to Halbach-array to boost flux linkage; on the stator side, cores are alternately arranged around a pair of ring windings in each phase to make use of the entire rotor flux that eliminates end windings. Analytical design considerations and finite element methods are used for an optimized design of a scooter in-wheel motor. Simulation results from Finite Element Analysis (FEA) show the motor achieved comparable torquemore » density to conventional rare-earth permanent magnet machines. This machine is a viable candidate for direct drive applications with low cost and high torque density.« less

Applications of Machine Learning for Radiation Therapy.

PubMed

Arimura, Hidetaka; Nakamoto, Takahiro

2016-01-01

Radiation therapy has been highly advanced as image guided radiation therapy (IGRT) by making advantage of image engineering technologies. Recently, novel frameworks based on image engineering technologies as well as machine learning technologies have been studied for sophisticating the radiation therapy. In this review paper, the author introduces several researches of applications of machine learning for radiation therapy. For examples, a method to determine the threshold values for standardized uptake value (SUV) for estimation of gross tumor volume (GTV) in positron emission tomography (PET) images, an approach to estimate the multileaf collimator (MLC) position errors between treatment plans and radiation delivery time, and prediction frameworks for esophageal stenosis and radiation pneumonitis risk after radiation therapy are described. Finally, the author introduces seven issues that one should consider when applying machine learning models to radiation therapy.

Applications of machine learning in cancer prediction and prognosis.

PubMed

Cruz, Joseph A; Wishart, David S

2007-02-11

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to "learn" from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on "older" technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15-25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression.

Precision aerial application for site-specific rice crop management

USDA-ARS?s Scientific Manuscript database

Precision agriculture includes different technologies that allow agricultural professional to use information management tools to optimize agriculture production. The new technologies allow aerial application applicators to improve application accuracy and efficiency, which saves time and money for...

Precisely Tailored DNA Nanostructures and their Theranostic Applications.

PubMed

Zhu, Bing; Wang, Lihua; Li, Jiang; Fan, Chunhai

2017-12-01

A critical challenge in nanotechnology is the limited precision and controllability of the structural parameters, which brings about concerns in uniformity, reproducibility and performance. Self-assembled DNA nanostructures, as a newly emerged type of nano-biomaterials, possess low-nanometer precision, excellent programmability and addressability. They can precisely arrange various molecules and materials to form spatially ordered complex, resulting in unambiguous physical or chemical properties. Because of these, DNA nanostructures have shown great promise in numerous biomedical theranostic applications. In this account, we briefly review the history and advances on construction of DNA nanoarchitectures and superstructures with accurate structural parameters. We focus on recent progress in exploiting these DNA nanostructures as platforms for quantitative biosensing, intracellular diagnosis, imaging, and smart drug delivery. We also discuss key challenges in practical applications. © 2017 The Chemical Society of Japan & Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Prompt and Precise Prototyping

NASA Technical Reports Server (NTRS)

2003-01-01

For Sanders Design International, Inc., of Wilton, New Hampshire, every passing second between the concept and realization of a product is essential to succeed in the rapid prototyping industry where amongst heavy competition, faster time-to-market means more business. To separate itself from its rivals, Sanders Design aligned with NASA's Marshall Space Flight Center to develop what it considers to be the most accurate rapid prototyping machine for fabrication of extremely precise tooling prototypes. The company's Rapid ToolMaker System has revolutionized production of high quality, small-to-medium sized prototype patterns and tooling molds with an exactness that surpasses that of computer numerically-controlled (CNC) machining devices. Created with funding and support from Marshall under a Small Business Innovation Research (SBIR) contract, the Rapid ToolMaker is a dual-use technology with applications in both commercial and military aerospace fields. The advanced technology provides cost savings in the design and manufacturing of automotive, electronic, and medical parts, as well as in other areas of consumer interest, such as jewelry and toys. For aerospace applications, the Rapid ToolMaker enables fabrication of high-quality turbine and compressor blades for jet engines on unmanned air vehicles, aircraft, and missiles.

Artificial Intelligence in Precision Cardiovascular Medicine.

PubMed

Krittanawong, Chayakrit; Zhang, HongJu; Wang, Zhen; Aydar, Mehmet; Kitai, Takeshi

2017-05-30

Artificial intelligence (AI) is a field of computer science that aims to mimic human thought processes, learning capacity, and knowledge storage. AI techniques have been applied in cardiovascular medicine to explore novel genotypes and phenotypes in existing diseases, improve the quality of patient care, enable cost-effectiveness, and reduce readmission and mortality rates. Over the past decade, several machine-learning techniques have been used for cardiovascular disease diagnosis and prediction. Each problem requires some degree of understanding of the problem, in terms of cardiovascular medicine and statistics, to apply the optimal machine-learning algorithm. In the near future, AI will result in a paradigm shift toward precision cardiovascular medicine. The potential of AI in cardiovascular medicine is tremendous; however, ignorance of the challenges may overshadow its potential clinical impact. This paper gives a glimpse of AI's application in cardiovascular clinical care and discusses its potential role in facilitating precision cardiovascular medicine. Copyright © 2017 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.

Studies of machinable ceramics for dental applications. 1. Color analysis.

PubMed

Taira, M; Wakasa, K; Yamaki, M; Tanaka, N; Shintani, H

1989-12-01

Machinable ceramics that can be cut and even lathed have recently been developed in industry. As a first step in evaluating the feasibility of such ceramics in dentistry, eight machinable ceramics were examined for color using the Vita shade guide and a chroma-meter reflectance instrument. We discovered that the studied machinable ceramics varied significantly from the Vita shade guide by the color difference vector, delta E. These machinable ceramics appeared very white and strongly opaque due to their high brightness (L*) values. For intra-oral applications, we expect that L* values of machinable ceramics will be reduced by modification of their microstructures, including their matrix and dispersed phases, while their excellent machinability due to the cleavage of dispersed crystals should be retained.

Application of Electro Chemical Machining for materials used in extreme conditions

NASA Astrophysics Data System (ADS)

Pandilov, Z.

2018-03-01

Electro-Chemical Machining (ECM) is the generic term for a variety of electrochemical processes. ECM is used to machine work pieces from metal and metal alloys irrespective of their hardness, strength or thermal properties, through the anodic dissolution, in aerospace, automotive, construction, medical equipment, micro-systems and power supply industries. The Electro Chemical Machining is extremely suitable for machining of materials used in extreme conditions. General overview of the Electro-Chemical Machining and its application for different materials used in extreme conditions is presented.

Precision wildlife medicine: applications of the human-centred precision medicine revolution to species conservation.

PubMed

Whilde, Jenny; Martindale, Mark Q; Duffy, David J

2017-05-01

The current species extinction crisis is being exacerbated by an increased rate of emergence of epizootic disease. Human-induced factors including habitat degradation, loss of biodiversity and wildlife population reductions resulting in reduced genetic variation are accelerating disease emergence. Novel, efficient and effective approaches are required to combat these epizootic events. Here, we present the case for the application of human precision medicine approaches to wildlife medicine in order to enhance species conservation efforts. We consider how the precision medicine revolution, coupled with the advances made in genomics, may provide a powerful and feasible approach to identifying and treating wildlife diseases in a targeted, effective and streamlined manner. A number of case studies of threatened species are presented which demonstrate the applicability of precision medicine to wildlife conservation, including sea turtles, amphibians and Tasmanian devils. These examples show how species conservation could be improved by using precision medicine techniques to determine novel treatments and management strategies for the specific medical conditions hampering efforts to restore population levels. Additionally, a precision medicine approach to wildlife health has in turn the potential to provide deeper insights into human health and the possibility of stemming and alleviating the impacts of zoonotic diseases. The integration of the currently emerging Precision Medicine Initiative with the concepts of EcoHealth (aiming for sustainable health of people, animals and ecosystems through transdisciplinary action research) and One Health (recognizing the intimate connection of humans, animal and ecosystem health and addressing a wide range of risks at the animal-human-ecosystem interface through a coordinated, collaborative, interdisciplinary approach) has great potential to deliver a deeper and broader interdisciplinary-based understanding of both wildlife and human

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1989-01-01

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

Applications of Machine Learning in Cancer Prediction and Prognosis

PubMed Central

Cruz, Joseph A.; Wishart, David S.

2006-01-01

Machine learning is a branch of artificial intelligence that employs a variety of statistical, probabilistic and optimization techniques that allows computers to “learn” from past examples and to detect hard-to-discern patterns from large, noisy or complex data sets. This capability is particularly well-suited to medical applications, especially those that depend on complex proteomic and genomic measurements. As a result, machine learning is frequently used in cancer diagnosis and detection. More recently machine learning has been applied to cancer prognosis and prediction. This latter approach is particularly interesting as it is part of a growing trend towards personalized, predictive medicine. In assembling this review we conducted a broad survey of the different types of machine learning methods being used, the types of data being integrated and the performance of these methods in cancer prediction and prognosis. A number of trends are noted, including a growing dependence on protein biomarkers and microarray data, a strong bias towards applications in prostate and breast cancer, and a heavy reliance on “older” technologies such artificial neural networks (ANNs) instead of more recently developed or more easily interpretable machine learning methods. A number of published studies also appear to lack an appropriate level of validation or testing. Among the better designed and validated studies it is clear that machine learning methods can be used to substantially (15–25%) improve the accuracy of predicting cancer susceptibility, recurrence and mortality. At a more fundamental level, it is also evident that machine learning is also helping to improve our basic understanding of cancer development and progression. PMID:19458758

Precision machining of optical surfaces with subaperture correction technologies MRF and IBF

NASA Astrophysics Data System (ADS)

Schmelzer, Olaf; Feldkamp, Roman

2015-10-01

Precision optical elements are used in a wide range of technical instrumentations. Many optical systems e.g. semiconductor inspection modules, laser heads for laser material processing or high end movie cameras, contain precision optics even aspherical or freeform surfaces. Critical parameters for such systems are wavefront error, image field curvature or scattered light. Following these demands the lens parameters are also critical concerning power and RMSi of the surface form error and micro roughness. How can we reach these requirements? The emphasis of this discussion is set on the application of subaperture correction technologies in the fabrication of high-end aspheres and free-forms. The presentation focuses on the technology chain necessary for the production of high-precision aspherical optical components and the characterization of the applied subaperture finishing tools MRF (magneto-rheological finishing) and IBF (ion beam figuring). These technologies open up the possibility of improving the performance of optical systems.

Applications of Support Vector Machines In Chemo And Bioinformatics

NASA Astrophysics Data System (ADS)

Jayaraman, V. K.; Sundararajan, V.

2010-10-01

Conventional linear & nonlinear tools for classification, regression & data driven modeling are being replaced on a rapid scale by newer techniques & tools based on artificial intelligence and machine learning. While the linear techniques are not applicable for inherently nonlinear problems, newer methods serve as attractive alternatives for solving real life problems. Support Vector Machine (SVM) classifiers are a set of universal feed-forward network based classification algorithms that have been formulated from statistical learning theory and structural risk minimization principle. SVM regression closely follows the classification methodology. In this work recent applications of SVM in Chemo & Bioinformatics will be described with suitable illustrative examples.

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning.

PubMed

Lenchik, Leon; Boutin, Robert D

2018-07-01

As populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunistic imaging, precision medicine, and machine learning. This review focuses on the state-of-the-art imaging of sarcopenia and provides context for such imaging by discussing the epidemiology, pathophysiology, consequences, and future directions in the field of sarcopenia. Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Material Choice for spindle of machine tools

NASA Astrophysics Data System (ADS)

Gouasmi, S.; Merzoug, B.; Abba, G.; Kherredine, L.

2012-02-01

The requirements of contemporary industry and the flashing development of modern sciences impose restrictions on the majority of the elements of machines; the resulting financial constraints can be satisfied by a better output of the production equipment. As for those concerning the design, the resistance and the correct operation of the product, these require the development of increasingly precise parts, therefore the use of increasingly powerful tools [5]. The precision of machining and the output of the machine tools are generally determined by the precision of rotation of the spindle, indeed, more this one is large more the dimensions to obtain are in the zone of tolerance and the defects of shape are minimized. During the development of the machine tool, the spindle which by definition is a rotating shaft receiving and transmitting to the work piece or the cutting tool the rotational movement, must be designed according to certain optimal parameters to be able to ensure the precision required. This study will be devoted to the choice of the material of the spindle fulfilling the imposed requirements of precision.

Characteristics for electrochemical machining with nanoscale voltage pulses.

PubMed

Lee, E S; Back, S Y; Lee, J T

2009-06-01

Electrochemical machining has traditionally been used in highly specialized fields, such as those of the aerospace and defense industries. It is now increasingly being applied in other industries, where parts with difficult-to-cut material, complex geometry and tribology, and devices of nanoscale and microscale are required. Electric characteristic plays a principal function role in and chemical characteristic plays an assistant function role in electrochemical machining. Therefore, essential parameters in electrochemical machining can be described current density, machining time, inter-electrode gap size, electrolyte, electrode shape etc. Electrochemical machining provides an economical and effective method for machining high strength, high tension and heat-resistant materials into complex shapes such as turbine blades of titanium and aluminum alloys. The application of nanoscale voltage pulses between a tool electrode and a workpiece in an electrochemical environment allows the three-dimensional machining of conducting materials with sub-micrometer precision. In this study, micro probe are developed by electrochemical etching and micro holes are manufactured using these micro probe as tool electrodes. Micro holes and microgroove can be accurately achieved by using nanoscale voltages pulses.

Challenges in mold manufacturing for high precision molded diffractive optical elements

NASA Astrophysics Data System (ADS)

Pongs, Guido; Bresseler, Bernd; Schweizer, Klaus; Bergs, Thomas

2016-09-01

Isothermal precision glass molding of imaging optics is the key technology for mass production of precise optical elements. Especially for numerous consumer applications (e.g. digital cameras, smart phones, …), high precision glass molding is applied for the manufacturing of aspherical lenses. The usage of diffractive optical elements (DOEs) can help to further reduce the number of lenses in the optical systems which will lead to a reduced weight of hand-held optical devices. But today the application of molded glass DOEs is limited due to the technological challenges in structuring the mold surfaces. Depending on the application submicrometer structures are required on the mold surface. Furthermore these structures have to be replicated very precisely to the glass lens surface. Especially the micro structuring of hard and brittle mold materials such as Tungsten Carbide is very difficult and not established. Thus a multitude of innovative approaches using diffractive optical elements cannot be realized. Aixtooling has investigated in different mold materials and different suitable machining technologies for the micro- and sub-micrometer structuring of mold surfaces. The focus of the work lays on ultra-precision grinding to generate the diffractive pattern on the mold surfaces. This paper presents the latest achievements in diffractive structuring of Tungsten Carbide mold surfaces by ultra-precision grinding.

Artificial intelligence, physiological genomics, and precision medicine.

PubMed

Williams, Anna Marie; Liu, Yong; Regner, Kevin R; Jotterand, Fabrice; Liu, Pengyuan; Liang, Mingyu

2018-04-01

Big data are a major driver in the development of precision medicine. Efficient analysis methods are needed to transform big data into clinically-actionable knowledge. To accomplish this, many researchers are turning toward machine learning (ML), an approach of artificial intelligence (AI) that utilizes modern algorithms to give computers the ability to learn. Much of the effort to advance ML for precision medicine has been focused on the development and implementation of algorithms and the generation of ever larger quantities of genomic sequence data and electronic health records. However, relevance and accuracy of the data are as important as quantity of data in the advancement of ML for precision medicine. For common diseases, physiological genomic readouts in disease-applicable tissues may be an effective surrogate to measure the effect of genetic and environmental factors and their interactions that underlie disease development and progression. Disease-applicable tissue may be difficult to obtain, but there are important exceptions such as kidney needle biopsy specimens. As AI continues to advance, new analytical approaches, including those that go beyond data correlation, need to be developed and ethical issues of AI need to be addressed. Physiological genomic readouts in disease-relevant tissues, combined with advanced AI, can be a powerful approach for precision medicine for common diseases.

Universal precision sine bar attachment

NASA Technical Reports Server (NTRS)

Mann, Franklin D. (Inventor)

1989-01-01

This invention relates to an attachment for a sine bar which can be used to perform measurements during lathe operations or other types of machining operations. The attachment can be used for setting precision angles on vises, dividing heads, rotary tables and angle plates. It can also be used in the inspection of machined parts, when close tolerances are required, and in the layout of precision hardware. The novelty of the invention is believed to reside in a specific versatile sine bar attachment for measuring a variety of angles on a number of different types of equipment.

Applications of Database Machines in Library Systems.

ERIC Educational Resources Information Center

Salmon, Stephen R.

1984-01-01

Characteristics and advantages of database machines are summarized and their applications to library functions are described. The ability to attach multiple hosts to the same database and flexibility in choosing operating and database management systems for different functions without loss of access to common database are noted. (EJS)

Technology of high-speed combined machining with brush electrode

NASA Astrophysics Data System (ADS)

Kirillov, O. N.; Smolentsev, V. P.; Yukhnevich, S. S.

2018-03-01

The new method was proposed for high-precision dimensional machining with a brush electrode when the true position of bundles of metal wire is adjusted by means of creating controlled centrifugal forces appeared due to the increased frequency of rotation of a tool. There are the ultimate values of circumferential velocity at which the bundles are pressed against a machined area of a workpiece in a stable manner despite the profile of the machined surface and variable stock of the workpiece. The special aspects of design of processing procedures for finishing standard parts, including components of products with low rigidity, are disclosed. The methodology of calculation and selection of processing modes which allow one to produce high-precision details and to provide corresponding surface roughness required to perform finishing operations (including the preparation of a surface for metal deposition) is presented. The production experience concerned with the use of high-speed combined machining with an unshaped tool electrode in knowledge-intensive branches of the machine-building industry for different types of production is analyzed. It is shown that the implementation of high-speed dimensional machining with an unshaped brush electrode allows one to expand the field of use of the considered process due to the application of a multipurpose tool in the form of a metal brush, as well as to obtain stable results of finishing and to provide the opportunities for long-term operation of the equipment without its changeover and readjustment.

Generation of gear tooth surfaces by application of CNC machines

NASA Technical Reports Server (NTRS)

Litvin, F. L.; Chen, N. X.

1994-01-01

This study will demonstrate the importance of application of computer numerically controlled (CNC) machines in generation of gear tooth surfaces with new topology. This topology decreases gear vibration and will extend the gear capacity and service life. A preliminary investigation by a tooth contact analysis (TCA) program has shown that gear tooth surfaces in line contact (for instance, involute helical gears with parallel axes, worm gear drives with cylindrical worms, etc.) are very sensitive to angular errors of misalignment that cause edge contact and an unfavorable shape of transmission errors and vibration. The new topology of gear tooth surfaces is based on the localization of bearing contact, and the synthesis of a predesigned parabolic function of transmission errors that is able to absorb a piecewise linear function of transmission errors caused by gear misalignment. The report will describe the following topics: description of kinematics of CNC machines with six degrees of freedom that can be applied for generation of gear tooth surfaces with new topology. A new method for grinding of gear tooth surfaces by a cone surface or surface of revolution based on application of CNC machines is described. This method provides an optimal approximation of the ground surface to the given one. This method is especially beneficial when undeveloped ruled surfaces are to be ground. Execution of motions of the CNC machine is also described. The solution to this problem can be applied as well for the transfer of machine tool settings from a conventional generator to the CNC machine. The developed theory required the derivation of a modified equation of meshing based on application of the concept of space curves, space curves represented on surfaces, geodesic curvature, surface torsion, etc. Condensed information on these topics of differential geometry is provided as well.

Drilling Precise Orifices and Slots

NASA Technical Reports Server (NTRS)

Richards, C. W.; Seidler, J. E.

1983-01-01

Reaction control thrustor injector requires precisely machined orifices and slots. Tooling setup consists of rotary table, numerical control system and torque sensitive drill press. Components used to drill oxidizer orifices. Electric discharge machine drills fuel-feed orifices. Device automates production of identical parts so several are completed in less time than previously.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1991-01-01

The Theory of Intelligent Machines proposes a hierarchical organization for the functions of an autonomous robot based on the Principle of Increasing Precision With Decreasing Intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed in recent years. A computer architecture that implements the lower two levels of the intelligent machine is presented. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Details of Execution Level controllers for motion and vision systems are addressed, as well as the Petri net transducer software used to implement Coordination Level functions. Extensions to UNIX and VxWorks operating systems which enable the development of a heterogeneous, distributed application are described. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Machine Tool Advanced Skills Technology (MAST). Common Ground: Toward a Standards-Based Training System for the U.S. Machine Tool and Metal Related Industries. Volume 15: Administrative Information, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

ERIC Educational Resources Information Center

Texas State Technical Coll., Waco.

This volume developed by the Machine Tool Advanced Skill Technology (MAST) program contains key administrative documents and provides additional sources for machine tool and precision manufacturing information and important points of contact in the industry. The document contains the following sections: a foreword; grant award letter; timeline for…

The application of machine learning techniques in the clinical drug therapy.

PubMed

Meng, Huan-Yu; Jin, Wan-Lin; Yan, Cheng-Kai; Yang, Huan

2018-05-25

The development of a novel drug is an extremely complicated process that includes the target identification, design and manufacture, and proper therapy of the novel drug, as well as drug dose selection, drug efficacy evaluation, and adverse drug reaction control. Due to the limited resources, high costs, long duration, and low hit-to-lead ratio in the development of pharmacogenetics and computer technology, machine learning techniques have assisted novel drug development and have gradually received more attention by researchers. According to current research, machine learning techniques are widely applied in the process of the discovery of new drugs and novel drug targets, the decision surrounding proper therapy and drug dose, and the prediction of drug efficacy and adverse drug reactions. In this article, we discussed the history, workflow, and advantages and disadvantages of machine learning techniques in the processes mentioned above. Although the advantages of machine learning techniques are fairly obvious, the application of machine learning techniques is currently limited. With further research, the application of machine techniques in drug development could be much more widespread and could potentially be one of the major methods used in drug development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

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

e-Learning Application for Machine Maintenance Process using Iterative Method in XYZ Company

NASA Astrophysics Data System (ADS)

Nurunisa, Suaidah; Kurniawati, Amelia; Pramuditya Soesanto, Rayinda; Yunan Kurnia Septo Hediyanto, Umar

2016-02-01

XYZ Company is a company based on manufacturing part for airplane, one of the machine that is categorized as key facility in the company is Millac 5H6P. As a key facility, the machines should be assured to work well and in peak condition, therefore, maintenance process is needed periodically. From the data gathering, it is known that there are lack of competency from the maintenance staff to maintain different type of machine which is not assigned by the supervisor, this indicate that knowledge which possessed by maintenance staff are uneven. The purpose of this research is to create knowledge-based e-learning application as a realization from externalization process in knowledge transfer process to maintain the machine. The application feature are adjusted for maintenance purpose using e-learning framework for maintenance process, the content of the application support multimedia for learning purpose. QFD is used in this research to understand the needs from user. The application is built using moodle with iterative method for software development cycle and UML Diagram. The result from this research is e-learning application as sharing knowledge media for maintenance staff in the company. From the test, it is known that the application make maintenance staff easy to understand the competencies.

Multiple man-machine interfaces

NASA Technical Reports Server (NTRS)

Stanton, L.; Cook, C. W.

1981-01-01

The multiple man machine interfaces inherent in military pilot training, their social implications, and the issue of possible negative feedback were explored. Modern technology has produced machines which can see, hear, and touch with greater accuracy and precision than human beings. Consequently, the military pilot is more a systems manager, often doing battle against a target he never sees. It is concluded that unquantifiable human activity requires motivation that is not intrinsic in a machine.

Precision displacement reference system

DOEpatents

Bieg, Lothar F.; Dubois, Robert R.; Strother, Jerry D.

2000-02-22

A precision displacement reference system is described, which enables real time accountability over the applied displacement feedback system to precision machine tools, positioning mechanisms, motion devices, and related operations. As independent measurements of tool location is taken by a displacement feedback system, a rotating reference disk compares feedback counts with performed motion. These measurements are compared to characterize and analyze real time mechanical and control performance during operation.

Video-rate or high-precision: a flexible range imaging camera

NASA Astrophysics Data System (ADS)

Dorrington, Adrian A.; Cree, Michael J.; Carnegie, Dale A.; Payne, Andrew D.; Conroy, Richard M.; Godbaz, John P.; Jongenelen, Adrian P. P.

2008-02-01

A range imaging camera produces an output similar to a digital photograph, but every pixel in the image contains distance information as well as intensity. This is useful for measuring the shape, size and location of objects in a scene, hence is well suited to certain machine vision applications. Previously we demonstrated a heterodyne range imaging system operating in a relatively high resolution (512-by-512) pixels and high precision (0.4 mm best case) configuration, but with a slow measurement rate (one every 10 s). Although this high precision range imaging is useful for some applications, the low acquisition speed is limiting in many situations. The system's frame rate and length of acquisition is fully configurable in software, which means the measurement rate can be increased by compromising precision and image resolution. In this paper we demonstrate the flexibility of our range imaging system by showing examples of high precision ranging at slow acquisition speeds and video-rate ranging with reduced ranging precision and image resolution. We also show that the heterodyne approach and the use of more than four samples per beat cycle provides better linearity than the traditional homodyne quadrature detection approach. Finally, we comment on practical issues of frame rate and beat signal frequency selection.

Quantitative approaches to energy and glucose homeostasis: machine learning and modelling for precision understanding and prediction

PubMed Central

Murphy, Kevin G.; Jones, Nick S.

2018-01-01

Obesity is a major global public health problem. Understanding how energy homeostasis is regulated, and can become dysregulated, is crucial for developing new treatments for obesity. Detailed recording of individual behaviour and new imaging modalities offer the prospect of medically relevant models of energy homeostasis that are both understandable and individually predictive. The profusion of data from these sources has led to an interest in applying machine learning techniques to gain insight from these large, relatively unstructured datasets. We review both physiological models and machine learning results across a diverse range of applications in energy homeostasis, and highlight how modelling and machine learning can work together to improve predictive ability. We collect quantitative details in a comprehensive mathematical supplement. We also discuss the prospects of forecasting homeostatic behaviour and stress the importance of characterizing stochasticity within and between individuals in order to provide practical, tailored forecasts and guidance to combat the spread of obesity. PMID:29367240

Precision manometer gauge

DOEpatents

McPherson, Malcolm J.; Bellman, Robert A.

1984-01-01

A precision manometer gauge which locates a zero height and a measured height of liquid using an open tube in communication with a reservoir adapted to receive the pressure to be measured. The open tube has a reference section carried on a positioning plate which is moved vertically with machine tool precision. Double scales are provided to read the height of the positioning plate accurately, the reference section being inclined for accurate meniscus adjustment, and means being provided to accurately locate a zero or reference position.

Precision manometer gauge

DOEpatents

McPherson, M.J.; Bellman, R.A.

1982-09-27

A precision manometer gauge which locates a zero height and a measured height of liquid using an open tube in communication with a reservoir adapted to receive the pressure to be measured. The open tube has a reference section carried on a positioning plate which is moved vertically with machine tool precision. Double scales are provided to read the height of the positioning plate accurately, the reference section being inclined for accurate meniscus adjustment, and means being provided to accurately locate a zero or reference position.

Sine-Bar Attachment For Machine Tools

NASA Technical Reports Server (NTRS)

Mann, Franklin D.

1988-01-01

Sine-bar attachment for collets, spindles, and chucks helps machinists set up quickly for precise angular cuts that require greater precision than provided by graduations of machine tools. Machinist uses attachment to index head, carriage of milling machine or lathe relative to table or turning axis of tool. Attachment accurate to 1 minute or arc depending on length of sine bar and precision of gauge blocks in setup. Attachment installs quickly and easily on almost any type of lathe or mill. Requires no special clamps or fixtures, and eliminates many trial-and-error measurements. More stable than improvised setups and not jarred out of position readily.

Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

PubMed

Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

2017-08-15

Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

Ultrashort pulse laser machining of metals and alloys

DOEpatents

Perry, Michael D.; Stuart, Brent C.

2003-09-16

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

Matching brain-machine interface performance to space applications.

PubMed

Citi, Luca; Tonet, Oliver; Marinelli, Martina

2009-01-01

A brain-machine interface (BMI) is a particular class of human-machine interface (HMI). BMIs have so far been studied mostly as a communication means for people who have little or no voluntary control of muscle activity. For able-bodied users, such as astronauts, a BMI would only be practical if conceived as an augmenting interface. A method is presented for pointing out effective combinations of HMIs and applications of robotics and automation to space. Latency and throughput are selected as performance measures for a hybrid bionic system (HBS), that is, the combination of a user, a device, and a HMI. We classify and briefly describe HMIs and space applications and then compare the performance of classes of interfaces with the requirements of classes of applications, both in terms of latency and throughput. Regions of overlap correspond to effective combinations. Devices requiring simpler control, such as a rover, a robotic camera, or environmental controls are suitable to be driven by means of BMI technology. Free flyers and other devices with six degrees of freedom can be controlled, but only at low-interactivity levels. More demanding applications require conventional interfaces, although they could be controlled by BMIs once the same levels of performance as currently recorded in animal experiments are attained. Robotic arms and manipulators could be the next frontier for noninvasive BMIs. Integrating smart controllers in HBSs could improve interactivity and boost the use of BMI technology in space applications.

Precision and manufacturing at the Lawrence Livermore National Laboratory

NASA Technical Reports Server (NTRS)

Saito, Theodore T.; Wasley, Richard J.; Stowers, Irving F.; Donaldson, Robert R.; Thompson, Daniel C.

1994-01-01

Precision Engineering is one of the Lawrence Livermore National Laboratory's core strengths. This paper discusses the past and present current technology transfer efforts of LLNL's Precision Engineering program and the Livermore Center for Advanced Manufacturing and Productivity (LCAMP). More than a year ago the Precision Machine Commercialization project embodied several successful methods of transferring high technology from the National Laboratories to industry. Currently, LCAMP has already demonstrated successful technology transfer and is involved in a broad spectrum of current programs. In addition, this paper discusses other technologies ripe for future transition including the Large Optics Diamond Turning Machine.

Precision and manufacturing at the Lawrence Livermore National Laboratory

NASA Astrophysics Data System (ADS)

Saito, Theodore T.; Wasley, Richard J.; Stowers, Irving F.; Donaldson, Robert R.; Thompson, Daniel C.

1994-02-01

Precision Engineering is one of the Lawrence Livermore National Laboratory's core strengths. This paper discusses the past and present current technology transfer efforts of LLNL's Precision Engineering program and the Livermore Center for Advanced Manufacturing and Productivity (LCAMP). More than a year ago the Precision Machine Commercialization project embodied several successful methods of transferring high technology from the National Laboratories to industry. Currently, LCAMP has already demonstrated successful technology transfer and is involved in a broad spectrum of current programs. In addition, this paper discusses other technologies ripe for future transition including the Large Optics Diamond Turning Machine.

Mining the Galaxy Zoo Database: Machine Learning Applications

NASA Astrophysics Data System (ADS)

Borne, Kirk D.; Wallin, J.; Vedachalam, A.; Baehr, S.; Lintott, C.; Darg, D.; Smith, A.; Fortson, L.

2010-01-01

The new Zooniverse initiative is addressing the data flood in the sciences through a transformative partnership between professional scientists, volunteer citizen scientists, and machines. As part of this project, we are exploring the application of machine learning techniques to data mining problems associated with the large and growing database of volunteer science results gathered by the Galaxy Zoo citizen science project. We will describe the basic challenge, some machine learning approaches, and early results. One of the motivators for this study is the acquisition (through the Galaxy Zoo results database) of approximately 100 million classification labels for roughly one million galaxies, yielding a tremendously large and rich set of training examples for improving automated galaxy morphological classification algorithms. In our first case study, the goal is to learn which morphological and photometric features in the Sloan Digital Sky Survey (SDSS) database correlate most strongly with user-selected galaxy morphological class. As a corollary to this study, we are also aiming to identify which galaxy parameters in the SDSS database correspond to galaxies that have been the most difficult to classify (based upon large dispersion in their volunter-provided classifications). Our second case study will focus on similar data mining analyses and machine leaning algorithms applied to the Galaxy Zoo catalog of merging and interacting galaxies. The outcomes of this project will have applications in future large sky surveys, such as the LSST (Large Synoptic Survey Telescope) project, which will generate a catalog of 20 billion galaxies and will produce an additional astronomical alert database of approximately 100 thousand events each night for 10 years -- the capabilities and algorithms that we are exploring will assist in the rapid characterization and classification of such massive data streams. This research has been supported in part through NSF award #0941610.

Micro Machining Enhances Precision Fabrication

NASA Technical Reports Server (NTRS)

2007-01-01

Advanced thermal systems developed for the Space Station Freedom project are now in use on the International Space Station. These thermal systems employ evaporative ammonia as their coolant, and though they employ the same series of chemical reactions as terrestrial refrigerators, the space-bound coolers are significantly smaller. Two Small Business Innovation Research (SBIR) contracts between Creare Inc. of Hanover, NH and Johnson Space Center developed an ammonia evaporator for thermal management systems aboard Freedom. The principal investigator for Creare Inc., formed Mikros Technologies Inc. to commercialize the work. Mikros Technologies then developed an advanced form of micro-electrical discharge machining (micro-EDM) to make tiny holes in the ammonia evaporator. Mikros Technologies has had great success applying this method to the fabrication of micro-nozzle array systems for industrial ink jet printing systems. The company is currently the world leader in fabrication of stainless steel micro-nozzles for this market, and in 2001 the company was awarded two SBIR research contracts from Goddard Space Flight Center to advance micro-fabrication and high-performance thermal management technologies.

High-Performance AC Power Source by Applying Robust Stability Control Technology for Precision Material Machining

NASA Astrophysics Data System (ADS)

Chang, En-Chih

2018-02-01

This paper presents a high-performance AC power source by applying robust stability control technology for precision material machining (PMM). The proposed technology associates the benefits of finite-time convergent sliding function (FTCSF) and firefly optimization algorithm (FOA). The FTCSF maintains the robustness of conventional sliding mode, and simultaneously speeds up the convergence speed of the system state. Unfortunately, when a highly nonlinear loading is applied, the chatter will occur. The chatter results in high total harmonic distortion (THD) output voltage of AC power source, and even deteriorates the stability of PMM. The FOA is therefore used to remove the chatter, and the FTCSF still preserves finite system-state convergence time. By combining FTCSF with FOA, the AC power source of PMM can yield good steady-state and transient performance. Experimental results are performed in support of the proposed technology.

A novel AFM-based 5-axis nanoscale machine tool for fabrication of nanostructures on a micro ball

NASA Astrophysics Data System (ADS)

Geng, Yanquan; Wang, Yuzhang; Yan, Yongda; Zhao, Xuesen

2017-11-01

This paper presents a novel atomic force microscopy (AFM)-based 5-axis nanoscale machine tool developed to fabricate nanostructures on different annuli of the micro ball. Different nanostructures can be obtained by combining the scratching trajectory of the AFM tip with the movement of the high precision air-bearing spindle. The center of the micro ball is aligned to be coincided with the gyration center of the high precision to guarantee the machining process during the rotating of the air-bearing spindle. Processing on different annuli of the micro ball is achieved by controlling the distance between the center of the micro ball and the rotation center of the AFM head. Nanostructures including square cavities, circular cavities, triangular cavities, and an annular nanochannel are machined successfully on the three different circumferences of a micro ball with a diameter of 1500 μm. Moreover, the influences of the error motions of the high precision air-bearing spindle and the eccentric between the micro ball and the gyration center of the high precision air-bearing spindle on the processing position error on the micro ball are also investigated. This proposed machining method has the potential to prepare the inertial confinement fusion target with the expected dimension defects, which would advance the application of the AFM tip-based nanomachining approach.

Security Aspects of Smart Cards vs. Embedded Security in Machine-to-Machine (M2M) Advanced Mobile Network Applications

NASA Astrophysics Data System (ADS)

Meyerstein, Mike; Cha, Inhyok; Shah, Yogendra

The Third Generation Partnership Project (3GPP) standardisation group currently discusses advanced applications of mobile networks such as Machine-to-Machine (M2M) communication. Several security issues arise in these contexts which warrant a fresh look at mobile networks’ security foundations, resting on smart cards. This paper contributes a security/efficiency analysis to this discussion and highlights the role of trusted platform technology to approach these issues.

Machine learning for epigenetics and future medical applications

PubMed Central

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

2017-01-01

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

Machine learning for epigenetics and future medical applications.

PubMed

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

2017-07-03

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

A Boltzmann machine for the organization of intelligent machines

NASA Technical Reports Server (NTRS)

Moed, Michael C.; Saridis, George N.

1990-01-01

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

Precision Machining Technologies. Occupational Competency Analysis Profile.

ERIC Educational Resources Information Center

Ohio State Univ., Columbus. Vocational Instructional Materials Lab.

This Occupational Competency Analysis Profile (OCAP), which is one of a series of OCAPs developed to identify the skills that Ohio employers deem necessary to entering a given occupation/occupational area, lists the occupational, academic, and employability skills required of individuals entering the occupation of precision machinist. The…

Machine-learning techniques for fast and accurate feature localization in holograms of colloidal particles

NASA Astrophysics Data System (ADS)

Hannel, Mark D.; Abdulali, Aidan; O'Brien, Michael; Grier, David G.

