Neuron-Type-Specific Utility in a Brain-Machine Interface: a Pilot Study.

Science.gov (United States)

Garcia-Garcia, Martha G; Bergquist, Austin J; Vargas-Perez, Hector; Nagai, Mary K; Zariffa, Jose; Marquez-Chin, Cesar; Popovic, Milos R

2017-11-01

Firing rates of single cortical neurons can be volitionally modulated through biofeedback (i.e. operant conditioning), and this information can be transformed to control external devices (i.e. brain-machine interfaces; BMIs). However, not all neurons respond to operant conditioning in BMI implementation. Establishing criteria that predict neuron utility will assist translation of BMI research to clinical applications. Single cortical neurons (n=7) were recorded extracellularly from primary motor cortex of a Long-Evans rat. Recordings were incorporated into a BMI involving up-regulation of firing rate to control the brightness of a light-emitting-diode and subsequent reward. Neurons were classified as 'fast-spiking', 'bursting' or 'regular-spiking' according to waveform-width and intrinsic firing patterns. Fast-spiking and bursting neurons were found to up-regulate firing rate by a factor of 2.43±1.16, demonstrating high utility, while regular-spiking neurons decreased firing rates on average by a factor of 0.73±0.23, demonstrating low utility. The ability to select neurons with high utility will be important to minimize training times and maximize information yield in future clinical BMI applications. The highly contrasting utility observed between fast-spiking and bursting neurons versus regular-spiking neurons allows for the hypothesis to be advanced that intrinsic electrophysiological properties may be useful criteria that predict neuron utility in BMI implementation.

An Investigation of Data Privacy and Utility Using Machine Learning as a Gauge

Science.gov (United States)

Mivule, Kato

2014-01-01

The purpose of this investigation is to study and pursue a user-defined approach in preserving data privacy while maintaining an acceptable level of data utility using machine learning classification techniques as a gauge in the generation of synthetic data sets. This dissertation will deal with data privacy, data utility, machine learning…

Machine Shop Suggested Job and Task Sheets. Part II. 21 Advanced Jobs.

Science.gov (United States)

Texas A and M Univ., College Station. Vocational Instructional Services.

This volume consists of advanced job and task sheets adaptable for use in the regular vocational industrial education programs for the training of machinists and machine shop operators. Twenty-one advanced machine shop job sheets are included. Some or all of this material is provided for each job: an introductory sheet with aim, checking…

Basic researches for advancement of man-machine systems

International Nuclear Information System (INIS)

Yoshikawa, Hidekazu

1994-01-01

The historical development of plant instrumentation and control system accompanying the introduction of automation is shown by the example of nuclear power plants. It is explained, and the change in the role of operators in the man-machine system is mentioned. Human errors are the serious problem in various fields, and automation resolves it. But complex systems also caused various disasters due to the relation of men and machines. The problem of human factors in high risk system automation is considered as the heightening of reliability and the reduction of burden on workers by decreasing human participation, and the increase of the risk of large accidents due to the lowering of reliability of human elements and the strengthening of the training of workers. Human model and the framework of human error analysis, the development of the system for man-machine system design and information analysis and evaluation, the significance of physiological index measurement and the perspective of the application, the analysis of the behavior of subjects in the abnormality diagnosis experiment using a plant simulator, and the development to the research on mutual adaptation interface are discussed. In this paper, the problem of human factors in system safety, that technical advancement brings about is examined, and the basic research on the advancement of man-machine systems by the author is reported. (K.I.)

Man-machine interface requirements - advanced technology

Science.gov (United States)

Remington, R. W.; Wiener, E. L.

1984-01-01

Research issues and areas are identified where increased understanding of the human operator and the interaction between the operator and the avionics could lead to improvements in the performance of current and proposed helicopters. Both current and advanced helicopter systems and avionics are considered. Areas critical to man-machine interface requirements include: (1) artificial intelligence; (2) visual displays; (3) voice technology; (4) cockpit integration; and (5) pilot work loads and performance.

ADVANCED DESIGN SOLUTIONS FOR HIGH-PRECISION WOODWORKING MACHINES

Directory of Open Access Journals (Sweden)

Giuseppe Lucisano

2016-03-01

Full Text Available With the aim at performing the highest precision during woodworking, a mix of alternative approaches, fruitfully integrated in a common design strategy, is essential. This paper represents an overview of technical solutions, recently developed by authors, in design of machine tools and their final effects on manufacturing. The most advanced solutions in machine design are reported side by side with common practices or little everyday expedients. These design actions are directly or indirectly related to the rational use of materials, sometimes very uncommon, as in the case of magnetorheological fluids chosen to implement an active control in speed and force on the electro-spindle, and permitting to improve the quality of wood machining. Other actions are less unusual, as in the case of the adoption of innovative anti-vibration supports for basement. Tradition or innovation, all these technical solutions contribute to the final result: the highest precision in wood machining.

Advances in machine learning and data mining for astronomy

CERN Document Server

Way, Michael J

2012-01-01

Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science. The book's introductory part provides context to issues in the astronomical sciences that are also important to health

Man-machine interface builders at the Advanced Photon Source

International Nuclear Information System (INIS)

Anderson, M.D.

1991-01-01

Argonne National Laboratory is constructing a 7-GeV Advanced Photon Source for use as a synchrotron radiation source in basic and applied research. The controls and computing environment for this accelerator complex includes graphical operator interfaces to the machine based on Motif, X11, and PHIGS/PEX. Construction and operation of the control system for this accelerator relies upon interactive interface builder and diagram/editor type tools, as well as a run-time environment for the constructed displays which communicate with the physical machine via network connections. This paper discusses our experience with several commercial CUI builders, the inadequacies found in these, motivation for the development of an application- specific builder, and design and implementation strategies employed in the development of our own Man-Machine Interface builder. 5 refs

Machining, joining and modifications of advanced materials

CERN Document Server

Altenbach, Holm

2016-01-01

This book presents the latest advances in mechanical and materials engineering applied to the machining, joining and modification of modern engineering materials. The contributions cover the classical fields of casting, forming and injection moulding as representative manufacturing methods, whereas additive manufacturing methods (rapid prototyping and laser sintering) are treated as more innovative and recent technologies that are paving the way for the manufacturing of shapes and features that traditional methods are unable to deliver. The book also explores water jet cutting as an innovative cutting technology that avoids the heat build-up typical of classical mechanical cutting. It introduces readers to laser cutting as an alternative technology for the separation of materials, and to classical bonding and friction stir welding approaches in the context of joining technologies. In many cases, forming and machining technologies require additional post-treatment to achieve the required level of surface quali...

Structural Analysis of Advanced Refueling Machine of APR1400

International Nuclear Information System (INIS)

Cho, J. R.; Kim, Y. H.; Park, B. T.; Park, J. B.; Jung, J. H.

2007-01-01

The Refueling Machine (RM) consists of two structural parts of bridge and trolley. The bridge structure is approximately 8.5 m long and 5 m wide and is primarily composed of two deep wide flange sections spanning the rector area at the operating level. The trolley is mounted on wheels that roll on the rails of the bridge. Vertical movements of trolley and bridge are restricted by guide rollers. In this paper, dynamic and structural analyses based on the earthquake spectrum are carried out to verify the structural integrity of advanced refueling machine. It is done by 3-dimensional finite element analysis using ANSYS software

Soft computing in machine learning

CERN Document Server

Park, Jooyoung; Inoue, Atsushi

2014-01-01

As users or consumers are now demanding smarter devices, intelligent systems are revolutionizing by utilizing machine learning. Machine learning as part of intelligent systems is already one of the most critical components in everyday tools ranging from search engines and credit card fraud detection to stock market analysis. You can train machines to perform some things, so that they can automatically detect, diagnose, and solve a variety of problems. The intelligent systems have made rapid progress in developing the state of the art in machine learning based on smart and deep perception. Using machine learning, the intelligent systems make widely applications in automated speech recognition, natural language processing, medical diagnosis, bioinformatics, and robot locomotion. This book aims at introducing how to treat a substantial amount of data, to teach machines and to improve decision making models. And this book specializes in the developments of advanced intelligent systems through machine learning. It...

MITI project on advanced man-machine system for nuclear power plants

International Nuclear Information System (INIS)

Kato, Kanji; Watanabe, Takao; Hayakawa, Hiroyasu; Naito, Norio; Masui, Takao; Ogino, Takamichi.

1988-01-01

A computerized operator support system (COSS) against abnormal plant conditions was developed as a five-year project from 1980 to 1984, under the sponsorship of the Ministry of International Trade and Industry. The main purpose of the COSS development was to implement the lessons learned from the Three Mile Island accident. The main nuclear industries in Japan participated in the project. The design concept of the operator support functions and the method to implement it were established, and the prototype systems of the COSS for BWR and PWR plants were developed. After the completion of the COSS development, the above participant group once again joined for the work on an advanced man-machine system for nuclear power plants (MMS-NPP). This eight-year project aims at realizing an advanced operator support system by applying artificial intelligence, especially knowledge engineering, and sophisticated man-machine interface devices. Its main objectives are shown. This system configuration, operating method decision system, man-machine communication system, operation and maintenance support functions and so on are described. (Kako, I.)

Advanced man-machine system for nuclear power plants

International Nuclear Information System (INIS)

Masui, Takao; Naito, Norio; Kato, Kanji.