2018-06-01

Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.

Recent developments in machine learning applications in landslide susceptibility mapping

NASA Astrophysics Data System (ADS)

Lun, Na Kai; Liew, Mohd Shahir; Matori, Abdul Nasir; Zawawi, Noor Amila Wan Abdullah

2017-11-01

While the prediction of spatial distribution of potential landslide occurrences is a primary interest in landslide hazard mitigation, it remains a challenging task. To overcome the scarceness of complete, sufficiently detailed geomorphological attributes and environmental conditions, various machine-learning techniques are increasingly applied to effectively map landslide susceptibility for large regions. Nevertheless, limited review papers are devoted to this field, particularly on the various domain specific applications of machine learning techniques. Available literature often report relatively good predictive performance, however, papers discussing the limitations of each approaches are quite uncommon. The foremost aim of this paper is to narrow these gaps in literature and to review up-to-date machine learning and ensemble learning techniques applied in landslide susceptibility mapping. It provides new readers an introductory understanding on the subject matter and researchers a contemporary review of machine learning advancements alongside the future direction of these techniques in the landslide mitigation field.

High Precision Prediction of Functional Sites in Protein Structures

PubMed Central

Buturovic, Ljubomir; Wong, Mike; Tang, Grace W.; Altman, Russ B.; Petkovic, Dragutin

2014-01-01

We address the problem of assigning biological function to solved protein structures. Computational tools play a critical role in identifying potential active sites and informing screening decisions for further lab analysis. A critical parameter in the practical application of computational methods is the precision, or positive predictive value. Precision measures the level of confidence the user should have in a particular computed functional assignment. Low precision annotations lead to futile laboratory investigations and waste scarce research resources. In this paper we describe an advanced version of the protein function annotation system FEATURE, which achieved 99% precision and average recall of 95% across 20 representative functional sites. The system uses a Support Vector Machine classifier operating on the microenvironment of physicochemical features around an amino acid. We also compared performance of our method with state-of-the-art sequence-level annotator Pfam in terms of precision, recall and localization. To our knowledge, no other functional site annotator has been rigorously evaluated against these key criteria. The software and predictive models are incorporated into the WebFEATURE service at http://feature.stanford.edu/wf4.0-beta. PMID:24632601

An experimental result of estimating an application volume by machine learning techniques.

PubMed

Hasegawa, Tatsuhito; Koshino, Makoto; Kimura, Haruhiko

2015-01-01

In this study, we improved the usability of smartphones by automating a user's operations. We developed an intelligent system using machine learning techniques that periodically detects a user's context on a smartphone. We selected the Android operating system because it has the largest market share and highest flexibility of its development environment. In this paper, we describe an application that automatically adjusts application volume. Adjusting the volume can be easily forgotten because users need to push the volume buttons to alter the volume depending on the given situation. Therefore, we developed an application that automatically adjusts the volume based on learned user settings. Application volume can be set differently from ringtone volume on Android devices, and these volume settings are associated with each specific application including games. Our application records a user's location, the volume setting, the foreground application name and other such attributes as learning data, thereby estimating whether the volume should be adjusted using machine learning techniques via Weka.

Engineering artificial machines from designable DNA materials for biomedical applications.

PubMed

Qi, Hao; Huang, Guoyou; Han, Yulong; Zhang, Xiaohui; Li, Yuhui; Pingguan-Murphy, Belinda; Lu, Tian Jian; Xu, Feng; Wang, Lin

2015-06-01

Deoxyribonucleic acid (DNA) emerges as building bricks for the fabrication of nanostructure with complete artificial architecture and geometry. The amazing ability of DNA in building two- and three-dimensional structures raises the possibility of developing smart nanomachines with versatile controllability for various applications. Here, we overviewed the recent progresses in engineering DNA machines for specific bioengineering and biomedical applications.

Motion Simulation Analysis of Rail Weld CNC Fine Milling Machine

NASA Astrophysics Data System (ADS)

Mao, Huajie; Shu, Min; Li, Chao; Zhang, Baojun

CNC fine milling machine is a new advanced equipment of rail weld precision machining with high precision, high efficiency, low environmental pollution and other technical advantages. The motion performance of this machine directly affects its machining accuracy and stability, which makes it an important consideration for its design. Based on the design drawings, this article completed 3D modeling of 60mm/kg rail weld CNC fine milling machine by using Solidworks. After that, the geometry was imported into Adams to finish the motion simulation analysis. The displacement, velocity, angular velocity and some other kinematical parameters curves of the main components were obtained in the post-processing and these are the scientific basis for the design and development for this machine.

The 26th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

Sydnor, Richard (Editor)

1995-01-01

This document is a compilation of technical papers presented at the 26th Annual PTTI Applications and Planning Meeting. Papers are in the following categories: (1) Recent developments in rubidium, cesium, and hydrogen-based frequency standards, and in cryogenic and trapped-ion technology; (2) International and transnational applications of Precise Time and Time Interval technology with emphasis on satellite laser tracking, GLONASS timing, intercomparison of national time scales and international telecommunications; (3) Applications of Precise Time and Time Interval technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; (4) Applications of PTTI technology to evolving military communications and navigation systems; and (5) Dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communications satellites.

Topic categorisation of statements in suicide notes with integrated rules and machine learning.

PubMed

Kovačević, Aleksandar; Dehghan, Azad; Keane, John A; Nenadic, Goran

2012-01-01

We describe and evaluate an automated approach used as part of the i2b2 2011 challenge to identify and categorise statements in suicide notes into one of 15 topics, including Love, Guilt, Thankfulness, Hopelessness and Instructions. The approach combines a set of lexico-syntactic rules with a set of models derived by machine learning from a training dataset. The machine learning models rely on named entities, lexical, lexico-semantic and presentation features, as well as the rules that are applicable to a given statement. On a testing set of 300 suicide notes, the approach showed the overall best micro F-measure of up to 53.36%. The best precision achieved was 67.17% when only rules are used, whereas best recall of 50.57% was with integrated rules and machine learning. While some topics (eg, Sorrow, Anger, Blame) prove challenging, the performance for relatively frequent (eg, Love) and well-scoped categories (eg, Thankfulness) was comparatively higher (precision between 68% and 79%), suggesting that automated text mining approaches can be effective in topic categorisation of suicide notes.

Engineering of Machine tool’s High-precision electric drives

NASA Astrophysics Data System (ADS)

Khayatov, E. S.; Korzhavin, M. E.; Naumovich, N. I.

2018-03-01

In the article it is shown that in mechanisms with numerical program control, high quality of processes can be achieved only in systems that provide adjustment of the working element’s position with high accuracy, and this requires an expansion of the regulation range by the torque. In particular, the use of synchronous reactive machines with independent excitation control makes it possible to substantially increase the moment overload in the sequential excitation circuit. Using mathematical and physical modeling methods, it is shown that in the electric drive with a synchronous reactive machine with independent excitation in a circuit with sequential excitation, it is possible to significantly expand the range of regulation by the torque and this is achieved by the effect of sequential excitation, which makes it possible to compensate for the transverse reaction of the armature.

Engineering Artificial Machines from Designable DNA Materials for Biomedical Applications

PubMed Central

Huang, Guoyou; Han, Yulong; Zhang, Xiaohui; Li, Yuhui; Pingguan-Murphy, Belinda; Lu, Tian Jian; Xu, Feng

2015-01-01

Deoxyribonucleic acid (DNA) emerges as building bricks for the fabrication of nanostructure with complete artificial architecture and geometry. The amazing ability of DNA in building two- and three-dimensional structures raises the possibility of developing smart nanomachines with versatile controllability for various applications. Here, we overviewed the recent progresses in engineering DNA machines for specific bioengineering and biomedical applications. PMID:25547514

Method for forming precision clockplate with pivot pins

DOEpatents

Wild, Ronald L [Albuquerque, NM

2010-06-01

Methods are disclosed for producing a precision clockplate with rotational bearing surfaces (e.g. pivot pins). The methods comprise providing an electrically conductive blank, conventionally machining oversize features comprising bearing surfaces into the blank, optionally machining of a relief on non-bearing surfaces, providing wire accesses adjacent to bearing surfaces, threading the wire of an electrical discharge machine through the accesses and finishing the bearing surfaces by wire electrical discharge machining. The methods have been shown to produce bearing surfaces of comparable dimension and tolerances as those produced by micro-machining methods such as LIGA, at reduced cost and complexity.

Human facial neural activities and gesture recognition for machine-interfacing applications.

PubMed

Hamedi, M; Salleh, Sh-Hussain; Tan, T S; Ismail, K; Ali, J; Dee-Uam, C; Pavaganun, C; Yupapin, P P

2011-01-01

The authors present a new method of recognizing different human facial gestures through their neural activities and muscle movements, which can be used in machine-interfacing applications. Human-machine interface (HMI) technology utilizes human neural activities as input controllers for the machine. Recently, much work has been done on the specific application of facial electromyography (EMG)-based HMI, which have used limited and fixed numbers of facial gestures. In this work, a multipurpose interface is suggested that can support 2-11 control commands that can be applied to various HMI systems. The significance of this work is finding the most accurate facial gestures for any application with a maximum of eleven control commands. Eleven facial gesture EMGs are recorded from ten volunteers. Detected EMGs are passed through a band-pass filter and root mean square features are extracted. Various combinations of gestures with a different number of gestures in each group are made from the existing facial gestures. Finally, all combinations are trained and classified by a Fuzzy c-means classifier. In conclusion, combinations with the highest recognition accuracy in each group are chosen. An average accuracy >90% of chosen combinations proved their ability to be used as command controllers.

Application of Taguchi method to optimization of surface roughness during precise turning of NiTi shape memory alloy

NASA Astrophysics Data System (ADS)

Kowalczyk, M.

2017-08-01

This paper describes the research results of surface quality research after the NiTi shape memory alloy (Nitinol) precise turning by the tools with edges made of polycrystalline diamonds (PCD). Nitinol, a nearly equiatomic nickel-titanium shape memory alloy, has wide applications in the arms industry, military, medicine and aerospace industry, and industrial robots. Due to their specific properties NiTi alloys are known to be difficult-to-machine materials particularly by using conventional techniques. The research trials were conducted for three independent parameters (vc, f, ap) affecting the surface roughness were analyzed. The choice of parameter configurations were performed by factorial design methods using orthogonal plan type L9, with three control factors, changing on three levels, developed by G. Taguchi. S/N ratio and ANOVA analyses were performed to identify the best of cutting parameters influencing surface roughness.

Electrochemical micro/nano-machining: principles and practices.

PubMed

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

2017-03-06

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

Classification of LIDAR Data for Generating a High-Precision Roadway Map

NASA Astrophysics Data System (ADS)

Jeong, J.; Lee, I.

2016-06-01

Generating of a highly precise map grows up with development of autonomous driving vehicles. The highly precise map includes a precision of centimetres level unlike an existing commercial map with the precision of meters level. It is important to understand road environments and make a decision for autonomous driving since a robust localization is one of the critical challenges for the autonomous driving car. The one of source data is from a Lidar because it provides highly dense point cloud data with three dimensional position, intensities and ranges from the sensor to target. In this paper, we focus on how to segment point cloud data from a Lidar on a vehicle and classify objects on the road for the highly precise map. In particular, we propose the combination with a feature descriptor and a classification algorithm in machine learning. Objects can be distinguish by geometrical features based on a surface normal of each point. To achieve correct classification using limited point cloud data sets, a Support Vector Machine algorithm in machine learning are used. Final step is to evaluate accuracies of obtained results by comparing them to reference data The results show sufficient accuracy and it will be utilized to generate a highly precise road map.

Effects of virtualization on a scientific application - Running a hyperspectral radiative transfer code on virtual machines.

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

Tikotekar, Anand A; Vallee, Geoffroy R; Naughton III, Thomas J

2008-01-01

The topic of system-level virtualization has recently begun to receive interest for high performance computing (HPC). This is in part due to the isolation and encapsulation offered by the virtual machine. These traits enable applications to customize their environments and maintain consistent software configurations in their virtual domains. Additionally, there are mechanisms that can be used for fault tolerance like live virtual machine migration. Given these attractive benefits to virtualization, a fundamental question arises, how does this effect my scientific application? We use this as the premise for our paper and observe a real-world scientific code running on a Xenmore » virtual machine. We studied the effects of running a radiative transfer simulation, Hydrolight, on a virtual machine. We discuss our methodology and report observations regarding the usage of virtualization with this application.« less

Supervised machine learning on a network scale: application to seismic event classification and detection

NASA Astrophysics Data System (ADS)

Reynen, Andrew; Audet, Pascal

2017-09-01

A new method using a machine learning technique is applied to event classification and detection at seismic networks. This method is applicable to a variety of network sizes and settings. The algorithm makes use of a small catalogue of known observations across the entire network. Two attributes, the polarization and frequency content, are used as input to regression. These attributes are extracted at predicted arrival times for P and S waves using only an approximate velocity model, as attributes are calculated over large time spans. This method of waveform characterization is shown to be able to distinguish between blasts and earthquakes with 99 per cent accuracy using a network of 13 stations located in Southern California. The combination of machine learning with generalized waveform features is further applied to event detection in Oklahoma, United States. The event detection algorithm makes use of a pair of unique seismic phases to locate events, with a precision directly related to the sampling rate of the generalized waveform features. Over a week of data from 30 stations in Oklahoma, United States are used to automatically detect 25 times more events than the catalogue of the local geological survey, with a false detection rate of less than 2 per cent. This method provides a highly confident way of detecting and locating events. Furthermore, a large number of seismic events can be automatically detected with low false alarm, allowing for a larger automatic event catalogue with a high degree of trust.

Machine Learning and Radiology

PubMed Central

Wang, Shijun; Summers, Ronald M.

2012-01-01

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

Precision Control Module For UV Laser 3D Micromachining

NASA Astrophysics Data System (ADS)

Wu, Wen-Hong; Hung, Min-Wei; Chang, Chun-Li

2011-01-01

UV laser has been widely used in various micromachining such as micro-scribing or patterning processing. At present, most of the semiconductors, LEDs, photovoltaic solar panels and touch panels industries need the UV laser processing system. However, most of the UV laser processing applications in the industries utilize two dimensional (2D) plane processing. And there are tremendous business opportunities that can be developed, such as three dimensional (3D) structures of micro-electromechanical (MEMS) sensor or the precision depth control of indium tin oxide (ITO) thin films edge insulation in touch panels. This research aims to develop a UV laser 3D micromachining module that can create the novel applications for industries. By special designed beam expender in optical system, the focal point of UV laser can be adjusted quickly and accurately through the optical path control lens of laser beam expender optical system. Furthermore, the integrated software for galvanometric scanner and focal point adjustment mechanism is developed as well, so as to carry out the precise 3D microstructure machining.

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 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.

Proceedings of the 8th Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

1977-01-01

The Proceedings contain the papers presented at the Eight Annual Precise Time and Tme Interval PTTI Applications and Planning Meeting. The edited record of the discussions following the papers and the panel discussions are also included. This meeting provided a forum for the exchange of information on precise time and frequency technology among members of the scientific community and persons with program applications. The 282 registered attendees came from various U.S. Government agencies, private industry, universities and a number of foreign countries were represented. In this meeting, papers were presented that emphasized: (1) definitions and international regulations of precise time sources and users, (2) the scientific foundations of Hydrogen Maser standards, the current developments in this field and the application experience, and (3) how to measure the stability performance properties of precise standards. As in the previous meetings, update and new papers were presented on system applications with past, present and future requirements identified.

Defining brain-machine interface applications by matching interface performance with device requirements.

PubMed

Tonet, Oliver; Marinelli, Martina; Citi, Luca; Rossini, Paolo Maria; Rossini, Luca; Megali, Giuseppe; Dario, Paolo

2008-01-15

Interaction with machines is mediated by human-machine interfaces (HMIs). Brain-machine interfaces (BMIs) are a particular class of HMIs and have so far been studied as a communication means for people who have little or no voluntary control of muscle activity. In this context, low-performing interfaces can be considered as prosthetic applications. On the other hand, for able-bodied users, a BMI would only be practical if conceived as an augmenting interface. In this paper, a method is introduced for pointing out effective combinations of interfaces and devices for creating real-world applications. First, devices for domotics, rehabilitation and assistive robotics, and their requirements, in terms of throughput and latency, are described. Second, HMIs are classified and their performance described, still in terms of throughput and latency. Then device requirements are matched with performance of available interfaces. Simple rehabilitation and domotics devices can be easily controlled by means of BMI technology. Prosthetic hands and wheelchairs are suitable applications but do not attain optimal interactivity. Regarding humanoid robotics, the head and the trunk can be controlled by means of BMIs, while other parts require too much throughput. Robotic arms, which have been controlled by means of cortical invasive interfaces in animal studies, could be the next frontier for non-invasive BMIs. Combining smart controllers with BMIs could improve interactivity and boost BMI applications.

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 1: Executive Summary

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions are explored. The specific tasks which will be required by future space projects are identified. ARAMIS options which are candidates for those space project tasks and the relative merits of these options are defined and evaluated. Promising applications of ARAMIS and specific areas for further research are identified. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Double Arm Linkage precision Linear motion (DALL) Carriage, a simplified, rugged, high performance linear motion stage for the moving mirror of an Fourier Transform Spectrometer or other system requiring precision linear motion

NASA Astrophysics Data System (ADS)

Johnson, Kendall B.; Hopkins, Greg

2017-08-01

The Double Arm Linkage precision Linear motion (DALL) carriage has been developed as a simplified, rugged, high performance linear motion stage. Initially conceived as a moving mirror stage for the moving mirror of a Fourier Transform Spectrometer (FTS), it is applicable to any system requiring high performance linear motion. It is based on rigid double arm linkages connecting a base to a moving carriage through flexures. It is a monolithic design. The system is fabricated from one piece of material including the flexural elements, using high precision machining. The monolithic design has many advantages. There are no joints to slip or creep and there are no CTE (coefficient of thermal expansion) issues. This provides a stable, robust design, both mechanically and thermally and is expected to provide a wide operating temperature range, including cryogenic temperatures, and high tolerance to vibration and shock. Furthermore, it provides simplicity and ease of implementation, as there is no assembly or alignment of the mechanism. It comes out of the machining operation aligned and there are no adjustments. A prototype has been fabricated and tested, showing superb shear performance and very promising tilt performance. This makes it applicable to both corner cube and flat mirror FTS systems respectively.

Machine learning and radiology.

PubMed

Wang, Shijun; Summers, Ronald M

2012-07-01

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

Microbiopsy/precision cutting devices

DOEpatents

Krulevitch, Peter A.; Lee, Abraham P.; Northrup, M. Allen; Benett, William J.

1999-01-01

Devices for performing tissue biopsy on a small scale (microbiopsy). By reducing the size of the biopsy tool and removing only a small amount of tissue or other material in a minimally invasive manner, the risks, costs, injury and patient discomfort associated with traditional biopsy procedures can be reduced. By using micromachining and precision machining capabilities, it is possible to fabricate small biopsy/cutting devices from silicon. These devices can be used in one of four ways 1) intravascularly, 2) extravascularly, 3) by vessel puncture, and 4) externally. Additionally, the devices may be used in precision surgical cutting.

Microbiopsy/precision cutting devices

DOEpatents

Krulevitch, P.A.; Lee, A.P.; Northrup, M.A.; Benett, W.J.

1999-07-27

Devices are disclosed for performing tissue biopsy on a small scale (microbiopsy). By reducing the size of the biopsy tool and removing only a small amount of tissue or other material in a minimally invasive manner, the risks, costs, injury and patient discomfort associated with traditional biopsy procedures can be reduced. By using micromachining and precision machining capabilities, it is possible to fabricate small biopsy/cutting devices from silicon. These devices can be used in one of four ways (1) intravascularly, (2) extravascularly, (3) by vessel puncture, and (4) externally. Additionally, the devices may be used in precision surgical cutting. 6 figs.

Multicutter machining of compound parametric surfaces

NASA Astrophysics Data System (ADS)

Hatna, Abdelmadjid; Grieve, R. J.; Broomhead, P.

2000-10-01

Parametric free forms are used in industries as disparate as footwear, toys, sporting goods, ceramics, digital content creation, and conceptual design. Optimizing tool path patterns and minimizing the total machining time is a primordial issue in numerically controlled (NC) machining of free form surfaces. We demonstrate in the present work that multi-cutter machining can achieve as much as 60% reduction in total machining time for compound sculptured surfaces. The given approach is based upon the pre-processing as opposed to the usual post-processing of surfaces for the detection and removal of interference followed by precise tracking of unmachined areas.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics

PubMed Central

HUANG, SHUJUN; CAI, NIANGUANG; PACHECO, PEDRO PENZUTI; NARANDES, SHAVIRA; WANG, YANG; XU, WAYNE

2017-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. PMID:29275361

Precision Parameter Estimation and Machine Learning

NASA Astrophysics Data System (ADS)

Wandelt, Benjamin D.

2008-12-01

I discuss the strategy of ``Acceleration by Parallel Precomputation and Learning'' (AP-PLe) that can vastly accelerate parameter estimation in high-dimensional parameter spaces and costly likelihood functions, using trivially parallel computing to speed up sequential exploration of parameter space. This strategy combines the power of distributed computing with machine learning and Markov-Chain Monte Carlo techniques efficiently to explore a likelihood function, posterior distribution or χ2-surface. This strategy is particularly successful in cases where computing the likelihood is costly and the number of parameters is moderate or large. We apply this technique to two central problems in cosmology: the solution of the cosmological parameter estimation problem with sufficient accuracy for the Planck data using PICo; and the detailed calculation of cosmological helium and hydrogen recombination with RICO. Since the APPLe approach is designed to be able to use massively parallel resources to speed up problems that are inherently serial, we can bring the power of distributed computing to bear on parameter estimation problems. We have demonstrated this with the CosmologyatHome project.

Web Mining: Machine Learning for Web Applications.

ERIC Educational Resources Information Center

Chen, Hsinchun; Chau, Michael

2004-01-01

Presents an overview of machine learning research and reviews methods used for evaluating machine learning systems. Ways that machine-learning algorithms were used in traditional information retrieval systems in the "pre-Web" era are described, and the field of Web mining and how machine learning has been used in different Web mining…

Remote sensing with unmanned aircraft systems for precision agriculture applications

USDA-ARS?s Scientific Manuscript database

The Federal Aviation Administration is revising regulations for using unmanned aircraft systems (UAS) in the national airspace. An important potential application of UAS may be as a remote-sensing platform for precision agriculture, but simply down-scaling remote sensing methodologies developed usi...

A Comprehensive Review of Permanent Magnet Transverse Flux Machines for Direct Drive Applications

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

Muljadi, Eduard; Husain, Tausif; Hasan, Iftekhar

The use of direct drive machines in renewable and industrial applications are increasing at a rapid rate. Transverse flux machines (TFM) are ideally suited for direct drive applications due to their high torque density. In this paper, a comprehensive review of the permanent magnet (PM) TFMs for direct drive applications is presented. The paper introduces TFMs and their operating principle and then reviews the different type of TFMs proposed in the literature. The TFMs are categorized according to the number of stator sides, types of stator cores and magnet arrangement in the rotor. The review covers different design topologies, materialsmore » used for manufacturing, structural and thermal analysis, modeling and design optimization and cogging torque minimization in TFMs. The paper also reviews various applications and comparisons for TFMs that have been presented in the literature.« less

[Design and application of medical electric leg-raising machine].

PubMed

Liang, Jintang; Chen, Jinyuan; Zhao, Zixian; Lin, Jinfeng; Li, Juanhong; Zhong, Jingliang

2017-08-01

Passive leg raising is widely used in clinic, but it lacks of specialized mechanical raise equipment. It requires medical staff to raise leg by hand or requires a multi-functional bed to raise leg, which takes time and effort. Therefore we have developed a new medical electric leg-raising machine. The equipment has the following characteristics: simple structure, stable performance, easy operation, fast and effective, safe and comfortable. The height range of the lifter is 50-120 cm, the range of the angle of raising leg is 10degree angle-80degree angle, the maximum supporting weight is 40 kg. Because of raising the height of the lower limbs and making precise angle, this equipment can completely replace the traditional manner of lifting leg by hand with multi-functional bed to lift patients' leg and can reduce the physical exhaustion and time consumption of medical staff. It can change the settings at any time to meet the needs of the patient; can be applied to the testing of PLR and dynamically assessing the hemodynamics; can prevent deep vein thrombosis and some related complications of staying in bed; and the machine is easy to be cleaned and disinfected, which can effectively avoid hospital acquired infection and cross infection; and can also be applied to emergency rescue of various disasters and emergencies.

Intelligent Machine Learning Approaches for Aerospace Applications

NASA Astrophysics Data System (ADS)

Sathyan, Anoop

Machine Learning is a type of artificial intelligence that provides machines or networks the ability to learn from data without the need to explicitly program them. There are different kinds of machine learning techniques. This thesis discusses the applications of two of these approaches: Genetic Fuzzy Logic and Convolutional Neural Networks (CNN). Fuzzy Logic System (FLS) is a powerful tool that can be used for a wide variety of applications. FLS is a universal approximator that reduces the need for complex mathematics and replaces it with expert knowledge of the system to produce an input-output mapping using If-Then rules. The expert knowledge of a system can help in obtaining the parameters for small-scale FLSs, but for larger networks we will need to use sophisticated approaches that can automatically train the network to meet the design requirements. This is where Genetic Algorithms (GA) and EVE come into the picture. Both GA and EVE can tune the FLS parameters to minimize a cost function that is designed to meet the requirements of the specific problem. EVE is an artificial intelligence developed by Psibernetix that is trained to tune large scale FLSs. The parameters of an FLS can include the membership functions and rulebase of the inherent Fuzzy Inference Systems (FISs). The main issue with using the GFS is that the number of parameters in a FIS increase exponentially with the number of inputs thus making it increasingly harder to tune them. To reduce this issue, the FLSs discussed in this thesis consist of 2-input-1-output FISs in cascade (Chapter 4) or as a layer of parallel FISs (Chapter 7). We have obtained extremely good results using GFS for different applications at a reduced computational cost compared to other algorithms that are commonly used to solve the corresponding problems. In this thesis, GFSs have been designed for controlling an inverted double pendulum, a task allocation problem of clustering targets amongst a set of UAVs, a fire

Machine intelligence applications to securities production

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

Johnson, C.K.

1987-01-01

The production of security documents provides a cache of interesting problems ranging across a broad spectrum. Some of the problems do not have rigorous scientific solutions available at this time and provide opportunities for less structured approaches such as AI. AI methods can be used in conjunction with traditional scientific and computational methods. The most productive applications of AI occur when this marriage of methods can be carried out without motivation to prove that one method is better than the other. Fields such as ink chemistry and technology, and machine inspection of graphic arts printing offer interesting challenges which willmore » continue to intrigue current and future generations of researchers into the 21st century.« less

Application of Machine Learning to Rotorcraft Health Monitoring

NASA Technical Reports Server (NTRS)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

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

Proceedings of the 7th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

1975-01-01

The Proceedings contain the papers presented at the Seventh Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting and the edited record of the discussion period following each paper. This meeting provided a forum to promote more effective, efficient, economical and skillful applications of PTTI technology to the many problem areas to which PTTI offers solutions. Specifically the purpose of the meeting is to: disseminate, coordinate, and exchange practical information associated with precise time and frequency; acquaint systems engineers, technicians and managers with precise time and frequency technology and its applications; and review present and future requirements for PTTI.

Analytical design of intelligent machines

NASA Technical Reports Server (NTRS)

Saridis, George N.; Valavanis, Kimon P.

1987-01-01

The problem of designing 'intelligent machines' to operate in uncertain environments with minimum supervision or interaction with a human operator is examined. The structure of an 'intelligent machine' is defined to be the structure of a Hierarchically Intelligent Control System, composed of three levels hierarchically ordered according to the principle of 'increasing precision with decreasing intelligence', namely: the organizational level, performing general information processing tasks in association with a long-term memory; the coordination level, dealing with specific information processing tasks with a short-term memory; and the control level, which performs the execution of various tasks through hardware using feedback control methods. The behavior of such a machine may be managed by controls with special considerations and its 'intelligence' is directly related to the derivation of a compatible measure that associates the intelligence of the higher levels with the concept of entropy, which is a sufficient analytic measure that unifies the treatment of all the levels of an 'intelligent machine' as the mathematical problem of finding the right sequence of internal decisions and controls for a system structured in the order of intelligence and inverse order of precision such that it minimizes its total entropy. A case study on the automatic maintenance of a nuclear plant illustrates the proposed approach.

Machinability of nickel based alloys using electrical discharge machining process

NASA Astrophysics Data System (ADS)

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

2018-04-01

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

27th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

Sydnor, Richard L. (Editor)

1996-01-01

This document is a compilation of technical papers presented at the 27th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting, held November 29 - December 1, 1995 at San Diego, CA. Papers are in the following categories: Recent developments in rubidium, cesium, and hydrogen-based frequency standards; and in cryogenic and trapped-ion technology; International and transnational applications of PTTI technology with emphasis on satellite laser tracking, GLONASS timing, intercomparison of national time scales and international telecommunications; Applications of PTTI technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; Applications of PTTI technology to evolving military communications and navigation systems; and Dissemination of precise time and frequency by means of Global Positioning System (GPS), Global Satellite Navigation System (GLONASS), MILSTAR, LORAN, and synchronous communications satellites.

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

DTIC Science & Technology

1993-01-01

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

Wax Reinforces Honeycomb During Machining

NASA Technical Reports Server (NTRS)

Towell, Timothy W.; Fahringer, David T.; Vasquez, Peter; Scheidegger, Alan P.

1995-01-01

Method of machining on conventional metal lathe devised for precise cutting of axisymmetric contours on honeycomb cores made of composite (matrix/fiber) materials. Wax filling reinforces honeycomb walls against bending and tearing while honeycomb being contoured on lathe. Innovative method of machining on lathe involves preparation in which honeycomb is placed in appropriate fixture and the fixture is then filled with molten water-soluble wax. Number of different commercial waxes have been tried.

The 25th Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

Sydnor, Richard L. (Editor)

1994-01-01

Papers in the following categories are presented: recent developments in rubidium, cesium, and hydrogen-based frequency standards, and in cryogenic and trapped-ion technology; international and transnational applications of precise time and time interval (PTTI) technology with emphasis on satellite laser tracking networks, GLONASS timing, intercomparison of national time scales and international telecommunication; applications of PTTI technology to the telecommunications, power distribution, platform positioning, and geophysical survey industries; application of PTTI technology to evolving military communications and navigation systems; and dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communications satellites.

Selected aspects of microelectronics technology and applications: Numerically controlled machine tools. Technology trends series no. 2

NASA Astrophysics Data System (ADS)

Sigurdson, J.; Tagerud, J.

1986-05-01

A UNIDO publication about machine tools with automatic control discusses the following: (1) numerical control (NC) machine tool perspectives, definition of NC, flexible manufacturing systems, robots and their industrial application, research and development, and sensors; (2) experience in developing a capability in NC machine tools; (3) policy issues; (4) procedures for retrieval of relevant documentation from data bases. Diagrams, statistics, bibliography are included.

Application of the SNoW machine learning paradigm to a set of transportation imaging problems

NASA Astrophysics Data System (ADS)

Paul, Peter; Burry, Aaron M.; Wang, Yuheng; Kozitsky, Vladimir

2012-01-01

Machine learning methods have been successfully applied to image object classification problems where there is clear distinction between classes and where a comprehensive set of training samples and ground truth are readily available. The transportation domain is an area where machine learning methods are particularly applicable, since the classification problems typically have well defined class boundaries and, due to high traffic volumes in most applications, massive roadway data is available. Though these classes tend to be well defined, the particular image noises and variations can be challenging. Another challenge is the extremely high accuracy typically required in most traffic applications. Incorrect assignment of fines or tolls due to imaging mistakes is not acceptable in most applications. For the front seat vehicle occupancy detection problem, classification amounts to determining whether one face (driver only) or two faces (driver + passenger) are detected in the front seat of a vehicle on a roadway. For automatic license plate recognition, the classification problem is a type of optical character recognition problem encompassing multiple class classification. The SNoW machine learning classifier using local SMQT features is shown to be successful in these two transportation imaging applications.

VIEW OF MICROMACHINING, HIGH PRECISION EQUIPMENT USED TO CUSTOM MAKE ...

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

VIEW OF MICRO-MACHINING, HIGH PRECISION EQUIPMENT USED TO CUSTOM MAKE SMALL PARTS. LUMPS OF CLAY; SHOWN IN THE PHOTOGRAPH, WERE USED TO STABILIZE PARTS BEING MACHINED. (11/1/87) - Rocky Flats Plant, Stainless Steel & Non-Nuclear Components Manufacturing, Southeast corner of intersection of Cottonwood & Third Avenues, Golden, Jefferson County, CO

Development of a CPM Machine for Injured Fingers.

PubMed

Fu, Yili; Zhang, Fuxiang; Ma, Xin; Meng, Qinggang

2005-01-01

Human fingers are easy to be injured. A CPM machine is a mechanism based on the rehabilitation theory of continuous passive motion (CPM). To develop a CPM machine for the clinic application in the rehabilitation of injured fingers is a significant task. Therefore, based on the theories of evidence based medicine (EBM) and CPM, we've developed a set of biomimetic mechanism after modeling the motions of fingers and analyzing its kinematics and dynamics analysis. We also design an embedded operating system based on ARM (a kind of 32-bit RISC microprocessor). The equipment can achieve the precise control of moving scope of fingers, finger's force and speed. It can serves as a rational checking method and a way of assessment for functional rehabilitation of human hands. Now, the first prototype has been finished and will start the clinical testing in Harbin Medical University shortly.

Precision molding of advanced glass optics: innovative production technology for lens arrays and free form optics

NASA Astrophysics Data System (ADS)

Pongs, Guido; Bresseler, Bernd; Bergs, Thomas; Menke, Gert

2012-10-01

Today isothermal precision molding of imaging glass optics has become a widely applied and integrated production technology in the optical industry. Especially in consumer electronics (e.g. digital cameras, mobile phones, Blu-ray) a lot of optical systems contain rotationally symmetrical aspherical lenses produced by precision glass molding. But due to higher demands on complexity and miniaturization of optical elements the established process chain for precision glass molding is not sufficient enough. Wafer based molding processes for glass optics manufacturing become more and more interesting for mobile phone applications. Also cylindrical lens arrays can be used in high power laser systems. The usage of unsymmetrical free-form optics allows an increase of efficiency in optical laser systems. Aixtooling is working on different aspects in the fields of mold manufacturing technologies and molding processes for extremely high complex optical components. In terms of array molding technologies, Aixtooling has developed a manufacturing technology for the ultra-precision machining of carbide molds together with European partners. The development covers the machining of multi lens arrays as well as cylindrical lens arrays. The biggest challenge is the molding of complex free-form optics having no symmetrical axis. A comprehensive CAD/CAM data management along the entire process chain is essential to reach high accuracies on the molded lenses. Within a national funded project Aixtooling is working on a consistent data handling procedure in the process chain for precision molding of free-form optics.

Machine for preparing phosphors for the fluorimetric determination of uranium

USGS Publications Warehouse

Stevens, R.E.; Wood, W.H.; Goetz, K.G.; Horr, C.A.

1956-01-01

The time saved by use of a machine for preparing many phosphors at one time increases the rate of productivity of the fluorimetric method for determining uranium. The machine prepares 18 phosphors at a time and eliminates the tedious and time-consuming step of preparing them by hand, while improving the precision of the method in some localities. The machine consists of a ring burner over which the platinum dishes, containing uranium and flux, are rotated. By placing the machine in an inclined position the molten flux comes into contact with all surfaces within th dish as the dishes rotate over the flame. Precision is improved because the heating and cooling conditions are the same for each of the 18 phosphors in one run as well as for successive runs.

Machine vision system for measuring conifer seedling morphology

NASA Astrophysics Data System (ADS)

Rigney, Michael P.; Kranzler, Glenn A.

1995-01-01

A PC-based machine vision system providing rapid measurement of bare-root tree seedling morphological features has been designed. The system uses backlighting and a 2048-pixel line- scan camera to acquire images with transverse resolutions as high as 0.05 mm for precise measurement of stem diameter. Individual seedlings are manually loaded on a conveyor belt and inspected by the vision system in less than 0.25 seconds. Designed for quality control and morphological data acquisition by nursery personnel, the system provides a user-friendly, menu-driven graphical interface. The system automatically locates the seedling root collar and measures stem diameter, shoot height, sturdiness ratio, root mass length, projected shoot and root area, shoot-root area ratio, and percent fine roots. Sample statistics are computed for each measured feature. Measurements for each seedling may be stored for later analysis. Feature measurements may be compared with multi-class quality criteria to determine sample quality or to perform multi-class sorting. Statistical summary and classification reports may be printed to facilitate the communication of quality concerns with grading personnel. Tests were conducted at a commercial forest nursery to evaluate measurement precision. Four quality control personnel measured root collar diameter, stem height, and root mass length on each of 200 conifer seedlings. The same seedlings were inspected four times by the machine vision system. Machine stem diameter measurement precision was four times greater than that of manual measurements. Machine and manual measurements had comparable precision for shoot height and root mass length.

Applications of Support Vector Machine (SVM) Learning in Cancer Genomics.