1990-01-01

Recent development of artificial intelligence(AI) seems to offer new possibility to strengthen the performance of the operator support system. From this point of view, a national project of Advanced Man-Machine System Development for Nuclear Power Plant (MMS-NPP) has been carried out since 1984 as 8-year project. This project aims at establishing advanced operator support functions which support operators in their knowledge-based behaviors and smoother interface with the system. This paper describes the role of MMS-NPP, the support functions and the main feature of the MMS-NPP detailed design with its focus placed on the realization methods using AI technology of the support functions for BWR and PWR plants. (author)

Advancing Control for Shield Tunneling Machine by Backstepping Design with LuGre Friction Model

Directory of Open Access Journals (Sweden)

Haibo Xie

2014-01-01

Full Text Available Shield tunneling machine is widely applied for underground tunnel construction. The shield machine is a complex machine with large momentum and ultralow advancing speed. The working condition underground is rather complicated and unpredictable, and brings big trouble in controlling the advancing speed. This paper focused on the advancing motion control on desired tunnel axis. A three-state dynamic model was established with considering unknown front face earth pressure force and unknown friction force. LuGre friction model was introduced to describe the friction force. Backstepping design was then proposed to make tracking error converge to zero. To have a comparison study, controller without LuGre model was designed. Tracking simulations of speed regulations and simulations when front face earth pressure changed were carried out to show the transient performances of the proposed controller. The results indicated that the controller had good tracking performance even under changing geological conditions. Experiments of speed regulations were carried out to have validations of the controllers.

International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines

CERN Document Server

Belyaev, Alexander; Krommer, Michael

2017-01-01

The papers in this volume present and discuss the frontiers in the mechanics of controlled machines and structures. They are based on papers presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines held in Vienna in September 2015. The workshop continues a series of international workshops held in Linz (2008) and St. Petersburg (2010).

Advanced induction machine model in phase coordinates for wind turbine applications

DEFF Research Database (Denmark)

Fajardo, L.A.; Iov, F.; Hansen, Anca Daniela

2007-01-01

In this paper an advanced phase coordinates squirrel cage induction machine model with time varying electrical parameters affected by magnetic saturation and rotor deep bar effects, is presented. The model uses standard data sheet for characterization of the electrical parameters, it is developed...

Accurate Identification of Cancerlectins through Hybrid Machine Learning Technology.

Science.gov (United States)

Zhang, Jieru; Ju, Ying; Lu, Huijuan; Xuan, Ping; Zou, Quan

2016-01-01

Cancerlectins are cancer-related proteins that function as lectins. They have been identified through computational identification techniques, but these techniques have sometimes failed to identify proteins because of sequence diversity among the cancerlectins. Advanced machine learning identification methods, such as support vector machine and basic sequence features (n-gram), have also been used to identify cancerlectins. In this study, various protein fingerprint features and advanced classifiers, including ensemble learning techniques, were utilized to identify this group of proteins. We improved the prediction accuracy of the original feature extraction methods and classification algorithms by more than 10% on average. Our work provides a basis for the computational identification of cancerlectins and reveals the power of hybrid machine learning techniques in computational proteomics.

Nontraditional machining processes research advances

CERN Document Server

2013-01-01

Nontraditional machining employs processes that remove material by various methods involving thermal, electrical, chemical and mechanical energy or even combinations of these. Nontraditional Machining Processes covers recent research and development in techniques and processes which focus on achieving high accuracies and good surface finishes, parts machined without burrs or residual stresses especially with materials that cannot be machined by conventional methods. With applications to the automotive, aircraft and mould and die industries, Nontraditional Machining Processes explores different aspects and processes through dedicated chapters. The seven chapters explore recent research into a range of topics including laser assisted manufacturing, abrasive water jet milling and hybrid processes. Students and researchers will find the practical examples and new processes useful for both reference and for developing further processes. Industry professionals and materials engineers will also find Nontraditional M...

Advanced man-machine interaction. Fundamentals and implementation

Energy Technology Data Exchange (ETDEWEB)

Kraiss, K.F. (ed.) [Aachen Technische Hochschule (Germany). Lehrstuhl fuer Technische Informatik und Computerwissenschaften

2006-07-01

Man-machine interaction is the gateway providing access to functions and services, which, due to the ever increasing complexity of smart systems, threatens to become a bottleneck. This book therefore introduces not only advanced interfacing concepts, but also gives insight into the related theoretical background.This refers mainly to the realization of video-based multimodal interaction via gesture, mimics, and speech, but also to interacting with virtual object in virtual environments, cooperating with local or remote robots, and user assistance. While most publications in the field of human factors engineering focus on interface design, this book puts special emphasis on implementation aspects. To this end it is accompanied by software development environments for image processing, classification, and virtual environment implementation. In addition a test data base is included for gestures, head pose, facial expressions, full-body person recognition, and people tracking. These data are used for the examples throughout the book, but are also meant to encourage the reader to start experimentation on his own. Thus the book may serve as a self-contained introduction both for researchers and developers of man-machine interfaces. It may also be used for graduate-level university courses. (orig.)

Utility Advanced Turbine Systems (ATS) technology readiness testing

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-05-01

The overall objective of the Advanced Turbine System (ATS) Phase 3 Cooperative Agreement between GE and the US Department of Energy (DOE) is the development of the GE 7H and 9H combined cycle power systems. The major effort will be expended on detail design. Validation of critical components and technologies will be performed, including: hot gas path component testing, sub-scale compressor testing, steam purity test trials, and rotational heat transfer confirmation testing. Processes will be developed to support the manufacture of the first system, which was to have been sited and operated in Phase 4 but will now be sited and operated commercially by GE. This change has resulted horn DOE's request to GE for deletion of Phase 4 in favor of a restructured Phase 3 (as Phase 3R) to include fill speed, no load (FSNL) testing of the 7H gas turbine. Technology enhancements that are not required for the first machine design but will be critical for future ATS advances in performance, reliability, and costs will be initiated. Long-term tests of materials to confirm design life predictions will continue. A schematic of the GE H machine is shown.

Structural health monitoring an advanced signal processing perspective

CERN Document Server

Chen, Xuefeng; Mukhopadhyay, Subhas

2017-01-01

This book highlights the latest advances and trends in advanced signal processing (such as wavelet theory, time-frequency analysis, empirical mode decomposition, compressive sensing and sparse representation, and stochastic resonance) for structural health monitoring (SHM). Its primary focus is on the utilization of advanced signal processing techniques to help monitor the health status of critical structures and machines encountered in our daily lives: wind turbines, gas turbines, machine tools, etc. As such, it offers a key reference guide for researchers, graduate students, and industry professionals who work in the field of SHM.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

Energy Technology Data Exchange (ETDEWEB)

Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)

2014-08-15

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.

Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring

International Nuclear Information System (INIS)

Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka

2014-01-01

The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology

Optimizing virtual machine placement for energy and SLA in clouds using utility functions

Directory of Open Access Journals (Sweden)

Abdelkhalik Mosa

2016-10-01

Full Text Available Abstract Cloud computing provides on-demand access to a shared pool of computing resources, which enables organizations to outsource their IT infrastructure. Cloud providers are building data centers to handle the continuous increase in cloud users’ demands. Consequently, these cloud data centers consume, and have the potential to waste, substantial amounts of energy. This energy consumption increases the operational cost and the CO2 emissions. The goal of this paper is to develop an optimized energy and SLA-aware virtual machine (VM placement strategy that dynamically assigns VMs to Physical Machines (PMs in cloud data centers. This placement strategy co-optimizes energy consumption and service level agreement (SLA violations. The proposed solution adopts utility functions to formulate the VM placement problem. A genetic algorithm searches the possible VMs-to-PMs assignments with a view to finding an assignment that maximizes utility. Simulation results using CloudSim show that the proposed utility-based approach reduced the average energy consumption by approximately 6 % and the overall SLA violations by more than 38 %, using fewer VM migrations and PM shutdowns, compared to a well-known heuristics-based approach.

Utility guidance to advanced LWR designers

International Nuclear Information System (INIS)

Yedidia, J.M.

1990-01-01

The purpose of this paper is to describe the process envisioned for the development of advanced reactors for future use by the utility industry. The role of the potential utility customer is gradually evolving from that of an owner-operator of such plants to that of a sponsor-participant in the actual design process. The author discusses development of a set of utility requirements, intended to describe in detail utility needs and expectations relative to the performance of future reactors. The reactor vendors, who participated actively in the preparation of the requirements documents, pledged to make every effort to meet them in their future designs. At that stage, when the requirements have been finalized and agreed to by all parties involved, including the Nuclear Regulatory Commission, the utilities were expected to move to the sidelines and wait for the reactor vendors to come up with the product

Human friendly man-machine system with advanced media technology

International Nuclear Information System (INIS)

Ogino, Takamichi; Sasaki, Kazunori

1993-01-01

This paper deals with the methodology to implement the man-machine system (MMS) with enhanced human friendliness for nuclear power plants. The relevant technologies are investigated from the two view points: One is integrated multi-media usage for user-computer interface and the other cognitive engineering for user-task interaction. Promising MMS design methodologies, concepts, and their limitations are discussed. To overcome uncertain factors found in human behaviors or individual differences in performance and preference of operators, a design appproach to natural and flexible man-computer interactive environment is proposed by intergrated use of not only cognitive and psychological knowledge but also advanced media technology. Multi-media operator support system under development is shown as an example to evaluate the effectiveness of the new approach and future advancement is prospected. (orig.)