PubMed

Huang, Shujun; Cai, Nianguang; Pacheco, Pedro Penzuti; Narrandes, Shavira; Wang, Yang; Xu, Wayne

2018-01-01

Machine learning with maximization (support) of separating margin (vector), called support vector machine (SVM) learning, is a powerful classification tool that has been used for cancer genomic classification or subtyping. Today, as advancements in high-throughput technologies lead to production of large amounts of genomic and epigenomic data, the classification feature of SVMs is expanding its use in cancer genomics, leading to the discovery of new biomarkers, new drug targets, and a better understanding of cancer driver genes. Herein we reviewed the recent progress of SVMs in cancer genomic studies. We intend to comprehend the strength of the SVM learning and its future perspective in cancer genomic applications. Copyright© 2018, International Institute of Anticancer Research (Dr. George J. Delinasios), All rights reserved.

A Machine-to-Machine protocol benchmark for eHealth applications - Use case: Respiratory rehabilitation.

PubMed

Talaminos-Barroso, Alejandro; Estudillo-Valderrama, Miguel A; Roa, Laura M; Reina-Tosina, Javier; Ortega-Ruiz, Francisco

2016-06-01

M2M (Machine-to-Machine) communications represent one of the main pillars of the new paradigm of the Internet of Things (IoT), and is making possible new opportunities for the eHealth business. Nevertheless, the large number of M2M protocols currently available hinders the election of a suitable solution that satisfies the requirements that can demand eHealth applications. In the first place, to develop a tool that provides a benchmarking analysis in order to objectively select among the most relevant M2M protocols for eHealth solutions. In the second place, to validate the tool with a particular use case: the respiratory rehabilitation. A software tool, called Distributed Computing Framework (DFC), has been designed and developed to execute the benchmarking tests and facilitate the deployment in environments with a large number of machines, with independence of the protocol and performance metrics selected. DDS, MQTT, CoAP, JMS, AMQP and XMPP protocols were evaluated considering different specific performance metrics, including CPU usage, memory usage, bandwidth consumption, latency and jitter. The results obtained allowed to validate a case of use: respiratory rehabilitation of chronic obstructive pulmonary disease (COPD) patients in two scenarios with different types of requirement: Home-Based and Ambulatory. The results of the benchmark comparison can guide eHealth developers in the choice of M2M technologies. In this regard, the framework presented is a simple and powerful tool for the deployment of benchmark tests under specific environments and conditions. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Machining of AISI D2 Tool Steel with Multiple Hole Electrodes by EDM Process

NASA Astrophysics Data System (ADS)

Prasad Prathipati, R.; Devuri, Venkateswarlu; Cheepu, Muralimohan; Gudimetla, Kondaiah; Uzwal Kiran, R.

2018-03-01

In recent years, with the increasing of technology the demand for machining processes is increasing for the newly developed materials. The conventional machining processes are not adequate to meet the accuracy of the machining of these materials. The non-conventional machining processes of electrical discharge machining is one of the most efficient machining processes is being widely used to machining of high accuracy products of various industries. The optimum selection of process parameters is very important in machining processes as that of an electrical discharge machining as they determine surface quality and dimensional precision of the obtained parts, even though time consumption rate is higher for machining of large dimension features. In this work, D2 high carbon and chromium tool steel has been machined using electrical discharge machining with the multiple hole electrode technique. The D2 steel has several applications such as forming dies, extrusion dies and thread rolling. But the machining of this tool steel is very hard because of it shard alloyed elements of V, Cr and Mo which enhance its strength and wear properties. However, the machining is possible by using electrical discharge machining process and the present study implemented a new technique to reduce the machining time using a multiple hole copper electrode. In this technique, while machining with multiple holes electrode, fin like projections are obtained, which can be removed easily by chipping. Then the finishing is done by using solid electrode. The machining time is reduced to around 50% while using multiple hole electrode technique for electrical discharge machining.

View north of inside machine shop 36; shop floor accommodates ...

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

View north of inside machine shop 36; shop floor accommodates lathes capable of machining a cylinder 60 inches in diameter and 75 feet long; other equipment includes horizontal and vertical jig borders, hydraulic tube straighteners and other equipment for precision machining of large ship components. - Naval Base Philadelphia-Philadelphia Naval Shipyard, Structure Shop, League Island, Philadelphia, Philadelphia County, PA

Investigation of fault modes in permanent magnet synchronous machines for traction applications

NASA Astrophysics Data System (ADS)

Choi, Gilsu

Over the past few decades, electric motor drives have been more widely adopted to power the transportation sector to reduce our dependence on foreign oil and carbon emissions. Permanent magnet synchronous machines (PMSMs) are popular in many applications in the aerospace and automotive industries that require high power density and high efficiency. However, the presence of magnets that cannot be turned off in the event of a fault has always been an issue that hinders adoption of PMSMs in these demanding applications. This work investigates the design and analysis of PMSMs for automotive traction applications with particular emphasis on fault-mode operation caused by faults appearing at the terminals of the machine. New models and analytical techniques are introduced for evaluating the steady-state and dynamic response of PMSM drives to various fault conditions. Attention is focused on modeling the PMSM drive including nonlinear magnetic behavior under several different fault conditions, evaluating the risks of irreversible demagnetization caused by the large fault currents, as well as developing fault mitigation techniques in terms of both the fault currents and demagnetization risks. Of the major classes of machine terminal faults that can occur in PMSMs, short-circuit (SC) faults produce much more dangerous fault currents than open-circuit faults. The impact of different PMSM topologies and parameters on their responses to symmetrical and asymmetrical short-circuit (SSC & ASC) faults has been investigated. A detailed investigation on both the SSC and ASC faults is presented including both closed-form and numerical analysis. The demagnetization characteristics caused by high fault-mode stator currents (i.e., armature reaction) for different types of PMSMs are investigated. A thorough analysis and comparison of the relative demagnetization vulnerability for different types of PMSMs is presented. This analysis includes design guidelines and recommendations for

Current status and future directions of precision agriculture for aerial application in the USA

USDA-ARS?s Scientific Manuscript database

Precision aerial application in the USA is less than a decade old since the development of the first variable-rate aerial application system. Many areas of the United States rely on readily available agricultural airplanes or helicopters for pest management. Variable-rate aerial application provides...

Micromechanical Machining Processes and their Application to Aerospace Structures, Devices and Systems

NASA Technical Reports Server (NTRS)

Friedrich, Craig R.; Warrington, Robert O.

1995-01-01

Micromechanical machining processes are those micro fabrication techniques which directly remove work piece material by either a physical cutting tool or an energy process. These processes are direct and therefore they can help reduce the cost and time for prototype development of micro mechanical components and systems. This is especially true for aerospace applications where size and weight are critical, and reliability and the operating environment are an integral part of the design and development process. The micromechanical machining processes are rapidly being recognized as a complementary set of tools to traditional lithographic processes (such as LIGA) for the fabrication of micromechanical components. Worldwide efforts in the U.S., Germany, and Japan are leading to results which sometimes rival lithography at a fraction of the time and cost. Efforts to develop processes and systems specific to aerospace applications are well underway.

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1990-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

Techniques and applications for binaural sound manipulation in human-machine interfaces

NASA Technical Reports Server (NTRS)

Begault, Durand R.; Wenzel, Elizabeth M.

1992-01-01

The implementation of binaural sound to speech and auditory sound cues (auditory icons) is addressed from both an applications and technical standpoint. Techniques overviewed include processing by means of filtering with head-related transfer functions. Application to advanced cockpit human interface systems is discussed, although the techniques are extendable to any human-machine interface. Research issues pertaining to three-dimensional sound displays under investigation at the Aerospace Human Factors Division at NASA Ames Research Center are described.

A forestry application simulation of man-machine techniques for analyzing remotely sensed data

NASA Technical Reports Server (NTRS)

Berkebile, J.; Russell, J.; Lube, B.

1976-01-01

The typical steps in the analysis of remotely sensed data for a forestry applications example are simulated. The example uses numerically-oriented pattern recognition techniques and emphasizes man-machine interaction.

Complex extreme learning machine applications in terahertz pulsed signals feature sets.

PubMed

Yin, X-X; Hadjiloucas, S; Zhang, Y

2014-11-01

This paper presents a novel approach to the automatic classification of very large data sets composed of terahertz pulse transient signals, highlighting their potential use in biochemical, biomedical, pharmaceutical and security applications. Two different types of THz spectra are considered in the classification process. Firstly a binary classification study of poly-A and poly-C ribonucleic acid samples is performed. This is then contrasted with a difficult multi-class classification problem of spectra from six different powder samples that although have fairly indistinguishable features in the optical spectrum, they also possess a few discernable spectral features in the terahertz part of the spectrum. Classification is performed using a complex-valued extreme learning machine algorithm that takes into account features in both the amplitude as well as the phase of the recorded spectra. Classification speed and accuracy are contrasted with that achieved using a support vector machine classifier. The study systematically compares the classifier performance achieved after adopting different Gaussian kernels when separating amplitude and phase signatures. The two signatures are presented as feature vectors for both training and testing purposes. The study confirms the utility of complex-valued extreme learning machine algorithms for classification of the very large data sets generated with current terahertz imaging spectrometers. The classifier can take into consideration heterogeneous layers within an object as would be required within a tomographic setting and is sufficiently robust to detect patterns hidden inside noisy terahertz data sets. The proposed study opens up the opportunity for the establishment of complex-valued extreme learning machine algorithms as new chemometric tools that will assist the wider proliferation of terahertz sensing technology for chemical sensing, quality control, security screening and clinic diagnosis. Furthermore, the proposed

Ultra-precision turning of complex spiral optical delay line

NASA Astrophysics Data System (ADS)

Zhang, Xiaodong; Li, Po; Fang, Fengzhou; Wang, Qichang

2011-11-01

Optical delay line (ODL) implements the vertical or depth scanning of optical coherence tomography, which is the most important factor affecting the scanning resolution and speed. The spinning spiral mirror is found as an excellent optical delay device because of the high-speed and high-repetition-rate. However, it is one difficult task to machine the mirror due to the special shape and precision requirement. In this paper, the spiral mirror with titled parabolic generatrix is proposed, and the ultra-precision turning method is studied for its machining using the spiral mathematic model. Another type of ODL with the segmental shape is also introduced and machined to make rotation balance for the mass equalization when scanning. The efficiency improvement is considered in details, including the rough cutting with the 5- axis milling machine, the machining coordinates unification, and the selection of layer direction in turning. The onmachine measuring method based on stylus gauge is designed to analyze the shape deviation. The air bearing is used as the measuring staff and the laser interferometer sensor as the position sensor, whose repeatability accuracy is proved up to 10nm and the stable feature keeps well. With this method developed, the complex mirror with nanometric finish of 10.7nm in Ra and the form error within 1um are achieved.

On the applicability of brain reading for predictive human-machine interfaces in robotics.

PubMed

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors.

On the Applicability of Brain Reading for Predictive Human-Machine Interfaces in Robotics

PubMed Central

Kirchner, Elsa Andrea; Kim, Su Kyoung; Straube, Sirko; Seeland, Anett; Wöhrle, Hendrik; Krell, Mario Michael; Tabie, Marc; Fahle, Manfred

2013-01-01

The ability of today's robots to autonomously support humans in their daily activities is still limited. To improve this, predictive human-machine interfaces (HMIs) can be applied to better support future interaction between human and machine. To infer upcoming context-based behavior relevant brain states of the human have to be detected. This is achieved by brain reading (BR), a passive approach for single trial EEG analysis that makes use of supervised machine learning (ML) methods. In this work we propose that BR is able to detect concrete states of the interacting human. To support this, we show that BR detects patterns in the electroencephalogram (EEG) that can be related to event-related activity in the EEG like the P300, which are indicators of concrete states or brain processes like target recognition processes. Further, we improve the robustness and applicability of BR in application-oriented scenarios by identifying and combining most relevant training data for single trial classification and by applying classifier transfer. We show that training and testing, i.e., application of the classifier, can be carried out on different classes, if the samples of both classes miss a relevant pattern. Classifier transfer is important for the usage of BR in application scenarios, where only small amounts of training examples are available. Finally, we demonstrate a dual BR application in an experimental setup that requires similar behavior as performed during the teleoperation of a robotic arm. Here, target recognition processes and movement preparation processes are detected simultaneously. In summary, our findings contribute to the development of robust and stable predictive HMIs that enable the simultaneous support of different interaction behaviors. PMID:24358125

Mechanism and experimental research on ultra-precision grinding of ferrite

NASA Astrophysics Data System (ADS)

Ban, Xinxing; Zhao, Huiying; Dong, Longchao; Zhu, Xueliang; Zhang, Chupeng; Gu, Yawen

2017-02-01

Ultra-precision grinding of ferrite is conducted to investigate the removal mechanism. Effect of the accuracy of machine tool key components on grinding surface quality is analyzed. The surface generation model of ferrite ultra-precision grinding machining is established. In order to reveal the surface formation mechanism of ferrite in the process of ultraprecision grinding, furthermore, the scientific and accurate of the calculation model are taken into account to verify the grinding surface roughness, which is proposed. Orthogonal experiment is designed using the high precision aerostatic turntable and aerostatic spindle for ferrite which is a typical hard brittle materials. Based on the experimental results, the influence factors and laws of ultra-precision grinding surface of ferrite are discussed through the analysis of the surface roughness. The results show that the quality of ferrite grinding surface is the optimal parameters, when the wheel speed of 20000r/mm, feed rate of 10mm/min, grinding depth of 0.005mm, and turntable rotary speed of 5r/min, the surface roughness Ra can up to 75nm.

Application of machine learning techniques to lepton energy reconstruction in water Cherenkov detectors

NASA Astrophysics Data System (ADS)

Drakopoulou, E.; Cowan, G. A.; Needham, M. D.; Playfer, S.; Taani, M.

2018-04-01

The application of machine learning techniques to the reconstruction of lepton energies in water Cherenkov detectors is discussed and illustrated for TITUS, a proposed intermediate detector for the Hyper-Kamiokande experiment. It is found that applying these techniques leads to an improvement of more than 50% in the energy resolution for all lepton energies compared to an approach based upon lookup tables. Machine learning techniques can be easily applied to different detector configurations and the results are comparable to likelihood-function based techniques that are currently used.

Full-band error control and crack-free surface fabrication techniques for ultra-precision fly cutting of large-aperture KDP crystals

NASA Astrophysics Data System (ADS)

Zhang, F. H.; Wang, S. F.; An, C. H.; Wang, J.; Xu, Q.

2017-06-01

Large-aperture potassium dihydrogen phosphate (KDP) crystals are widely used in the laser path of inertial confinement fusion (ICF) systems. The most common method of manufacturing half-meter KDP crystals is ultra-precision fly cutting. When processing KDP crystals by ultra-precision fly cutting, the dynamic characteristics of the fly cutting machine and fluctuations in the fly cutting environment are translated into surface errors at different spatial frequency bands. These machining errors should be suppressed effectively to guarantee that KDP crystals meet the full-band machining accuracy specified in the evaluation index. In this study, the anisotropic machinability of KDP crystals and the causes of typical surface errors in ultra-precision fly cutting of the material are investigated. The structures of the fly cutting machine and existing processing parameters are optimized to improve the machined surface quality. The findings are theoretically and practically important in the development of high-energy laser systems in China.

Classifying smoking urges via machine learning.

PubMed

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

2016-12-01

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

Classifying smoking urges via machine learning

PubMed Central

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

2016-01-01

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

Ontology-based coupled optimisation design method using state-space analysis for the spindle box system of large ultra-precision optical grinding machine

NASA Astrophysics Data System (ADS)

Wang, Qianren; Chen, Xing; Yin, Yuehong; Lu, Jian

2017-08-01

With the increasing complexity of mechatronic products, traditional empirical or step-by-step design methods are facing great challenges with various factors and different stages having become inevitably coupled during the design process. Management of massive information or big data, as well as the efficient operation of information flow, is deeply involved in the process of coupled design. Designers have to address increased sophisticated situations when coupled optimisation is also engaged. Aiming at overcoming these difficulties involved in conducting the design of the spindle box system of ultra-precision optical grinding machine, this paper proposed a coupled optimisation design method based on state-space analysis, with the design knowledge represented by ontologies and their semantic networks. An electromechanical coupled model integrating mechanical structure, control system and driving system of the motor is established, mainly concerning the stiffness matrix of hydrostatic bearings, ball screw nut and rolling guide sliders. The effectiveness and precision of the method are validated by the simulation results of the natural frequency and deformation of the spindle box when applying an impact force to the grinding wheel.

Machining of Silicon-Ribbon-Forming Dies

NASA Technical Reports Server (NTRS)

Menna, A. A.

1985-01-01

Carbon extension for dies used in forming silicon ribbon crystals machined precisely with help of special tool. Die extension has edges beveled toward narrow flats at top, with slot precisely oriented and centered between flats and bevels. Cutting tool assembled from standard angle cutter and circular saw or saws. Angle cutters cuts bevels while slot saw cuts slot between them. In alternative version, custom-ground edges or additional circular saws also cut flats simultaneously.

Methods for the Precise Locating and Forming of Arrays of Curved Features into a Workpiece

DOEpatents

Gill, David Dennis; Keeler, Gordon A.; Serkland, Darwin K.; Mukherjee, Sayan D.

2008-10-14

Methods for manufacturing high precision arrays of curved features (e.g. lenses) in the surface of a workpiece are described utilizing orthogonal sets of inter-fitting locating grooves to mate a workpiece to a workpiece holder mounted to the spindle face of a rotating machine tool. The matching inter-fitting groove sets in the workpiece and the chuck allow precisely and non-kinematically indexing the workpiece to locations defined in two orthogonal directions perpendicular to the turning axis of the machine tool. At each location on the workpiece a curved feature can then be on-center machined to create arrays of curved features on the workpiece. The averaging effect of the corresponding sets of inter-fitting grooves provide for precise repeatability in determining, the relative locations of the centers of each of the curved features in an array of curved features.

Comparison study on disturbance estimation techniques in precise slow motion control

NASA Astrophysics Data System (ADS)

Fan, S.; Nagamune, R.; Altintas, Y.; Fan, D.; Zhang, Z.

2010-08-01

Precise low speed motion control is important for the industrial applications of both micro-milling machine tool feed drives and electro-optical tracking servo systems. It calls for precise position and instantaneous velocity measurement and disturbance, which involves direct drive motor force ripple, guide way friction and cutting force etc., estimation. This paper presents a comparison study on dynamic response and noise rejection performance of three existing disturbance estimation techniques, including the time-delayed estimators, the state augmented Kalman Filters and the conventional disturbance observers. The design technique essentials of these three disturbance estimators are introduced. For designing time-delayed estimators, it is proposed to substitute Kalman Filter for Luenberger state observer to improve noise suppression performance. The results show that the noise rejection performances of the state augmented Kalman Filters and the time-delayed estimators are much better than the conventional disturbance observers. These two estimators can give not only the estimation of the disturbance but also the low noise level estimations of position and instantaneous velocity. The bandwidth of the state augmented Kalman Filters is wider than the time-delayed estimators. In addition, the state augmented Kalman Filters can give unbiased estimations of the slow varying disturbance and the instantaneous velocity, while the time-delayed estimators can not. The simulation and experiment conducted on X axis of a 2.5-axis prototype micro milling machine are provided.

Application of Multimodality Imaging Fusion Technology in Diagnosis and Treatment of Malignant Tumors under the Precision Medicine Plan.

PubMed

Wang, Shun-Yi; Chen, Xian-Xia; Li, Yi; Zhang, Yu-Ying

2016-12-20

The arrival of precision medicine plan brings new opportunities and challenges for patients undergoing precision diagnosis and treatment of malignant tumors. With the development of medical imaging, information on different modality imaging can be integrated and comprehensively analyzed by imaging fusion system. This review aimed to update the application of multimodality imaging fusion technology in the precise diagnosis and treatment of malignant tumors under the precision medicine plan. We introduced several multimodality imaging fusion technologies and their application to the diagnosis and treatment of malignant tumors in clinical practice. The data cited in this review were obtained mainly from the PubMed database from 1996 to 2016, using the keywords of "precision medicine", "fusion imaging", "multimodality", and "tumor diagnosis and treatment". Original articles, clinical practice, reviews, and other relevant literatures published in English were reviewed. Papers focusing on precision medicine, fusion imaging, multimodality, and tumor diagnosis and treatment were selected. Duplicated papers were excluded. Multimodality imaging fusion technology plays an important role in tumor diagnosis and treatment under the precision medicine plan, such as accurate location, qualitative diagnosis, tumor staging, treatment plan design, and real-time intraoperative monitoring. Multimodality imaging fusion systems could provide more imaging information of tumors from different dimensions and angles, thereby offing strong technical support for the implementation of precision oncology. Under the precision medicine plan, personalized treatment of tumors is a distinct possibility. We believe that multimodality imaging fusion technology will find an increasingly wide application in clinical practice.

Nanometric edge profile measurement of cutting tools on a diamond turning machine

NASA Astrophysics Data System (ADS)

Asai, Takemi; Arai, Yoshikazu; Cui, Yuguo; Gao, Wei

2008-10-01

Single crystal diamond tools are used for fabrication of precision parts [1-5]. Although there are many types of tools that are supplied, the tools with round nose are popular for machining very smooth surfaces. Tools with small nose radii, small wedge angles and included angles are also being utilized for fabrication of micro structured surfaces such as microlens arrays [6], diffractive optical elements and so on. In ultra precision machining, tools are very important as a part of the machining equipment. The roughness or profile of machined surface may become out of desired tolerance. It is thus necessary to know the state of the tool edge accurately. To meet these requirements, an atomic force microscope (AFM) for measuring the 3D edge profiles of tools having nanometer-scale cutting edge radii with high resolution has been developed [7-8]. Although the AFM probe unit is combined with an optical sensor for aligning the measurement probe with the tools edge top to be measured in short time in this system, this time only the AFM probe unit was used. During the measurement time, that was attached onto the ultra precision turning machine to confirm the possibility of profile measurement system.

Model-based machine learning.

PubMed

Bishop, Christopher M

2013-02-13

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications.

Model-based machine learning

PubMed Central

Bishop, Christopher M.

2013-01-01

Several decades of research in the field of machine learning have resulted in a multitude of different algorithms for solving a broad range of problems. To tackle a new application, a researcher typically tries to map their problem onto one of these existing methods, often influenced by their familiarity with specific algorithms and by the availability of corresponding software implementations. In this study, we describe an alternative methodology for applying machine learning, in which a bespoke solution is formulated for each new application. The solution is expressed through a compact modelling language, and the corresponding custom machine learning code is then generated automatically. This model-based approach offers several major advantages, including the opportunity to create highly tailored models for specific scenarios, as well as rapid prototyping and comparison of a range of alternative models. Furthermore, newcomers to the field of machine learning do not have to learn about the huge range of traditional methods, but instead can focus their attention on understanding a single modelling environment. In this study, we show how probabilistic graphical models, coupled with efficient inference algorithms, provide a very flexible foundation for model-based machine learning, and we outline a large-scale commercial application of this framework involving tens of millions of users. We also describe the concept of probabilistic programming as a powerful software environment for model-based machine learning, and we discuss a specific probabilistic programming language called Infer.NET, which has been widely used in practical applications. PMID:23277612

[Precision nutrition in the era of precision medicine].

PubMed

Chen, P Z; Wang, H

2016-12-06

Precision medicine has been increasingly incorporated into clinical practice and is enabling a new era for disease prevention and treatment. As an important constituent of precision medicine, precision nutrition has also been drawing more attention during physical examinations. The main aim of precision nutrition is to provide safe and efficient intervention methods for disease treatment and management, through fully considering the genetics, lifestyle (dietary, exercise and lifestyle choices), metabolic status, gut microbiota and physiological status (nutrient level and disease status) of individuals. Three major components should be considered in precision nutrition, including individual criteria for sufficient nutritional status, biomarker monitoring or techniques for nutrient detection and the applicable therapeutic or intervention methods. It was suggested that, in clinical practice, many inherited and chronic metabolic diseases might be prevented or managed through precision nutritional intervention. For generally healthy populations, because lifestyles, dietary factors, genetic factors and environmental exposures vary among individuals, precision nutrition is warranted to improve their physical activity and reduce disease risks. In summary, research and practice is leading toward precision nutrition becoming an integral constituent of clinical nutrition and disease prevention in the era of precision medicine.

Applications of RNA Indexes for Precision Oncology in Breast Cancer.

PubMed

Ma, Liming; Liang, Zirui; Zhou, Hui; Qu, Lianghu

2018-05-09

Precision oncology aims to offer the most appropriate treatments to cancer patients mainly based on their individual genetic information. Genomics has provided numerous valuable data on driver mutations and risk loci; however, it remains a formidable challenge to transform these data into therapeutic agents. Transcriptomics describes the multifarious expression patterns of both mRNAs and non-coding RNAs (ncRNAs), which facilitates the deciphering of genomic codes. In this review, we take breast cancer as an example to demonstrate the applications of these rich RNA resources in precision medicine exploration. These include the use of mRNA profiles in triple-negative breast cancer (TNBC) subtyping to inform corresponding candidate targeted therapies; current advancements and achievements of high-throughput RNA interference (RNAi) screening technologies in breast cancer; and microRNAs as functional signatures for defining cell identities and regulating the biological activities of breast cancer cells. We summarize the benefits of transcriptomic analyses in breast cancer management and propose that unscrambling the core signaling networks of cancer may be an important task of multiple-omic data integration for precision oncology. Copyright © 2018 The Authors. Production and hosting by Elsevier B.V. All rights reserved.

Lateral vibration control of a precise machine using magneto-rheological mounts featuring multiple directional damping effect

NASA Astrophysics Data System (ADS)

Kim, Hyung Tae; Jeong, An Mok; Kim, Hyo Young; An, Jong Wook; Kim, Cheol Ho; Jin, Kyung Chan; Choi, Seung-Bok

2018-03-01

In a previous work, magneto-rheological (MR) dampers were originally designed and implemented for reducing the vertical low-frequency vibration occurring in precise semi-conductor manufacturing equipment. To reduce the vibrations, an isolator levitated the manufacturing machine from the floor using pneumatic pressure which cut off the external vibration, while the MR damper was used to decrease the transient response of the isolator. However, it has been found that the MR damper also provides a damping effect on the lateral vibration induced by the high-speed plane motions. Therefore, in this work both vertical and lateral vibrations are controlled using the yield and shear stresses of the lateral directions generated from the MR fluids by applying a magnetic field. After deriving a vibration control model, an overall control logic is formulated considering both vertical and lateral vibrations. In this control strategy, a feedback loop associated with the laser sensor is used for vertical vibration control, while a feed-forward loop with the motion information is used for lateral vibration control. The experimental results show that the proposed concept is highly effective for lateral vibration control using the damping effect on multiple directions.

High efficiency machining technology and equipment for edge chamfer of KDP crystals

NASA Astrophysics Data System (ADS)

Chen, Dongsheng; Wang, Baorui; Chen, Jihong

2016-10-01

Potassium dihydrogen phosphate (KDP) is a type of nonlinear optical crystal material. To Inhibit the transverse stimulated Raman scattering of laser beam and then enhance the optical performance of the optics, the edges of the large-sized KDP crystal needs to be removed to form chamfered faces with high surface quality (RMS<5 nm). However, as the depth of cut (DOC) of fly cutting is usually several, its machining efficiency is too low to be accepted for chamfering of the KDP crystal as the amount of materials to be removed is in the order of millimeter. This paper proposes a novel hybrid machining method, which combines precision grinding with fly cutting, for crackless and high efficiency chamfer of KDP crystal. A specialized machine tool, which adopts aerostatic bearing linear slide and aerostatic bearing spindle, was developed for chamfer of the KDP crystal. The aerostatic bearing linear slide consists of an aerostatic bearing guide with linearity of 0.1 μm/100mm and a linear motor to achieve linear feeding with high precision and high dynamic performance. The vertical spindle consists of an aerostatic bearing spindle with the rotation accuracy (axial) of 0.05 microns and Fork type flexible connection precision driving mechanism. The machining experiment on flying and grinding was carried out, the optimize machining parameters was gained by a series of experiment. Surface roughness of 2.4 nm has been obtained. The machining efficiency can be improved by six times using the combined method to produce the same machined surface quality.

Diamond Machining of an Off-Axis Biconic Aspherical Mirror

NASA Technical Reports Server (NTRS)

Ohl, Raymond G.; Preuss, Werner; Sohn, Alex; MacKenty, John

2009-01-01

Two diamond-machining methods have been developed as part of an effort to design and fabricate an off-axis, biconic ellipsoidal, concave aluminum mirror for an infrared spectrometer at the Kitt Peak National Observatory. Beyond this initial application, the methods can be expected to enable satisfaction of requirements for future instrument mirrors having increasingly complex (including asymmetrical), precise shapes that, heretofore, could not readily be fabricated by diamond machining or, in some cases, could not be fabricated at all. In the initial application, the mirror is prescribed, in terms of Cartesian coordinates x and y, by aperture dimensions of 94 by 76 mm, placements of -2 mm off axis in x and 227 mm off axis in y, an x radius of curvature of 377 mm, a y radius of curvature of 407 mm, an x conic constant of 0.078, and a y conic constant of 0.127. The aspect ratio of the mirror blank is about 6. One common, "diamond machining" process uses single-point diamond turning (SPDT). However, it is impossible to generate the required off-axis, biconic ellipsoidal shape by conventional SPDT because (1) rotational symmetry is an essential element of conventional SPDT and (2) the present off-axis biconic mirror shape lacks rotational symmetry. Following conventional practice, it would be necessary to make this mirror from a glass blank by computer-controlled polishing, which costs more than diamond machining and yields a mirror that is more difficult to mount to a metal bench. One of the two present diamond machining methods involves the use of an SPDT machine equipped with a fast tool servo (FTS). The SPDT machine is programmed to follow the rotationally symmetric asphere that best fits the desired off-axis, biconic ellipsoidal surface. The FTS is actuated in synchronism with the rotation of the SPDT machine to generate the difference between the desired surface and the best-fit rotationally symmetric asphere. In order to minimize the required stroke of the FTS

Graphite fiber reinforced structure for supporting machine tools

DOEpatents

Knight, Jr., Charles E.; Kovach, Louis; Hurst, John S.

1978-01-01

Machine tools utilized in precision machine operations require tool support structures which exhibit minimal deflection, thermal expansion and vibration characteristics. The tool support structure of the present invention is a graphite fiber reinforced composite in which layers of the graphite fibers or yarn are disposed in a 0/90.degree. pattern and bonded together with an epoxy resin. The finished composite possesses a low coefficient of thermal expansion and a substantially greater elastic modulus, stiffness-to-weight ratio, and damping factor than a conventional steel tool support utilized in similar machining operations.

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Application of ARAMIS capabilities to space project functional elements

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities and their related ground support functions are studied, so that informed decisions can be made on which aspects of ARAMIS to develop. The specific tasks which will be required by future space project tasks are identified and the relative merits of these options are evaluated. The ARAMIS options defined and researched span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Atomically precise edge chlorination of nanographenes and its application in graphene nanoribbons

PubMed Central

Tan, Yuan-Zhi; Yang, Bo; Parvez, Khaled; Narita, Akimitsu; Osella, Silvio; Beljonne, David; Feng, Xinliang; Müllen, Klaus

2013-01-01

Chemical functionalization is one of the most powerful and widely used strategies to control the properties of nanomaterials, particularly in the field of graphene. However, the ill-defined structure of the present functionalized graphene inhibits atomically precise structural characterization and structure-correlated property modulation. Here we present a general edge chlorination protocol for atomically precise functionalization of nanographenes at different scales from 1.2 to 3.4 nm and its application in graphene nanoribbons. The well-defined edge chlorination is unambiguously confirmed by X-ray single-crystal analysis, which also discloses the characteristic non-planar molecular shape and detailed bond lengths of chlorinated nanographenes. Chlorinated nanographenes and graphene nanoribbons manifest enhanced solution processability associated with decreases in the optical band gap and frontier molecular orbital energy levels, exemplifying the structure-correlated property modulation by precise edge chlorination. PMID:24212200

Addressing uncertainty in atomistic machine learning.

PubMed

Peterson, Andrew A; Christensen, Rune; Khorshidi, Alireza

2017-05-10

Machine-learning regression has been demonstrated to precisely emulate the potential energy and forces that are output from more expensive electronic-structure calculations. However, to predict new regions of the potential energy surface, an assessment must be made of the credibility of the predictions. In this perspective, we address the types of errors that might arise in atomistic machine learning, the unique aspects of atomistic simulations that make machine-learning challenging, and highlight how uncertainty analysis can be used to assess the validity of machine-learning predictions. We suggest this will allow researchers to more fully use machine learning for the routine acceleration of large, high-accuracy, or extended-time simulations. In our demonstrations, we use a bootstrap ensemble of neural network-based calculators, and show that the width of the ensemble can provide an estimate of the uncertainty when the width is comparable to that in the training data. Intriguingly, we also show that the uncertainty can be localized to specific atoms in the simulation, which may offer hints for the generation of training data to strategically improve the machine-learned representation.

Machine learning applications in proteomics research: how the past can boost the future.

PubMed

Kelchtermans, Pieter; Bittremieux, Wout; De Grave, Kurt; Degroeve, Sven; Ramon, Jan; Laukens, Kris; Valkenborg, Dirk; Barsnes, Harald; Martens, Lennart

2014-03-01

Machine learning is a subdiscipline within artificial intelligence that focuses on algorithms that allow computers to learn solving a (complex) problem from existing data. This ability can be used to generate a solution to a particularly intractable problem, given that enough data are available to train and subsequently evaluate an algorithm on. Since MS-based proteomics has no shortage of complex problems, and since publicly available data are becoming available in ever growing amounts, machine learning is fast becoming a very popular tool in the field. We here therefore present an overview of the different applications of machine learning in proteomics that together cover nearly the entire wet- and dry-lab workflow, and that address key bottlenecks in experiment planning and design, as well as in data processing and analysis. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Normal contour error measurement on-machine and compensation method for polishing complex surface by MRF

NASA Astrophysics Data System (ADS)

Chen, Hua; Chen, Jihong; Wang, Baorui; Zheng, Yongcheng

2016-10-01

The Magnetorheological finishing (MRF) process, based on the dwell time method with the constant normal spacing for flexible polishing, would bring out the normal contour error in the fine polishing complex surface such as aspheric surface. The normal contour error would change the ribbon's shape and removal characteristics of consistency for MRF. Based on continuously scanning the normal spacing between the workpiece and the finder by the laser range finder, the novel method was put forward to measure the normal contour errors while polishing complex surface on the machining track. The normal contour errors was measured dynamically, by which the workpiece's clamping precision, multi-axis machining NC program and the dynamic performance of the MRF machine were achieved for the verification and security check of the MRF process. The unit for measuring the normal contour errors of complex surface on-machine was designed. Based on the measurement unit's results as feedback to adjust the parameters of the feed forward control and the multi-axis machining, the optimized servo control method was presented to compensate the normal contour errors. The experiment for polishing 180mm × 180mm aspherical workpiece of fused silica by MRF was set up to validate the method. The results show that the normal contour error was controlled in less than 10um. And the PV value of the polished surface accuracy was improved from 0.95λ to 0.09λ under the conditions of the same process parameters. The technology in the paper has been being applied in the PKC600-Q1 MRF machine developed by the China Academe of Engineering Physics for engineering application since 2014. It is being used in the national huge optical engineering for processing the ultra-precision optical parts.

Extreme learning machine for ranking: generalization analysis and applications.

PubMed

Chen, Hong; Peng, Jiangtao; Zhou, Yicong; Li, Luoqing; Pan, Zhibin

2014-05-01

The extreme learning machine (ELM) has attracted increasing attention recently with its successful applications in classification and regression. In this paper, we investigate the generalization performance of ELM-based ranking. A new regularized ranking algorithm is proposed based on the combinations of activation functions in ELM. The generalization analysis is established for the ELM-based ranking (ELMRank) in terms of the covering numbers of hypothesis space. Empirical results on the benchmark datasets show the competitive performance of the ELMRank over the state-of-the-art ranking methods. Copyright © 2014 Elsevier Ltd. All rights reserved.

Military and Government Applications of Human-Machine Communication by Voice

NASA Astrophysics Data System (ADS)

Weinstein, Clifford J.

1995-10-01

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs.

Machine learning models for lipophilicity and their domain of applicability.

PubMed

Schroeter, Timon; Schwaighofer, Anton; Mika, Sebastian; Laak, Antonius Ter; Suelzle, Detlev; Ganzer, Ursula; Heinrich, Nikolaus; Müller, Klaus-Robert

2007-01-01

Unfavorable lipophilicity and water solubility cause many drug failures; therefore these properties have to be taken into account early on in lead discovery. Commercial tools for predicting lipophilicity usually have been trained on small and neutral molecules, and are thus often unable to accurately predict in-house data. Using a modern Bayesian machine learning algorithm--a Gaussian process model--this study constructs a log D7 model based on 14,556 drug discovery compounds of Bayer Schering Pharma. Performance is compared with support vector machines, decision trees, ridge regression, and four commercial tools. In a blind test on 7013 new measurements from the last months (including compounds from new projects) 81% were predicted correctly within 1 log unit, compared to only 44% achieved by commercial software. Additional evaluations using public data are presented. We consider error bars for each method (model based error bars, ensemble based, and distance based approaches), and investigate how well they quantify the domain of applicability of each model.