Advances in Machine Technology.

Science.gov (United States)

Clark, William R; Villa, Gianluca; Neri, Mauro; Ronco, Claudio

2018-01-01

Continuous renal replacement therapy (CRRT) machines have evolved into devices specifically designed for critically ill over the past 40 years. In this chapter, a brief history of this evolution is first provided, with emphasis on the manner in which changes have been made to address the specific needs of the critically ill patient with acute kidney injury. Subsequently, specific examples of technology developments for CRRT machines are discussed, including the user interface, pumps, pressure monitoring, safety features, and anticoagulation capabilities. © 2018 S. Karger AG, Basel.

Advanced energy utilization MHD power generation

International Nuclear Information System (INIS)

2008-01-01

The 'Technical Committee on Advanced Energy Utilization MHD Power Generation' was started to establish advanced energy utilization technologies in Japan, and has been working for three years from June 2004 to May 2007. This committee investigated closed cycle MHD, open cycle MHD, and liquid metal MHD power generation as high-efficiency power generation systems on the earth. Then, aero-space application and deep space exploration technologies were investigated as applications of MHD technology. The spin-off from research and development on MHD power generation such as acceleration and deceleration of supersonic flows was expected to solve unstart phenomena in scramjet engine and also to solve abnormal heating of aircrafts by shock wave. In addition, this committee investigated researches on fuel cells, on secondary batteries, on connection of wind power system to power grid, and on direct energy conversion system from nuclear fusion reactor for future. The present technical report described results of investigations by the committee. (author)

Acoustic monitoring of rotating machine by advanced signal processing technology

International Nuclear Information System (INIS)

Kanemoto, Shigeru

2010-01-01

The acoustic data remotely measured by hand held type microphones are investigated for monitoring and diagnosing the rotational machine integrity in nuclear power plants. The plant operator's patrol monitoring is one of the important activities for condition monitoring. However, remotely measured sound has some difficulties to be considered for precise diagnosis or quantitative judgment of rotating machine anomaly, since the measurement sensitivity is different in each measurement, and also, the sensitivity deteriorates in comparison with an attached type sensor. Hence, in the present study, several advanced signal processing methods are examined and compared in order to find optimum anomaly monitoring technology from the viewpoints of both sensitivity and robustness of performance. The dimension of pre-processed signal feature patterns are reduced into two-dimensional space for the visualization by using the standard principal component analysis (PCA) or the kernel based PCA. Then, the normal state is classified by using probabilistic neural network (PNN) or support vector data description (SVDD). By using the mockup test facility of rotating machine, it is shown that the appropriate combination of the above algorithms gives sensitive and robust anomaly monitoring performance. (author)

Traditional machining processes research advances

CERN Document Server

2015-01-01

This book collects several examples of research in machining processes. Chapter 1 provides information on polycrystalline diamond tool material and its emerging applications. Chapter 2 is dedicated to the analysis of orthogonal cutting experiments using diamond-coated tools with force and temperature measurements. Chapter 3 describes the estimation of cutting forces and tool wear using modified mechanistic models in high performance turning. Chapter 4 contains information on cutting under gas shields for industrial applications. Chapter 5 is dedicated to the machinability of magnesium and its alloys. Chapter 6 provides information on grinding science. Finally, chapter 7 is dedicated to flexible integration of shape and functional modelling of machine tool spindles in a design framework.    

Investigation of Effect of Machine Layout on Productivity and Utilization Level: What If Simulation Approach

Directory of Open Access Journals (Sweden)

Islam Faisal Bourini

2018-03-01

Full Text Available Designing and selecting the material handling system is a vital factor for any production line, and as result for the whole manufacturing system. Poor design and unsuitable handling equipment may increase the risk of having bottlenecks, longer production time and as a result the higher total production cost. One of the useful and effective tools are using “what if” simulation techniques. However, this technique needs effective simulation software. The main objective for this research is to simulate different types of handling system using what if scenario. To achieve the objective of the research, Delmia Quest software has been used to simulate two different systems: manual system and conveyers system for the same production line and analyses the differences in terms of utilization and production rate. The results obtained have been analysed and appraised to induce the bottleneck locations, productivity and utilizations of the machines and material handling systems used in the design system. Finally, the best model have been developed to increase the productivity, utilizations of the machines, material handling systems and to minimize the bottleneck locations.

Machine learning and computer vision approaches for phenotypic profiling.

Science.gov (United States)

Grys, Ben T; Lo, Dara S; Sahin, Nil; Kraus, Oren Z; Morris, Quaid; Boone, Charles; Andrews, Brenda J

2017-01-02

With recent advances in high-throughput, automated microscopy, there has been an increased demand for effective computational strategies to analyze large-scale, image-based data. To this end, computer vision approaches have been applied to cell segmentation and feature extraction, whereas machine-learning approaches have been developed to aid in phenotypic classification and clustering of data acquired from biological images. Here, we provide an overview of the commonly used computer vision and machine-learning methods for generating and categorizing phenotypic profiles, highlighting the general biological utility of each approach. © 2017 Grys et al.

Analysis of machining and machine tools

CERN Document Server

Liang, Steven Y

2016-01-01

This book delivers the fundamental science and mechanics of machining and machine tools by presenting systematic and quantitative knowledge in the form of process mechanics and physics. It gives readers a solid command of machining science and engineering, and familiarizes them with the geometry and functionality requirements of creating parts and components in today’s markets. The authors address traditional machining topics, such as: single and multiple point cutting processes grinding components accuracy and metrology shear stress in cutting cutting temperature and analysis chatter They also address non-traditional machining, such as: electrical discharge machining electrochemical machining laser and electron beam machining A chapter on biomedical machining is also included. This book is appropriate for advanced undergraduate and graduate mechani cal engineering students, manufacturing engineers, and researchers. Each chapter contains examples, exercises and their solutions, and homework problems that re...

Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Science.gov (United States)

Slomka, Piotr J; Dey, Damini; Sitek, Arkadiusz; Motwani, Manish; Berman, Daniel S; Germano, Guido

2017-03-01

Non-invasive imaging plays a critical role in managing patients with cardiovascular disease. Although subjective visual interpretation remains the clinical mainstay, quantitative analysis facilitates objective, evidence-based management, and advances in clinical research. This has driven developments in computing and software tools aimed at achieving fully automated image processing and quantitative analysis. In parallel, machine learning techniques have been used to rapidly integrate large amounts of clinical and quantitative imaging data to provide highly personalized individual patient-based conclusions. Areas covered: This review summarizes recent advances in automated quantitative imaging in cardiology and describes the latest techniques which incorporate machine learning principles. The review focuses on the cardiac imaging techniques which are in wide clinical use. It also discusses key issues and obstacles for these tools to become utilized in mainstream clinical practice. Expert commentary: Fully-automated processing and high-level computer interpretation of cardiac imaging are becoming a reality. Application of machine learning to the vast amounts of quantitative data generated per scan and integration with clinical data also facilitates a move to more patient-specific interpretation. These developments are unlikely to replace interpreting physicians but will provide them with highly accurate tools to detect disease, risk-stratify, and optimize patient-specific treatment. However, with each technological advance, we move further from human dependence and closer to fully-automated machine interpretation.

Advanced Machine Learning Emulators of Radiative Transfer Models

Science.gov (United States)

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

2017-12-01

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

Assisting the Tooling and Machining Industry to Become Energy Efficient

Energy Technology Data Exchange (ETDEWEB)

Curry, Bennett [Arizona Commerce Authority, Phoenix, AZ (United States)

2016-12-30

The Arizona Commerce Authority (ACA) conducted an Innovation in Advanced Manufacturing Grant Competition to support and grow southern and central Arizona’s Aerospace and Defense (A&D) industry and its supply chain. The problem statement for this grant challenge was that many A&D machining processes utilize older generation CNC machine tool technologies that can result an inefficient use of resources – energy, time and materials – compared to the latest state-of-the-art CNC machines. Competitive awards funded projects to develop innovative new tools and technologies that reduce energy consumption for older generation machine tools and foster working relationships between industry small to medium-sized manufacturing enterprises and third-party solution providers. During the 42-month term of this grant, 12 competitive awards were made. Final reports have been included with this submission.

Support vector machines applications

CERN Document Server

Guo, Guodong

2014-01-01

Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.

Environmentally Friendly Machining

CERN Document Server

Dixit, U S; Davim, J Paulo

2012-01-01

Environment-Friendly Machining provides an in-depth overview of environmentally-friendly machining processes, covering numerous different types of machining in order to identify which practice is the most environmentally sustainable. The book discusses three systems at length: machining with minimal cutting fluid, air-cooled machining and dry machining. Also covered is a way to conserve energy during machining processes, along with useful data and detailed descriptions for developing and utilizing the most efficient modern machining tools. Researchers and engineers looking for sustainable machining solutions will find Environment-Friendly Machining to be a useful volume.