Precise positioning method for multi-process connecting based on binocular vision

NASA Astrophysics Data System (ADS)

Liu, Wei; Ding, Lichao; Zhao, Kai; Li, Xiao; Wang, Ling; Jia, Zhenyuan

2016-01-01

With the rapid development of aviation and aerospace, the demand for metal coating parts such as antenna reflector, eddy-current sensor and signal transmitter, etc. is more and more urgent. Such parts with varied feature dimensions, complex three-dimensional structures, and high geometric accuracy are generally fabricated by the combination of different manufacturing technology. However, it is difficult to ensure the machining precision because of the connection error between different processing methods. Therefore, a precise positioning method is proposed based on binocular micro stereo vision in this paper. Firstly, a novel and efficient camera calibration method for stereoscopic microscope is presented to solve the problems of narrow view field, small depth of focus and too many nonlinear distortions. Secondly, the extraction algorithms for law curve and free curve are given, and the spatial position relationship between the micro vision system and the machining system is determined accurately. Thirdly, a precise positioning system based on micro stereovision is set up and then embedded in a CNC machining experiment platform. Finally, the verification experiment of the positioning accuracy is conducted and the experimental results indicated that the average errors of the proposed method in the X and Y directions are 2.250 μm and 1.777 μm, respectively.

High-precision processing and detection of the high-caliber off-axis aspheric mirror

NASA Astrophysics Data System (ADS)

Dai, Chen; Li, Ang; Xu, Lingdi; Zhang, Yingjie

2017-10-01

To achieve the efficient, controllable, digital processing and high-precision detection of the high-caliber off-axis aspheric mirror, meeting the high-level development needs of the modern high-resolution, large field of space optical remote sensing camera, we carried out the research on high precision machining and testing technology of off-axis aspheric mirror. First, we forming the off-axis aspheric sample with diameter of 574mm × 302mm by milling it with milling machine, and then the intelligent robot equipment was used for off-axis aspheric high precision polishing. Surface detection of the sample will be proceed with the off-axis aspheric contact contour detection technology and offaxis non-spherical surface interference detection technology after its fine polishing using ion beam equipment. The final surface accuracy RMS is 12nm.

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.

Probability machines: consistent probability estimation using nonparametric learning machines.

PubMed

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

2012-01-01

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

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

PubMed

Casey, M

1996-08-15

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

High-intensity fibre laser design for micro-machining applications

NASA Astrophysics Data System (ADS)

Ortiz-Neria, D. I.; Martinez-Piñón, F.; Hernandez-Escamilla, H.; Alvarez-Chavez, J. A.

2010-11-01

This work is focused on the design of a 250W high-intensity continuous-wave fibre optic laser with a 15μm spot size beam and a beam parameter product (BPP) of 1.8 for its use on Laser-assisted Cold Spray process (LCS) in the micro-machining areas. The metal-powder deposition process LCS, is a novel method based on Cold Spray technique (CS) assisted by laser technology. The LCS accelerates metal powders by the use of a high-pressure gas in order to achieve flash welding of particles over substrate. In LCS, the critical velocity of impact is lower with respect with CS while the powder particle is heated before the deposition by a laser beam. Furthermore, LCS does not heat the powder to achieve high temperatures as it happens in plasma processes. This property puts aside cooling problems which normally happen in sintered processes with high oxygen/nitrogen concentration levels. LCS will be used not only in deposition of thin layers. After careful design, proof of concept, experimental data, and prototype development, it should be feasible to perform micro-machining precise work with the use of the highintensity fibre laser presented in this work, and selective deposition of particles, in a similar way to the well-known Direct Metal Laser Sintering process (DMLS). The fibre laser consists on a large-mode area, Yb3+-doped, semi-diffraction limited, 25-m fibre laser cavity, operating in continuous wave regime. The fibre shows an arguably high slope-efficiency with no signs of roll-over. The measured M2 value is 1.8 and doping concentration of 15000ppm. It was made with a slight modification of the traditional MCVD technique. A full optical characterization will be presented.

Design Enhancement and Performance Examination of External Rotor Switched Flux Permanent Magnet Machine for Downhole Application

NASA Astrophysics Data System (ADS)

Kumar, R.; Sulaiman, E.; Soomro, H. A.; Jusoh, L. I.; Bahrim, F. S.; Omar, M. F.

2017-08-01

The recent change in innovation and employments of high-temperature magnets, permanent magnet flux switching machine (PMFSM) has turned out to be one of the suitable contenders for seaward boring, however, less intended for downhole because of high atmospheric temperature. Subsequently, this extensive review manages the design enhancement and performance examination of external rotor PMFSM for the downhole application. Preparatory, the essential design parameters required for machine configuration are computed numerically. At that point, the design enhancement strategy is actualized through deterministic technique. At last, preliminary and refined execution of the machine is contrasted and as a consequence, the yield torque is raised from 16.39Nm to 33.57Nm while depreciating the cogging torque and PM weight up to 1.77Nm and 0.79kg, individually. In this manner, it is inferred that purposed enhanced design of 12slot-22pole with external rotor is convenient for the downhole application.

Manufacturing and Machining Challenges of Hybrid Aluminium Metal Matix Composites

NASA Astrophysics Data System (ADS)

Baburaja, Kammuluri; Sainadh Teja, S.; Karthik Sri, D.; Kuldeep, J.; Gowtham, V.

2017-08-01

Manufacturing which involves material removal processes or material addition processes or material transformation processes. One or all the processes to obtain the final desired properties for a material with desired shape which meets the required precision and accuracy values for the expected service life of a material in working conditions. Researchers found the utility of aluminium to be the second largest after steel. Aluminium and its metal matrix composite possess wide applications in various applications in aerospace industry, automobile industry, Constructions and even in kitchen utensils. Hybrid Al-MMCconsist of two different materials, and one will be from organic origin along with the base material. In this paper an attempt is made to bring out the importance of utilization of aluminium and the challenges concerned in manufacturing and machining of hybrid aluminium MMC.

Open source machine-learning algorithms for the prediction of optimal cancer drug therapies.

PubMed

Huang, Cai; Mezencev, Roman; McDonald, John F; Vannberg, Fredrik

2017-01-01

Precision medicine is a rapidly growing area of modern medical science and open source machine-learning codes promise to be a critical component for the successful development of standardized and automated analysis of patient data. One important goal of precision cancer medicine is the accurate prediction of optimal drug therapies from the genomic profiles of individual patient tumors. We introduce here an open source software platform that employs a highly versatile support vector machine (SVM) algorithm combined with a standard recursive feature elimination (RFE) approach to predict personalized drug responses from gene expression profiles. Drug specific models were built using gene expression and drug response data from the National Cancer Institute panel of 60 human cancer cell lines (NCI-60). The models are highly accurate in predicting the drug responsiveness of a variety of cancer cell lines including those comprising the recent NCI-DREAM Challenge. We demonstrate that predictive accuracy is optimized when the learning dataset utilizes all probe-set expression values from a diversity of cancer cell types without pre-filtering for genes generally considered to be "drivers" of cancer onset/progression. Application of our models to publically available ovarian cancer (OC) patient gene expression datasets generated predictions consistent with observed responses previously reported in the literature. By making our algorithm "open source", we hope to facilitate its testing in a variety of cancer types and contexts leading to community-driven improvements and refinements in subsequent applications.

Automated Inspection And Precise Grinding Of Gears

NASA Technical Reports Server (NTRS)

Frint, Harold; Glasow, Warren

1995-01-01

Method of precise grinding of spiral bevel gears involves automated inspection of gear-tooth surfaces followed by adjustments of machine-tool settings to minimize differences between actual and nominal surfaces. Similar to method described in "Computerized Inspection of Gear-Tooth Surfaces" (LEW-15736). Yields gears of higher quality, with significant reduction in manufacturing and inspection time.

Military and government applications of human-machine communication by voice.

PubMed Central

Weinstein, C J

1995-01-01

This paper describes a range of opportunities for military and government applications of human-machine communication by voice, based on visits and contacts with numerous user organizations in the United States. The applications include some that appear to be feasible by careful integration of current state-of-the-art technology and others that will require a varying mix of advances in speech technology and in integration of the technology into applications environments. Applications that are described include (1) speech recognition and synthesis for mobile command and control; (2) speech processing for a portable multifunction soldier's computer; (3) speech- and language-based technology for naval combat team tactical training; (4) speech technology for command and control on a carrier flight deck; (5) control of auxiliary systems, and alert and warning generation, in fighter aircraft and helicopters; and (6) voice check-in, report entry, and communication for law enforcement agents or special forces. A phased approach for transfer of the technology into applications is advocated, where integration of applications systems is pursued in parallel with advanced research to meet future needs. Images Fig. 1 Fig. 2 Fig. 3 Fig. 4 Fig. 5 Fig. 6 PMID:7479718

High-efficiency machining methods for aviation materials

NASA Astrophysics Data System (ADS)

Kononov, V. K.

1991-07-01

The papers contained in this volume present results of theoretical and experimental studies aimed at increasing the efficiency of cutting tools during the machining of high-temperature materials and titanium alloys. Specific topics discussed include a study of the performance of disk cutters during the machining of flexible parts of a high-temperature alloy, VZhL14N; a study of the wear resistance of cutters of hard alloys of various types; effect of a deformed electric field on the precision of the electrochemical machining of gas turbine engine components; and efficient machining of parts of composite materials. The discussion also covers the effect of the technological process structure on the residual stress distribution in the blades of gas turbine engines; modeling of the multiparameter assembly of engineering products for a specified priority of geometrical output parameters; and a study of the quality of the surface and surface layer of specimens machined by a high-temperature pulsed plasma.

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

PubMed Central

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

2013-01-01

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

Machine learning in genetics and genomics

PubMed Central

Libbrecht, Maxwell W.; Noble, William Stafford

2016-01-01

The field of machine learning promises to enable computers to assist humans in making sense of large, complex data sets. In this review, we outline some of the main applications of machine learning to genetic and genomic data. In the process, we identify some recurrent challenges associated with this type of analysis and provide general guidelines to assist in the practical application of machine learning to real genetic and genomic data. PMID:25948244

Applications of color machine vision in the agricultural and food industries

NASA Astrophysics Data System (ADS)

Zhang, Min; Ludas, Laszlo I.; Morgan, Mark T.; Krutz, Gary W.; Precetti, Cyrille J.

1999-01-01

Color is an important factor in Agricultural and the Food Industry. Agricultural or prepared food products are often grade by producers and consumers using color parameters. Color is used to estimate maturity, sort produce for defects, but also perform genetic screenings or make an aesthetic judgement. The task of sorting produce following a color scale is very complex, requires special illumination and training. Also, this task cannot be performed for long durations without fatigue and loss of accuracy. This paper describes a machine vision system designed to perform color classification in real-time. Applications for sorting a variety of agricultural products are included: e.g. seeds, meat, baked goods, plant and wood.FIrst the theory of color classification of agricultural and biological materials is introduced. Then, some tools for classifier development are presented. Finally, the implementation of the algorithm on real-time image processing hardware and example applications for industry is described. This paper also presented an image analysis algorithm and a prototype machine vision system which was developed for industry. This system will automatically locate the surface of some plants using digital camera and predict information such as size, potential value and type of this plant. The algorithm developed will be feasible for real-time identification in an industrial environment.

Contributions a l'etude et a l'application industrielle de la machine asynchrone

NASA Astrophysics Data System (ADS)

Ouhrouche, Mohand-Ameziane

The work presented in this thesis, done in the Electrical Drives Laboratory of Electrical and Computer Engineering Department, deals with the industrial applications of a three-phase induction machine (electrical drives and electricity generation). This thesis, characterized by its multidisciplinary content, has two major parts. The first one deals with the on-line and off-line parametric identification of the induction machine model necessary to achieve accurate vector control strategy. The second part, which is a resume of a research work sponsored by Hydro-Quebec, deals with the application of an induction machine in Asynchronous Non Utility Generators units (ANUG). As it is shown in the following, major scientific contributions are made in both two parts. In the first part of our research work, we propose a new speed sensorless vector control strategy for an induction machine, which is adaptive to the rotor resistance variations. The proposed control strategy is based on the Extended Kalman Filter approach and a decoupling controller which takes into account the rotor resistance variations. The consideration of coupled electrical and mechanical modes leads to a fifth order nonlinear model of the induction machine. The load torque is taken as a function of the rotor angular speed. The Extended Kalman Filter, based on the process's nonlinear (bilinear) model, estimate simultaneously the rotor resistance, angular speed and the flux vector from the startup to the steady state equilibrium point. The machine-converter-control system is implemented in MATLAB/SIMULINK environment and the obtained results confirm the robustness of the proposed scheme. As in the electrical drives erea, the induction machine is now widely used by small to medium power Non Utility Generator units (NUG) to produce electricity. In Quebec, these NUGs units are integrated into the Hydro-Quebec 25 kV distribution system via transformer which exhibit nonlinear characteristics. We have shown by

Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis

PubMed Central

Balto, Julia M; Kinnett-Hopkins, Dominique L

2016-01-01

Background There is increased interest in the application of smartphone applications and wearable motion sensors among multiple sclerosis (MS) patients. Objective This study examined the accuracy and precision of common smartphone applications and motion sensors for measuring steps taken by MS patients while walking on a treadmill. Methods Forty-five MS patients (Expanded Disability Status Scale (EDSS) = 1.0–5.0) underwent two 500-step walking trials at comfortable walking speed on a treadmill. Participants wore five motion sensors: the Digi-Walker SW-200 pedometer (Yamax), the UP2 and UP Move (Jawbone), and the Flex and One (Fitbit). The smartphone applications were Health (Apple), Health Mate (Withings), and Moves (ProtoGeo Oy). Results The Fitbit One had the best absolute (mean = 490.6 steps, 95% confidence interval (CI) = 485.6–495.5 steps) and relative accuracy (1.9% error), and absolute (SD = 16.4) and relative precision (coefficient of variation (CV) = 0.0), for the first 500-step walking trial; this was repeated with the second trial. Relative accuracy was correlated with slower walking speed for the first (rs = −.53) and second (rs = −.53) trials. Conclusion The results suggest that the waist-worn Fitbit One is the most precise and accurate sensor for measuring steps when walking on a treadmill, but future research is needed (testing the device across a broader range of disability, at different speeds, and in real-life walking conditions) before inclusion in clinical research and practice with MS patients. PMID:28607720

Accuracy and precision of smartphone applications and commercially available motion sensors in multiple sclerosis.

PubMed

Balto, Julia M; Kinnett-Hopkins, Dominique L; Motl, Robert W

2016-01-01

There is increased interest in the application of smartphone applications and wearable motion sensors among multiple sclerosis (MS) patients. This study examined the accuracy and precision of common smartphone applications and motion sensors for measuring steps taken by MS patients while walking on a treadmill. Forty-five MS patients (Expanded Disability Status Scale (EDSS) = 1.0-5.0) underwent two 500-step walking trials at comfortable walking speed on a treadmill. Participants wore five motion sensors: the Digi-Walker SW-200 pedometer (Yamax), the UP2 and UP Move (Jawbone), and the Flex and One (Fitbit). The smartphone applications were Health (Apple), Health Mate (Withings), and Moves (ProtoGeo Oy). The Fitbit One had the best absolute (mean = 490.6 steps, 95% confidence interval (CI) = 485.6-495.5 steps) and relative accuracy (1.9% error), and absolute (SD = 16.4) and relative precision (coefficient of variation (CV) = 0.0), for the first 500-step walking trial; this was repeated with the second trial. Relative accuracy was correlated with slower walking speed for the first ( r s  =  -.53) and second ( r s  =  -.53) trials. The results suggest that the waist-worn Fitbit One is the most precise and accurate sensor for measuring steps when walking on a treadmill, but future research is needed (testing the device across a broader range of disability, at different speeds, and in real-life walking conditions) before inclusion in clinical research and practice with MS patients.

A Study on Improvement of Machining Precision in a Medical Milling Robot

NASA Astrophysics Data System (ADS)

Sugita, Naohiko; Osa, Takayuki; Nakajima, Yoshikazu; Mori, Masahiko; Saraie, Hidenori; Mitsuishi, Mamoru

Minimal invasiveness and increasing of precision have recently become important issues in orthopedic surgery. The femur and tibia must be cut precisely for successful knee arthroplasty. The recent trend towards Minimally Invasive Surgery (MIS) has increased surgical difficulty since the incision length and open access area are small. In this paper, the result of deformation analysis of the robot and an active compensation method of robot deformation, which is based on an error map, are proposed and evaluated.

A strategy to apply machine learning to small datasets in materials science

NASA Astrophysics Data System (ADS)

Zhang, Ying; Ling, Chen

2018-12-01

There is growing interest in applying machine learning techniques in the research of materials science. However, although it is recognized that materials datasets are typically smaller and sometimes more diverse compared to other fields, the influence of availability of materials data on training machine learning models has not yet been studied, which prevents the possibility to establish accurate predictive rules using small materials datasets. Here we analyzed the fundamental interplay between the availability of materials data and the predictive capability of machine learning models. Instead of affecting the model precision directly, the effect of data size is mediated by the degree of freedom (DoF) of model, resulting in the phenomenon of association between precision and DoF. The appearance of precision-DoF association signals the issue of underfitting and is characterized by large bias of prediction, which consequently restricts the accurate prediction in unknown domains. We proposed to incorporate the crude estimation of property in the feature space to establish ML models using small sized materials data, which increases the accuracy of prediction without the cost of higher DoF. In three case studies of predicting the band gap of binary semiconductors, lattice thermal conductivity, and elastic properties of zeolites, the integration of crude estimation effectively boosted the predictive capability of machine learning models to state-of-art levels, demonstrating the generality of the proposed strategy to construct accurate machine learning models using small materials dataset.

Proceedings of the Eleventh Annual Precise Time and Time Interval (PTTI) Application and Planning Meeting. [conference

NASA Technical Reports Server (NTRS)

Wardrip, S. C. (Editor)

1979-01-01

Thirty eight papers are presented addressing various aspects of precise time and time interval applications. Areas discussed include: past accomplishments; state of the art systems; new and useful applications, procedures, and techniques; and fruitful directions for research efforts.

A computer architecture for intelligent machines

NASA Technical Reports Server (NTRS)

Lefebvre, D. R.; Saridis, G. N.

1992-01-01

The theory of intelligent machines proposes a hierarchical organization for the functions of an autonomous robot based on the principle of increasing precision with decreasing intelligence. An analytic formulation of this theory using information-theoretic measures of uncertainty for each level of the intelligent machine has been developed. The authors present a computer architecture that implements the lower two levels of the intelligent machine. The architecture supports an event-driven programming paradigm that is independent of the underlying computer architecture and operating system. Execution-level controllers for motion and vision systems are briefly addressed, as well as the Petri net transducer software used to implement coordination-level functions. A case study illustrates how this computer architecture integrates real-time and higher-level control of manipulator and vision systems.

Proceedings of the 30th Annual Precise Time and Time Interval (PTTI) Systems and Applications Meeting

NASA Technical Reports Server (NTRS)

Breakiron, Lee A. (Editor)

1999-01-01

This document is a compilation of technical papers presented at the 30th Annual Precise Time and Time Interval (PTTI) Systems and Applications Meeting held 1-3 December 1998 at the Hyatt Regency Hotel at Reston Town Center, Reston, Virginia. Papers are in the following categories: 1) Recent developments in rubidium, cesium, and hydrogen-based atomic frequency standards, and in trapped-ion and space clock technology; 2) National and international applications of PTTI technology with emphasis on GPS and GLONASS timing, atomic time scales, and telecommunications; 3) Applications of PTTI technology to evolving military navigation and communication systems; geodesy; aviation; and pulsars; and 4) Dissemination of precise time and frequency by means of GPS, geosynchronous communication satellites, computer networks, WAAS, and LORAN.

Research on carrying capacity of hydrostatic slideway on heavy-duty gantry CNC machine

NASA Astrophysics Data System (ADS)

Cui, Chao; Guo, Tieneng; Wang, Yijie; Dai, Qin

2017-05-01

Hydrostatic slideway is a key part in the heavy-duty gantry CNC machine, which supports the total weight of the gantry and moves smoothly along the table. Therefore, the oil film between sliding rails plays an important role on the carrying capacity and precision of machine. In this paper, the oil film in no friction is simulated with three-dimensional CFD. The carrying capacity of heavy hydrostatic slideway, pressure and velocity characteristic of the flow field are analyzed. The simulation result is verified through comparing with the experimental data obtained from the heavy-duty gantry machine. For the requirement of engineering, the oil film carrying capacity is analyzed with simplified theoretical method. The precision of the simplified method is evaluated and the effectiveness is verified with the experimental data. The simplified calculation method is provided for designing oil pad on heavy-duty gantry CNC machine hydrostatic slideway.

Low-cost precision rotary index calibration

NASA Astrophysics Data System (ADS)

Ng, T. W.; Lim, T. S.

2005-08-01

The traditional method for calibrating angular indexing repeatability of rotary axes on machine tools and measuring equipment is with a precision polygon (usually 12 sided) and an autocollimator or angular interferometer. Such a setup is typically expensive. Here, we propose a far more cost-effective approach that uses just a laser, diffractive optical element, and CCD camera. We show that significantly high accuracies can be achieved for angular index calibration.

The 22nd Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

Sydnor, Richard L. (Editor)

1990-01-01

Papers presented at the 22nd Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting are compiled. The following subject areas are covered: Rb, Cs, and H-based frequency standards and cryogenic and trapped-ion technology; satellite laser tracking networks, GLONASS timing, intercomparison of national time scales and international telecommunications; telecommunications, power distribution, platform positioning, and geophysical survey industries; military communications and navigation systems; and dissemination of precise time and frequency by means of GPS, GLONASS, MILSTAR, LORAN, and synchronous communication satellites.

Light-operated machines based on threaded molecular structures.

PubMed

Credi, Alberto; Silvi, Serena; Venturi, Margherita

2014-01-01

Rotaxanes and related species represent the most common implementation of the concept of artificial molecular machines, because the supramolecular nature of the interactions between the components and their interlocked architecture allow a precise control on the position and movement of the molecular units. The use of light to power artificial molecular machines is particularly valuable because it can play the dual role of "writing" and "reading" the system. Moreover, light-driven machines can operate without accumulation of waste products, and photons are the ideal inputs to enable autonomous operation mechanisms. In appropriately designed molecular machines, light can be used to control not only the stability of the system, which affects the relative position of the molecular components but also the kinetics of the mechanical processes, thereby enabling control on the direction of the movements. This step forward is necessary in order to make a leap from molecular machines to molecular motors.

Precision tool holder with flexure-adjustable, three degrees of freedom for a four-axis lathe

DOEpatents

Bono, Matthew J [Pleasanton, CA; Hibbard, Robin L [Livermore, CA

2008-03-04

A precision tool holder for precisely positioning a single point cutting tool on 4-axis lathe, such that the center of the radius of the tool nose is aligned with the B-axis of the machine tool, so as to facilitate the machining of precision meso-scale components with complex three-dimensional shapes with sub-.mu.m accuracy on a four-axis lathe. The device is designed to fit on a commercial diamond turning machine and can adjust the cutting tool position in three orthogonal directions with sub-micrometer resolution. In particular, the tool holder adjusts the tool position using three flexure-based mechanisms, with two flexure mechanisms adjusting the lateral position of the tool to align the tool with the B-axis, and a third flexure mechanism adjusting the height of the tool. Preferably, the flexures are driven by manual micrometer adjusters. In this manner, this tool holder simplifies the process of setting a tool with sub-.mu.m accuracy, to substantially reduce the time required to set the tool.

Achieving Small Structures in Thin NiTi Sheets for Medical Applications with Water Jet and Micro Machining: A Comparison

NASA Astrophysics Data System (ADS)

Frotscher, M.; Kahleyss, F.; Simon, T.; Biermann, D.; Eggeler, G.

2011-07-01

NiTi shape memory alloys (SMA) are used for a variety of applications including medical implants and tools as well as actuators, making use of their unique properties. However, due to the hardness and strength, in combination with the high elasticity of the material, the machining of components can be challenging. The most common machining techniques used today are laser cutting and electrical discharge machining (EDM). In this study, we report on the machining of small structures into binary NiTi sheets, applying alternative processing methods being well-established for other metallic materials. Our results indicate that water jet machining and micro milling can be used to machine delicate structures, even in very thin NiTi sheets. Further work is required to optimize the cut quality and the machining speed in order to increase the cost-effectiveness and to make both methods more competitive.

Integration of USB and firewire cameras in machine vision applications

NASA Astrophysics Data System (ADS)

Smith, Timothy E.; Britton, Douglas F.; Daley, Wayne D.; Carey, Richard

1999-08-01

Digital cameras have been around for many years, but a new breed of consumer market cameras is hitting the main stream. By using these devices, system designers and integrators will be well posited to take advantage of technological advances developed to support multimedia and imaging applications on the PC platform. Having these new cameras on the consumer market means lower cost, but it does not necessarily guarantee ease of integration. There are many issues that need to be accounted for like image quality, maintainable frame rates, image size and resolution, supported operating system, and ease of software integration. This paper will describe briefly a couple of the consumer digital standards, and then discuss some of the advantages and pitfalls of integrating both USB and Firewire cameras into computer/machine vision applications.

Vertical high-precision Michelson wavemeter

NASA Astrophysics Data System (ADS)

Morales, A.; de Urquijo, J.; Mendoza, A.

1993-01-01

We have designed and tested a traveling, Michelson-type vertical wavemeter for the wavelength measurement of tunable continuous-wave lasers in the visible part of the spectrum. The interferometer has two movable corner cubes, suspending vertically from a driving setup resembling Atwood's machine. To reduce the fraction-of-fringe error, a vernier-type coincidence circuit was used. Although simple, this wavemeter has a relative precision of 3.2 parts in 109 for an overall fringe count of about 7×106.

Fault Tolerant State Machines

NASA Technical Reports Server (NTRS)

Burke, Gary R.; Taft, Stephanie

2004-01-01

State machines are commonly used to control sequential logic in FPGAs and ASKS. An errant state machine can cause considerable damage to the device it is controlling. For example in space applications, the FPGA might be controlling Pyros, which when fired at the wrong time will cause a mission failure. Even a well designed state machine can be subject to random errors us a result of SEUs from the radiation environment in space. There are various ways to encode the states of a state machine, and the type of encoding makes a large difference in the susceptibility of the state machine to radiation. In this paper we compare 4 methods of state machine encoding and find which method gives the best fault tolerance, as well as determining the resources needed for each method.

Laser machining of explosives

DOEpatents

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

2000-01-01

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

Micro-machined resonator

DOEpatents

Godshall, N.A.; Koehler, D.R.; Liang, A.Y.; Smith, B.K.

1993-03-30

A micro-machined resonator, typically quartz, with upper and lower micro-machinable support members, or covers, having etched wells which may be lined with conductive electrode material, between the support members is a quartz resonator having an energy trapping quartz mesa capacitively coupled to the electrode through a diaphragm; the quartz resonator is supported by either micro-machined cantilever springs or by thin layers extending over the surfaces of the support. If the diaphragm is rigid, clock applications are available, and if the diaphragm is resilient, then transducer applications can be achieved. Either the thin support layers or the conductive electrode material can be integral with the diaphragm. In any event, the covers are bonded to form a hermetic seal and the interior volume may be filled with a gas or may be evacuated. In addition, one or both of the covers may include oscillator and interface circuitry for the resonator.

Micro-machined resonator

DOEpatents

Godshall, Ned A.; Koehler, Dale R.; Liang, Alan Y.; Smith, Bradley K.

1993-01-01

A micro-machined resonator, typically quartz, with upper and lower micro-machinable support members, or covers, having etched wells which may be lined with conductive electrode material, between the support members is a quartz resonator having an energy trapping quartz mesa capacitively coupled to the electrode through a diaphragm; the quartz resonator is supported by either micro-machined cantilever springs or by thin layers extending over the surfaces of the support. If the diaphragm is rigid, clock applications are available, and if the diaphragm is resilient, then transducer applications can be achieved. Either the thin support layers or the conductive electrode material can be integral with the diaphragm. In any event, the covers are bonded to form a hermetic seal and the interior volume may be filled with a gas or may be evacuated. In addition, one or both of the covers may include oscillator and interface circuitry for the resonator.

Man-machine communication - A transparent switchboard for computers

NASA Technical Reports Server (NTRS)

Rasmussen, H.

1971-01-01

Device uses pattern of transparent contact touch points that are put on cathode ray tube screen. Touch point system compels more precise and unambiguous communication between man and machine than is possible with any other means, and speeds up operation responses.

Genomically Encoded Analog Memory with Precise In vivo DNA Writing in Living Cell Populations

PubMed Central

Farzadfard, Fahim; Lu, Timothy K.

2014-01-01

Cellular memory is crucial to many natural biological processes and for sophisticated synthetic-biology applications. Existing cellular memories rely on epigenetic switches or recombinases, which are limited in scalability and recording capacity. Here, we use the DNA of living cell populations as genomic ‘tape recorders’ for the analog and distributed recording of long-term event histories. We describe a platform for generating single-stranded DNA (ssDNA) in vivo in response to arbitrary transcriptional signals. When co-expressed with a recombinase, these intracellularly expressed ssDNAs target specific genomic DNA addresses, resulting in precise mutations that accumulate in cell populations as a function of the magnitude and duration of the inputs. This platform could enable long-term cellular recorders for environmental and biomedical applications, biological state machines, and enhanced genome engineering strategies. PMID:25395541

Differences in liver stiffness values obtained with new ultrasound elastography machines and Fibroscan: A comparative study.

PubMed

Piscaglia, Fabio; Salvatore, Veronica; Mulazzani, Lorenzo; Cantisani, Vito; Colecchia, Antonio; Di Donato, Roberto; Felicani, Cristina; Ferrarini, Alessia; Gamal, Nesrine; Grasso, Valentina; Marasco, Giovanni; Mazzotta, Elena; Ravaioli, Federico; Ruggieri, Giacomo; Serio, Ilaria; Sitouok Nkamgho, Joules Fabrice; Serra, Carla; Festi, Davide; Schiavone, Cosima; Bolondi, Luigi

2017-07-01

Whether Fibroscan thresholds can be immediately adopted for none, some or all other shear wave elastography techniques has not been tested. The aim of the present study was to test the concordance of the findings obtained from 7 of the most recent ultrasound elastography machines with respect to Fibroscan. Sixteen hepatitis C virus-related patients with fibrosis ≥2 and having reliable results at Fibroscan were investigated in two intercostal spaces using 7 different elastography machines. Coefficients of both precision (an index of data dispersion) and accuracy (an index of bias correction factors expressing different magnitudes of changes in comparison to the reference) were calculated. Median stiffness values differed among the different machines as did coefficients of both precision (range 0.54-0.72) and accuracy (range 0.28-0.87). When the average of the measurements of two intercostal spaces was considered, coefficients of precision significantly increased with all machines (range 0.72-0.90) whereas of accuracy improved more scatteredly and by a smaller degree (range 0.40-0.99). The present results showed only moderate concordance of the majority of elastography machines with the Fibroscan results, preventing the possibility of the immediate universal adoption of Fibroscan thresholds for defining liver fibrosis staging for all new machines. Copyright © 2017 Editrice Gastroenterologica Italiana S.r.l. Published by Elsevier Ltd. All rights reserved.

Monel Machining

NASA Technical Reports Server (NTRS)

1983-01-01

Castle Industries, Inc. is a small machine shop manufacturing replacement plumbing repair parts, such as faucet, tub and ballcock seats. Therese Castley, president of Castle decided to introduce Monel because it offered a chance to improve competitiveness and expand the product line. Before expanding, Castley sought NERAC assistance on Monel technology. NERAC (New England Research Application Center) provided an information package which proved very helpful. The NASA database was included in NERAC's search and yielded a wealth of information on machining Monel.

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.

Modeling and Analysis of High Torque Density Transverse Flux Machines for Direct-Drive Applications

NASA Astrophysics Data System (ADS)

Hasan, Iftekhar

Commercially available permanent magnet synchronous machines (PMSM) typically use rare-earth-based permanent magnets (PM). However, volatility and uncertainty associated with the supply and cost of rare-earth magnets have caused a push for increased research into the development of non-rare-earth based PM machines and reluctance machines. Compared to other PMSM topologies, the Transverse Flux Machine (TFM) is a promising candidate to get higher torque densities at low speed for direct-drive applications, using non-rare-earth based PMs. The TFMs can be designed with a very small pole pitch which allows them to attain higher force density than conventional radial flux machines (RFM) and axial flux machines (AFM). This dissertation presents the modeling, electromagnetic design, vibration analysis, and prototype development of a novel non-rare-earth based PM-TFM for a direct-drive wind turbine application. The proposed TFM addresses the issues of low power factor, cogging torque, and torque ripple during the electromagnetic design phase. An improved Magnetic Equivalent Circuit (MEC) based analytical model was developed as an alternative to the time-consuming 3D Finite Element Analysis (FEA) for faster electromagnetic analysis of the TFM. The accuracy and reliability of the MEC model were verified, both with 3D-FEA and experimental results. The improved MEC model was integrated with a Particle Swarm Optimization (PSO) algorithm to further enhance the capability of the analytical tool for performing rigorous optimization of performance-sensitive machine design parameters to extract the highest torque density for rated speed. A novel concept of integrating the rotary transformer within the proposed TFM design was explored to completely eliminate the use of magnets from the TFM. While keeping the same machine envelope, and without changing the stator or rotor cores, the primary and secondary of a rotary transformer were embedded into the double-sided TFM. The proposed

Topologies for three-phase wound-field salient rotor switched-flux machines for HEV applications

NASA Astrophysics Data System (ADS)

Khan, Faisal; Sulaiman, Erwan; Ahmad, Md Zarafi; Husin, Zhafir Aizat; Mazlan, Mohamed Mubin Aizat

2015-05-01

Wound-field switched-flux machines (WFSFM) have an intrinsic simplicity and high speed that make them well suited to many hybrid electric vehicle (HEV) applications. However, overlap armature and field windings raised the copper losses in these machines. Furthermore, in previous design segmented-rotor is used which made the rotor less robust. To overcome these problems, this paper presents novel topologies for three-phase wound-field switched-flux machines. Both armature and field winding are located on the stator and rotor is composed of only stack of iron. Non-overlap armature and field windings and toothed-rotor are the clear advantages of these topologies as the copper losses gets reduce and rotor becomes more robust. Design feasibility and performance analysis of 12 slots and different rotor pole numbers are examined on the basis of coil arrangement test, peak armature flux linkage, back emf, cogging torque and average torque by using Finite Element Analysis(FEA).

Application of Smart Infrastructure Systems approach to precision medicine.

PubMed

Govindaraju, Diddahally R; Annaswamy, Anuradha M

2015-12-01

All biological variation is hierarchically organized dynamic network system of genomic components, organelles, cells, tissues, organs, individuals, families, populations and metapopulations. Individuals are axial in this hierarchy, as they represent antecedent, attendant and anticipated aspects of health, disease, evolution and medical care. Humans show individual specific genetic and clinical features such as complexity, cooperation, resilience, robustness, vulnerability, self-organization, latent and emergent behavior during their development, growth and senescence. Accurate collection, measurement, organization and analyses of individual specific data, embedded at all stratified levels of biological, demographic and cultural diversity - the big data - is necessary to make informed decisions on health, disease and longevity; which is a central theme of precision medicine initiative (PMI). This initiative also calls for the development of novel analytical approaches to handle complex multidimensional data. Here we suggest the application of Smart Infrastructure Systems (SIS) approach to accomplish some of the goals set forth by the PMI on the premise that biological systems and the SIS share many common features. The latter has been successfully employed in managing complex networks of non-linear adaptive controls, commonly encountered in smart engineering systems. We highlight their concordance and discuss the utility of the SIS approach in precision medicine programs.

In pursuit of precision: the calibration of minds and machines in late nineteenth-century psychology.

PubMed

Benschop, R; Draaisma, D

2000-01-01

A prominent feature of late nineteenth-century psychology was its intense preoccupation with precision. Precision was at once an ideal and an argument: the quest for precision helped psychology to establish its status as a mature science, sharing a characteristic concern with the natural sciences. We will analyse how psychologists set out to produce precision in 'mental chronometry', the measurement of the duration of psychological processes. In his Leipzig laboratory, Wundt inaugurated an elaborate research programme on mental chronometry. We will look at the problem of calibration of experimental apparatus and will describe the intricate material, literary, and social technologies involved in the manufacture of precision. First, we shall discuss some of the technical problems involved in the measurement of ever shorter time-spans. Next, the Cattell-Berger experiments will help us to argue against the received view that all the precision went into the hardware, and practically none into the social organization of experimentation. Experimenters made deliberate efforts to bring themselves and their subjects under a regime of control and calibration similar to that which reigned over the experimental machinery. In Leipzig psychology, the particular blend of material and social technology resulted in a specific object of study: the generalized mind. We will then show that the distribution of precision in experimental psychology outside Leipzig demanded a concerted effort of instruments, texts, and people. It will appear that the forceful attempts to produce precision and uniformity had some rather paradoxical consequences.

Machine compliance in compression tests

NASA Astrophysics Data System (ADS)

Sousa, Pedro; Ivens, Jan; Lomov, Stepan V.

2018-05-01

The compression behavior of a material cannot be accurately determined if the machine compliance is not accounted prior to the measurements. This work discusses the machine compliance during a compressibility test with fiberglass fabrics. The thickness variation was measured during loading and unloading cycles with a relaxation stage of 30 minutes between them. The measurements were performed using an indirect technique based on the comparison between the displacement at a free compression cycle and the displacement with a sample. Relating to the free test, it has been noticed the nonexistence of machine relaxation during relaxation stage. Considering relaxation or not, the characteristic curves for a free compression cycle can be overlapped precisely in the majority of the points. For the compression test with sample, it was noticed a non-physical decrease of about 30 µm during the relaxation stage, what can be explained by the greater fabric relaxation in relation to the machine relaxation. Beyond the technique normally used, another technique was used which allows a constant thickness during relaxation. Within this second method, machine displacement with sample is simply subtracted to the machine displacement without sample being imposed as constant. If imposed as a constant it will remain constant during relaxation stage and it will suddenly decrease after relaxation. If constantly calculated it will decrease gradually during relaxation stage. Independently of the technique used the final result will remain unchanged. The uncertainty introduced by this imprecision is about ±15 µm.

Sliding mode control of magnetic suspensions for precision pointing and tracking applications

NASA Technical Reports Server (NTRS)

Misovec, Kathleen M.; Flynn, Frederick J.; Johnson, Bruce G.; Hedrick, J. Karl

1991-01-01

A recently developed nonlinear control method, sliding mode control, is examined as a means of advancing the achievable performance of space-based precision pointing and tracking systems that use nonlinear magnetic actuators. Analytic results indicate that sliding mode control improves performance compared to linear control approaches. In order to realize these performance improvements, precise knowledge of the plant is required. Additionally, the interaction of an estimating scheme and the sliding mode controller has not been fully examined in the literature. Estimation schemes were designed for use with this sliding mode controller that do not seriously degrade system performance. The authors designed and built a laboratory testbed to determine the feasibility of utilizing sliding mode control in these types of applications. Using this testbed, experimental verification of the authors' analyses is ongoing.

Developing a New Wireless Sensor Network Platform and Its Application in Precision Agriculture

PubMed Central

Aquino-Santos, Raúl; González-Potes, Apolinar; Edwards-Block, Arthur; Virgen-Ortiz, Raúl Alejandro

2011-01-01

Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of “smart dust” offer great advantages due to their small size, low power consumption, easy integration and support for “green” applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modified version of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network. PMID:22346622

Developing a new wireless sensor network platform and its application in precision agriculture.

PubMed

Aquino-Santos, Raúl; González-Potes, Apolinar; Edwards-Block, Arthur; Virgen-Ortiz, Raúl Alejandro

2011-01-01

Wireless sensor networks are gaining greater attention from the research community and industrial professionals because these small pieces of "smart dust" offer great advantages due to their small size, low power consumption, easy integration and support for "green" applications. Green applications are considered a hot topic in intelligent environments, ubiquitous and pervasive computing. This work evaluates a new wireless sensor network platform and its application in precision agriculture, including its embedded operating system and its routing algorithm. To validate the technological platform and the embedded operating system, two different routing strategies were compared: hierarchical and flat. Both of these routing algorithms were tested in a small-scale network applied to a watermelon field. However, we strongly believe that this technological platform can be also applied to precision agriculture because it incorporates a modified version of LORA-CBF, a wireless location-based routing algorithm that uses cluster-based flooding. Cluster-based flooding addresses the scalability concerns of wireless sensor networks, while the modified LORA-CBF routing algorithm includes a metric to monitor residual battery energy. Furthermore, results show that the modified version of LORA-CBF functions well with both the flat and hierarchical algorithms, although it functions better with the flat algorithm in a small-scale agricultural network.

Design and application of the falling vertical sorting machine

NASA Astrophysics Data System (ADS)

Zuo, Ping; Peng, Tao; Yang, Hai

2018-04-01

In the process of tobacco production, it is necessary to pack the smoke according to the needs of different customers. A sorting machine is used to pick up the cigarette at present, there is a launch channel machine, a percussible vertical machine, But in the sorting process, the rolling channel machine is different in terms of the quality of smoke and the frictional force. It is difficult to ensure the location and posture of the belt sorting line, which causes the manipulator to not grasp. The strike type vertical machine is difficult to control the parallelism of the smoke. Now this team has developed a falling sorting machine, which has solved the smoke drop of a cigarette to the transmission belt. There will not be no code, can satisfy most of the different types of smoke sorting and no damage to smoke status. The dynamic characteristics such as the angular error of the opening and closing mechanism are carried out by ADAMS software. The simulation results show that the maximum angular error is 0.016rad. Through the test of the device, the goods falling speed is 7031/hour, the good of the falling position error within 2mm, meet the crawl accuracy requirements of the palletizing robot.

Nanocomposites for Machining Tools

PubMed Central

Loginov, Pavel; Mishnaevsky, Leon; Levashov, Evgeny

2017-01-01

Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance. PMID:29027926

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

NASA Astrophysics Data System (ADS)

Karunakaran, K.; Chandrasekaran, M.

2017-03-01

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

Precision diamond grinding of ceramics and glass

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

Smith, S.; Paul, H.; Scattergood, R.O.

A new research initiative will be undertaken to investigate the effect of machine parameters and material properties on precision diamond grinding of ceramics and glass. The critical grinding depth to initiate the plastic flow-to-brittle fracture regime will be directly measured using plunge-grind tests. This information will be correlated with machine parameters such as wheel bonding and diamond grain size. Multiaxis grinding tests will then be made to provide data more closely coupled with production technology. One important aspect of the material property studies involves measuring fracture toughness at the very short crack sizes commensurate with grinding damage. Short crack toughnessmore » value`s can be much less than the long-crack toughness values measured in conventional fracture tests.« less

Optimization design about gimbal structure of high-precision autonomous celestial navigation tracking mirror system

NASA Astrophysics Data System (ADS)

Huang, Wei; Yang, Xiao-xu; Han, Jun-feng; Wei, Yu; Zhang, Jing; Xie, Mei-lin; Yue, Peng

2016-01-01

High precision tracking platform of celestial navigation with control mirror servo structure form, to solve the disadvantages of big volume and rotational inertia, slow response speed, and so on. It improved the stability and tracking accuracy of platform. Due to optical sensor and mirror are installed on the middle-gimbal, stiffness and resonant frequency requirement for high. Based on the application of finite element modality analysis theory, doing Research on dynamic characteristics of the middle-gimbal, and ANSYS was used for the finite element dynamic emulator analysis. According to the result of the computer to find out the weak links of the structure, and Put forward improvement suggestions and reanalysis. The lowest resonant frequency of optimization middle-gimbal avoid the bandwidth of the platform servo mechanism, and much higher than the disturbance frequency of carrier aircraft, and reduces mechanical resonance of the framework. Reaching provides a theoretical basis for the whole machine structure optimization design of high-precision of autonomous Celestial navigation tracking mirror system.

High-precision laser microcutting and laser microdrilling using diffractive beam-splitting and high-precision flexible beam alignment

NASA Astrophysics Data System (ADS)

Zibner, F.; Fornaroli, C.; Holtkamp, J.; Shachaf, Lior; Kaplan, Natan; Gillner, A.

2017-08-01

High-precision laser micro machining gains more importance in industrial applications every month. Optical systems like the helical optics offer highest quality together with controllable and adjustable drilling geometry, thus as taper angle, aspect ratio and heat effected zone. The helical optics is based on a rotating Dove-prism which is mounted in a hollow shaft engine together with other optical elements like wedge prisms and plane plates. Although the achieved quality can be interpreted as extremely high the low process efficiency is a main reason that this manufacturing technology has only limited demand within the industrial market. The objective of the research studies presented in this paper is to dramatically increase process efficiency as well as process flexibility. During the last years, the average power of commercial ultra-short pulsed laser sources has increased significantly. The efficient utilization of the high average laser power in the field of material processing requires an effective distribution of the laser power onto the work piece. One approach to increase the efficiency is the application of beam splitting devices to enable parallel processing. Multi beam processing is used to parallelize the fabrication of periodic structures as most application only require a partial amount of the emitted ultra-short pulsed laser power. In order to achieve highest flexibility while using multi beam processing the single beams are diverted and re-guided in a way that enables the opportunity to process with each partial beam on locally apart probes or semimanufactures.

Streamlining machine learning in mobile devices for remote sensing

NASA Astrophysics Data System (ADS)

Coronel, Andrei D.; Estuar, Ma. Regina E.; Garcia, Kyle Kristopher P.; Dela Cruz, Bon Lemuel T.; Torrijos, Jose Emmanuel; Lim, Hadrian Paulo M.; Abu, Patricia Angela R.; Victorino, John Noel C.

2017-09-01

Mobile devices have been at the forefront of Intelligent Farming because of its ubiquitous nature. Applications on precision farming have been developed on smartphones to allow small farms to monitor environmental parameters surrounding crops. Mobile devices are used for most of these applications, collecting data to be sent to the cloud for storage, analysis, modeling and visualization. However, with the issue of weak and intermittent connectivity in geographically challenged areas of the Philippines, the solution is to provide analysis on the phone itself. Given this, the farmer gets a real time response after data submission. Though Machine Learning is promising, hardware constraints in mobile devices limit the computational capabilities, making model development on the phone restricted and challenging. This study discusses the development of a Machine Learning based mobile application using OpenCV libraries. The objective is to enable the detection of Fusarium oxysporum cubense (Foc) in juvenile and asymptomatic bananas using images of plant parts and microscopic samples as input. Image datasets of attached, unattached, dorsal, and ventral views of leaves were acquired through sampling protocols. Images of raw and stained specimens from soil surrounding the plant, and sap from the plant resulted to stained and unstained samples respectively. Segmentation and feature extraction techniques were applied to all images. Initial findings show no significant differences among the different feature extraction techniques. For differentiating infected from non-infected leaves, KNN yields highest average accuracy, as opposed to Naive Bayes and SVM. For microscopic images using MSER feature extraction, KNN has been tested as having a better accuracy than SVM or Naive-Bayes.

Precise time and time interval applications to electric power systems

NASA Technical Reports Server (NTRS)

Wilson, Robert E.

1992-01-01

There are many applications of precise time and time interval (frequency) in operating modern electric power systems. Many generators and customer loads are operated in parallel. The reliable transfer of electrical power to the consumer partly depends on measuring power system frequency consistently in many locations. The internal oscillators in the widely dispersed frequency measuring units must be syntonized. Elaborate protection and control systems guard the high voltage equipment from short and open circuits. For the highest reliability of electric service, engineers need to study all control system operations. Precise timekeeping networks aid in the analysis of power system operations by synchronizing the clocks on recording instruments. Utility engineers want to reproduce events that caused loss of service to customers. Precise timekeeping networks can synchronize protective relay test-sets. For dependable electrical service, all generators and large motors must remain close to speed synchronism. The stable response of a power system to perturbations is critical to continuity of electrical service. Research shows that measurement of the power system state vector can aid in the monitoring and control of system stability. If power system operators know that a lightning storm is approaching a critical transmission line or transformer, they can modify operating strategies. Knowledge of the location of a short circuit fault can speed the re-energizing of a transmission line. One fault location technique requires clocks synchronized to one microsecond. Current research seeks to find out if one microsecond timekeeping can aid and improve power system control and operation.

Application of high-precision two-way ranging to Galileo Earth-1 encounter navigation

NASA Technical Reports Server (NTRS)

Pollmeier, V. M.; Thurman, S. W.

1992-01-01

The application of precision two-way ranging to orbit determination with relatively short data arcs is investigated for the Galileo spacecraft's approach to its first Earth encounter (December 8, 1990). Analysis of previous S-band (2.3-GHz) ranging data acquired from Galileo indicated that under good signal conditions submeter precision and 10-m ranging accuracy were achieved. It is shown that ranging data of sufficient accuracy, when acquired from multiple stations, can sense the geocentric angular position of a distant spacecraft. A range data filtering technique, in which explicit modeling of range measurement bias parameters for each station pass is utilized, is shown to largely remove the systematic ground system calibration errors and transmission media effects from the Galileo range measurements, which would otherwise corrupt the angle-finding capabilities of the data. The accuracy of the Galileo orbit solutions obtained with S-band Doppler and precision ranging were found to be consistent with simple theoretical calculations, which predicted that angular accuracies of 0.26-0.34 microrad were achievable. In addition, the navigation accuracy achieved with precision ranging was marginally better than that obtained using delta-differenced one-way range (delta DOR), the principal data type that was previously used to obtain spacecraft angular position measurements operationally.

Monte-Carlo Method Application for Precising Meteor Velocity from TV Observations

NASA Astrophysics Data System (ADS)

Kozak, P.

2014-12-01

Monte-Carlo method (method of statistical trials) as an application for meteor observations processing was developed in author's Ph.D. thesis in 2005 and first used in his works in 2008. The idea of using the method consists in that if we generate random values of input data - equatorial coordinates of the meteor head in a sequence of TV frames - in accordance with their statistical distributions we get a possibility to plot the probability density distributions for all its kinematical parameters, and to obtain their mean values and dispersions. At that the theoretical possibility appears to precise the most important parameter - geocentric velocity of a meteor - which has the highest influence onto precision of meteor heliocentric orbit elements calculation. In classical approach the velocity vector was calculated in two stages: first we calculate the vector direction as a vector multiplication of vectors of poles of meteor trajectory big circles, calculated from two observational points. Then we calculated the absolute value of velocity independently from each observational point selecting any of them from some reasons as a final parameter. In the given method we propose to obtain a statistical distribution of velocity absolute value as an intersection of two distributions corresponding to velocity values obtained from different points. We suppose that such an approach has to substantially increase the precision of meteor velocity calculation and remove any subjective inaccuracies.

The design and improvement of radial tire molding machine

NASA Astrophysics Data System (ADS)

Wang, Wenhao; Zhang, Tao

2018-04-01

This paper presented that the high accuracy semisteel meridian tire molding machine structure configurations, combining tyre high precision characteristics, the original structure and parameter optimization, technology improvement innovation design period of opening and closing machine rotary shaping drum institutions. This way out of the shaft from the structure to the push-pull type movable shaping drum of thinking limit, compared with the specifications and shaping drum can smaller contraction, is conducive to forming the tire and reduce the tire deformation.

Metabolomics Applications in Precision Medicine: An Oncological Perspective

PubMed Central

Puchades-Carrasco, Leonor; Pineda-Lucena, Antonio

2017-01-01

Nowadays, cancer therapy remains limited by the conventional one-size-fits-all approach. In this context, treatment decisions are based on the clinical stage of disease but fail to ascertain the individual´s underlying biology and its role in driving malignancy. The identification of better therapies for cancer treatment is thus limited by the lack of sufficient data regarding the characterization of specific biochemical signatures associated with each particular cancer patient or group of patients. Metabolomics approaches promise a better understanding of cancer, a disease characterized by significant alterations in bioenergetic metabolism, by identifying changes in the pattern of metabolite expression in addition to changes in the concentration of individual metabolites as well as alterations in biochemical pathways. These approaches hold the potential of identifying novel biomarkers with different clinical applications, including the development of more specific diagnostic methods based on the characterization of metabolic subtypes, the monitoring of currently used cancer therapeutics to evaluate the response and the prognostic outcome with a given therapy, and the evaluation of the mechanisms involved in disease relapse and drug resistance. This review discusses metabolomics applications in different oncological processes underlining the potential of this omics approach to further advance the implementation of precision medicine in the oncology area. PMID:28685691

Estimation of Alpine Skier Posture Using Machine Learning Techniques

PubMed Central

Nemec, Bojan; Petrič, Tadej; Babič, Jan; Supej, Matej

2014-01-01

High precision Global Navigation Satellite System (GNSS) measurements are becoming more and more popular in alpine skiing due to the relatively undemanding setup and excellent performance. However, GNSS provides only single-point measurements that are defined with the antenna placed typically behind the skier's neck. A key issue is how to estimate other more relevant parameters of the skier's body, like the center of mass (COM) and ski trajectories. Previously, these parameters were estimated by modeling the skier's body with an inverted-pendulum model that oversimplified the skier's body. In this study, we propose two machine learning methods that overcome this shortcoming and estimate COM and skis trajectories based on a more faithful approximation of the skier's body with nine degrees-of-freedom. The first method utilizes a well-established approach of artificial neural networks, while the second method is based on a state-of-the-art statistical generalization method. Both methods were evaluated using the reference measurements obtained on a typical giant slalom course and compared with the inverted-pendulum method. Our results outperform the results of commonly used inverted-pendulum methods and demonstrate the applicability of machine learning techniques in biomechanical measurements of alpine skiing. PMID:25313492

Using Machine Learning to Advance Personality Assessment and Theory.

PubMed

Bleidorn, Wiebke; Hopwood, Christopher James

2018-05-01

Machine learning has led to important advances in society. One of the most exciting applications of machine learning in psychological science has been the development of assessment tools that can powerfully predict human behavior and personality traits. Thus far, machine learning approaches to personality assessment have focused on the associations between social media and other digital records with established personality measures. The goal of this article is to expand the potential of machine learning approaches to personality assessment by embedding it in a more comprehensive construct validation framework. We review recent applications of machine learning to personality assessment, place machine learning research in the broader context of fundamental principles of construct validation, and provide recommendations for how to use machine learning to advance our understanding of personality.

Machine-Learning Algorithms to Code Public Health Spending Accounts

PubMed Central

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

2017-01-01

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

Machine-Learning Algorithms to Code Public Health Spending Accounts.

PubMed

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

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

Verification and validation of a Work Domain Analysis with turing machine task analysis.

PubMed

Rechard, J; Bignon, A; Berruet, P; Morineau, T

2015-03-01

While the use of Work Domain Analysis as a methodological framework in cognitive engineering is increasing rapidly, verification and validation of work domain models produced by this method are becoming a significant issue. In this article, we propose the use of a method based on Turing machine formalism named "Turing Machine Task Analysis" to verify and validate work domain models. The application of this method on two work domain analyses, one of car driving which is an "intentional" domain, and the other of a ship water system which is a "causal domain" showed the possibility of highlighting improvements needed by these models. More precisely, the step by step analysis of a degraded task scenario in each work domain model pointed out unsatisfactory aspects in the first modelling, like overspecification, underspecification, omission of work domain affordances, or unsuitable inclusion of objects in the work domain model. Copyright © 2014 Elsevier Ltd and The Ergonomics Society. All rights reserved.

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

PubMed

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

2017-11-01

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

Space Applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 4: Supplement, Appendix 4.3: Candidate ARAMIS Capabilities

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Potential applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and to their related ground support functions, in the years 1985-2000, so that NASA may make informed decisions on which aspects of ARAMIS to develop. The study first identifies the specific tasks which will be required by future space projects. It then defines ARAMIS options which are candidates for those space project tasks, and evaluates the relative merits of these options. Finally, the study identifies promising applications of ARAMIS, and recommends specific areas for further research. The ARAMIS options defined and researched by the study group span the range from fully human to fully machine, including a number of intermediate options (e.g., humans assisted by computers, and various levels of teleoperation). By including this spectrum, the study searches for the optimum mix of humans and machines for space project tasks.

Architectures for intelligent machines

NASA Technical Reports Server (NTRS)

Saridis, George N.

1991-01-01

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

Safety Features in Anaesthesia Machine

PubMed Central

Subrahmanyam, M; Mohan, S

2013-01-01

Anaesthesia is one of the few sub-specialties of medicine, which has quickly adapted technology to improve patient safety. This application of technology can be seen in patient monitoring, advances in anaesthesia machines, intubating devices, ultrasound for visualisation of nerves and vessels, etc., Anaesthesia machines have come a long way in the last 100 years, the improvements being driven both by patient safety as well as functionality and economy of use. Incorporation of safety features in anaesthesia machines and ensuring that a proper check of the machine is done before use on a patient ensures patient safety. This review will trace all the present safety features in the machine and their evolution. PMID:24249880

Precision Machining. FasTrak Specialization Integrated Technical and Academic Competency (ITAC). 2002 Revision.

ERIC Educational Resources Information Center

Ohio State Dept. of Education, Columbus. Div. of Career-Technical and Adult Education.

This publication provided the competencies and key indicators for a program that enables students to prepare for a number of occupations within the broader metalworking industry. Specializations include machinist, computer numerical control programmers, and maintenance and machine builders. Competencies and the related key indicators are presented…

Rare events modeling with support vector machine: Application to forecasting large-amplitude geomagnetic substorms and extreme events in financial markets.

NASA Astrophysics Data System (ADS)

Gavrishchaka, V. V.; Ganguli, S. B.

2001-12-01

Reliable forecasting of rare events in a complex dynamical system is a challenging problem that is important for many practical applications. Due to the nature of rare events, data set available for construction of the statistical and/or machine learning model is often very limited and incomplete. Therefore many widely used approaches including such robust algorithms as neural networks can easily become inadequate for rare events prediction. Moreover in many practical cases models with high-dimensional inputs are required. This limits applications of the existing rare event modeling techniques (e.g., extreme value theory) that focus on univariate cases. These approaches are not easily extended to multivariate cases. Support vector machine (SVM) is a machine learning system that can provide an optimal generalization using very limited and incomplete training data sets and can efficiently handle high-dimensional data. These features may allow to use SVM to model rare events in some applications. We have applied SVM-based system to the problem of large-amplitude substorm prediction and extreme event forecasting in stock and currency exchange markets. Encouraging preliminary results will be presented and other possible applications of the system will be discussed.

CESAR research in intelligent machines

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

Weisbin, C.R.

1986-01-01

The Center for Engineering Systems Advanced Research (CESAR) was established in 1983 as a national center for multidisciplinary, long-range research and development in machine intelligence and advanced control theory for energy-related applications. Intelligent machines of interest here are artificially created operational systems that are capable of autonomous decision making and action. The initial emphasis for research is remote operations, with specific application to dexterous manipulation in unstructured dangerous environments where explosives, toxic chemicals, or radioactivity may be present, or in other environments with significant risk such as coal mining or oceanographic missions. Potential benefits include reduced risk to man inmore » hazardous situations, machine replication of scarce expertise, minimization of human error due to fear or fatigue, and enhanced capability using high resolution sensors and powerful computers. A CESAR goal is to explore the interface between the advanced teleoperation capability of today, and the autonomous machines of the future.« less

Monitoring of laser material processing using machine integrated low-coherence interferometry

NASA Astrophysics Data System (ADS)

Kunze, Rouwen; König, Niels; Schmitt, Robert

2017-06-01

Laser material processing has become an indispensable tool in modern production. With the availability of high power pico- and femtosecond laser sources, laser material processing is advancing into applications, which demand for highest accuracies such as laser micro milling or laser drilling. In order to enable narrow tolerance windows, a closedloop monitoring of the geometrical properties of the processed work piece is essential for achieving a robust manufacturing process. Low coherence interferometry (LCI) is a high-precision measuring principle well-known from surface metrology. In recent years, we demonstrated successful integrations of LCI into several different laser material processing methods. Within this paper, we give an overview about the different machine integration strategies, that always aim at a complete and ideally telecentric integration of the measurement device into the existing beam path of the processing laser. Thus, highly accurate depth measurements within machine coordinates and a subsequent process control and quality assurance are possible. First products using this principle have already found its way to the market, which underlines the potential of this technology for the monitoring of laser material processing.

Application de la methode de la reponse frequentielle a l'arret "SSFR", sur une machine synchrone a poles saillants de grande puissance

NASA Astrophysics Data System (ADS)

Belqorchi, Abdelghafour

Forty years after Watson and Manchur conducted the Stand-Still Frequency Response (SSFR) test on a large turbogenerator, the applicability of this technic on a powerful salient pole synchronous generator has yet to be confirmed. The scientific literature on the subject is rare and very few have attempted to compare SSFR parameter results with those deduced by classical tests. The validity of SSFR on large salient pole machines has still to be proven. The present work aims in participating to fill this knowledge gap. It can be used to build a database of measurements highly needed to draw the validity of the technic. Also, the author hopes to demonstrate the potential of SSFR model to represent the machine, not only in cases of weak disturbances but also strong ones such as instantaneous three-phase short-circuit faults. The difficulties raised by previous searchers are: The lack of accuracy in very low frequency measurements; The difficulty in rotor positioning, according to d and q axes, in case of salient pole machines; The measurement current level influence on magnetizing inductances, in axes-d and; The rotation impact on damper circuits for some rotors design. Aware of the above difficulties, the author conducted an SSFR test on a large salient pole machine (285 MVA). The generator under test has laminated non isolated rotor and an integral slot number. The damper windings in adjacent poles are connected together, via the polar core and the rotor rim. Finally, the damping circuit is unaffected by rotation. To improve the measurement accuracy, in very low frequencies, the most precise frequency response analyser available on the market was used. Besides, the frequency responses of the signals conditioning modules (i.e., isolation, amplification...) were accounted for to correct the four measured SSFR transfer functions. Immunization against noise and use of instrumentation in their optimum range, were other technics rigorously applied. Magnetizing inductances

[Precision and personalized medicine].

PubMed

Sipka, Sándor

2016-10-01

The author describes the concept of "personalized medicine" and the newly introduced "precision medicine". "Precision medicine" applies the terms of "phenotype", "endotype" and "biomarker" in order to characterize more precisely the various diseases. Using "biomarkers" the homogeneous type of a disease (a "phenotype") can be divided into subgroups called "endotypes" requiring different forms of treatment and financing. The good results of "precision medicine" have become especially apparent in relation with allergic and autoimmune diseases. The application of this new way of thinking is going to be necessary in Hungary, too, in the near future for participants, controllers and financing boards of healthcare. Orv. Hetil., 2016, 157(44), 1739-1741.

Dynamic analysis and vibration testing of CFRP drive-line system used in heavy-duty machine tool

NASA Astrophysics Data System (ADS)

Yang, Mo; Gui, Lin; Hu, Yefa; Ding, Guoping; Song, Chunsheng

2018-03-01

Low critical rotary speed and large vibration in the metal drive-line system of heavy-duty machine tool affect the machining precision seriously. Replacing metal drive-line with the CFRP drive-line can effectively solve this problem. Based on the composite laminated theory and the transfer matrix method (TMM), this paper puts forward a modified TMM to analyze dynamic characteristics of CFRP drive-line system. With this modified TMM, the CFRP drive-line of a heavy vertical miller is analyzed. And the finite element modal analysis model of the shafting is established. The results of the modified TMM and finite element analysis (FEA) show that the modified TMM can effectively predict the critical rotary speed of CFRP drive-line. And the critical rotary speed of CFRP drive-line is 20% higher than that of the original metal drive-line. Then, the vibration of the CFRP and the metal drive-line were tested. The test results show that application of the CFRP drive shaft in the drive-line can effectively reduce the vibration of the heavy-duty machine tool.

Machine learning-based dual-energy CT parametric mapping

NASA Astrophysics Data System (ADS)

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W.; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Helo, Rose Al; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C.; Rassouli, Negin; Gilkeson, Robert C.; Traughber, Bryan J.; Cheng, Chee-Wai; Muzic, Raymond F., Jr.

2018-06-01

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Zeff), relative electron density (ρ e), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Machine learning-based dual-energy CT parametric mapping.

PubMed

Su, Kuan-Hao; Kuo, Jung-Wen; Jordan, David W; Van Hedent, Steven; Klahr, Paul; Wei, Zhouping; Al Helo, Rose; Liang, Fan; Qian, Pengjiang; Pereira, Gisele C; Rassouli, Negin; Gilkeson, Robert C; Traughber, Bryan J; Cheng, Chee-Wai; Muzic, Raymond F

2018-06-08

The aim is to develop and evaluate machine learning methods for generating quantitative parametric maps of effective atomic number (Z eff ), relative electron density (ρ e ), mean excitation energy (I x ), and relative stopping power (RSP) from clinical dual-energy CT data. The maps could be used for material identification and radiation dose calculation. Machine learning methods of historical centroid (HC), random forest (RF), and artificial neural networks (ANN) were used to learn the relationship between dual-energy CT input data and ideal output parametric maps calculated for phantoms from the known compositions of 13 tissue substitutes. After training and model selection steps, the machine learning predictors were used to generate parametric maps from independent phantom and patient input data. Precision and accuracy were evaluated using the ideal maps. This process was repeated for a range of exposure doses, and performance was compared to that of the clinically-used dual-energy, physics-based method which served as the reference. The machine learning methods generated more accurate and precise parametric maps than those obtained using the reference method. Their performance advantage was particularly evident when using data from the lowest exposure, one-fifth of a typical clinical abdomen CT acquisition. The RF method achieved the greatest accuracy. In comparison, the ANN method was only 1% less accurate but had much better computational efficiency than RF, being able to produce parametric maps in 15 s. Machine learning methods outperformed the reference method in terms of accuracy and noise tolerance when generating parametric maps, encouraging further exploration of the techniques. Among the methods we evaluated, ANN is the most suitable for clinical use due to its combination of accuracy, excellent low-noise performance, and computational efficiency.

Machine tools and fixtures: A compilation

NASA Technical Reports Server (NTRS)

1971-01-01

As part of NASA's Technology Utilizations Program, a compilation was made of technological developments regarding machine tools, jigs, and fixtures that have been produced, modified, or adapted to meet requirements of the aerospace program. The compilation is divided into three sections that include: (1) a variety of machine tool applications that offer easier and more efficient production techniques; (2) methods, techniques, and hardware that aid in the setup, alignment, and control of machines and machine tools to further quality assurance in finished products: and (3) jigs, fixtures, and adapters that are ancillary to basic machine tools and aid in realizing their greatest potential.

Application of grey-fuzzy approach in parametric optimization of EDM process in machining of MDN 300 steel

NASA Astrophysics Data System (ADS)

Protim Das, Partha; Gupta, P.; Das, S.; Pradhan, B. B.; Chakraborty, S.

2018-01-01

Maraging steel (MDN 300) find its application in many industries as it exhibits high hardness which are very difficult to machine material. Electro discharge machining (EDM) is an extensively popular machining process which can be used in machining of such materials. Optimization of response parameters are essential for effective machining of these materials. Past researchers have already used Taguchi for obtaining the optimal responses of EDM process for this material with responses such as material removal rate (MRR), tool wear rate (TWR), relative wear ratio (RWR), and surface roughness (SR) considering discharge current, pulse on time, pulse off time, arc gap, and duty cycle as process parameters. In this paper, grey relation analysis (GRA) with fuzzy logic is applied to this multi objective optimization problem to check the responses by an implementation of the derived parametric setting. It was found that the parametric setting derived by the proposed method results in better a response than those reported by the past researchers. Obtained results are also verified using the technique for order of preference by similarity to ideal solution (TOPSIS). The predicted result also shows that there is a significant improvement in comparison to the results of past researchers.

UV laser-assisted wire stripping and micro-machining

NASA Astrophysics Data System (ADS)

Martyniuk, Jerry

1994-02-01

Results are reported for the use of a 266 nm frequency quadrupled Nd:YAG ultraviolet laser in the areas of wire stripping of small coaxial type transmission lines and for micro-machining of various materials including copper, glass, polyimide and DuPont TEFLONTM. This new laser is typically run with a 2 KHz repetition rate, 40 ns FWHM pulse and a fluence of about 50 joules/cm2 which makes it possible to micro-machine metals, polymers, glasses and ceramics. The high fluence of this laser allows shielding structures such as Al-MylarTM, Al-KaptonTM or the plated copper used in small coaxial cables to be precisely cut. Cut rates are reported for the above materials as well as results and photos of wire stripping and micro- machining.

Providing QoS through machine-learning-driven adaptive multimedia applications.

PubMed

Ruiz, Pedro M; Botía, Juan A; Gómez-Skarmeta, Antonio

2004-06-01

We investigate the optimization of the quality of service (QoS) offered by real-time multimedia adaptive applications through machine learning algorithms. These applications are able to adapt in real time their internal settings (i.e., video sizes, audio and video codecs, among others) to the unpredictably changing capacity of the network. Traditional adaptive applications just select a set of settings to consume less than the available bandwidth. We propose a novel approach in which the selected set of settings is the one which offers a better user-perceived QoS among all those combinations which satisfy the bandwidth restrictions. We use a genetic algorithm to decide when to trigger the adaptation process depending on the network conditions (i.e., loss-rate, jitter, etc.). Additionally, the selection of the new set of settings is done according to a set of rules which model the user-perceived QoS. These rules are learned using the SLIPPER rule induction algorithm over a set of examples extracted from scores provided by real users. We will demonstrate that the proposed approach guarantees a good user-perceived QoS even when the network conditions are constantly changing.

Reverse time migration: A seismic processing application on the connection machine

NASA Technical Reports Server (NTRS)

Fiebrich, Rolf-Dieter

1987-01-01

The implementation of a reverse time migration algorithm on the Connection Machine, a massively parallel computer is described. Essential architectural features of this machine as well as programming concepts are presented. The data structures and parallel operations for the implementation of the reverse time migration algorithm are described. The algorithm matches the Connection Machine architecture closely and executes almost at the peak performance of this machine.

Machine learning in heart failure: ready for prime time.

PubMed

Awan, Saqib Ejaz; Sohel, Ferdous; Sanfilippo, Frank Mario; Bennamoun, Mohammed; Dwivedi, Girish

2018-03-01

The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

Accuracy and Reliability of a New Tennis Ball Machine

PubMed Central

Brechbuhl, Cyril; Millet, Grégoire; Schmitt, Laurent

2016-01-01

The aim was to evaluate the reliability of a newly-developed ball machine named 'Hightof', on the field and to assess its accuracy. The experiment was conducted in the collaboration of the 'Hawk-Eye' technology. The accuracy and reliability of this ball machine were assessed during an incremental test, with 1 min of exercise and 30 sec of recovery, where the frequency of the balls increased from 10 to 30 balls·min-1. The initial frequency was 10 and increased by 2 until 22, then by 1 until 30 balls·min-1. The reference points for the impact were 8.39m from the net and 2.70m from lateral line for the right side and 2.83m for the left side. The precision of the machine was similar on the right and left sides (0.63 ± 0.39 vs 0.63 ± 0.34 m). The distances to the reference point were 0.52 ± 0.42, 0.26 ± 0.19, 0.52 ± 0.37, 0.28 ± 0.19 m for the Y-right, X-right, Y-left and X-left impacts. The precision was constant and did not increase with the intensity. (e.g ball frequency). The ball velocity was 86.3 ± 1.5 and 86.5 ± 1.3 km·h-1 for the right and the left side, respectively. The coefficient of variation for the velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min-1). Conclusion: both the accuracy and the reliability of this new ball machine appear satisfying enough for field testing and training. Key points The reliability and accuracy of a new ball machine named 'Hightof' were assessed. The impact point was reproducible and similar on the right and left sides (±0.63 m). The precision was constant and did not increase with the intensity (e.g ball frequency). The coefficient of variation of the ball velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min-1). PMID:27274663

Accuracy and Reliability of a New Tennis Ball Machine.

PubMed

Brechbuhl, Cyril; Millet, Grégoire; Schmitt, Laurent

2016-06-01

The aim was to evaluate the reliability of a newly-developed ball machine named 'Hightof', on the field and to assess its accuracy. The experiment was conducted in the collaboration of the 'Hawk-Eye' technology. The accuracy and reliability of this ball machine were assessed during an incremental test, with 1 min of exercise and 30 sec of recovery, where the frequency of the balls increased from 10 to 30 balls·min(-1). The initial frequency was 10 and increased by 2 until 22, then by 1 until 30 balls·min(-1). The reference points for the impact were 8.39m from the net and 2.70m from lateral line for the right side and 2.83m for the left side. The precision of the machine was similar on the right and left sides (0.63 ± 0.39 vs 0.63 ± 0.34 m). The distances to the reference point were 0.52 ± 0.42, 0.26 ± 0.19, 0.52 ± 0.37, 0.28 ± 0.19 m for the Y-right, X-right, Y-left and X-left impacts. The precision was constant and did not increase with the intensity. (e.g ball frequency). The ball velocity was 86.3 ± 1.5 and 86.5 ± 1.3 km·h(-1) for the right and the left side, respectively. The coefficient of variation for the velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min(-1)). both the accuracy and the reliability of this new ball machine appear satisfying enough for field testing and training. Key pointsThe reliability and accuracy of a new ball machine named 'Hightof' were assessed.The impact point was reproducible and similar on the right and left sides (±0.63 m).The precision was constant and did not increase with the intensity (e.g ball frequency).The coefficient of variation of the ball velocity ranged between 1 and 2% in all stages (ball velocity ranging from 10 to 30 balls·min(-1)).

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”.

Lifelong personal health data and application software via virtual machines in the cloud.

PubMed

Van Gorp, Pieter; Comuzzi, Marco

2014-01-01

Personal Health Records (PHRs) should remain the lifelong property of patients, who should be able to show them conveniently and securely to selected caregivers and institutions. In this paper, we present MyPHRMachines, a cloud-based PHR system taking a radically new architectural solution to health record portability. In MyPHRMachines, health-related data and the application software to view and/or analyze it are separately deployed in the PHR system. After uploading their medical data to MyPHRMachines, patients can access them again from remote virtual machines that contain the right software to visualize and analyze them without any need for conversion. Patients can share their remote virtual machine session with selected caregivers, who will need only a Web browser to access the pre-loaded fragments of their lifelong PHR. We discuss a prototype of MyPHRMachines applied to two use cases, i.e., radiology image sharing and personalized medicine.