Condition Assessment of Foundation Piles and Utility Poles Based on Guided Wave Propagation Using a Network of Tactile Transducers and Support Vector Machines

Directory of Open Access Journals (Sweden)

Ulrike Dackermann

2017-12-01

Full Text Available This paper presents a novel non-destructive testing and health monitoring system using a network of tactile transducers and accelerometers for the condition assessment and damage classification of foundation piles and utility poles. While in traditional pile integrity testing an impact hammer with broadband frequency excitation is typically used, the proposed testing system utilizes an innovative excitation system based on a network of tactile transducers to induce controlled narrow-band frequency stress waves. Thereby, the simultaneous excitation of multiple stress wave types and modes is avoided (or at least reduced, and targeted wave forms can be generated. The new testing system enables the testing and monitoring of foundation piles and utility poles where the top is inaccessible, making the new testing system suitable, for example, for the condition assessment of pile structures with obstructed heads and of poles with live wires. For system validation, the new system was experimentally tested on nine timber and concrete poles that were inflicted with several types of damage. The tactile transducers were excited with continuous sine wave signals of 1 kHz frequency. Support vector machines were employed together with advanced signal processing algorithms to distinguish recorded stress wave signals from pole structures with different types of damage. The results show that using fast Fourier transform signals, combined with principal component analysis as the input feature vector for support vector machine (SVM classifiers with different kernel functions, can achieve damage classification with accuracies of 92.5% ± 7.5%.

Advice taking from humans and machines: an fMRI and effective connectivity study

Directory of Open Access Journals (Sweden)

Kimberly Goodyear

2016-11-01

Full Text Available With new technological advances, advice can come from different sources such as machines or humans, but how individuals respond to such advice and the neural correlates involved need to be better understood. We combined functional MRI and multivariate Granger causality analysis with an X-ray luggage-screening task to investigate the neural basis and corresponding effective connectivity involved with advice utilization from agents framed as experts. Participants were asked to accept or reject good or bad advice from a human or machine agent with low reliability (high false alarm rate. We showed that unreliable advice decreased performance overall and participants interacting with the human agent had a greater depreciation of advice utilization during bad advice compared to the machine agent. These differences in advice utilization can be perceivably due to reevaluation of expectations arising from association of dispositional credibility for each agent. We demonstrated that differences in advice utilization engaged brain regions that may be associated with evaluation of personal characteristics and traits (precuneus, posterior cingulate cortex, temporoparietal junction and interoception (posterior insula. We found that the right posterior insula and left precuneus were the drivers of the advice utilization network that were reciprocally connected to each other and also projected to all other regions. Our behavioral and neuroimaging results have significant implications for society because of progressions in technology and increased interactions with machines.

Indonesian Stock Prediction using Support Vector Machine (SVM

Directory of Open Access Journals (Sweden)

Santoso Murtiyanto

2018-01-01

Full Text Available This project is part of developing software to provide predictive information technology-based services artificial intelligence (Machine Intelligence or Machine Learning that will be utilized in the money market community. The prediction method used in this early stages uses the combination of Gaussian Mixture Model and Support Vector Machine with Python programming. The system predicts the price of Astra International (stock code: ASII.JK stock data. The data used was taken during 17 yr period of January 2000 until September 2017. Some data was used for training/modeling (80 % of data and the remainder (20 % was used for testing. An integrated model comprising Gaussian Mixture Model and Support Vector Machine system has been tested to predict stock market of ASII.JK for l d in advance. This model has been compared with the Market Cummulative Return. From the results, it is depicts that the Gaussian Mixture Model-Support Vector Machine based stock predicted model, offers significant improvement over the compared models resulting sharpe ratio of 3.22.

Utility industry evaluation of the Sodium Advanced Fast Reactor

International Nuclear Information System (INIS)

Burstein, S.; DelGeorge, L.O.; Tramm, T.R.; Gibbons, J.P.; High, M.D.; Neils, G.H.; Pilmer, D.F.; Tomonto, J.R.; Wells, J.T.

1990-02-01

A team of utility industry representatives evaluated the Sodium Advanced Fast Reactor plant design, a current liquid metal reactor design created by an industrial team led by Rockwell International under Department of Energy sponsorship. The utility industry team concluded that the plant design offers several attractive characteristics, especially in the safety arena, as well as preserving the traditional attraction of liquid metal reactors, very high fuel utilization. Specific comments and recommendations are provided as a contribution towards improving an already attractive plant design. 18 refs

Liquid lens: advances in adaptive optics

Science.gov (United States)

Casey, Shawn Patrick

2010-12-01

'Liquid lens' technologies promise significant advancements in machine vision and optical communications systems. Adaptations for machine vision, human vision correction, and optical communications are used to exemplify the versatile nature of this technology. Utilization of liquid lens elements allows the cost effective implementation of optical velocity measurement. The project consists of a custom image processor, camera, and interface. The images are passed into customized pattern recognition and optical character recognition algorithms. A single camera would be used for both speed detection and object recognition.

Advance reactor and fuel-cycle systems--potentials and limitations for United States utilities

International Nuclear Information System (INIS)

Zebroski, E.L.; Williams, R.F.

1979-01-01

This paper reviews the potential benefits and limitations of advance reactor and fuel-cycle systems for United States utilities. The results of the review of advanced technologies show that for the near and midterm, the only advance reactor and fuel-cycle system with significant potential for United States utilities is the current LWR, and evolutionary, not revolutionary, enhancements. For the long term, the liquid-metal breeder reactor continues to be the most promising advance nuclear option. The major factors leading to this conclusion are summarized

DOE FreedomCAR and vehicle technologies program advanced power electronic and electrical machines annual review report

Energy Technology Data Exchange (ETDEWEB)

Olszewski, Mitch [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

2006-10-11

This report is a summary of the Review Panel at the FY06 DOE FreedomCAR and Vehicle Technologies (FCVT) Annual Review of Advanced Power Electronics and Electric Machine (APEEM) research activities held on August 15-17, 2006.

Data management and communication networks for Man-Machine Interface System in Korea Advanced Liquid MEtal Reactor : its functionality and design requirements

International Nuclear Information System (INIS)

Cha, Kyung Ho; Park, Gun Ok; Suh, Sang Moon; Kim, Jang Yeol; Kwon, Kee Choon

1998-01-01

The DAta management and Communication NETworks(DACONET), which it is designed as a subsystem for Man-Machine Interface System of Korea Advanced LIquid MEtal Reactor(KALIMER MMIS) and advanced design concept is approached, is described. The DACONET has its roles of providing the real-time data transmission and communication paths between MMIS systems, providing the quality data for protection, monitoring and control of KALIMER and logging the static and dynamic behavioral data during KALIMER operation. The DACONET is characterized as the distributed real-time system architecture with high performance. Future direction, in which advanced technology is being continually applied to Man-Machine Interface System development and communication networks of KALIMER MMIS

Data management and communication networks for Man-Machine Interface System in Korea Advanced Liquid MEtal Reactor : its functionality and design requirements

Energy Technology Data Exchange (ETDEWEB)

Cha, Kyung Ho; Park, Gun Ok; Suh, Sang Moon; Kim, Jang Yeol; Kwon, Kee Choon [KAERI, Taejon (Korea, Republic of)

1998-05-01

The DAta management and Communication NETworks(DACONET), which it is designed as a subsystem for Man-Machine Interface System of Korea Advanced LIquid MEtal Reactor(KALIMER MMIS) and advanced design concept is approached, is described. The DACONET has its roles of providing the real-time data transmission and communication paths between MMIS systems, providing the quality data for protection, monitoring and control of KALIMER and logging the static and dynamic behavioral data during KALIMER operation. The DACONET is characterized as the distributed real-time system architecture with high performance. Future direction, in which advanced technology is being continually applied to Man-Machine Interface System development and communication networks of KALIMER MMIS.

UTILITY ADVANCED TURBINE SYSTEMS (ATS) TECHNOLOGY READINESS TESTING

Energy Technology Data Exchange (ETDEWEB)

Unknown

1999-04-01

The overall objective of the Advanced Turbine System (ATS) Phase 3 Cooperative Agreement between GE and the U.S. Department of Energy (DOE) is the development of the GE 7H and 9H combined cycle power systems. The major effort will be expended on detail design. Validation of critical components and technologies will be performed, including: hot gas path component testing, sub-scale compressor testing, steam purity test trials, and rotational heat transfer conflation testing. Processes will be developed to support the manufacture of the first system, which was to have been sited and operated in Phase 4 but will now be sited and operated commercially by GE. This change has resulted from DOE's request to GE for deletion of Phase 4 in favor of a restructured Phase 3 (as Phase 3R) to include full speed, no load (FSNL) testing of the 7H gas turbine. Technology enhancements that are not required for the first machine design but will be critical for future ATS advances in performance, reliability, and costs will be initiated. Long-term tests of materials to confirm design life predictions will continue. The objective of this task is to design 7H and 9H compressor rotor and stator structures with the goal of achieving high efficiency at lower cost and greater durability by applying proven GE Power Systems (GEPS) heavy-duty use design practices. The designs will be based on the GE Aircraft Engines (GEAE) CF6-80C2 compressor. Transient and steady-state thermo-mechanical stress analyses will be run to ensure compliance with GEPS life standards. Drawings will be prepared for forgings, castings, machining, and instrumentation for full speed, no load (FSNL) tests of the first unit on both 9H and 7H applications.

Recent advances in intelligent machine technologies

International Nuclear Information System (INIS)

Bartholet, T.G.