CNC water-jet machining and cutting center

NASA Astrophysics Data System (ADS)

Bartlett, D. C.

1991-09-01

Computer Numerical Control (CNC) water-jet machining was investigated to determine the potential applications and cost-effectiveness that would result by establishing this capability in the engineering shops of Allied-Signal Inc., Kansas City Division (KCD). Both conductive and nonconductive samples were machined at KCD on conventional machining equipment (a three-axis conversational programmed mill and a wire electrical discharge machine) and on two current-technology water-jet machines at outside vendors. These samples were then inspected, photographed, and evaluated. The current-technology water-jet machines were not as accurate as the conventional equipment. The resolution of the water-jet equipment was only +/- 0.005 inch, as compared to +/- 0.0002 inch for the conventional equipment. The principal use for CNC water-jet machining would be as follows: Contouring to near finished shape those items made from 300 and 400 series stainless steels, titanium, Inconel, aluminum, glass, or any material whose fabrication tolerance is less than the machine resolution of +/- 0.005 inch; and contouring to finished shape those items made from Kevlar, rubber, fiberglass, foam, aluminum, or any material whose fabrication specifications allow the use of a machine with +/- 0.005 inch tolerance. Additional applications are possible because there is minimal force generated on the material being cut and because the water-jet cuts without generating dust.

The influence of negative training set size on machine learning-based virtual screening.

PubMed

Kurczab, Rafał; Smusz, Sabina; Bojarski, Andrzej J

2014-01-01

The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening.

The influence of negative training set size on machine learning-based virtual screening

PubMed Central

2014-01-01

Background The paper presents a thorough analysis of the influence of the number of negative training examples on the performance of machine learning methods. Results The impact of this rather neglected aspect of machine learning methods application was examined for sets containing a fixed number of positive and a varying number of negative examples randomly selected from the ZINC database. An increase in the ratio of positive to negative training instances was found to greatly influence most of the investigated evaluating parameters of ML methods in simulated virtual screening experiments. In a majority of cases, substantial increases in precision and MCC were observed in conjunction with some decreases in hit recall. The analysis of dynamics of those variations let us recommend an optimal composition of training data. The study was performed on several protein targets, 5 machine learning algorithms (SMO, Naïve Bayes, Ibk, J48 and Random Forest) and 2 types of molecular fingerprints (MACCS and CDK FP). The most effective classification was provided by the combination of CDK FP with SMO or Random Forest algorithms. The Naïve Bayes models appeared to be hardly sensitive to changes in the number of negative instances in the training set. Conclusions In conclusion, the ratio of positive to negative training instances should be taken into account during the preparation of machine learning experiments, as it might significantly influence the performance of particular classifier. What is more, the optimization of negative training set size can be applied as a boosting-like approach in machine learning-based virtual screening. PMID:24976867

Radiomic machine-learning classifiers for prognostic biomarkers of advanced nasopharyngeal carcinoma.

PubMed

Zhang, Bin; He, Xin; Ouyang, Fusheng; Gu, Dongsheng; Dong, Yuhao; Zhang, Lu; Mo, Xiaokai; Huang, Wenhui; Tian, Jie; Zhang, Shuixing

2017-09-10

We aimed to identify optimal machine-learning methods for radiomics-based prediction of local failure and distant failure in advanced nasopharyngeal carcinoma (NPC). We enrolled 110 patients with advanced NPC. A total of 970 radiomic features were extracted from MRI images for each patient. Six feature selection methods and nine classification methods were evaluated in terms of their performance. We applied the 10-fold cross-validation as the criterion for feature selection and classification. We repeated each combination for 50 times to obtain the mean area under the curve (AUC) and test error. We observed that the combination methods Random Forest (RF) + RF (AUC, 0.8464 ± 0.0069; test error, 0.3135 ± 0.0088) had the highest prognostic performance, followed by RF + Adaptive Boosting (AdaBoost) (AUC, 0.8204 ± 0.0095; test error, 0.3384 ± 0.0097), and Sure Independence Screening (SIS) + Linear Support Vector Machines (LSVM) (AUC, 0.7883 ± 0.0096; test error, 0.3985 ± 0.0100). Our radiomics study identified optimal machine-learning methods for the radiomics-based prediction of local failure and distant failure in advanced NPC, which could enhance the applications of radiomics in precision oncology and clinical practice. Copyright © 2017 Elsevier B.V. All rights reserved.

The dynamic analysis of drum roll lathe for machining of rollers

NASA Astrophysics Data System (ADS)

Qiao, Zheng; Wu, Dongxu; Wang, Bo; Li, Guo; Wang, Huiming; Ding, Fei

2014-08-01

An ultra-precision machine tool for machining of the roller has been designed and assembled, and due to the obvious impact which dynamic characteristic of machine tool has on the quality of microstructures on the roller surface, the dynamic characteristic of the existing machine tool is analyzed in this paper, so is the influence of circumstance that a large scale and slender roller is fixed in the machine on dynamic characteristic of the machine tool. At first, finite element model of the machine tool is built and simplified, and based on that, the paper carries on with the finite element mode analysis and gets the natural frequency and shaking type of four steps of the machine tool. According to the above model analysis results, the weak stiffness systems of machine tool can be further improved and the reasonable bandwidth of control system of the machine tool can be designed. In the end, considering the shock which is caused by Z axis as a result of fast positioning frequently to feeding system and cutting tool, transient analysis is conducted by means of ANSYS analysis in this paper. Based on the results of transient analysis, the vibration regularity of key components of machine tool and its impact on cutting process are explored respectively.

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation.

PubMed

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment.

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.

Using Big Data Analytics to Advance Precision Radiation Oncology.

PubMed

McNutt, Todd R; Benedict, Stanley H; Low, Daniel A; Moore, Kevin; Shpitser, Ilya; Jiang, Wei; Lakshminarayanan, Pranav; Cheng, Zhi; Han, Peijin; Hui, Xuan; Nakatsugawa, Minoru; Lee, Junghoon; Moore, Joseph A; Robertson, Scott P; Shah, Veeraj; Taylor, Russ; Quon, Harry; Wong, John; DeWeese, Theodore

2018-06-01

Big clinical data analytics as a primary component of precision medicine is discussed, identifying where these emerging tools fit in the spectrum of genomics and radiomics research. A learning health system (LHS) is conceptualized that uses clinically acquired data with machine learning to advance the initiatives of precision medicine. The LHS is comprehensive and can be used for clinical decision support, discovery, and hypothesis derivation. These developing uses can positively impact the ultimate management and therapeutic course for patients. The conceptual model for each use of clinical data, however, is different, and an overview of the implications is discussed. With advancements in technologies and culture to improve the efficiency, accuracy, and breadth of measurements of the patient condition, the concept of an LHS may be realized in precision radiation therapy. Copyright © 2018 Elsevier Inc. All rights reserved.

Simulation-driven machine learning: Bearing fault classification

NASA Astrophysics Data System (ADS)

Sobie, Cameron; Freitas, Carina; Nicolai, Mike

2018-01-01

Increasing the accuracy of mechanical fault detection has the potential to improve system safety and economic performance by minimizing scheduled maintenance and the probability of unexpected system failure. Advances in computational performance have enabled the application of machine learning algorithms across numerous applications including condition monitoring and failure detection. Past applications of machine learning to physical failure have relied explicitly on historical data, which limits the feasibility of this approach to in-service components with extended service histories. Furthermore, recorded failure data is often only valid for the specific circumstances and components for which it was collected. This work directly addresses these challenges for roller bearings with race faults by generating training data using information gained from high resolution simulations of roller bearing dynamics, which is used to train machine learning algorithms that are then validated against four experimental datasets. Several different machine learning methodologies are compared starting from well-established statistical feature-based methods to convolutional neural networks, and a novel application of dynamic time warping (DTW) to bearing fault classification is proposed as a robust, parameter free method for race fault detection.

A review on application of nanofluid MQL in machining

NASA Astrophysics Data System (ADS)

Rifat, Mustafa; Rahman, Md. Habibor; Das, Debashish

2017-12-01

Heat generation is an inevitable phenomenon during machining. To eradicate heat oriented detrimental effects like surface burning, tool wear and so on-different types of cooling system are being used. Traditional flood cooling method is the most widely used technique; however the consumption rate of coolant is very high. Moreover, if it is not deposited or recycled properly, it may also cause environmental hazard. Minimum Quantity Lubrication (MQL), on the other hand, sprays lubricant which decreases the frictional force and heat produced during machining. Nanofluid MQL is the incorporation of especially engineered nanoparticles into the lubricant that increases the heat carrying capacity. In this paper, four manufacturing processes (grinding, turning, milling, and drilling) and the effect of using nanofluid MQL in them are studied and summarized. Parameters that are considered in this study are cutting force, surface roughness, machining temperature, tool wear and environmental aspects. It can be observed that using nanofluids in an optimized manner can be beneficial to the machining processes because of their superior characteristics.

A cost-effective, accurate machine for testing the torsional strength of sheep long bones.

PubMed

Jämsä, T; Jalovaara, P

1996-07-01

A cost-effective torsional testing machine for sheep long bones was constructed. The machine was fabricated on a disused standard turning lathe. The angular speed used was 6.5 degrees/s. A precision amplifier using modern low-noise, low-drift operational amplifiers was developed. The maximum torsional load was 250 Nm, the sensitivity 0.5 Nm and the total machine inaccuracy less than 1.0%. The standard error of torsional testing was 3.0% when seven pairs of intact sheep tibiae were tested.

Applications of picosecond lasers and pulse-bursts in precision manufacturing

NASA Astrophysics Data System (ADS)

Knappe, Ralf

2012-03-01

Just as CW and quasi-CW lasers have revolutionized the materials processing world, picosecond lasers are poised to change the world of micromachining, where lasers outperform mechanical tools due to their flexibility, reliability, reproducibility, ease of programming, and lack of mechanical force or contamination to the part. Picosecond lasers are established as powerful tools for micromachining. Industrial processes like micro drilling, surface structuring and thin film ablation benefit from a process, which provides highest precision and minimal thermal impact for all materials. Applications such as microelectronics, semiconductor, and photovoltaic industries use picosecond lasers for maximum quality, flexibility, and cost efficiency. The range of parts, manufactured with ps lasers spans from microscopic diamond tools over large printing cylinders with square feet of structured surface. Cutting glass for display and PV is a large application, as well. With a smart distribution of energy into groups of ps-pulses at ns-scale separation (known as burst mode) ablation rates can be increased by one order of magnitude or more for some materials, also providing a better surface quality under certain conditions. The paper reports on the latest results of the laser technology, scaling of ablation rates, and various applications in ps-laser micromachining.

Six-Port Based Interferometry for Precise Radar and Sensing Applications.

PubMed

Koelpin, Alexander; Lurz, Fabian; Linz, Sarah; Mann, Sebastian; Will, Christoph; Lindner, Stefan

2016-09-22

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology.

Six-Port Based Interferometry for Precise Radar and Sensing Applications

PubMed Central

Koelpin, Alexander; Lurz, Fabian; Linz, Sarah; Mann, Sebastian; Will, Christoph; Lindner, Stefan

2016-01-01

Microwave technology plays a more important role in modern industrial sensing applications. Pushed by the significant progress in monolithic microwave integrated circuit technology over the past decades, complex sensing systems operating in the microwave and even millimeter-wave range are available for reasonable costs combined with exquisite performance. In the context of industrial sensing, this stimulates new approaches for metrology based on microwave technology. An old measurement principle nearly forgotten over the years has recently gained more and more attention in both academia and industry: the six-port interferometer. This paper reviews the basic concept, investigates promising applications in remote, as well as contact-based sensing and compares the system with state-of-the-art metrology. The significant advantages will be discussed just as the limitations of the six-port architecture. Particular attention will be paid to impairment effects and non-ideal behavior, as well as compensation and linearization concepts. It will be shown that in application fields, like remote distance sensing, precise alignment measurements, as well as interferometrically-evaluated mechanical strain analysis, the six-port architecture delivers extraordinary measurement results combined with high measurement data update rates for reasonable system costs. This makes the six-port architecture a promising candidate for industrial metrology. PMID:27669246

Whole-machine calibration approach for phased array radar with self-test

NASA Astrophysics Data System (ADS)

Shen, Kai; Yao, Zhi-Cheng; Zhang, Jin-Chang; Yang, Jian

2017-06-01

The performance of the missile-borne phased array radar is greatly influenced by the inter-channel amplitude and phase inconsistencies. In order to ensure its performance, the amplitude and the phase characteristics of radar should be calibrated. Commonly used methods mainly focus on antenna calibration, such as FFT, REV, etc. However, the radar channel also contains T / R components, channels, ADC and messenger. In order to achieve on-based phased array radar amplitude information for rapid machine calibration and compensation, we adopt a high-precision plane scanning test platform for phase amplitude test. A calibration approach for the whole channel system based on the radar frequency source test is proposed. Finally, the advantages and the application prospect of this approach are analysed.

Sub-cell turning to accomplish micron-level alignment of precision assemblies

NASA Astrophysics Data System (ADS)

Kumler, James J.; Buss, Christian

2017-08-01

Higher performance expectations for complex optical systems demand tighter alignment requirements for lens assembly alignment. In order to meet diffraction limited imaging performance over wide spectral bands across the UV and visible wavebands, new manufacturing approaches and tools must be developed if the optical systems will be produced consistently in volume production. This is especially applicable in the field of precision microscope objectives for life science, semiconductor inspection and laser material processing systems. We observe a rising need for the improvement in the optical imaging performance of objective lenses. The key challenge lies in the micron-level decentration and tilt of each lens element. One solution for the production of high quality lens systems is sub-cell assembly with alignment turning. This process relies on an automatic alignment chuck to align the optical axis of a mounted lens to the spindle axis of the machine. Subsequently, the mount is cut with diamond tools on a lathe with respect to the optical axis of the mount. Software controlled integrated measurement technology ensures highest precision. In addition to traditional production processes, further dimensions can be controlled in a very precise manner, e.g. the air gaps between the lenses. Using alignment turning simplifies further alignment steps and reduces the risk of errors. This paper describes new challenges in microscope objective design and manufacturing, and addresses difficulties with standard production processes. A new measurement and alignment technique is described, and strengths and limitations are outlined.

Research on precise pneumatic-electric displacement sensor with large measurement range

NASA Astrophysics Data System (ADS)

Yin, Zhehao; Yuan, Yibao; Liu, Baoshuai

2017-10-01

This research mainly focuses on precise pneumatic-electric displacement sensor which has large measurement range. Under the high precision, measurement range can be expanded so that the need of high precision as well as large range can be satisfied in the field of machining inspection technology. This research was started by the analysis of pneumatic-measuring theory. Then, an gas circuit measuring system which is based on differential pressure was designed. This designed system can reach two aims: Firstly, to convert displacement signal into gas signal; Secondly, to reduce the measurement error which caused by pressure and environmental turbulence. Furthermore, in consideration of the high requirement for linearity, sensitivity and stability, the project studied the pneumatic-electric transducer which puts the SCX series pressure sensor as a key part. The main purpose of this pneumatic-electric transducer is to convert gas signal to suitable electrical signal. Lastly, a broken line subsection linearization circuit was designed, which can nonlinear correct the output characteristic curve so as to enlarge the linear measurement range. The final result could be briefly described like this: under the condition that measuring error is less than 1μm, measurement range could be extended to approximately 200μm which is much higher than the measurement range of traditional pneumatic measuring instrument. Meanwhile, it can reach higher exchangeability and stability in order to become more suitable to engineering application.

Clustering and Candidate Motif Detection in Exosomal miRNAs by Application of Machine Learning Algorithms.

PubMed

Gaur, Pallavi; Chaturvedi, Anoop

2017-07-22

The clustering pattern and motifs give immense information about any biological data. An application of machine learning algorithms for clustering and candidate motif detection in miRNAs derived from exosomes is depicted in this paper. Recent progress in the field of exosome research and more particularly regarding exosomal miRNAs has led much bioinformatic-based research to come into existence. The information on clustering pattern and candidate motifs in miRNAs of exosomal origin would help in analyzing existing, as well as newly discovered miRNAs within exosomes. Along with obtaining clustering pattern and candidate motifs in exosomal miRNAs, this work also elaborates the usefulness of the machine learning algorithms that can be efficiently used and executed on various programming languages/platforms. Data were clustered and sequence candidate motifs were detected successfully. The results were compared and validated with some available web tools such as 'BLASTN' and 'MEME suite'. The machine learning algorithms for aforementioned objectives were applied successfully. This work elaborated utility of machine learning algorithms and language platforms to achieve the tasks of clustering and candidate motif detection in exosomal miRNAs. With the information on mentioned objectives, deeper insight would be gained for analyses of newly discovered miRNAs in exosomes which are considered to be circulating biomarkers. In addition, the execution of machine learning algorithms on various language platforms gives more flexibility to users to try multiple iterations according to their requirements. This approach can be applied to other biological data-mining tasks as well.

Application of Fuzzy TOPSIS for evaluating machining techniques using sustainability metrics

NASA Astrophysics Data System (ADS)

Digalwar, Abhijeet K.

2018-04-01

Sustainable processes and techniques are getting increased attention over the last few decades due to rising concerns over the environment, improved focus on productivity and stringency in environmental as well as occupational health and safety norms. The present work analyzes the research on sustainable machining techniques and identifies techniques and parameters on which sustainability of a process is evaluated. Based on the analysis these parameters are then adopted as criteria’s to evaluate different sustainable machining techniques such as Cryogenic Machining, Dry Machining, Minimum Quantity Lubrication (MQL) and High Pressure Jet Assisted Machining (HPJAM) using a fuzzy TOPSIS framework. In order to facilitate easy arithmetic, the linguistic variables represented by fuzzy numbers are transformed into crisp numbers based on graded mean representation. Cryogenic machining was found to be the best alternative sustainable technique as per the fuzzy TOPSIS framework adopted. The paper provides a method to deal with multi criteria decision making problems in a complex and linguistic environment.

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.

Precision laser processing for micro electronics and fiber optic manufacturing

NASA Astrophysics Data System (ADS)

Webb, Andrew; Osborne, Mike; Foster-Turner, Gideon; Dinkel, Duane W.

2008-02-01

The application of laser based materials processing for precision micro scale manufacturing in the electronics and fiber optic industry is becoming increasingly widespread and accepted. This presentation will review latest laser technologies available and discuss the issues to be considered in choosing the most appropriate laser and processing parameters. High repetition rate, short duration pulsed lasers have improved rapidly in recent years in terms of both performance and reliability enabling flexible, cost effective processing of many material types including metal, silicon, plastic, ceramic and glass. Demonstrating the relevance of laser micromachining, application examples where laser processing is in use for production will be presented, including miniaturization of surface mount capacitors by applying a laser technique for demetalization of tracks in the capacitor manufacturing process and high quality laser machining of fiber optics including stripping, cleaving and lensing, resulting in optical quality finishes without the need for traditional polishing. Applications include telecoms, biomedical and sensing. OpTek Systems was formed in 2000 and provide fully integrated systems and sub contract services for laser processes. They are headquartered in the UK and are establishing a presence in North America through a laser processing facility in South Carolina and sales office in the North East.

Precision medicine: In need of guidance and surveillance.

PubMed

Lin, Jian-Zhen; Long, Jun-Yu; Wang, An-Qiang; Zheng, Ying; Zhao, Hai-Tao

2017-07-28

Precision medicine, currently a hotspot in mainstream medicine, has been strongly promoted in recent years. With rapid technological development, such as next-generation sequencing, and fierce competition in molecular targeted drug exploitation, precision medicine represents an advance in science and technology; it also fulfills needs in public health care. The clinical translation and application of precision medicine - especially in the prevention and treatment of tumors - is far from satisfactory; however, the aims of precision medicine deserve approval. Thus, this medical approach is currently in its infancy; it has promising prospects, but it needs to overcome numbers of problems and deficiencies. It is expected that in addition to conventional symptoms and signs, precision medicine will define disease in terms of the underlying molecular characteristics and other environmental susceptibility factors. Those expectations should be realized by constructing a novel data network, integrating clinical data from individual patients and personal genomic background with existing research on the molecular makeup of diseases. In addition, multi-omics analysis and multi-discipline collaboration will become crucial elements in precision medicine. Precision medicine deserves strong support, and its development demands directed momentum. We propose three kinds of impetus (research, application and collaboration impetus) for such directed momentum toward promoting precision medicine and accelerating its clinical translation and application.

Precision medicine: In need of guidance and surveillance

PubMed Central

Lin, Jian-Zhen; Long, Jun-Yu; Wang, An-Qiang; Zheng, Ying; Zhao, Hai-Tao

2017-01-01

Precision medicine, currently a hotspot in mainstream medicine, has been strongly promoted in recent years. With rapid technological development, such as next-generation sequencing, and fierce competition in molecular targeted drug exploitation, precision medicine represents an advance in science and technology; it also fulfills needs in public health care. The clinical translation and application of precision medicine - especially in the prevention and treatment of tumors - is far from satisfactory; however, the aims of precision medicine deserve approval. Thus, this medical approach is currently in its infancy; it has promising prospects, but it needs to overcome numbers of problems and deficiencies. It is expected that in addition to conventional symptoms and signs, precision medicine will define disease in terms of the underlying molecular characteristics and other environmental susceptibility factors. Those expectations should be realized by constructing a novel data network, integrating clinical data from individual patients and personal genomic background with existing research on the molecular makeup of diseases. In addition, multi-omics analysis and multi-discipline collaboration will become crucial elements in precision medicine. Precision medicine deserves strong support, and its development demands directed momentum. We propose three kinds of impetus (research, application and collaboration impetus) for such directed momentum toward promoting precision medicine and accelerating its clinical translation and application. PMID:28811702

Intelligible machine learning with malibu.

PubMed

Langlois, Robert E; Lu, Hui

2008-01-01

malibu is an open-source machine learning work-bench developed in C/C++ for high-performance real-world applications, namely bioinformatics and medical informatics. It leverages third-party machine learning implementations for more robust bug-free software. This workbench handles several well-studied supervised machine learning problems including classification, regression, importance-weighted classification and multiple-instance learning. The malibu interface was designed to create reproducible experiments ideally run in a remote and/or command line environment. The software can be found at: http://proteomics.bioengr. uic.edu/malibu/index.html.

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.

What can neuromorphic event-driven precise timing add to spike-based pattern recognition?

PubMed

Akolkar, Himanshu; Meyer, Cedric; Clady, Zavier; Marre, Olivier; Bartolozzi, Chiara; Panzeri, Stefano; Benosman, Ryad

2015-03-01

This letter introduces a study to precisely measure what an increase in spike timing precision can add to spike-driven pattern recognition algorithms. The concept of generating spikes from images by converting gray levels into spike timings is currently at the basis of almost every spike-based modeling of biological visual systems. The use of images naturally leads to generating incorrect artificial and redundant spike timings and, more important, also contradicts biological findings indicating that visual processing is massively parallel, asynchronous with high temporal resolution. A new concept for acquiring visual information through pixel-individual asynchronous level-crossing sampling has been proposed in a recent generation of asynchronous neuromorphic visual sensors. Unlike conventional cameras, these sensors acquire data not at fixed points in time for the entire array but at fixed amplitude changes of their input, resulting optimally sparse in space and time-pixel individually and precisely timed only if new, (previously unknown) information is available (event based). This letter uses the high temporal resolution spiking output of neuromorphic event-based visual sensors to show that lowering time precision degrades performance on several recognition tasks specifically when reaching the conventional range of machine vision acquisition frequencies (30-60 Hz). The use of information theory to characterize separability between classes for each temporal resolution shows that high temporal acquisition provides up to 70% more information that conventional spikes generated from frame-based acquisition as used in standard artificial vision, thus drastically increasing the separability between classes of objects. Experiments on real data show that the amount of information loss is correlated with temporal precision. Our information-theoretic study highlights the potentials of neuromorphic asynchronous visual sensors for both practical applications and theoretical

Experimental Machine Learning of Quantum States

NASA Astrophysics Data System (ADS)

Gao, Jun; Qiao, Lu-Feng; Jiao, Zhi-Qiang; Ma, Yue-Chi; Hu, Cheng-Qiu; Ren, Ruo-Jing; Yang, Ai-Lin; Tang, Hao; Yung, Man-Hong; Jin, Xian-Min

2018-06-01

Quantum information technologies provide promising applications in communication and computation, while machine learning has become a powerful technique for extracting meaningful structures in "big data." A crossover between quantum information and machine learning represents a new interdisciplinary area stimulating progress in both fields. Traditionally, a quantum state is characterized by quantum-state tomography, which is a resource-consuming process when scaled up. Here we experimentally demonstrate a machine-learning approach to construct a quantum-state classifier for identifying the separability of quantum states. We show that it is possible to experimentally train an artificial neural network to efficiently learn and classify quantum states, without the need of obtaining the full information of the states. We also show how adding a hidden layer of neurons to the neural network can significantly boost the performance of the state classifier. These results shed new light on how classification of quantum states can be achieved with limited resources, and represent a step towards machine-learning-based applications in quantum information processing.

Proceedings of the Thirteenth Annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting

NASA Technical Reports Server (NTRS)

Wardrip, S. C.

1982-01-01

Proceedings of an annual Precise Time and Time Interval (PTTI) Applications and Planning Meeting are summarized. A transparent view of the state-of-the-art, an opportunity to express needs, a view of important future trends, and a review of relevant past accomplishments were considered for PTTI managers, systems engineers, and program planner. Specific aims were: to provide PTTI users with new and useful applications, procedures, and techniques; to allow the PTTI researcher to better assess fruitful directions for research efforts.

Synthetic biology. Genomically encoded analog memory with precise in vivo DNA writing in living cell populations.

PubMed

Farzadfard, Fahim; Lu, Timothy K

2014-11-14

Cellular memory is crucial to many natural biological processes and sophisticated synthetic biology applications. Existing cellular memories rely on epigenetic switches or recombinases, which are limited in scalability and recording capacity. In this work, we use the DNA of living cell populations as genomic "tape recorders" for the analog and distributed recording of long-term event histories. We describe a platform for generating single-stranded DNA (ssDNA) in vivo in response to arbitrary transcriptional signals. When coexpressed with a recombinase, these intracellularly expressed ssDNAs target specific genomic DNA addresses, resulting in precise mutations that accumulate in cell populations as a function of the magnitude and duration of the inputs. This platform could enable long-term cellular recorders for environmental and biomedical applications, biological state machines, and enhanced genome engineering strategies. Copyright © 2014, American Association for the Advancement of Science.

Introduction to machine learning for brain imaging.

PubMed

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

2011-05-15

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

Application of Abrasive-Waterjets for Machining Fatigue-Critical Aircraft Aluminum Parts

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

Liu, H T; Hovanski, Yuri; Dahl, Michael E

2010-08-19

Current specifications require AWJ-cut aluminum parts for fatigue critical aerospace structures to go through subsequent processing due to concerns of degradation in fatigue performance. The requirement of secondary process for AWJ-machined parts greatly negates the cost effectiveness of waterjet technology. Some cost savings are envisioned if it can be shown that AWJ net cut parts have comparable durability properties as those conventionally machined. To revisit and upgrade the specifications for AWJ machining of aircraft aluminum, “Dog-bone” specimens, with and without secondary processes, were prepared for independent fatigue tests at Boeing and Pacific Northwest National Laboratory (PNNL). Test results show thatmore » the fatigue life is proportional to quality levels of machined edges or inversely proportional to the surface roughness Ra . Even at highest quality level, the average fatigue life of AWJ-machined parts is about 30% shorter than those of conventionally machined counterparts. Between two secondary processes, dry-grit blasting with aluminum oxide abrasives until the striation is removed visually yields excellent result. It actually prolongs the fatigue life of parts at least three times higher than that achievable with conventional machining. Dry-grit blasting is relatively simple and inexpensive to administrate and, equally important, alleviates the concerns of garnet embedment.« less

Osteoblast adhesion on novel machinable calcium phosphate/lanthanum phosphate composites for orthopedic applications.

PubMed

Ergun, Celaletdin; Liu, Huinan; Webster, Thomas J

2009-06-01

Lanthanum phosphate (LaPO(4), LP) was combined with either hydroxyapatite (HA) or tricalcium phosphate (TCP) to form novel composites for orthopedic applications. In this study, these composites were prepared by wet chemistry synthesis and subsequent powder mixing. These HA/LP and TCP/LP composites were characterized in terms of phase stability and microstructure evolution during sintering using X-ray diffraction (XRD) and scanning electron microscopy (SEM). Their machinability was evaluated using a direct drilling test. For HA/LP composites, LP reacted with HA during sintering and formed a new phase, Ca(8)La(2)(PO(4))(6)O(2), as a reaction by-product. However, TCP/LP composites showed phase stability and the formation of a weak interface between TCP and LP machinability when sintered at 1100 degrees C, which is crucial for achieving desirable properties. Thus, these novel TCP/LP composites fulfilled the requirements for machinability, a key consideration for manufacturing orthopedic implants. Moreover, the biocompatibility of these novel LP composites was studied, for the first time, in this paper. In vitro cell culture tests demonstrated that the LP and its composites supported osteoblast (bone-forming cell) adhesion similar to natural bioceramics (such as HA and TCP). In conclusion, these novel LP composites should be further studied and developed for more effectively treating bone related diseases or injuries. 2008 Wiley Periodicals, Inc.

Analysis and design of asymmetrical reluctance machine

NASA Astrophysics Data System (ADS)

Harianto, Cahya A.

Over the past few decades the induction machine has been chosen for many applications due to its structural simplicity and low manufacturing cost. However, modest torque density and control challenges have motivated researchers to find alternative machines. The permanent magnet synchronous machine has been viewed as one of the alternatives because it features higher torque density for a given loss than the induction machine. However, the assembly and permanent magnet material cost, along with safety under fault conditions, have been concerns for this class of machine. An alternative machine type, namely the asymmetrical reluctance machine, is proposed in this work. Since the proposed machine is of the reluctance machine type, it possesses desirable feature, such as near absence of rotor losses, low assembly cost, low no-load rotational losses, modest torque ripple, and rather benign fault conditions. Through theoretical analysis performed herein, it is shown that this machine has a higher torque density for a given loss than typical reluctance machines, although not as high as the permanent magnet machines. Thus, the asymmetrical reluctance machine is a viable and advantageous machine alternative where the use of permanent magnet machines are undesirable.

Machine Translation-Assisted Language Learning: Writing for Beginners

ERIC Educational Resources Information Center

Garcia, Ignacio; Pena, Maria Isabel

2011-01-01

The few studies that deal with machine translation (MT) as a language learning tool focus on its use by advanced learners, never by beginners. Yet, freely available MT engines (i.e. Google Translate) and MT-related web initiatives (i.e. Gabble-on.com) position themselves to cater precisely to the needs of learners with a limited command of a…

Big genomics and clinical data analytics strategies for precision cancer prognosis.

PubMed

Ow, Ghim Siong; Kuznetsov, Vladimir A

2016-11-07

The field of personalized and precise medicine in the era of big data analytics is growing rapidly. Previously, we proposed our model of patient classification termed Prognostic Signature Vector Matching (PSVM) and identified a 37 variable signature comprising 36 let-7b associated prognostic significant mRNAs and the age risk factor that stratified large high-grade serous ovarian cancer patient cohorts into three survival-significant risk groups. Here, we investigated the predictive performance of PSVM via optimization of the prognostic variable weights, which represent the relative importance of one prognostic variable over the others. In addition, we compared several multivariate prognostic models based on PSVM with classical machine learning techniques such as K-nearest-neighbor, support vector machine, random forest, neural networks and logistic regression. Our results revealed that negative log-rank p-values provides more robust weight values as opposed to the use of other quantities such as hazard ratios, fold change, or a combination of those factors. PSVM, together with the classical machine learning classifiers were combined in an ensemble (multi-test) voting system, which collectively provides a more precise and reproducible patient stratification. The use of the multi-test system approach, rather than the search for the ideal classification/prediction method, might help to address limitations of the individual classification algorithm in specific situation.

Interferometric correction system for a numerically controlled machine

DOEpatents

Burleson, Robert R.

1978-01-01

An interferometric correction system for a numerically controlled machine is provided to improve the positioning accuracy of a machine tool, for example, for a high-precision numerically controlled machine. A laser interferometer feedback system is used to monitor the positioning of the machine tool which is being moved by command pulses to a positioning system to position the tool. The correction system compares the commanded position as indicated by a command pulse train applied to the positioning system with the actual position of the tool as monitored by the laser interferometer. If the tool position lags the commanded position by a preselected error, additional pulses are added to the pulse train applied to the positioning system to advance the tool closer to the commanded position, thereby reducing the lag error. If the actual tool position is leading in comparison to the commanded position, pulses are deleted from the pulse train where the advance error exceeds the preselected error magnitude to correct the position error of the tool relative to the commanded position.

From machine learning to deep learning: progress in machine intelligence for rational drug discovery.

PubMed

Zhang, Lu; Tan, Jianjun; Han, Dan; Zhu, Hao

2017-11-01

Machine intelligence, which is normally presented as artificial intelligence, refers to the intelligence exhibited by computers. In the history of rational drug discovery, various machine intelligence approaches have been applied to guide traditional experiments, which are expensive and time-consuming. Over the past several decades, machine-learning tools, such as quantitative structure-activity relationship (QSAR) modeling, were developed that can identify potential biological active molecules from millions of candidate compounds quickly and cheaply. However, when drug discovery moved into the era of 'big' data, machine learning approaches evolved into deep learning approaches, which are a more powerful and efficient way to deal with the massive amounts of data generated from modern drug discovery approaches. Here, we summarize the history of machine learning and provide insight into recently developed deep learning approaches and their applications in rational drug discovery. We suggest that this evolution of machine intelligence now provides a guide for early-stage drug design and discovery in the current big data era. Copyright © 2017 Elsevier Ltd. All rights reserved.

Quantum machine learning for quantum anomaly detection

NASA Astrophysics Data System (ADS)

Liu, Nana; Rebentrost, Patrick

2018-04-01

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

Classification of Astrocytomas and Oligodendrogliomas from Mass Spectrometry Data Using Sparse Kernel Machines

PubMed Central

Huang, Jacob; Gholami, Behnood; Agar, Nathalie Y. R.; Norton, Isaiah; Haddad, Wassim M.; Tannenbaum, Allen R.

2013-01-01

Glioma histologies are the primary factor in prognostic estimates and are used in determining the proper course of treatment. Furthermore, due to the sensitivity of cranial environments, real-time tumor-cell classification and boundary detection can aid in the precision and completeness of tumor resection. A recent improvement to mass spectrometry known as desorption electrospray ionization operates in an ambient environment without the application of a preparation compound. This allows for a real-time acquisition of mass spectra during surgeries and other live operations. In this paper, we present a framework using sparse kernel machines to determine a glioma sample’s histopathological subtype by analyzing its chemical composition acquired by desorption electrospray ionization mass spectrometry. PMID:22256188

Precision Heating Process

NASA Technical Reports Server (NTRS)

1992-01-01

A heat sealing process was developed by SEBRA based on technology that originated in work with NASA's Jet Propulsion Laboratory. The project involved connecting and transferring blood and fluids between sterile plastic containers while maintaining a closed system. SEBRA markets the PIRF Process to manufacturers of medical catheters. It is a precisely controlled method of heating thermoplastic materials in a mold to form or weld catheters and other products. The process offers advantages in fast, precise welding or shape forming of catheters as well as applications in a variety of other industries.

Applications of inertial-sensor high-inheritance instruments to DSN precision antenna pointing

NASA Technical Reports Server (NTRS)

Goddard, R. E.

1992-01-01

Laboratory test results of the initialization and tracking performance of an existing inertial-sensor-based instrument are given. The instrument, although not primarily designed for precision antenna pointing applications, demonstrated an on-average 10-hour tracking error of several millidegrees. The system-level instrument performance is shown by analysis to be sensor limited. Simulated instrument improvements show a tracking error of less than 1 mdeg, which would provide acceptable performance, i.e., low pointing loss, for the DSN 70-m antenna sub network, operating at Ka-band (1-cm wavelength).

Applications of inertial-sensor high-inheritance instruments to DSN precision antenna pointing

NASA Technical Reports Server (NTRS)

Goddard, R. E.

1992-01-01

Laboratory test results of the initialization and tracking performance of an existing inertial-sensor-based instrument are given. The instrument, although not primarily designed for precision antenna pointing applications, demonstrated an on-average 10-hour tracking error of several millidegrees. The system-level instrument performance is shown by analysis to be sensor limited. Simulated instrument improvements show a tracking error of less than 1 mdeg, which would provide acceptable performance, i.e., low pointing loss, for the Deep Space Network 70-m antenna subnetwork, operating at Ka-band (1-cm wavelength).

Influence of forces acting on side of machine on precision machining of large diameter holes

NASA Astrophysics Data System (ADS)

Fedorenko, M. A.; Bondarenko, J. A.; Sanina, T. M.

2018-03-01

One of the most important factors that increase efficiency, durability and reliability of rotating units is precision installation, preventive maintenance work, timely replacing of a failed or worn components and assemblies. These works should be carried out in the operation of the equipment, as the downtime in many cases leads to large financial losses. Stop of one unit of an industrial enterprise can interrupt the technological chain of production, resulting in a possible stop of the entire equipment. Improving the efficiency and optimization of the repair process increases accuracy of installation work when installing equipment, conducting restoration under operating conditions relevant for enterprises of different industries because it eliminates dismantling the equipment, sending it to maintenance, the expectation of equipment return, the new installation with the required quality and accuracy of repair.

Using machine learning for sequence-level automated MRI protocol selection in neuroradiology.

PubMed

Brown, Andrew D; Marotta, Thomas R

2018-05-01

Incorrect imaging protocol selection can lead to important clinical findings being missed, contributing to both wasted health care resources and patient harm. We present a machine learning method for analyzing the unstructured text of clinical indications and patient demographics from magnetic resonance imaging (MRI) orders to automatically protocol MRI procedures at the sequence level. We compared 3 machine learning models - support vector machine, gradient boosting machine, and random forest - to a baseline model that predicted the most common protocol for all observations in our test set. The gradient boosting machine model significantly outperformed the baseline and demonstrated the best performance of the 3 models in terms of accuracy (95%), precision (86%), recall (80%), and Hamming loss (0.0487). This demonstrates the feasibility of automating sequence selection by applying machine learning to MRI orders. Automated sequence selection has important safety, quality, and financial implications and may facilitate improvements in the quality and safety of medical imaging service delivery.

Machine Translation and Other Translation Technologies.

ERIC Educational Resources Information Center

Melby, Alan

1996-01-01

Examines the application of linguistic theory to machine translation and translator tools, discusses the use of machine translation and translator tools in the real world of translation, and addresses the impact of translation technology on conceptions of language and other issues. Findings indicate that the human mind is flexible and linguistic…

Autoresonant control of nonlinear mode in ultrasonic transducer for machining applications.

PubMed

Babitsky, V I; Astashev, V K; Kalashnikov, A N

2004-04-01

Experiments conducted in several countries have shown that the improvement of machining quality can be promoted through conversion of the cutting process into one involving controllable high-frequency vibration at the cutting zone. This is achieved through the generation and maintenance of ultrasonic vibration of the cutting tool to alter the fracture process of work-piece material cutting to one in which loading of the materials at the tool tip is incremental, repetitive and controlled. It was shown that excitation of the high-frequency vibro-impact mode of the tool-workpiece interaction is the most effective way of ultrasonic influence on the dynamic characteristics of machining. The exploitation of this nonlinear mode needs a new method of adaptive control for excitation and stabilisation of ultrasonic vibration known as autoresonance. An approach has been developed to design an autoresonant ultrasonic cutting unit as an oscillating system with an intelligent electronic feedback controlling self-excitation in the entire mechatronic system. The feedback produces the exciting force by means of transformation and amplification of the motion signal. This allows realisation for robust control of fine resonant tuning to bring the nonlinear high Q-factor systems into technological application. The autoresonant control provides the possibility of self-tuning and self-adaptation mechanisms for the system to keep the nonlinear resonant mode of oscillation under unpredictable variation of load, structure and parameters. This allows simple regulation of intensity of the process whilst keeping maximum efficiency at all times. An autoresonant system with supervisory computer control was developed, tested and used for the control of the piezoelectric transducer during ultrasonically assisted cutting. The system has been developed as combined analog-digital, where analog devices process the control signal, and parameters of the devices are controlled digitally by computer. The

POOL server: machine learning application for functional site prediction in proteins.

PubMed

Somarowthu, Srinivas; Ondrechen, Mary Jo

2012-08-01

We present an automated web server for partial order optimum likelihood (POOL), a machine learning application that combines computed electrostatic and geometric information for high-performance prediction of catalytic residues from 3D structures. Input features consist of THEMATICS electrostatics data and pocket information from ConCavity. THEMATICS measures deviation from typical, sigmoidal titration behavior to identify functionally important residues and ConCavity identifies binding pockets by analyzing the surface geometry of protein structures. Both THEMATICS and ConCavity (structure only) do not require the query protein to have any sequence or structure similarity to other proteins. Hence, POOL is applicable to proteins with novel folds and engineered proteins. As an additional option for cases where sequence homologues are available, users can include evolutionary information from INTREPID for enhanced accuracy in site prediction. The web site is free and open to all users with no login requirements at http://www.pool.neu.edu. m.ondrechen@neu.edu Supplementary data are available at Bioinformatics online.

Design and control of a macro-micro robot for precise force applications

NASA Technical Reports Server (NTRS)

Wang, Yulun; Mangaser, Amante; Laby, Keith; Jordan, Steve; Wilson, Jeff

1993-01-01

Creating a robot which can delicately interact with its environment has been the goal of much research. Primarily two difficulties have made this goal hard to attain. The execution of control strategies which enable precise force manipulations are difficult to implement in real time because such algorithms have been too computationally complex for available controllers. Also, a robot mechanism which can quickly and precisely execute a force command is difficult to design. Actuation joints must be sufficiently stiff, frictionless, and lightweight so that desired torques can be accurately applied. This paper describes a robotic system which is capable of delicate manipulations. A modular high-performance multiprocessor control system was designed to provide sufficient compute power for executing advanced control methods. An 8 degree of freedom macro-micro mechanism was constructed to enable accurate tip forces. Control algorithms based on the impedance control method were derived, coded, and load balanced for maximum execution speed on the multiprocessor system. Delicate force tasks such as polishing, finishing, cleaning, and deburring, are the target applications of the robot.

Breakthrough in current-in-plane tunneling measurement precision by application of multi-variable fitting algorithm.

PubMed

Cagliani, Alberto; Østerberg, Frederik W; Hansen, Ole; Shiv, Lior; Nielsen, Peter F; Petersen, Dirch H

2017-09-01

We present a breakthrough in micro-four-point probe (M4PP) metrology to substantially improve precision of transmission line (transfer length) type measurements by application of advanced electrode position correction. In particular, we demonstrate this methodology for the M4PP current-in-plane tunneling (CIPT) technique. The CIPT method has been a crucial tool in the development of magnetic tunnel junction (MTJ) stacks suitable for magnetic random-access memories for more than a decade. On two MTJ stacks, the measurement precision of resistance-area product and tunneling magnetoresistance was improved by up to a factor of 3.5 and the measurement reproducibility by up to a factor of 17, thanks to our improved position correction technique.

Routine and timely sub-picoNewton force stability and precision for biological applications of atomic force microscopy.

PubMed

Churnside, Allison B; Sullan, Ruby May A; Nguyen, Duc M; Case, Sara O; Bull, Matthew S; King, Gavin M; Perkins, Thomas T

2012-07-11

Force drift is a significant, yet unresolved, problem in atomic force microscopy (AFM). We show that the primary source of force drift for a popular class of cantilevers is their gold coating, even though they are coated on both sides to minimize drift. Drift of the zero-force position of the cantilever was reduced from 900 nm for gold-coated cantilevers to 70 nm (N = 10; rms) for uncoated cantilevers over the first 2 h after wetting the tip; a majority of these uncoated cantilevers (60%) showed significantly less drift (12 nm, rms). Removing the gold also led to ∼10-fold reduction in reflected light, yet short-term (0.1-10 s) force precision improved. Moreover, improved force precision did not require extended settling; most of the cantilevers tested (9 out of 15) achieved sub-pN force precision (0.54 ± 0.02 pN) over a broad bandwidth (0.01-10 Hz) just 30 min after loading. Finally, this precision was maintained while stretching DNA. Hence, removing gold enables both routine and timely access to sub-pN force precision in liquid over extended periods (100 s). We expect that many current and future applications of AFM can immediately benefit from these improvements in force stability and precision.

Finite element computation on nearest neighbor connected machines

NASA Technical Reports Server (NTRS)

Mcaulay, A. D.

1984-01-01

Research aimed at faster, more cost effective parallel machines and algorithms for improving designer productivity with finite element computations is discussed. A set of 8 boards, containing 4 nearest neighbor connected arrays of commercially available floating point chips and substantial memory, are inserted into a commercially available machine. One-tenth Mflop (64 bit operation) processors provide an 89% efficiency when solving the equations arising in a finite element problem for a single variable regular grid of size 40 by 40 by 40. This is approximately 15 to 20 times faster than a much more expensive machine such as a VAX 11/780 used in double precision. The efficiency falls off as faster or more processors are envisaged because communication times become dominant. A novel successive overrelaxation algorithm which uses cyclic reduction in order to permit data transfer and computation to overlap in time is proposed.

A Strategy for DoD Manufacturing Science and Technology R and D in Precision Fabrication

DTIC Science & Technology

1994-01-01

3-11 vii Contents (Continued) Bibliography Appendix A. Progress Since the 1991 Plan Appendix B. Why "Precision" Appendix C...preci- sion fabrication R&D. Appendix A summarizes progress in precision fabrication R&D since the previous plan was prepared in 1991. Appendix B...lathe’s power consumption may indicate worn bearings. Detecting and acting on this condition can prevent costly spindle damage and associated machine down

Effect of cleaning status on accuracy and precision of oxygen flowmeters of various ages.

PubMed

Fissekis, Stephanie; Hodgson, David S; Bello, Nora M

2017-07-01

To evaluate oxygen flowmeters for accuracy and precision, assess the effects of cleaning and assess conformity to the American Society for Testing Materials (ASTM) standards. Experimental study. The flow of oxygen flowmeters from 31 anesthesia machines aged 1-45 years was measured before and after cleaning using a volumetric flow analyzer set at 0.5, 1.0, 2.0, 3.0, and 4.0 L minute -1 . A general linear mixed models approach was used to assess flow accuracy and precision. Flowmeters 1 year of age delivered accurate mean oxygen flows at all settings regardless of cleaning status. Flowmeters ≥5 years of age underdelivered at flows of 3.0 and 4.0 L minute -1 . Flowmeters ≥12 years underdelivered at flows of 2.0, 3.0 and 4.0 L minute -1 prior to cleaning. There was no evidence of any beneficial effect of cleaning on accuracy of flowmeters 5-12 years of age (p > 0.22), but the accuracy of flowmeters ≥15 years of age was improved by cleaning (p < 0.05). Regardless of age, cleaning increased precision, decreasing flow variability by approximately 17%. Nine of 31 uncleaned flowmeters did not meet ASTM standards. After cleaning, a different set of nine flowmeters did not meet standards, including three that had met standards prior to cleaning. Older flowmeters were more likely to underdeliver oxygen, especially at higher flows. Regardless of age, cleaning decreased flow variability, improving precision. However, flowmeters still may fail to meet ASTM standards, regardless of cleaning status. Cleaning anesthesia machine oxygen flowmeters improved precision for all tested machines and partially corrected inaccuracies in flowmeters ≥15 years old. A notable proportion of flowmeters did not meet ASTM standards. Cleaning did not ensure that they subsequently conformed to ASTM standards. We recommend annual flow output validation to identify whether flowmeters are acceptable for continued clinical use. Copyright © 2017 Association of Veterinary Anaesthetists and American

Stereodivergent synthesis with a programmable molecular machine

NASA Astrophysics Data System (ADS)

Kassem, Salma; Lee, Alan T. L.; Leigh, David A.; Marcos, Vanesa; Palmer, Leoni I.; Pisano, Simone

2017-09-01

It has been convincingly argued that molecular machines that manipulate individual atoms, or highly reactive clusters of atoms, with Ångström precision are unlikely to be realized. However, biological molecular machines routinely position rather less reactive substrates in order to direct chemical reaction sequences, from sequence-specific synthesis by the ribosome to polyketide synthases, where tethered molecules are passed from active site to active site in multi-enzyme complexes. Artificial molecular machines have been developed for tasks that include sequence-specific oligomer synthesis and the switching of product chirality, a photo-responsive host molecule has been described that is able to mechanically twist a bound molecular guest, and molecular fragments have been selectively transported in either direction between sites on a molecular platform through a ratchet mechanism. Here we detail an artificial molecular machine that moves a substrate between different activating sites to achieve different product outcomes from chemical synthesis. This molecular robot can be programmed to stereoselectively produce, in a sequential one-pot operation, an excess of any one of four possible diastereoisomers from the addition of a thiol and an alkene to an α,β-unsaturated aldehyde in a tandem reaction process. The stereodivergent synthesis includes diastereoisomers that cannot be selectively synthesized through conventional iminium-enamine organocatalysis. We anticipate that future generations of programmable molecular machines may have significant roles in chemical synthesis and molecular manufacturing.

Newton Methods for Large Scale Problems in Machine Learning

ERIC Educational Resources Information Center

Hansen, Samantha Leigh

2014-01-01

The focus of this thesis is on practical ways of designing optimization algorithms for minimizing large-scale nonlinear functions with applications in machine learning. Chapter 1 introduces the overarching ideas in the thesis. Chapters 2 and 3 are geared towards supervised machine learning applications that involve minimizing a sum of loss…

Method and apparatus for improving the quality and efficiency of ultrashort-pulse laser machining

DOEpatents

Stuart, Brent C.; Nguyen, Hoang T.; Perry, Michael D.

2001-01-01

A method and apparatus for improving the quality and efficiency of machining of materials with laser pulse durations shorter than 100 picoseconds by orienting and maintaining the polarization of the laser light such that the electric field vector is perpendicular relative to the edges of the material being processed. Its use is any machining operation requiring remote delivery and/or high precision with minimal collateral dames.

Industrial Inspection with Open Eyes: Advance with Machine Vision Technology

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

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

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

Method and apparatus for precision laser micromachining

DOEpatents

Chang, Jim; Warner, Bruce E.; Dragon, Ernest P.

2000-05-02

A method and apparatus for micromachining and microdrilling which results in a machined part of superior surface quality is provided. The system uses a near diffraction limited, high repetition rate, short pulse length, visible wavelength laser. The laser is combined with a high speed precision tilting mirror and suitable beam shaping optics, thus allowing a large amount of energy to be accurately positioned and scanned on the workpiece. As a result of this system, complicated, high resolution machining patterns can be achieved. A cover plate may be temporarily attached to the workpiece. Then as the workpiece material is vaporized during the machining process, the vapors condense on the cover plate rather than the surface of the workpiece. In order to eliminate cutting rate variations as the cutting direction is varied, a randomly polarized laser beam is utilized. A rotating half-wave plate is used to achieve the random polarization. In order to correctly locate the focus at the desired location within the workpiece, the position of the focus is first determined by monitoring the speckle size while varying the distance between the workpiece and the focussing optics. When the speckle size reaches a maximum, the focus is located at the first surface of the workpiece. After the location of the focus has been determined, it is repositioned to the desired location within the workpiece, thus optimizing the quality of the machined area.

Machine vision for digital microfluidics

NASA Astrophysics Data System (ADS)

Shin, Yong-Jun; Lee, Jeong-Bong

2010-01-01

Machine vision is widely used in an industrial environment today. It can perform various tasks, such as inspecting and controlling production processes, that may require humanlike intelligence. The importance of imaging technology for biological research or medical diagnosis is greater than ever. For example, fluorescent reporter imaging enables scientists to study the dynamics of gene networks with high spatial and temporal resolution. Such high-throughput imaging is increasingly demanding the use of machine vision for real-time analysis and control. Digital microfluidics is a relatively new technology with expectations of becoming a true lab-on-a-chip platform. Utilizing digital microfluidics, only small amounts of biological samples are required and the experimental procedures can be automatically controlled. There is a strong need for the development of a digital microfluidics system integrated with machine vision for innovative biological research today. In this paper, we show how machine vision can be applied to digital microfluidics by demonstrating two applications: machine vision-based measurement of the kinetics of biomolecular interactions and machine vision-based droplet motion control. It is expected that digital microfluidics-based machine vision system will add intelligence and automation to high-throughput biological imaging in the future.

Multi-Cultural Competency-Based Vocational Curricula. Machine Trades. Multi-Cultural Competency-Based Vocational/Technical Curricula Series.

ERIC Educational Resources Information Center

Hepburn, Larry; Shin, Masako

This document, one of eight in a multi-cultural competency-based vocational/technical curricula series, is on machine trades. This program is designed to run 36 weeks and cover 6 instructional areas: use of measuring tools; benchwork/tool bit grinding; lathe work; milling work; precision grinding; and combination machine work. A duty-task index…

Experimental investigation of the tip based micro/nano machining

NASA Astrophysics Data System (ADS)

Guo, Z.; Tian, Y.; Liu, X.; Wang, F.; Zhou, C.; Zhang, D.

2017-12-01

Based on the self-developed three dimensional micro/nano machining system, the effects of machining parameters and sample material on micro/nano machining are investigated. The micro/nano machining system is mainly composed of the probe system and micro/nano positioning stage. The former is applied to control the normal load and the latter is utilized to realize high precision motion in the xy plane. A sample examination method is firstly introduced to estimate whether the sample is placed horizontally. The machining parameters include scratching direction, speed, cycles, normal load and feed. According to the experimental results, the scratching depth is significantly affected by the normal load in all four defined scratching directions but is rarely influenced by the scratching speed. The increase of scratching cycle number can increase the scratching depth as well as smooth the groove wall. In addition, the scratching tests of silicon and copper attest that the harder material is easier to be removed. In the scratching with different feed amount, the machining results indicate that the machined depth increases as the feed reduces. Further, a cubic polynomial is used to fit the experimental results to predict the scratching depth. With the selected machining parameters of scratching direction d3/d4, scratching speed 5 μm/s and feed 0.06 μm, some more micro structures including stair, sinusoidal groove, Chinese character '田', 'TJU' and Chinese panda have been fabricated on the silicon substrate.

Evaluating the Security of Machine Learning Algorithms

DTIC Science & Technology

2008-05-20

Two far-reaching trends in computing have grown in significance in recent years. First, statistical machine learning has entered the mainstream as a...computing applications. The growing intersection of these trends compels us to investigate how well machine learning performs under adversarial conditions... machine learning has a structure that we can use to build secure learning systems. This thesis makes three high-level contributions. First, we develop a

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.

A wearable computing platform for developing cloud-based machine learning models for health monitoring applications.

PubMed

Patel, Shyamal; McGinnis, Ryan S; Silva, Ikaro; DiCristofaro, Steve; Mahadevan, Nikhil; Jortberg, Elise; Franco, Jaime; Martin, Albert; Lust, Joseph; Raj, Milan; McGrane, Bryan; DePetrillo, Paolo; Aranyosi, A J; Ceruolo, Melissa; Pindado, Jesus; Ghaffari, Roozbeh

2016-08-01

Wearable sensors have the potential to enable clinical-grade ambulatory health monitoring outside the clinic. Technological advances have enabled development of devices that can measure vital signs with great precision and significant progress has been made towards extracting clinically meaningful information from these devices in research studies. However, translating measurement accuracies achieved in the controlled settings such as the lab and clinic to unconstrained environments such as the home remains a challenge. In this paper, we present a novel wearable computing platform for unobtrusive collection of labeled datasets and a new paradigm for continuous development, deployment and evaluation of machine learning models to ensure robust model performance as we transition from the lab to home. Using this system, we train activity classification models across two studies and track changes in model performance as we go from constrained to unconstrained settings.

a Contemporary Approach for Evaluation of the best Measurement Capability of a Force Calibration Machine

NASA Astrophysics Data System (ADS)

Kumar, Harish

The present paper discusses the procedure for evaluation of best measurement capability of a force calibration machine. The best measurement capability of force calibration machine is evaluated by a comparison through the precision force transfer standards to the force standard machines. The force transfer standards are calibrated by the force standard machine and then by the force calibration machine by adopting the similar procedure. The results are reported and discussed in the paper and suitable discussion has been made for force calibration machine of 200 kN capacity. Different force transfer standards of nominal capacity 20 kN, 50 kN and 200 kN are used. It is found that there are significant variations in the .uncertainty of force realization by the force calibration machine according to the proposed method in comparison to the earlier method adopted.

High-precision arithmetic in mathematical physics

DOE PAGES

Bailey, David H.; Borwein, Jonathan M.

2015-05-12

For many scientific calculations, particularly those involving empirical data, IEEE 32-bit floating-point arithmetic produces results of sufficient accuracy, while for other applications IEEE 64-bit floating-point is more appropriate. But for some very demanding applications, even higher levels of precision are often required. Furthermore, this article discusses the challenge of high-precision computation, in the context of mathematical physics, and highlights what facilities are required to support future computation, in light of emerging developments in computer architecture.

A Novel Application of Machine Learning Methods to Model Microcontroller Upset Due to Intentional Electromagnetic Interference

NASA Astrophysics Data System (ADS)

Bilalic, Rusmir

A novel application of support vector machines (SVMs), artificial neural networks (ANNs), and Gaussian processes (GPs) for machine learning (GPML) to model microcontroller unit (MCU) upset due to intentional electromagnetic interference (IEMI) is presented. In this approach, an MCU performs a counting operation (0-7) while electromagnetic interference in the form of a radio frequency (RF) pulse is direct-injected into the MCU clock line. Injection times with respect to the clock signal are the clock low, clock rising edge, clock high, and the clock falling edge periods in the clock window during which the MCU is performing initialization and executing the counting procedure. The intent is to cause disruption in the counting operation and model the probability of effect (PoE) using machine learning tools. Five experiments were executed as part of this research, each of which contained a set of 38,300 training points and 38,300 test points, for a total of 383,000 total points with the following experiment variables: injection times with respect to the clock signal, injected RF power, injected RF pulse width, and injected RF frequency. For the 191,500 training points, the average training error was 12.47%, while for the 191,500 test points the average test error was 14.85%, meaning that on average, the machine was able to predict MCU upset with an 85.15% accuracy. Leaving out the results for the worst-performing model (SVM with a linear kernel), the test prediction accuracy for the remaining machines is almost 89%. All three machine learning methods (ANNs, SVMs, and GPML) showed excellent and consistent results in their ability to model and predict the PoE on an MCU due to IEMI. The GP approach performed best during training with a 7.43% average training error, while the ANN technique was most accurate during the test with a 10.80% error.

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.

Machine learning: novel bioinformatics approaches for combating antimicrobial resistance.

PubMed

Macesic, Nenad; Polubriaginof, Fernanda; Tatonetti, Nicholas P

2017-12-01

Antimicrobial resistance (AMR) is a threat to global health and new approaches to combating AMR are needed. Use of machine learning in addressing AMR is in its infancy but has made promising steps. We reviewed the current literature on the use of machine learning for studying bacterial AMR. The advent of large-scale data sets provided by next-generation sequencing and electronic health records make applying machine learning to the study and treatment of AMR possible. To date, it has been used for antimicrobial susceptibility genotype/phenotype prediction, development of AMR clinical decision rules, novel antimicrobial agent discovery and antimicrobial therapy optimization. Application of machine learning to studying AMR is feasible but remains limited. Implementation of machine learning in clinical settings faces barriers to uptake with concerns regarding model interpretability and data quality.Future applications of machine learning to AMR are likely to be laboratory-based, such as antimicrobial susceptibility phenotype prediction.

An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation

PubMed Central

Nguyen, Huu-Tho; Md Dawal, Siti Zawiah; Nukman, Yusoff; Aoyama, Hideki; Case, Keith

2015-01-01

Globalization of business and competitiveness in manufacturing has forced companies to improve their manufacturing facilities to respond to market requirements. Machine tool evaluation involves an essential decision using imprecise and vague information, and plays a major role to improve the productivity and flexibility in manufacturing. The aim of this study is to present an integrated approach for decision-making in machine tool selection. This paper is focused on the integration of a consistent fuzzy AHP (Analytic Hierarchy Process) and a fuzzy COmplex PRoportional ASsessment (COPRAS) for multi-attribute decision-making in selecting the most suitable machine tool. In this method, the fuzzy linguistic reference relation is integrated into AHP to handle the imprecise and vague information, and to simplify the data collection for the pair-wise comparison matrix of the AHP which determines the weights of attributes. The output of the fuzzy AHP is imported into the fuzzy COPRAS method for ranking alternatives through the closeness coefficient. Presentation of the proposed model application is provided by a numerical example based on the collection of data by questionnaire and from the literature. The results highlight the integration of the improved fuzzy AHP and the fuzzy COPRAS as a precise tool and provide effective multi-attribute decision-making for evaluating the machine tool in the uncertain environment. PMID:26368541

Space applications of Automation, Robotics and Machine Intelligence Systems (ARAMIS). Volume 2: Space projects overview

NASA Technical Reports Server (NTRS)

Miller, R. H.; Minsky, M. L.; Smith, D. B. S.

1982-01-01

Applications of automation, robotics, and machine intelligence systems (ARAMIS) to space activities, and their related ground support functions are studied so that informed decisions can be made on which aspects of ARAMIS to develop. The space project breakdowns, which are used to identify tasks ('functional elements'), are described. The study method concentrates on the production of a matrix relating space project tasks to pieces of ARAMIS.

Applications of Brain–Machine Interface Systems in Stroke Recovery and Rehabilitation

PubMed Central

Francisco, Gerard E.; Contreras-Vidal, Jose L.

2014-01-01

Stroke is a leading cause of disability, significantly impacting the quality of life (QOL) in survivors, and rehabilitation remains the mainstay of treatment in these patients. Recent engineering and technological advances such as brain-machine interfaces (BMI) and robotic rehabilitative devices are promising to enhance stroke neu-rorehabilitation, to accelerate functional recovery and improve QOL. This review discusses the recent applications of BMI and robotic-assisted rehabilitation in stroke patients. We present the framework for integrated BMI and robotic-assisted therapies, and discuss their potential therapeutic, assistive and diagnostic functions in stroke rehabilitation. Finally, we conclude with an outlook on the potential challenges and future directions of these neurotechnologies, and their impact on clinical rehabilitation. PMID:25110624

The forthcoming era of precision medicine.

PubMed

Gamulin, Stjepan

2016-11-01

The aim of this essay is to present the definition and principles of personalized or precision medicine, the perspective and barriers to its development and clinical application. The implementation of precision medicine in health care requires the coordinated efforts of all health care stakeholders (the biomedical community, government, regulatory bodies, patients' groups). Particularly, translational research with the integration of genomic and comprehensive data from all levels of the organism ("big data"), development of bioinformatics platforms enabling network analysis of disease etiopathogenesis, development of a legislative framework for handling personal data, and new paradigms of medical education are necessary for successful application of the concept of precision medicine in health care. In the present and future era of precision medicine, the collaboration of all participants in health care is necessary for its realization, resulting in improvement of diagnosis, prevention and therapy, based on a holistic, individually tailored approach. Copyright © 2016 by Academy of Sciences and Arts of Bosnia and Herzegovina.

Weldability, machinability and surfacing of commercial duplex stainless steel AISI2205 for marine applications - A recent review.

PubMed

Vinoth Jebaraj, A; Ajaykumar, L; Deepak, C R; Aditya, K V V

2017-05-01

In the present review, attempts have been made to analyze the metallurgical, mechanical, and corrosion properties of commercial marine alloy duplex stainless steel AISI 2205 with special reference to its weldability, machinability, and surfacing. In the first part, effects of various fusion and solid-state welding processes on joining DSS 2205 with similar and dissimilar metals are addressed. Microstructural changes during the weld cooling cycle such as austenite reformation, partitioning of alloying elements, HAZ transformations, and the intermetallic precipitations are analyzed and compared with the different welding techniques. In the second part, machinability of DSS 2205 is compared with the commercial ASS grades in order to justify the quality of machining. In the third part, the importance of surface quality in a marine exposure is emphasized and the enhancement of surface properties through peening techniques is highlighted. The research gaps and inferences highlighted in this review will be more useful for the fabrications involved in the marine applications.

Machine learning classification with confidence: application of transductive conformal predictors to MRI-based diagnostic and prognostic markers in depression.

PubMed

Nouretdinov, Ilia; Costafreda, Sergi G; Gammerman, Alexander; Chervonenkis, Alexey; Vovk, Vladimir; Vapnik, Vladimir; Fu, Cynthia H Y

2011-05-15

There is rapidly accumulating evidence that the application of machine learning classification to neuroimaging measurements may be valuable for the development of diagnostic and prognostic prediction tools in psychiatry. However, current methods do not produce a measure of the reliability of the predictions. Knowing the risk of the error associated with a given prediction is essential for the development of neuroimaging-based clinical tools. We propose a general probabilistic classification method to produce measures of confidence for magnetic resonance imaging (MRI) data. We describe the application of transductive conformal predictor (TCP) to MRI images. TCP generates the most likely prediction and a valid measure of confidence, as well as the set of all possible predictions for a given confidence level. We present the theoretical motivation for TCP, and we have applied TCP to structural and functional MRI data in patients and healthy controls to investigate diagnostic and prognostic prediction in depression. We verify that TCP predictions are as accurate as those obtained with more standard machine learning methods, such as support vector machine, while providing the additional benefit of a valid measure of confidence for each prediction. Copyright © 2010 Elsevier Inc. All rights reserved.

Revisit of Machine Learning Supported Biological and Biomedical Studies.

PubMed

Yu, Xiang-Tian; Wang, Lu; Zeng, Tao

2018-01-01

Generally, machine learning includes many in silico methods to transform the principles underlying natural phenomenon to human understanding information, which aim to save human labor, to assist human judge, and to create human knowledge. It should have wide application potential in biological and biomedical studies, especially in the era of big biological data. To look through the application of machine learning along with biological development, this review provides wide cases to introduce the selection of machine learning methods in different practice scenarios involved in the whole biological and biomedical study cycle and further discusses the machine learning strategies for analyzing omics data in some cutting-edge biological studies. Finally, the notes on new challenges for machine learning due to small-sample high-dimension are summarized from the key points of sample unbalance, white box, and causality.

Different Techniques For Producing Precision Holes (>20 mm) In Hardened Steel—Comparative Results

NASA Astrophysics Data System (ADS)

Coelho, R. T.; Tanikawa, S. T.

2009-11-01

High speed machining (HSM), or high performance machining, has been one of the most recent technological advances. When applied to milling operations, using adequate machines, CAM programs and tooling, it allows cutting hardened steels, which was not feasible just a couple of years ago. The use of very stiff and precision machines has created the possibilities of machining holes in hardened steels, such as AISI H13 with 48-50 HRC, using helical interpolations, for example. Such process is particularly useful for holes with diameter bigger than normal solid carbide drills commercially available, around 20 mm, or higher. Such holes may need narrow tolerances, fine surface finishing, which can be obtained just by end milling operations. The present work compares some of the strategies used to obtain such holes by end milling, and also some techniques employed to finish them, by milling, boring and also by fine grinding at the same machine. Results indicate that it is possible to obtain holes with less than 0.36 m in circularity, 7.41 m in cylindricity and 0.12 m in surface roughness Ra. Additionally, there is less possibilities of obtaining heat affected layers when using such technique.

[Role and management of cancer clinical database in the application of gastric cancer precision medicine].

PubMed

Li, Yuanfang; Zhou, Zhiwei

2016-02-01

Precision medicine is a new medical concept and medical model, which is based on personalized medicine, rapid progress of genome sequencing technology and cross application of biological information and big data science. Precision medicine improves the diagnosis and treatment of gastric cancer to provide more convenience through more profound analyses of characteristics, pathogenesis and other core issues in gastric cancer. Cancer clinical database is important to promote the development of precision medicine. Therefore, it is necessary to pay close attention to the construction and management of the database. The clinical database of Sun Yat-sen University Cancer Center is composed of medical record database, blood specimen bank, tissue bank and medical imaging database. In order to ensure the good quality of the database, the design and management of the database should follow the strict standard operation procedure(SOP) model. Data sharing is an important way to improve medical research in the era of medical big data. The construction and management of clinical database must also be strengthened and innovated.

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data

PubMed Central

Hepworth, Philip J.; Nefedov, Alexey V.; Muchnik, Ilya B.; Morgan, Kenton L.

2012-01-01

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide. PMID:22319115

Broiler chickens can benefit from machine learning: support vector machine analysis of observational epidemiological data.

PubMed

Hepworth, Philip J; Nefedov, Alexey V; Muchnik, Ilya B; Morgan, Kenton L

2012-08-07

Machine-learning algorithms pervade our daily lives. In epidemiology, supervised machine learning has the potential for classification, diagnosis and risk factor identification. Here, we report the use of support vector machine learning to identify the features associated with hock burn on commercial broiler farms, using routinely collected farm management data. These data lend themselves to analysis using machine-learning techniques. Hock burn, dermatitis of the skin over the hock, is an important indicator of broiler health and welfare. Remarkably, this classifier can predict the occurrence of high hock burn prevalence with accuracy of 0.78 on unseen data, as measured by the area under the receiver operating characteristic curve. We also compare the results with those obtained by standard multi-variable logistic regression and suggest that this technique provides new insights into the data. This novel application of a machine-learning algorithm, embedded in poultry management systems could offer significant improvements in broiler health and welfare worldwide.

Protein contact prediction by integrating deep multiple sequence alignments, coevolution and machine learning.

PubMed

Adhikari, Badri; Hou, Jie; Cheng, Jianlin

2018-03-01

In this study, we report the evaluation of the residue-residue contacts predicted by our three different methods in the CASP12 experiment, focusing on studying the impact of multiple sequence alignment, residue coevolution, and machine learning on contact prediction. The first method (MULTICOM-NOVEL) uses only traditional features (sequence profile, secondary structure, and solvent accessibility) with deep learning to predict contacts and serves as a baseline. The second method (MULTICOM-CONSTRUCT) uses our new alignment algorithm to generate deep multiple sequence alignment to derive coevolution-based features, which are integrated by a neural network method to predict contacts. The third method (MULTICOM-CLUSTER) is a consensus combination of the predictions of the first two methods. We evaluated our methods on 94 CASP12 domains. On a subset of 38 free-modeling domains, our methods achieved an average precision of up to 41.7% for top L/5 long-range contact predictions. The comparison of the three methods shows that the quality and effective depth of multiple sequence alignments, coevolution-based features, and machine learning integration of coevolution-based features and traditional features drive the quality of predicted protein contacts. On the full CASP12 dataset, the coevolution-based features alone can improve the average precision from 28.4% to 41.6%, and the machine learning integration of all the features further raises the precision to 56.3%, when top L/5 predicted long-range contacts are evaluated. And the correlation between the precision of contact prediction and the logarithm of the number of effective sequences in alignments is 0.66. © 2017 Wiley Periodicals, Inc.

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

EDITORIAL: Precision Measurement Technology at the 56th International Scientific Colloquium in Ilmenau Precision Measurement Technology at the 56th International Scientific Colloquium in Ilmenau

NASA Astrophysics Data System (ADS)

Manske, E.; Froehlich, T.

2012-07-01

The 56th International Scientific Colloquium was held from 12th to 16th September 2011 at the Ilmenau University of Technology in Germany. This event was organized by the Faculty of Mechanical Engineering under the title: 'Innovation in Mechanical Engineering—Shaping the Future' and was intended to reflect the entire scope of modern mechanical engineering. In three main topics many research areas, all involving innovative mechanical engineering, were addressed, especially in the fields of Precision Engineering and Precision Measurement Technology, Mechatronics and Ambient-Assisted Living and Systems Technology. The participants were scientists from 21 countries, and 166 presentations were given. This special issue of Measurement Science and Technology presents selected contributions on 'Precision Engineering and Precision Measurement Technology'. Over three days the conference participants discussed novel scientific results in two sessions. The main topics of these sessions were: Measurement and Sensor Technology Process measurement Laser measurement Force measurement Weighing technology Temperature measurement Measurement dynamics and Nanopositioning and Nanomeasuring Technology Nanopositioning and nanomeasuring machines Nanometrology Probes and tools Mechanical design Signal processing Control and visualization in NPM devices Significant research results from the Collaborative Research Centre SFB 622 'Nanopositioning and Nanomeasuring Machines' funded by the German Research Foundation (DFG) were presented as part of this topic. As the Chairmen, our special thanks are due to the International Programme Committee, the Organization Committee and the conference speakers as well as colleagues from the Institute of Process Measurement and Sensor Technology who helped make the conference a success. We would like to thank all the authors for their contributions, the referees for their time spent reviewing the contributions and their valuable comments, and the whole

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.

Machine learning techniques to predict sensitive patterns to fault attack in the Java Card application

NASA Astrophysics Data System (ADS)

Chahrazed, Yahiaoui; Jean-Louis, Lanet; Mohamed, Mezghiche; Karim, Tamine

2018-01-01

Fault attack represents one of the serious threats against Java Card security. It consists of physical perturbation of chip components to introduce faults in the code execution. A fault may be induced using a laser beam to impact opcodes and operands of instructions. This could lead to a mutation of the application code in such a way that it becomes hostile. Any successful attack may reveal a secret information stored in the card or grant an undesired authorisation. We propose a methodology to recognise, during the development step, the sensitive patterns to the fault attack in the Java Card applications. It is based on the concepts from text categorisation and machine learning. In fact, in this method, we represented the patterns using opcodes n-grams as features, and we evaluated different machine learning classifiers. The results show that the classifiers performed poorly when classifying dangerous sensitive patterns, due to the imbalance of our data-set. The number of dangerous sensitive patterns is much lower than the number of not dangerous patterns. We used resampling techniques to balance the class distribution in our data-set. The experimental results indicated that the resampling techniques improved the accuracy of the classifiers. In addition, our proposed method reduces the execution time of sensitive patterns classification in comparison to the SmartCM tool. This tool is used in our study to evaluate the effect of faults on Java Card applications.