1987-01-01

Further developments in intelligent machine technologies have recently been accomplished under sponsorship by the Department of Energy (DOE), the Electric Power Research Institute (EPRI), the U.S. Army and NASA. This paper describes these developments and presents actual results achieved and demonstrated. These projects encompass new developments in manipulators, vision and walking machines. Continuing developments will add increasing degrees of autonomy as appropriate to applications in the fields of nuclear power, space, defense and industrial or commercial marketplaces

UTILITY ADVANCED TURBINE SYSTEMS (ATS) TECHNOLOGY READINESS TESTING: PHASE 3R

Energy Technology Data Exchange (ETDEWEB)

None

1999-09-01

The overall objective of the Advanced Turbine System (ATS) Phase 3 Cooperative Agreement between GE and the US Department of Energy (DOE) is the development of the GE 7H and 9H combined cycle power systems. The major effort will be expended on detail design. Validation of critical components and technologies will be performed, including: hot gas path component testing, sub-scale compressor testing, steam purity test trials, and rotational heat transfer confirmation testing. Processes will be developed to support the manufacture of the first system, which was to have been sited and operated in Phase 4 but will now be sited and operated commercially by GE. This change has resulted from DOE's request to GE for deletion of Phase 4 in favor of a restructured Phase 3 (as Phase 3R) to include full speed, no load (FSNL) testing of the 7H gas turbine. Technology enhancements that are not required for the first machine design but will be critical for future ATS advances in performance, reliability, and costs will be initiated. Long-term tests of materials to confirm design life predictions will continue. A schematic of the GE H machine is shown. This report summarizes work accomplished in 2Q99.

Machining with abrasives

CERN Document Server

Jackson, Mark J

2011-01-01

Abrasive machining is key to obtaining the desired geometry and surface quality in manufacturing. This book discusses the fundamentals and advances in the abrasive machining processes. It provides a complete overview of developing areas in the field.

Recent Advances in Technologies Required for a ``Salad Machine''

Science.gov (United States)

Kliss, M.; Heyenga, A. G.; Hoehn, A.; Stodieck, L. S.

Future long duration, manned space flight missions will require life support systems that minimize resupply requirements and ultimately approach self-sufficiency in space. Bioregenerative life support systems are a promising approach, but they are far from mature. Early in the development of the NASA Controlled Ecological Life Support System Program, the idea of onboard cultivation of salad-type vegetables for crew consumption was proposed as a first step away from the total reliance on resupply for food in space. Since that time, significant advances in space-based plant growth hardware have occurred, and considerable flight experience has been gained. This paper revisits the ``Salad Machine'' concept and describes recent developments in subsystem technologies for both plant root and shoot environments that are directly relevant to the development of such a facility

Diagnosing Coronary Heart Disease using Ensemble Machine Learning

OpenAIRE

Kathleen H. Miao; Julia H. Miao; George J. Miao

2016-01-01

Globally, heart disease is the leading cause of death for both men and women. One in every four people is afflicted with and dies of heart disease. Early and accurate diagnoses of heart disease thus are crucial in improving the chances of long-term survival for patients and saving millions of lives. In this research, an advanced ensemble machine learning technology, utilizing an adaptive Boosting algorithm, is developed for accurate coronary heart disease diagnosis and outcome predictions. Th...

Electric vehicle traction motors - The development of an advanced motor concept

Science.gov (United States)

Campbell, P.

1980-01-01

An axial-field permanent magnet traction motor is described, similar to several advanced motors that are being developed in the United States. This type of machine has several advantages over conventional dc motors, particularly in the electric vehicle application. The rapidly changing cost of magnetic materials, particularly cobalt, makes it important to study the utilization of permanent magnet materials in such machines. The impact of different magnets on machine design is evaluated, and the advantages of using iron powder composites in the armature are assessed.

Mechanical design of machine components

CERN Document Server

Ugural, Ansel C

2015-01-01

Mechanical Design of Machine Components, Second Edition strikes a balance between theory and application, and prepares students for more advanced study or professional practice. It outlines the basic concepts in the design and analysis of machine elements using traditional methods, based on the principles of mechanics of materials. The text combines the theory needed to gain insight into mechanics with numerical methods in design. It presents real-world engineering applications, and reveals the link between basic mechanics and the specific design of machine components and machines. Divided into three parts, this revised text presents basic background topics, deals with failure prevention in a variety of machine elements and covers applications in design of machine components as well as entire machines. Optional sections treating special and advanced topics are also included.Key Features of the Second Edition:Incorporates material that has been completely updated with new chapters, problems, practical examples...

Advanced man-machine interface systems and advanced information management systems programs

International Nuclear Information System (INIS)

Naser, J.; Gray, S.; Machiels, A.

1997-01-01

The Advanced Light Water Reactor (ALWR) Program started in the early 1980's. This work involves the development and NRC review of the ALWR Utility Requirements Documents, the development and design certification of ALWR designs, the analysis of the Early Site Permit process, and the First-of-a-Kind Engineering for two of the ALWR plant designs. ALWRs will embody modern proven technology. However, technologies expected to be used in these plants are changing very rapidly so that additional capabilities will become available that will be beneficial for future plants. To remain competitive on a life-cycle basis in the future, the ALWR must take advantage of the best and most modem technologies available. 1 ref

Data management and communication networks for man-machine interface system in Korea Advanced LIquid MEtal Reactor : Its functionality and design requirements

Energy Technology Data Exchange (ETDEWEB)

Cha, Kyung Ho; Park, Gun Ok; Suh, Sang Moon; Kim, Jang Yeol; Kwon, Kee Choon [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1999-12-31

The DAta management and COmmunication NETworks(DACONET), which it is designed as a subsystem for Man-Machine Interface System of Korea Advanced LIquid MEtal Reactor (KALIMER MMIS) and advanced design concept is approached, is described. The DACONET has its roles of providing the real-time data transmission and communication paths between MMIS systems, providing the quality data for protection, monitoring and control of KALIMER and logging the static and dynamic behavioral data during KALIMER operation. The DACONET is characterized as the distributed real-time system architecture with high performance. Future direction, in which advanced technology is being continually applied to Man-Machine Interface System development of Nuclear Power Plants, will be considered for designing data management and communication networks of KALIMER MMIS. 9 refs., 1 fig. (Author)

Data management and communication networks for man-machine interface system in Korea Advanced LIquid MEtal Reactor : Its functionality and design requirements

Energy Technology Data Exchange (ETDEWEB)

Cha, Kyung Ho; Park, Gun Ok; Suh, Sang Moon; Kim, Jang Yeol; Kwon, Kee Choon [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1998-12-31

The DAta management and COmmunication NETworks(DACONET), which it is designed as a subsystem for Man-Machine Interface System of Korea Advanced LIquid MEtal Reactor (KALIMER MMIS) and advanced design concept is approached, is described. The DACONET has its roles of providing the real-time data transmission and communication paths between MMIS systems, providing the quality data for protection, monitoring and control of KALIMER and logging the static and dynamic behavioral data during KALIMER operation. The DACONET is characterized as the distributed real-time system architecture with high performance. Future direction, in which advanced technology is being continually applied to Man-Machine Interface System development of Nuclear Power Plants, will be considered for designing data management and communication networks of KALIMER MMIS. 9 refs., 1 fig. (Author)

Numerical controlled diamond fly cutting machine for grazing incidence X-ray reflection mirrors

International Nuclear Information System (INIS)

Uchida, Fumihiko; Moriyama, Shigeo; Seya, Eiiti

1992-01-01

Synchrotron radiation has reached the stage of practical use, and the application to the wide fields that support future advanced technologies such as spectroscopy, the structural analysis of matters, semiconductor lithography and medical light source is expected. For the optical system of the equipment utilizing synchrotron radiation, the total reflection mirrors of oblique incidence are used for collimating and collecting X-ray. In order to restrain their optical aberration, nonspherical shape is required, and as the manufacturing method with high precision for nonspherical mirrors, a numerically controlled diamond cutting machine was developed. As for the cutting of soft metals with diamond tools, the high precision machining of any form can be done by numerical control, the machining time can be reduced as compared with grinding, and the cooling effect is large in metals. The construction of the cutting machine, the principle of machining, the control system, the method of calculating numerical control data, the investigation of machinable forms and the result of evaluation are reported. (K.I.)

The Three Pillars of Machine Programming

OpenAIRE

Gottschlich, Justin; Solar-Lezama, Armando; Tatbul, Nesime; Carbin, Michael; Rinard, Martin; Barzilay, Regina; Amarasinghe, Saman; Tenenbaum, Joshua B; Mattson, Tim

2018-01-01

In this position paper, we describe our vision of the future of machine programming through a categorical examination of three pillars of research. Those pillars are: (i) intention, (ii) invention, and(iii) adaptation. Intention emphasizes advancements in the human-to-computer and computer-to-machine-learning interfaces. Invention emphasizes the creation or refinement of algorithms or core hardware and software building blocks through machine learning (ML). Adaptation emphasizes advances in t...

Recent Advances in Predictive (Machine) Learning

Energy Technology Data Exchange (ETDEWEB)

Friedman, J

2004-01-24

Prediction involves estimating the unknown value of an attribute of a system under study given the values of other measured attributes. In prediction (machine) learning the prediction rule is derived from data consisting of previously solved cases. Most methods for predictive learning were originated many years ago at the dawn of the computer age. Recently two new techniques have emerged that have revitalized the field. These are support vector machines and boosted decision trees. This paper provides an introduction to these two new methods tracing their respective ancestral roots to standard kernel methods and ordinary decision trees.

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 11: Computer-Aided Manufacturing & Advanced CNC, of a 15-Volume Set of Skill Standards and Curriculum Training Materials for the Precision Manufacturing Industry.

Science.gov (United States)

Texas State Technical Coll., Waco.

This document is intended to help education and training institutions deliver the Machine Tool Advanced Skills Technology (MAST) curriculum to a variety of individuals and organizations. MAST consists of industry-specific skill standards and model curricula for 15 occupational specialty areas within the U.S. machine tool and metals-related…

Sentiment Analysis in the Sales Review of Indonesian Marketplace by Utilizing Support Vector Machine

Directory of Open Access Journals (Sweden)

Anang Anggono Lutfi

2018-04-01

Full Text Available The online store is changing people’s shopping behavior. Despite the fact, the potential customer’s distrust in the quality of products and service is one of the online store’s weaknesses. A review is provided by the online stores to overcome this weakness. Customers often write a review using languages that are not well structured. Sentiment analysis is used to extract the polarity of the unstructured texts. This research attempted to do a sentiment analysis in the sales review. Sentiment analysis in sales reviews can be used as a tool to evaluate the sales. This research intends to conduct a sentiment analysis in the sales review of Indonesian marketplace by utilizing Support Vector Machine and Naive Bayes. The reviews of the data are gathered from one of Indonesian marketplace, Bukalapak. The data are classified into positive or negative class. TF-IDF is used to feature extraction. The experiment shows that Support Vector Machine with linear kernel provides higher accuracy than Naive Bayes. Support Vector Machine shows the highest accuracy average. The generated accuracy is 93.65%. This approach of sentiment analysis in sales review can be used as the base of intelligent sales evaluation for online stores in the future.

Utility requirements for advanced light water reactors

International Nuclear Information System (INIS)

Machiels, A.; Gray, S.; Mulford, T.; Rodwell, E.

1996-01-01

The nuclear energy industry is actively engaged in developing advanced light water reactor (ALWR) designs for the next century. The new designs take advantage of the thousands of reactor-years of experience that have been accumulated by operating over 400 plants worldwide. The EPRI effort began in the early 1980's, when a survey of utility executives was conducted to determine their prerequisites for ordering nuclear power plants. The results were clear: new plants had to be simpler and safer, and have greater design margins, i.e., be more forgiving. The utility executives also supported making improvements to the established light water reactor technology, rather than trying to develop new reactor concepts. Finally, they wanted the option to build mid-size plants (∼600 MWe) in addition to full-size plants of more than 1200 MWe. 4 refs

Induction machine handbook

CERN Document Server

Boldea, Ion

2002-01-01

Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to

Book review: A first course in Machine Learning

DEFF Research Database (Denmark)

Ortiz-Arroyo, Daniel

2016-01-01

"The new edition of A First Course in Machine Learning by Rogers and Girolami is an excellent introduction to the use of statistical methods in machine learning. The book introduces concepts such as mathematical modeling, inference, and prediction, providing ‘just in time’ the essential background...... to change models and parameter values to make [it] easier to understand and apply these models in real applications. The authors [also] introduce more advanced, state-of-the-art machine learning methods, such as Gaussian process models and advanced mixture models, which are used across machine learning....... This makes the book interesting not only to students with little or no background in machine learning but also to more advanced graduate students interested in statistical approaches to machine learning." —Daniel Ortiz-Arroyo, Associate Professor, Aalborg University Esbjerg, Denmark...

Quantum machine learning.

Science.gov (United States)

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

2017-09-13

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

Rotary Ultrasonic Machining of Poly-Crystalline Cubic Boron Nitride

Directory of Open Access Journals (Sweden)

Kuruc Marcel

2014-12-01

Full Text Available Poly-crystalline cubic boron nitride (PCBN is one of the hardest material. Generally, so hard materials could not be machined by conventional machining methods. Therefore, for this purpose, advanced machining methods have been designed. Rotary ultrasonic machining (RUM is included among them. RUM is based on abrasive removing mechanism of ultrasonic vibrating diamond particles, which are bonded on active part of rotating tool. It is suitable especially for machining hard and brittle materials (such as glass and ceramics. This contribution investigates this advanced machining method during machining of PCBN.

Utility Leadership in Defining Requirements for Advanced Light Water Reactors

International Nuclear Information System (INIS)

Sugnet, William R.; Layman, William H.

1990-01-01

It is appropriate, based on twenty five years of operating experience, that utilities take a position of leadership in developing the technical design and performance requirements for the next generations of nuclear electric generating plants. The U. S. utilities, through the Electric Power Research Institute, began an initiative in 1985 to develop such Utility requirements. Many international Utility organizations, including Korea Electric Power Corporation, have joined as full participants in this important Utility industry initiative. In light of the closer linkage among countries of the world due to rapid travel and telecommunications, it is also appropriate that there be international dialogue and agreement on the principal standards for nuclear power plant acceptability and performance. The Utility/EPRI Advanced Light Water Reactor Program guided by the ALRR Utility Steering Committee has been very successful in developing these Utility requirements. This paper will summarize the state of development of the ALRR Utility Requirements for Evolutionary Plants, recent developments in their review by the U. S. Nuclear Regulatory Commission, resolution of open issues, and the extension of this effort to develop a companion set of ALRR Utility Requirements for plants employing passive safety features

Space Weather in the Machine Learning Era: A Multidisciplinary Approach

Science.gov (United States)

Camporeale, E.; Wing, S.; Johnson, J.; Jackman, C. M.; McGranaghan, R.

2018-01-01

The workshop entitled Space Weather: A Multidisciplinary Approach took place at the Lorentz Center, University of Leiden, Netherlands, on 25-29 September 2017. The aim of this workshop was to bring together members of the Space Weather, Mathematics, Statistics, and Computer Science communities to address the use of advanced techniques such as Machine Learning, Information Theory, and Deep Learning, to better understand the Sun-Earth system and to improve space weather forecasting. Although individual efforts have been made toward this goal, the community consensus is that establishing interdisciplinary collaborations is the most promising strategy for fully utilizing the potential of these advanced techniques in solving Space Weather-related problems.

Machine medical ethics

CERN Document Server

Pontier, Matthijs

2015-01-01

The essays in this book, written by researchers from both humanities and sciences, describe various theoretical and experimental approaches to adding medical ethics to a machine in medical settings. Medical machines are in close proximity with human beings, and getting closer: with patients who are in vulnerable states of health, who have disabilities of various kinds, with the very young or very old, and with medical professionals. In such contexts, machines are undertaking important medical tasks that require emotional sensitivity, knowledge of medical codes, human dignity, and privacy. As machine technology advances, ethical concerns become more urgent: should medical machines be programmed to follow a code of medical ethics? What theory or theories should constrain medical machine conduct? What design features are required? Should machines share responsibility with humans for the ethical consequences of medical actions? How ought clinical relationships involving machines to be modeled? Is a capacity for e...

A Multi-scale, Multi-Model, Machine-Learning Solar Forecasting Technology

Energy Technology Data Exchange (ETDEWEB)

Hamann, Hendrik F. [IBM, Yorktown Heights, NY (United States). Thomas J. Watson Research Center

2017-05-31

The goal of the project was the development and demonstration of a significantly improved solar forecasting technology (short: Watt-sun), which leverages new big data processing technologies and machine-learnt blending between different models and forecast systems. The technology aimed demonstrating major advances in accuracy as measured by existing and new metrics which themselves were developed as part of this project. Finally, the team worked with Independent System Operators (ISOs) and utilities to integrate the forecasts into their operations.

A study on advanced man-machine interface system for autonomous nuclear power plants

International Nuclear Information System (INIS)

Matsuoka, Takeshi; Numano, Masayoshi; Fukuto, Junji; Sugasawa, Shinobu; Miyazaki, Keiko; Someya, Minoru; Haraki, Nobuo

1994-01-01

A man-machine interface(MMI) system of an autonomous nuclear power plant has an advanced function compared with that of the present nuclear power plants. The MMI has a function model of a plant state, and updates and revises this function model by itself. This paper describes the concept of autonomous nuclear power plants, a plant simulator of an autonomous power plant, a contracted function model of a plant state, three-dimensional color graphic display of a plant state, and an event-tree like expression for plant states. (author)

Human-machine communication for educational systems design : NATO Advanced Study Institute proceedings, Eindhoven August 16-26, 1993

NARCIS (Netherlands)

Janse, M.D.; Harrington, T.L.

1994-01-01

This book contains the papers presented at the NATO Advanced Study Institute (ASI) on the Basics of Man-Machine Communication for the Design of Educational Systems, held August 16-26, 1993 in Eindhoven, The Netherlands. The ASI addressed the state of the art in the design of educational systems with

Virtual Machine in Automation Projects

OpenAIRE

Xing, Xiaoyuan

2010-01-01

Virtual machine, as an engineering tool, has recently been introduced into automation projects in Tetra Pak Processing System AB. The goal of this paper is to examine how to better utilize virtual machine for the automation projects. This paper designs different project scenarios using virtual machine. It analyzes installability, performance and stability of virtual machine from the test results. Technical solutions concerning virtual machine are discussed such as the conversion with physical...

Design of rotating electrical machines

CERN Document Server

Pyrhonen , Juha; Hrabovcova , Valeria

2013-01-01

In one complete volume, this essential reference presents an in-depth overview of the theoretical principles and techniques of electrical machine design. This timely new edition offers up-to-date theory and guidelines for the design of electrical machines, taking into account recent advances in permanent magnet machines as well as synchronous reluctance machines. New coverage includes: Brand new material on the ecological impact of the motors, covering the eco-design principles of rotating electrical machinesAn expanded section on the design of permanent magnet synchronous machines, now repo

Comparative implementation of Handwritten and Machine written Gurmukhi text utilizing appropriate parameters

Science.gov (United States)

Kaur, Jaswinder; Jagdev, Gagandeep, Dr.