Advancing the large-scale CCS database for metabolomics and lipidomics at the machine-learning era.

PubMed

Zhou, Zhiwei; Tu, Jia; Zhu, Zheng-Jiang

2018-02-01

Metabolomics and lipidomics aim to comprehensively measure the dynamic changes of all metabolites and lipids that are present in biological systems. The use of ion mobility-mass spectrometry (IM-MS) for metabolomics and lipidomics has facilitated the separation and the identification of metabolites and lipids in complex biological samples. The collision cross-section (CCS) value derived from IM-MS is a valuable physiochemical property for the unambiguous identification of metabolites and lipids. However, CCS values obtained from experimental measurement and computational modeling are limited available, which significantly restricts the application of IM-MS. In this review, we will discuss the recently developed machine-learning based prediction approach, which could efficiently generate precise CCS databases in a large scale. We will also highlight the applications of CCS databases to support metabolomics and lipidomics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Computerized Machine for Cutting Space Shuttle Thermal Tiles

NASA Technical Reports Server (NTRS)

Ramirez, Luis E.; Reuter, Lisa A.

2009-01-01

A report presents the concept of a machine aboard the space shuttle that would cut oversized thermal-tile blanks to precise sizes and shapes needed to replace tiles that were damaged or lost during ascent to orbit. The machine would include a computer-controlled jigsaw enclosed in a clear acrylic shell that would prevent escape of cutting debris. A vacuum motor would collect the debris into a reservoir and would hold a tile blank securely in place. A database stored in the computer would contain the unique shape and dimensions of every tile. Once a broken or missing tile was identified, its identification number would be entered into the computer, wherein the cutting pattern associated with that number would be retrieved from the database. A tile blank would be locked into a crib in the machine, the shell would be closed (proximity sensors would prevent activation of the machine while the shell was open), and a "cut" command would be sent from the computer. A blade would be moved around the crib like a plotter, cutting the tile to the required size and shape. Once the tile was cut, an astronaut would take a space walk for installation.

Combining Machine Learning Systems and Multiple Docking Simulation Packages to Improve Docking Prediction Reliability for Network Pharmacology

PubMed Central

Hsin, Kun-Yi; Ghosh, Samik; Kitano, Hiroaki

2013-01-01

Increased availability of bioinformatics resources is creating opportunities for the application of network pharmacology to predict drug effects and toxicity resulting from multi-target interactions. Here we present a high-precision computational prediction approach that combines two elaborately built machine learning systems and multiple molecular docking tools to assess binding potentials of a test compound against proteins involved in a complex molecular network. One of the two machine learning systems is a re-scoring function to evaluate binding modes generated by docking tools. The second is a binding mode selection function to identify the most predictive binding mode. Results from a series of benchmark validations and a case study show that this approach surpasses the prediction reliability of other techniques and that it also identifies either primary or off-targets of kinase inhibitors. Integrating this approach with molecular network maps makes it possible to address drug safety issues by comprehensively investigating network-dependent effects of a drug or drug candidate. PMID:24391846

Understanding and Optimizing Asynchronous Low-Precision Stochastic Gradient Descent

PubMed Central

De Sa, Christopher; Feldman, Matthew; Ré, Christopher; Olukotun, Kunle

2018-01-01

Stochastic gradient descent (SGD) is one of the most popular numerical algorithms used in machine learning and other domains. Since this is likely to continue for the foreseeable future, it is important to study techniques that can make it run fast on parallel hardware. In this paper, we provide the first analysis of a technique called Buckwild! that uses both asynchronous execution and low-precision computation. We introduce the DMGC model, the first conceptualization of the parameter space that exists when implementing low-precision SGD, and show that it provides a way to both classify these algorithms and model their performance. We leverage this insight to propose and analyze techniques to improve the speed of low-precision SGD. First, we propose software optimizations that can increase throughput on existing CPUs by up to 11×. Second, we propose architectural changes, including a new cache technique we call an obstinate cache, that increase throughput beyond the limits of current-generation hardware. We also implement and analyze low-precision SGD on the FPGA, which is a promising alternative to the CPU for future SGD systems. PMID:29391770

Manipulating Crop Density to Optimize Nitrogen and Water Use: An Application of Precision Agroecology

NASA Astrophysics Data System (ADS)

Brown, T. T.; Huggins, D. R.; Smith, J. L.; Keller, C. K.; Kruger, C.

2011-12-01

Rising levels of reactive nitrogen (Nr) in the environment coupled with increasing population positions agriculture as a major contributor for supplying food and ecosystem services to the world. The concept of Precision Agroecology (PA) explicitly recognizes the importance of time and place by combining the principles of precision farming with ecology creating a framework that can lead to improvements in Nr use efficiency. In the Palouse region of the Pacific Northwest, USA, relationships between productivity, N dynamics and cycling, water availability, and environmental impacts result from intricate spatial and temporal variations in soil, ecosystem processes, and socioeconomic factors. Our research goal is to investigate N use efficiency (NUE) in the context of factors that regulate site-specific environmental and economic conditions and to develop the concept of PA for use in sustainable agroecosystems and science-based Nr policy. Nitrogen and plant density field trials with winter wheat (Triticum aestivum L.) were conducted at the Washington State University Cook Agronomy Farm near Pullman, WA under long-term no-tillage management in 2010 and 2011. Treatments were imposed across environmentally heterogeneous field conditions to assess soil, crop and environmental interactions. Microplots with a split N application using 15N-labeled fertilizer were established in 2011 to examine the impact of N timing on uptake of fertilizer and soil N throughout the growing season for two plant density treatments. Preliminary data show that plant density manipulation combined with precision N applications regulated water and N use and resulted in greater wheat yield with less seed and N inputs. These findings indicate that improvements to NUE and agroecosystem sustainability should consider landscape-scale patterns driving productivity (e.g., spatial and temporal dynamics of water availability and N transformations) and would benefit from policy incentives that promote a PA

Automated planning for intelligent machines in energy-related applications

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

Weisbin, C.R.; de Saussure, G.; Barhen, J.

1984-01-01

This paper discusses the current activities of the Center for Engineering Systems Advanced Research (CESAR) program related to plan generation and execution by an intelligent machine. The system architecture for the CESAR mobile robot (named HERMIES-1) is described. The minimal cut-set approach is developed to reduce the tree search time of conventional backward chaining planning techniques. Finally, a real-time concept of an Intelligent Machine Operating System is presented in which planning and reasoning is embedded in a system for resource allocation and process management.

Microfabrication of Silicon/Ceramic Hybrid Cantilever for Scanning Probe Microscope and Sensor Applications

NASA Astrophysics Data System (ADS)

Wakayama, Takayuki; Kobayashi, Toshinari; Iwata, Nobuya; Tanifuji, Nozomi; Matsuda, Yasuaki; Yamada, Syoji

2003-12-01

We present here new cantilevers for scanning probe microscopy (SPM) and sensor applications, which consist of silicon cantilever beam and ceramic pedestal. Silicon is only used to make cantilever beams and tips. Precision-machinery-made ceramics replaces silicon pedestal part. The ceramics was recently developed by Sumikin Ceramics and Quarts Co., Ltd. and can be machined precisely with end mill cutting. Many silicon beams are fabricated at once from a wafer using batch fabrication method. Therefore, SPM probes can be fabricated in high productivity and in low cost. These beams are transferred with transfer technique and are bonded on the ceramic pedestal with epoxy glue. We demonstrate here atomic force microscope (AFM) and gas sensor applications of the hybrid structure. In a gas sensor application, the ends of the cantilever are selectively modified with zeolite crystals as a sensitive layer. The bonding strength is enough for each application.

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling

PubMed Central

Cuperlovic-Culf, Miroslava

2018-01-01

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies. PMID:29324649

Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling.

PubMed

Cuperlovic-Culf, Miroslava

2018-01-11

Machine learning uses experimental data to optimize clustering or classification of samples or features, or to develop, augment or verify models that can be used to predict behavior or properties of systems. It is expected that machine learning will help provide actionable knowledge from a variety of big data including metabolomics data, as well as results of metabolism models. A variety of machine learning methods has been applied in bioinformatics and metabolism analyses including self-organizing maps, support vector machines, the kernel machine, Bayesian networks or fuzzy logic. To a lesser extent, machine learning has also been utilized to take advantage of the increasing availability of genomics and metabolomics data for the optimization of metabolic network models and their analysis. In this context, machine learning has aided the development of metabolic networks, the calculation of parameters for stoichiometric and kinetic models, as well as the analysis of major features in the model for the optimal application of bioreactors. Examples of this very interesting, albeit highly complex, application of machine learning for metabolism modeling will be the primary focus of this review presenting several different types of applications for model optimization, parameter determination or system analysis using models, as well as the utilization of several different types of machine learning technologies.

Whole brain white matter connectivity analysis using machine learning: An application to autism.

PubMed

Zhang, Fan; Savadjiev, Peter; Cai, Weidong; Song, Yang; Rathi, Yogesh; Tunç, Birkan; Parker, Drew; Kapur, Tina; Schultz, Robert T; Makris, Nikos; Verma, Ragini; O'Donnell, Lauren J

2018-05-15

In this paper, we propose an automated white matter connectivity analysis method for machine learning classification and characterization of white matter abnormality via identification of discriminative fiber tracts. The proposed method uses diffusion MRI tractography and a data-driven approach to find fiber clusters corresponding to subdivisions of the white matter anatomy. Features extracted from each fiber cluster describe its diffusion properties and are used for machine learning. The method is demonstrated by application to a pediatric neuroimaging dataset from 149 individuals, including 70 children with autism spectrum disorder (ASD) and 79 typically developing controls (TDC). A classification accuracy of 78.33% is achieved in this cross-validation study. We investigate the discriminative diffusion features based on a two-tensor fiber tracking model. We observe that the mean fractional anisotropy from the second tensor (associated with crossing fibers) is most affected in ASD. We also find that local along-tract (central cores and endpoint regions) differences between ASD and TDC are helpful in differentiating the two groups. These altered diffusion properties in ASD are associated with multiple robustly discriminative fiber clusters, which belong to several major white matter tracts including the corpus callosum, arcuate fasciculus, uncinate fasciculus and aslant tract; and the white matter structures related to the cerebellum, brain stem, and ventral diencephalon. These discriminative fiber clusters, a small part of the whole brain tractography, represent the white matter connections that could be most affected in ASD. Our results indicate the potential of a machine learning pipeline based on white matter fiber clustering. Copyright © 2017 Elsevier Inc. All rights reserved.

Traveling wire electrode increases productivity of Electrical Discharge Machining /EDM/ equipment

NASA Technical Reports Server (NTRS)

Kotora, J., Jr.; Smith, S. V.

1967-01-01

Traveling wire electrode on electrical discharge machining /EDM/ equipment reduces the time requirements for precision cutting. This device enables cutting with a minimum of lost material and without inducing stress beyond that inherent in the material. The use of wire increases accuracy and enables tighter tolerances to be maintained.

Precision digital control systems

NASA Astrophysics Data System (ADS)

Vyskub, V. G.; Rozov, B. S.; Savelev, V. I.

This book is concerned with the characteristics of digital control systems of great accuracy. A classification of such systems is considered along with aspects of stabilization, programmable control applications, digital tracking systems and servomechanisms, and precision systems for the control of a scanning laser beam. Other topics explored are related to systems of proportional control, linear devices and methods for increasing precision, approaches for further decreasing the response time in the case of high-speed operation, possibilities for the implementation of a logical control law, and methods for the study of precision digital control systems. A description is presented of precision automatic control systems which make use of electronic computers, taking into account the existing possibilities for an employment of computers in automatic control systems, approaches and studies required for including a computer in such control systems, and an analysis of the structure of automatic control systems with computers. Attention is also given to functional blocks in the considered systems.

Precision cosmological parameter estimation

NASA Astrophysics Data System (ADS)

Fendt, William Ashton, Jr.

2009-09-01

Experimental efforts of the last few decades have brought. a golden age to mankind's endeavor to understand tine physical properties of the Universe throughout its history. Recent measurements of the cosmic microwave background (CMB) provide strong confirmation of the standard big bang paradigm, as well as introducing new mysteries, to unexplained by current physical models. In the following decades. even more ambitious scientific endeavours will begin to shed light on the new physics by looking at the detailed structure of the Universe both at very early and recent times. Modern data has allowed us to begins to test inflationary models of the early Universe, and the near future will bring higher precision data and much stronger tests. Cracking the codes hidden in these cosmological observables is a difficult and computationally intensive problem. The challenges will continue to increase as future experiments bring larger and more precise data sets. Because of the complexity of the problem, we are forced to use approximate techniques and make simplifying assumptions to ease the computational workload. While this has been reasonably sufficient until now, hints of the limitations of our techniques have begun to come to light. For example, the likelihood approximation used for analysis of CMB data from the Wilkinson Microwave Anistropy Probe (WMAP) satellite was shown to have short falls, leading to pre-emptive conclusions drawn about current cosmological theories. Also it can he shown that an approximate method used by all current analysis codes to describe the recombination history of the Universe will not be sufficiently accurate for future experiments. With a new CMB satellite scheduled for launch in the coming months, it is vital that we develop techniques to improve the analysis of cosmological data. This work develops a novel technique of both avoiding the use of approximate computational codes as well as allowing the application of new, more precise analysis

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.

Micro-machined thermo-conductivity detector

DOEpatents

Yu, Conrad

2003-01-01

A micro-machined thermal conductivity detector for a portable gas chromatograph. The detector is highly sensitive and has fast response time to enable detection of the small size gas samples in a portable gas chromatograph which are in the order of nanoliters. The high sensitivity and fast response time are achieved through micro-machined devices composed of a nickel wire, for example, on a silicon nitride window formed in a silicon member and about a millimeter square in size. In addition to operating as a thermal conductivity detector, the silicon nitride window with a micro-machined wire therein of the device can be utilized for a fast response heater for PCR applications.

Evaluation of machinability and flexural strength of a novel dental machinable glass-ceramic.

PubMed

Qin, Feng; Zheng, Shucan; Luo, Zufeng; Li, Yong; Guo, Ling; Zhao, Yunfeng; Fu, Qiang

2009-10-01

To evaluate the machinability and flexural strength of a novel dental machinable glass-ceramic (named PMC), and to compare the machinability property with that of Vita Mark II and human enamel. The raw batch materials were selected and mixed. Four groups of novel glass-ceramics were formed at different nucleation temperatures, and were assigned to Group 1, Group 2, Group 3 and Group 4. The machinability of the four groups of novel glass-ceramics, Vita Mark II ceramic and freshly extracted human premolars were compared by means of drilling depth measurement. A three-point bending test was used to measure the flexural strength of the novel glass-ceramics. The crystalline phases of the group with the best machinability were identified by X-ray diffraction. In terms of the drilling depth, Group 2 of the novel glass-ceramics proves to have the largest drilling depth. There was no statistical difference among Group 1, Group 4 and the natural teeth. The drilling depth of Vita MK II was statistically less than that of Group 1, Group 4 and the natural teeth. Group 3 had the least drilling depth. In respect of the flexural strength, Group 2 exhibited the maximum flexural strength; Group 1 was statistically weaker than Group 2; there was no statistical difference between Group 3 and Group 4, and they were the weakest materials. XRD of Group 2 ceramic showed that a new type of dental machinable glass-ceramic containing calcium-mica had been developed by the present study and was named PMC. PMC is promising for application as a dental machinable ceramic due to its good machinability and relatively high strength.

Comparing and Validating Machine Learning Models for Mycobacterium tuberculosis Drug Discovery.

PubMed

Lane, Thomas; Russo, Daniel P; Zorn, Kimberley M; Clark, Alex M; Korotcov, Alexandru; Tkachenko, Valery; Reynolds, Robert C; Perryman, Alexander L; Freundlich, Joel S; Ekins, Sean

2018-04-26

Tuberculosis is a global health dilemma. In 2016, the WHO reported 10.4 million incidences and 1.7 million deaths. The need to develop new treatments for those infected with Mycobacterium tuberculosis ( Mtb) has led to many large-scale phenotypic screens and many thousands of new active compounds identified in vitro. However, with limited funding, efforts to discover new active molecules against Mtb needs to be more efficient. Several computational machine learning approaches have been shown to have good enrichment and hit rates. We have curated small molecule Mtb data and developed new models with a total of 18,886 molecules with activity cutoffs of 10 μM, 1 μM, and 100 nM. These data sets were used to evaluate different machine learning methods (including deep learning) and metrics and to generate predictions for additional molecules published in 2017. One Mtb model, a combined in vitro and in vivo data Bayesian model at a 100 nM activity yielded the following metrics for 5-fold cross validation: accuracy = 0.88, precision = 0.22, recall = 0.91, specificity = 0.88, kappa = 0.31, and MCC = 0.41. We have also curated an evaluation set ( n = 153 compounds) published in 2017, and when used to test our model, it showed the comparable statistics (accuracy = 0.83, precision = 0.27, recall = 1.00, specificity = 0.81, kappa = 0.36, and MCC = 0.47). We have also compared these models with additional machine learning algorithms showing Bayesian machine learning models constructed with literature Mtb data generated by different laboratories generally were equivalent to or outperformed deep neural networks with external test sets. Finally, we have also compared our training and test sets to show they were suitably diverse and different in order to represent useful evaluation sets. Such Mtb machine learning models could help prioritize compounds for testing in vitro and in vivo.

Testing and Validating Machine Learning Classifiers by Metamorphic Testing☆

PubMed Central

Xie, Xiaoyuan; Ho, Joshua W. K.; Murphy, Christian; Kaiser, Gail; Xu, Baowen; Chen, Tsong Yueh

2011-01-01

Machine Learning algorithms have provided core functionality to many application domains - such as bioinformatics, computational linguistics, etc. However, it is difficult to detect faults in such applications because often there is no “test oracle” to verify the correctness of the computed outputs. To help address the software quality, in this paper we present a technique for testing the implementations of machine learning classification algorithms which support such applications. Our approach is based on the technique “metamorphic testing”, which has been shown to be effective to alleviate the oracle problem. Also presented include a case study on a real-world machine learning application framework, and a discussion of how programmers implementing machine learning algorithms can avoid the common pitfalls discovered in our study. We also conduct mutation analysis and cross-validation, which reveal that our method has high effectiveness in killing mutants, and that observing expected cross-validation result alone is not sufficiently effective to detect faults in a supervised classification program. The effectiveness of metamorphic testing is further confirmed by the detection of real faults in a popular open-source classification program. PMID:21532969

High-Strength Undiffused Brushless (HSUB) Machine

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

Hsu, John S; Tolbert, Leon M; Lee, Seong T

2007-01-01

This paper introduces a new high-strength undiffused brushless machine that transfers the stationary excitation magnetomotive force to the rotor without any brushes. For a conventional permanent magnet (PM) machine, the air gap flux density cannot be enhanced effectively but can be weakened. In the new machine, both the stationary excitation coil and the PM in the rotor produce an enhanced air gap flux. The PM in the rotor prevents magnetic flux diffusion between the poles and guides the reluctance flux path. The pole flux density in the air gap can be much higher than what the PM alone can produce.more » A high-strength machine is thus obtained. The air gap flux density can be weakened through the stationary excitation winding. This type of machine is particularly suitable for electric and hybrid-electric vehicle applications. Patents of this new technology are either granted or pending.« less

An intelligent CNC machine control system architecture

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

Miller, D.J.; Loucks, C.S.

1996-10-01

Intelligent, agile manufacturing relies on automated programming of digitally controlled processes. Currently, processes such as Computer Numerically Controlled (CNC) machining are difficult to automate because of highly restrictive controllers and poor software environments. It is also difficult to utilize sensors and process models for adaptive control, or to integrate machining processes with other tasks within a factory floor setting. As part of a Laboratory Directed Research and Development (LDRD) program, a CNC machine control system architecture based on object-oriented design and graphical programming has been developed to address some of these problems and to demonstrate automated agile machining applications usingmore » platform-independent software.« less

Machine learning of atmospheric chemistry. Applications to a global chemistry transport model.

NASA Astrophysics Data System (ADS)

Evans, M. J.; Keller, C. A.

2017-12-01

Atmospheric chemistry is central to many environmental issues such as air pollution, climate change, and stratospheric ozone loss. Chemistry Transport Models (CTM) are a central tool for understanding these issues, whether for research or for forecasting. These models split the atmosphere in a large number of grid-boxes and consider the emission of compounds into these boxes and their subsequent transport, deposition, and chemical processing. The chemistry is represented through a series of simultaneous ordinary differential equations, one for each compound. Given the difference in life-times between the chemical compounds (mili-seconds for O(1D) to years for CH4) these equations are numerically stiff and solving them consists of a significant fraction of the computational burden of a CTM.We have investigated a machine learning approach to solving the differential equations instead of solving them numerically. From an annual simulation of the GEOS-Chem model we have produced a training dataset consisting of the concentration of compounds before and after the differential equations are solved, together with some key physical parameters for every grid-box and time-step. From this dataset we have trained a machine learning algorithm (random regression forest) to be able to predict the concentration of the compounds after the integration step based on the concentrations and physical state at the beginning of the time step. We have then included this algorithm back into the GEOS-Chem model, bypassing the need to integrate the chemistry.This machine learning approach shows many of the characteristics of the full simulation and has the potential to be substantially faster. There are a wide range of application for such an approach - generating boundary conditions, for use in air quality forecasts, chemical data assimilation systems, centennial scale climate simulations etc. We discuss our approches' speed and accuracy, and highlight some potential future directions for

A Review on Parametric Analysis of Magnetic Abrasive Machining Process

NASA Astrophysics Data System (ADS)

Khattri, Krishna; Choudhary, Gulshan; Bhuyan, B. K.; Selokar, Ashish

2018-03-01

The magnetic abrasive machining (MAM) process is a highly developed unconventional machining process. It is frequently used in manufacturing industries for nanometer range surface finishing of workpiece with the help of Magnetic abrasive particles (MAPs) and magnetic force applied in the machining zone. It is precise and faster than conventional methods and able to produce defect free finished components. This paper provides a comprehensive review on the recent advancement of MAM process carried out by different researcher till date. The effect of different input parameters such as rotational speed of electromagnet, voltage, magnetic flux density, abrasive particles size and working gap on the performances of Material Removal Rate (MRR) and surface roughness (Ra) have been discussed. On the basis of review, it is observed that the rotational speed of electromagnet, voltage and mesh size of abrasive particles have significant impact on MAM process.

Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining

PubMed Central

Mendikute, Alberto; Zatarain, Mikel; Bertelsen, Álvaro; Leizea, Ibai

2017-01-01

Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g., 0.1 mm error in 1 m) with

Self-Calibrated In-Process Photogrammetry for Large Raw Part Measurement and Alignment before Machining.

PubMed

Mendikute, Alberto; Yagüe-Fabra, José A; Zatarain, Mikel; Bertelsen, Álvaro; Leizea, Ibai

2017-09-09

Photogrammetry methods are being used more and more as a 3D technique for large scale metrology applications in industry. Optical targets are placed on an object and images are taken around it, where measuring traceability is provided by precise off-process pre-calibrated digital cameras and scale bars. According to the 2D target image coordinates, target 3D coordinates and camera views are jointly computed. One of the applications of photogrammetry is the measurement of raw part surfaces prior to its machining. For this application, post-process bundle adjustment has usually been adopted for computing the 3D scene. With that approach, a high computation time is observed, leading in practice to time consuming and user dependent iterative review and re-processing procedures until an adequate set of images is taken, limiting its potential for fast, easy-to-use, and precise measurements. In this paper, a new efficient procedure is presented for solving the bundle adjustment problem in portable photogrammetry. In-process bundle computing capability is demonstrated on a consumer grade desktop PC, enabling quasi real time 2D image and 3D scene computing. Additionally, a method for the self-calibration of camera and lens distortion has been integrated into the in-process approach due to its potential for highest precision when using low cost non-specialized digital cameras. Measurement traceability is set only by scale bars available in the measuring scene, avoiding the uncertainty contribution of off-process camera calibration procedures or the use of special purpose calibration artifacts. The developed self-calibrated in-process photogrammetry has been evaluated both in a pilot case scenario and in industrial scenarios for raw part measurement, showing a total in-process computing time typically below 1 s per image up to a maximum of 2 s during the last stages of the computed industrial scenes, along with a relative precision of 1/10,000 (e.g. 0.1 mm error in 1 m) with

The Automatic Measuring Machines and Ground-Based Astrometry

NASA Astrophysics Data System (ADS)

Sergeeva, T. P.

The introduction of the automatic measuring machines into the astronomical investigations a little more then a quarter of the century ago has increased essentially the range and the scale of projects which the astronomers could capable to realize since then. During that time, there have been dozens photographic sky surveys, which have covered all of the sky more then once. Due to high accuracy and speed of automatic measuring machines the photographic astrometry has obtained the opportunity to create the high precision catalogs such as CpC2. Investigations of the structure and kinematics of the stellar components of our Galaxy has been revolutionized in the last decade by the advent of automated plate measuring machines. But in an age of rapidly evolving electronic detectors and space-based catalogs, expected soon, one could think that the twilight hours of astronomical photography have become. On opposite of that point of view such astronomers as D.Monet (U.S.N.O.), L.G.Taff (STScI), M.K.Tsvetkov (IA BAS) and some other have contended the several ways of the photographic astronomy evolution. One of them sounds as: "...special efforts must be taken to extract useful information from the photographic archives before the plates degrade and the technology required to measure them disappears". Another is the minimization of the systematic errors of ground-based star catalogs by employment of certain reduction technology and a dense enough and precise space-based star reference catalogs. In addition to that the using of the higher resolution and quantum efficiency emulsions such as Tech Pan and some of the new methods of processing of the digitized information hold great promise for future deep (B<25) surveys (Bland-Hawthorn et al. 1993, AJ, 106, 2154). Thus not only the hard working of all existing automatic measuring machines is apparently needed but the designing, development and employment of a new generation of portable, mobile scanners is very necessary. The

System software for the finite element machine

NASA Technical Reports Server (NTRS)

Crockett, T. W.; Knott, J. D.

1985-01-01

The Finite Element Machine is an experimental parallel computer developed at Langley Research Center to investigate the application of concurrent processing to structural engineering analysis. This report describes system-level software which has been developed to facilitate use of the machine by applications researchers. The overall software design is outlined, and several important parallel processing issues are discussed in detail, including processor management, communication, synchronization, and input/output. Based on experience using the system, the hardware architecture and software design are critiqued, and areas for further work are suggested.

Analysing exoplanetary data using unsupervised machine-learning

NASA Astrophysics Data System (ADS)

Waldmann, I. P.

2012-04-01

The field of transiting extrasolar planets and especially the study of their atmospheres is one of the youngest and most dynamic subjects in current astrophysics. Permanently at the edge of technical feasibility, we are successfully discovering and characterising smaller and smaller planets. To study exoplanetary atmospheres, we typically require a 10-4 to 10-5 level of accuracy in flux. Achieving such a precision has become the central challenge to exoplanetary research and is often impeded by systematic (nongaussian) noise from either the instrument, stellar activity or both. Dedicated missions, such as Kepler, feature an a priori instrument calibration plan to the required accuracy but nonetheless remain limited by stellar systematics. More generic instruments often lack a sufficiently defined instrument response function, making it very hard to calibrate. In these cases, it becomes interesting to know how well we can calibrate the data without any additional or prior knowledge of the instrument or star. In this conference, we present a non-parametric machine-learning algorithm, based on the concept of independent component analysis, to de-convolve the systematic noise and all non-Gaussian signals from the desired astrophysical signal. Such a 'blind' signal de-mixing is commonly known as the 'Cocktail Party problem' in signal-processing. We showcase the importance and broad applicability of unsupervised machine learning in exoplanetary data analysis by discussing: 1) the removal of instrument systematics in a re-analysis of an HD189733b transmission spectrum obtained with Hubble/NICMOS; 2) the removal of time-correlated stellar noise in individual lightcurves observed by the Kepler mission.

30 CFR 18.21 - Machines equipped with powered dust collectors.

Code of Federal Regulations, 2011 CFR

2011-07-01

... 30 Mineral Resources 1 2011-07-01 2011-07-01 false Machines equipped with powered dust collectors... Construction and Design Requirements § 18.21 Machines equipped with powered dust collectors. Powered dust collectors on machines submitted for approval shall meet the applicable requirements of Part 33 of this...

Machining of Aircraft Titanium with Abrasive-Waterjets for Fatigue Critical Applications

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

Liu, H. T.; Hovanski, Yuri; Dahl, Michael E.

2010-10-04

Laboratory tests were conducted to determine the fatigue performance of AWJ-machined aircraft titanium. Dog-bone specimens machined with AWJs were prepared and tested with and without sanding and dry-grit blasting with Al2O3 as secondary processes. The secondary processes were applied to remove the visual appearance of AWJ-generated striations and to clean up the garnet embedment. The fatigue performance of AWJ-machined specimens was compared with baseline specimens machined with CNC milling. Fatigue test results not only confirmed the findings of the aluminum dog-bone specimens but also further enhance the fatigue performance. In addition, titanium is known to be notoriously difficult to cutmore » with contact tools while AWJs cut it 34% faster than stainless steel. AWJ cutting and dry-grit blasting are shown to be a preferred combination for processing aircraft titanium that is fatigue critical.« less

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.

The applications of machine learning algorithms in the modeling of estrogen-like chemicals.

PubMed

Liu, Huanxiang; Yao, Xiaojun; Gramatica, Paola

2009-06-01

Increasing concern is being shown by the scientific community, government regulators, and the public about endocrine-disrupting chemicals that, in the environment, are adversely affecting human and wildlife health through a variety of mechanisms, mainly estrogen receptor-mediated mechanisms of toxicity. Because of the large number of such chemicals in the environment, there is a great need for an effective means of rapidly assessing endocrine-disrupting activity in the toxicology assessment process. When faced with the challenging task of screening large libraries of molecules for biological activity, the benefits of computational predictive models based on quantitative structure-activity relationships to identify possible estrogens become immediately obvious. Recently, in order to improve the accuracy of prediction, some machine learning techniques were introduced to build more effective predictive models. In this review we will focus our attention on some recent advances in the use of these methods in modeling estrogen-like chemicals. The advantages and disadvantages of the machine learning algorithms used in solving this problem, the importance of the validation and performance assessment of the built models as well as their applicability domains will be discussed.

Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview.

PubMed

Shah, Sanjiv J

2017-06-01

There are few proven therapies for heart failure with preserved ejection fraction (HFpEF). The lack of therapies, along with increased recognition of the disorder and its underlying pathophysiology, has led to the acknowledgement that HFpEF is heterogeneous and is not likely to respond to a one-size-fits-all approach. Thus, HFpEF is a prime candidate to benefit from a precision medicine approach. For this reason, we have assembled a compendium of papers on the topic of precision medicine in HFpEF in the Journal of Cardiovascular Translational Research. These papers cover a variety of topics relevant to precision medicine in HFpEF, including automated identification of HFpEF patients; machine learning, novel molecular approaches, genomics, and deep phenotyping of HFpEF; and clinical trial designs that can be used to advance precision medicine in HFpEF. In this introductory article, we provide an overview of precision medicine in HFpEF with the hope that the work described here and in the other papers in this special theme issue will stimulate investigators and clinicians to advance a more targeted approach to HFpEF classification and treatment.

Precision Medicine for Heart Failure with Preserved Ejection Fraction: An Overview

PubMed Central

Shah, Sanjiv J.

2017-01-01

There are few proven therapies for heart failure with preserved ejection fraction (HFpEF). The lack of therapies, along with increased recognition of the disorder and its underlying pathophysiology, has led to the acknowledgement that HFpEF is heterogeneous and is not likely to respond to a one-size-fits-all approach. Thus, HFpEF is a prime candidate to benefit from a precision medicine approach. For this reason, we have assembled a compendium of papers on the topic of precision medicine in HFpEF in the Journal of Cardiovascular Translational Research. These papers cover a variety of topics relevant to precision medicine in HFpEF, including automated identification of HFpEF patients; machine learning, novel molecular approaches, genomics, and deep phenotyping of HFpEF; and clinical trial designs that can be used to advance precision medicine in HFpEF. In this introductory article, we provide an overview of precision medicine in HFpEF with the hope that the work described here and in the other papers in this special theme issue will stimulate investigators and clinicians to advance a more targeted approach to HFpEF classification and treatment. PMID:28585183

Synthesis of bioactive and machinable miserite glass-ceramics for dental implant applications.

PubMed

Saadaldin, Selma A; Dixon, S Jeffrey; Costa, Daniel O; Rizkalla, Amin S

2013-06-01

To synthesize and characterize machinable, bioactive glass-ceramics (GCs) suitable for dental implant applications. A glass in the SiO2-Al2O3-CaO-CaF2-K2O-B2O3-La2O3 system was synthesized by wet chemical methods, followed by calcination, melting and quenching. Crystallization kinetics were determined by differential thermal analysis (DTA). GC discs were produced by cold pressing of the glass powder and sintered using schedules determined by DTA. The crystalline phases and microstructure of GC samples were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM), respectively. Dynamic Young's modulus (E), true hardness (Ho), fracture toughness (KIC) and brittleness index (BI) were evaluated. Bioactivity was studied by examining the formation of hydroxyapatite (HA) on the GC surfaces after soaking in simulated body fluid (SBF). Attachment and proliferation of MC3T3-E1 osteoblastic cells were assessed in vitro. Miserite [KCa5(Si2O7)(Si6O15)(OH)F] was the main crystalline phase of the GC with additional secondary phases. Microstructural studies revealed interlocking lath-like crystalline morphology. E, Ho, and KIC values for the GCs were 96±3 GPa, 5.27±0.26 GPa and 4.77±0.27 MPa m(0.5), respectively. The BI was found to be 1.11±0.05 μm(-0.5), indicating outstanding machinability. An HA surface layer was formed on the GC surfaces when soaked in SBF, indicating potential bioactivity. MC3T3-E1 cells exhibited attachment, spreading and proliferation on GC surfaces, demonstrating excellent biocompatibility. We present a novel approach for the synthesis of miserite GC with the physical and biological properties required for non-metallic dental implant applications. Copyright © 2013 Academy of Dental Materials. Published by Elsevier Ltd. All rights reserved.

Gott Time Machines, BTZ Black Hole Formation, and Choptuik Scaling

NASA Astrophysics Data System (ADS)

Birmingham, Danny; Sen, Siddhartha

2000-02-01

We study the formation of Bañados-Teitelboim-Zanelli black holes by the collision of point particles. It is shown that the Gott time machine, originally constructed for the case of vanishing cosmological constant, provides a precise mechanism for black hole formation. As a result, one obtains an exact analytic understanding of the Choptuik scaling.

Investigation of Anisotropic Bonded Magnets in Permanent Magnet Machine Applications

NASA Astrophysics Data System (ADS)

Khazdozian, H. A.; McCall, S. K.; Kramer, M. J.; Paranthaman, M. P.; Nlebedim, I. C.

Rare earth elements (REE) provide the high energy product necessary for permanent magnets, such as sintered Nd2Fe14B, in many applications like wind energy generators. However, REEs are considered critical materials due to risk in their supply. To reduce the use of critical materials in permanent magnet machines, the performance of anisotropic bonded NdFeB magnets, aligned under varying magnetic field strength, was simulated using 3D finite element analysis in a 3MW direct-drive permanent magnet generator (DDPMG), with sintered N42 magnets used as a baseline for comparison. For direct substitution of the anisotropic bonded magnets, approximately 85% of the efficiency of the baseline model was achieved, irrespective of the alignment field. The torque and power generation of the DDPMG was not found to vary significantly with increase in the alignment field. Finally, design changes were studied to allow for the achievement of rated torque and power with the use of anisotropic bonded magnets, demonstrating the potential for reduction of critical materials in permanent magnets for renewable energy applications. This work was supported by the Critical Materials Institute, an Energy Innovation Hub funded by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office.

A new fabrication method for precision antenna reflectors for space flight and ground test

NASA Technical Reports Server (NTRS)

Sharp, G. Richard; Wanhainen, Joyce S.; Ketelsen, Dean A.

1991-01-01

Communications satellites are using increasingly higher frequencies that require increasingly precise antenna reflectors for use in space. Traditional industry fabrication methods for space antenna reflectors employ successive modeling techniques using high- and low-temperature molds for reflector face sheets and then a final fit-up of the completed honeycomb sandwich panel antenna reflector to a master pattern. However, as new missions are planned at much higher frequencies, greater accuracies will be necessary than are achievable using these present methods. A new approach for the fabrication of ground-test solid-surface antenna reflectors is to build a rigid support structure with an easy-to-machine surface. This surface is subsequently machined to the desired reflector contour and coated with a radio-frequency-reflective surface. This method was used to fabricate a 2.7-m-diameter ground-test antenna reflector to an accuracy of better than 0.013 mm (0.0005 in.) rms. A similar reflector for use on spacecraft would be constructed in a similar manner but with space-qualified materials. The design, analysis, and fabrication of the 2.7-m-diameter precision antenna reflector for antenna ground tests and the extension of this technology to precision, space-based antenna reflectors are described.