2018-01-01

Optical character recognition is concerned with the recognition of optically processed characters. The recognition is done offline after the writing or printing has been completed, unlike online recognition where the computer has to recognize the characters instantly as they are drawn. The performance of character recognition depends upon the quality of scanned documents. The preprocessing steps are used for removing low-frequency background noise and normalizing the intensity of individual scanned documents. Several filters are used for reducing certain image details and enabling an easier or faster evaluation. The primary aim of the research work is to recognize handwritten and machine written characters and differentiate them. The language opted for the research work is Punjabi Gurmukhi and tool utilized is Matlab.

Experimental Investigation – Magnetic Assisted Electro Discharge Machining

Science.gov (United States)

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.

Machine learning in autistic spectrum disorder behavioral research: A review and ways forward.

Science.gov (United States)

Thabtah, Fadi

2018-02-13

Autistic Spectrum Disorder (ASD) is a mental disorder that retards acquisition of linguistic, communication, cognitive, and social skills and abilities. Despite being diagnosed with ASD, some individuals exhibit outstanding scholastic, non-academic, and artistic capabilities, in such cases posing a challenging task for scientists to provide answers. In the last few years, ASD has been investigated by social and computational intelligence scientists utilizing advanced technologies such as machine learning to improve diagnostic timing, precision, and quality. Machine learning is a multidisciplinary research topic that employs intelligent techniques to discover useful concealed patterns, which are utilized in prediction to improve decision making. Machine learning techniques such as support vector machines, decision trees, logistic regressions, and others, have been applied to datasets related to autism in order to construct predictive models. These models claim to enhance the ability of clinicians to provide robust diagnoses and prognoses of ASD. However, studies concerning the use of machine learning in ASD diagnosis and treatment suffer from conceptual, implementation, and data issues such as the way diagnostic codes are used, the type of feature selection employed, the evaluation measures chosen, and class imbalances in data among others. A more serious claim in recent studies is the development of a new method for ASD diagnoses based on machine learning. This article critically analyses these recent investigative studies on autism, not only articulating the aforementioned issues in these studies but also recommending paths forward that enhance machine learning use in ASD with respect to conceptualization, implementation, and data. Future studies concerning machine learning in autism research are greatly benefitted by such proposals.

Abrasives and Grinding Machines; Machine Shop Work--Advanced: 9557.02.

Science.gov (United States)

Dade County Public Schools, Miami, FL.

The course outline has been prepared as a guide to assist the instructor in systematically planning and presenting a variety of meaningful lessons to facilitate the necessary training for the machine shop student. The material contained in the outline is designed to enable the student to learn the manipulative skills and related knowledge…

Man-machine communication based on the computerized operator support system

International Nuclear Information System (INIS)

Sano, Y.; Fukumoto, A.; Seki, E.; Tai, I.; Mori, N.; Tsuchida, M.; Sato, N.

1985-01-01

Development of a man-machine communication system in a nuclear power plant has been performed, utilizing the new communication technologies and an advanced diagnosis system. In the course of elaborating the communication concept, selection and rearrangement of communication functions in a control room were made based on the human factors engineering. Guidelines and criteria for information display system and operational equipments were also studied and evaluated. The outline of the communication concept and some evaluation test results are described. (author)

Advanced electric drives analysis, control, and modeling using MATLAB/Simulink

CERN Document Server

Mohan, Ned

2014-01-01

Advanced Electric Drives utilizes a physics-based approach to explain the fundamental concepts of modern electric drive control and its operation under dynamic conditions. Gives readers a "physical" picture of electric machines and drives without resorting to mathematical transformations for easy visualization Confirms the physics-based analysis of electric drives mathematically Provides readers with an analysis of electric machines in a way that can be easily interfaced to common power electronic converters and controlled using any control scheme Makes the MATLAB/Simulink files used in exampl

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

Science.gov (United States)

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

2018-03-01

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

Advanced Cell Classifier: User-Friendly Machine-Learning-Based Software for Discovering Phenotypes in High-Content Imaging Data.

Science.gov (United States)

Piccinini, Filippo; Balassa, Tamas; Szkalisity, Abel; Molnar, Csaba; Paavolainen, Lassi; Kujala, Kaisa; Buzas, Krisztina; Sarazova, Marie; Pietiainen, Vilja; Kutay, Ulrike; Smith, Kevin; Horvath, Peter

2017-06-28

High-content, imaging-based screens now routinely generate data on a scale that precludes manual verification and interrogation. Software applying machine learning has become an essential tool to automate analysis, but these methods require annotated examples to learn from. Efficiently exploring large datasets to find relevant examples remains a challenging bottleneck. Here, we present Advanced Cell Classifier (ACC), a graphical software package for phenotypic analysis that addresses these difficulties. ACC applies machine-learning and image-analysis methods to high-content data generated by large-scale, cell-based experiments. It features methods to mine microscopic image data, discover new phenotypes, and improve recognition performance. We demonstrate that these features substantially expedite the training process, successfully uncover rare phenotypes, and improve the accuracy of the analysis. ACC is extensively documented, designed to be user-friendly for researchers without machine-learning expertise, and distributed as a free open-source tool at www.cellclassifier.org. Copyright © 2017 Elsevier Inc. All rights reserved.

Utility guide to advanced UT systems for PSI and ISI

International Nuclear Information System (INIS)

Anon.

1987-01-01

The number of automated UT inspection systems and techniques, currently in the marketplace or being developed, has grown in recent years to the point where a utility engineer reviewing this field is faced with a major task in trying to decide what inspection technique and system will meet his inspection requirements. Recognizing the utility engineer's problem, EPRI initiated this project to produce a utility engineer's guide to advanced, automated UT systems. Of principal concern are those that have been recently introduced, and designed for problem areas such as BWR piping. Older automated scanning systems, used primarily for pressure vessel inspection, are not being ignored, but are not covered here. Costs, benefits and inspection time are addressed for several systems in this report

VIRTUAL MODELING OF A NUMERICAL CONTROL MACHINE TOOL USED FOR COMPLEX MACHINING OPERATIONS

Directory of Open Access Journals (Sweden)

POPESCU Adrian

2015-11-01

Full Text Available This paper presents the 3D virtual model of the numerical control machine Modustar 100, in terms of machine elements. This is a CNC machine of modular construction, all components allowing the assembly in various configurations. The paper focused on the design of the subassemblies specific to the axes numerically controlled by means of CATIA v5, which contained different drive kinematic chains of different translation modules that ensures translation on X, Y and Z axis. Machine tool development for high speed and highly precise cutting demands employment of advanced simulation techniques witch it reflect on cost of total development of the machine.

Utility advanced turbine systems (ATS) technology readiness testing -- Phase 3. Annual report, October 1, 1996--September 30, 1997

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-12-31

The overall objective of the Advanced Turbine System (ATS) Phase 3 Cooperative Agreement between GE and the US Department of Energy (DOE) is the development of the GE 7H and 9H combined cycle power systems. The major effort will be expended on detail design. Validation of critical components and technologies will be performed including: hot gas path component testing, sub-scale compressor testing, steam purity test trials, and rotational heat transfer confirmation testing. Processes will be developed to support the manufacture of the first system. Technology enhancements that are not required for the first machine design but will be critical for future ATS advances in performance, reliability, and costs will be initiated. Long-term tests of materials to confirm design life predictions will continue. A schematic of the GE H machine is shown.

Machine Learning Technologies and Their Applications for Science and Engineering Domains Workshop -- Summary Report

Science.gov (United States)

Ambur, Manjula; Schwartz, Katherine G.; Mavris, Dimitri N.

2016-01-01

The fields of machine learning and big data analytics have made significant advances in recent years, which has created an environment where cross-fertilization of methods and collaborations can achieve previously unattainable outcomes. The Comprehensive Digital Transformation (CDT) Machine Learning and Big Data Analytics team planned a workshop at NASA Langley in August 2016 to unite leading experts the field of machine learning and NASA scientists and engineers. The primary goal for this workshop was to assess the state-of-the-art in this field, introduce these leading experts to the aerospace and science subject matter experts, and develop opportunities for collaboration. The workshop was held over a three day-period with lectures from 15 leading experts followed by significant interactive discussions. This report provides an overview of the 15 invited lectures and a summary of the key discussion topics that arose during both formal and informal discussion sections. Four key workshop themes were identified after the closure of the workshop and are also highlighted in the report. Furthermore, several workshop attendees provided their feedback on how they are already utilizing machine learning algorithms to advance their research, new methods they learned about during the workshop, and collaboration opportunities they identified during the workshop.

Machine Learning

CERN Multimedia

CERN. Geneva

2017-01-01

Machine learning, which builds on ideas in computer science, statistics, and optimization, focuses on developing algorithms to identify patterns and regularities in data, and using these learned patterns to make predictions on new observations. Boosted by its industrial and commercial applications, the field of machine learning is quickly evolving and expanding. Recent advances have seen great success in the realms of computer vision, natural language processing, and broadly in data science. Many of these techniques have already been applied in particle physics, for instance for particle identification, detector monitoring, and the optimization of computer resources. Modern machine learning approaches, such as deep learning, are only just beginning to be applied to the analysis of High Energy Physics data to approach more and more complex problems. These classes will review the framework behind machine learning and discuss recent developments in the field.

Recent advances in modeling nutrient utilization in ruminants.

Science.gov (United States)

Kebreab, E; Dijkstra, J; Bannink, A; France, J

2009-04-01

Mathematical modeling techniques have been applied to study various aspects of the ruminant, such as rumen function, postabsorptive metabolism, and product composition. This review focuses on advances made in modeling rumen fermentation and its associated rumen disorders, and energy and nutrient utilization and excretion with respect to environmental issues. Accurate prediction of fermentation stoichiometry has an impact on estimating the type of energy-yielding substrate available to the animal, and the ratio of lipogenic to glucogenic VFA is an important determinant of methanogenesis. Recent advances in modeling VFA stoichiometry offer ways for dietary manipulation to shift the fermentation in favor of glucogenic VFA. Increasing energy to the animal by supplementing with starch can lead to health problems such as subacute rumen acidosis caused by rumen pH depression. Mathematical models have been developed to describe changes in rumen pH and rumen fermentation. Models that relate rumen temperature to rumen pH have also been developed and have the potential to aid in the diagnosis of subacute rumen acidosis. The effect of pH has been studied mechanistically, and in such models, fractional passage rate has a large impact on substrate degradation and microbial efficiency in the rumen and should be an important theme in future studies. The efficiency with which energy is utilized by ruminants has been updated in recent studies. Mechanistic models of N utilization indicate that reducing dietary protein concentration, matching protein degradability to the microbial requirement, and increasing the energy status of the animal will reduce the output of N as waste. Recent mechanistic P models calculate the P requirement by taking into account P recycled through saliva and endogenous losses. Mechanistic P models suggest reducing current P amounts for lactating dairy cattle to at least 0.35% P in the diet, with a potential reduction of up to 1.3 kt/yr. A model that

A fluidics comparison of Alcon Infiniti, Bausch & Lomb Stellaris, and Advanced Medical Optics Signature phacoemulsification machines.

Science.gov (United States)

Georgescu, Dan; Kuo, Annie F; Kinard, Krista I; Olson, Randall J

2008-06-01

To compare three phacoemulsification machines for measurement accuracy and postocclusion surge (POS) in human cadaver eyes. In vitro comparisons of machine accuracy and POS. Tip vacuum and flow were compared with machine indicated vacuum and flow. All machines were placed in two human cadaver eyes and POS was determined. Vacuum (% of actual) was 101.9% +/- 1.7% for Infiniti (Alcon, Fort Worth, Texas, USA), 93.2% +/- 3.9% for Stellaris (Bausch & Lomb, Rochester, New York, USA), and 107.8% +/- 4.6% for Signature (Advanced Medical Optics, Santa, Ana, California, USA; P Infiniti, 53.5 +/- 0.0 ml/minute and 179.8 +/- 0.9 mm Hg for Stellaris, and 58.5 +/- 0.0 ml/minute and 115.1 +/- 2.3 mm Hg for Signature (P Infiniti, 0.16 +/- 0.06 mm for Stellaris, and 0.13 +/- 0.04 mm for Signature at 550 mm Hg, 60 cm bottle height, 45 ml/minute flow with 19-gauge tips (P Infiniti vs Stellaris and Signature). POS in an 81-year-old eye was 1.51 +/- 0.22 mm for Infiniti, 0.83 +/- 0.06 mm for Stellaris, 0.67 +/- 0.01 mm for Signature at 400 mm Hg vacuum, 70 cm bottle height, 40 ml/minute flow with 19-gauge tips (P Infiniti and Stellaris were similar. Minimizing POS and vacuum to maintain flow potentially are important in avoiding ocular damage and surgical complications.

Machining of titanium alloys

CERN Document Server

2014-01-01

This book presents a collection of examples illustrating the resent research advances in the machining of titanium alloys. These materials have excellent strength and fracture toughness as well as low density and good corrosion resistance; however, machinability is still poor due to their low thermal conductivity and high chemical reactivity with cutting tool materials. This book presents solutions to enhance machinability in titanium-based alloys and serves as a useful reference to professionals and researchers in aerospace, automotive and biomedical fields.

Advanced Turbine Systems (ATS) program conceptual design and product development. Quarterly progress report, December 1, 1995--February 29, 1996

Energy Technology Data Exchange (ETDEWEB)

NONE

1997-06-01

This report describes the overall program status of the General Electric Advanced Gas Turbine Development program, and reports progress on three main task areas. The program is focused on two specific products: (1) a 70-MW class industrial gas turbine based on the GE90 core technology, utilizing a new air cooling methodology; and (2) a 200-MW class utility gas turbine based on an advanced GE heavy-duty machine, utilizing advanced cooling and enhancement in component efficiency. The emphasis for the industrial system is placed on cycle design and low emission combustion. For the utility system, the focus is on developing a technology base for advanced turbine cooling while achieving low emission combustion. The three tasks included in this progress report are on: conversion to a coal-fueled advanced turbine system, integrated program plan, and design and test of critical components. 13 figs., 1 tab.

5-axes modular CNC machining center

Directory of Open Access Journals (Sweden)

Breaz Radu-Eugen

2017-01-01

Full Text Available The paper presents the development of a 5-axes CNC machining center. The main goal of the machine was to provide the students a practical layout for training in advanced CAM techniques. The mechanical structure of the machine was built in a modular way by a specialized company, which also implemented the CNC controller. The authors of this paper developed the geometric and kinematic model of the CNC machining center and the post-processor, in order to use the machine in a CAM environment.

Introduction to AC machine design

CERN Document Server

Lipo, Thomas A

2018-01-01

AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...

Parametric study of an absorption refrigeration machine using advanced exergy analysis

International Nuclear Information System (INIS)

Gong, Sunyoung; Goni Boulama, Kiari

2014-01-01

An advanced exergy analysis of a water–lithium bromide absorption refrigeration machine was conducted. For each component of the machine, the proposed analysis quantified the irreversibility that can be avoided and the irreversibility that is unavoidable. It also identified the irreversibility originating from inefficiencies within the component and the irreversibility that does not originate from the operation of the considered component. It was observed that the desorber and absorber concentrated most of the exergy destruction. Furthermore, the exergy destruction at these components was found to be dominantly endogenous and unavoidable. A parametrical study has been presented discussing the sensitivity of the different performance indicators to the temperature at which the heat source is available, the temperature of the refrigerated environment, and the temperature of the cooling medium used at the condenser and absorber. It was observed that the endogenous avoidable exergy destruction at the desorber, i.e. the portion of the desorber irreversibility that could be avoided by improving the design and operation of the desorber, decreased when the heat source or the temperature at which the cooling effect was generated increased, and it decreased when the heat sink temperature increased. The endogenous avoidable exergy destruction at the absorber displayed the same variations, though it was observed to be less affected by the heat source temperature. Contrary to the aforementioned two components, the exergy destruction at the evaporator and condenser were dominantly endogenous and avoidable, with little sensitivity to the cycle operating parameters. - Highlights: • Endogenous, exogenous, avoidable and unavoidable irreversibilities were calculated for a water–LiBr absorption machine. • Overall, desorber and absorber concentrated most of the exergy destruction of the cycle. • The exergy destruction was mainly endogenous and unavoidable for the desorber and

AP600 level of automation: United States utility perspective

International Nuclear Information System (INIS)

Bekkerman, A.Y.

1997-01-01

Design of the AP600 advanced nuclear plant man-machine interface system (M-MIS) is guided by the applicable requirements from the Utility Requirements Document (URD). However, the URD has left certain aspects of the M-MIS to be determined by the designer working together with utilities sponsoring the work. This is particularly true in the case of the level of automation to be designed into the M-MIS. Based on experience from currently operating plants, utilities have specified the identity and roles of personnel in the control room, which has led to establishing a number of level of automation issues for the AP600. The key role of automated computerized procedures in the AP600 automation has been determined and resolved. 5 refs

Human-machine interactions

Science.gov (United States)

Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM

2009-04-28

Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.

Clinical utility of ramucirumab in advanced gastric cancer

Directory of Open Access Journals (Sweden)

Chan MMK

2015-09-01

Full Text Available Matthew MK Chan,1,2 Katrin M Sjoquist,1,3 John R Zalcberg4 1NHMRC Clinical Trials Centre, University of Sydney, Sydney, NSW, Australia; 2Department of Medical Oncology, Central Coast Cancer Centre, Gosford Hospital, Gosford, NSW, Australia; 3Cancer Care Centre, St George Hospital, Sydney, NSW, Australia; 4School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia Abstract: Gastric cancer is currently the third most common cause of cancer deaths worldwide. Prognosis remains poor with most patients presenting with advanced or metastatic disease. A better understanding of angiogenesis has led to the investigation of drugs that inhibit the vascular endothelial growth factor (VEGF pathway including anti-VEGF antibody therapy (eg, bevacizumab, inhibitors of angiogenic receptor tyrosine kinases (eg, sunitinib, sorafenib, apatinib, regorafenib, and inhibitors of vascular endothelial growth factor receptors (VEGFRs (eg, ramucirumab. Ramucirumab, a VEGFR-2 inhibitor, is the first anti-angiogenic agent approved by the US Food and Drug Administration for use in the treatment of advanced gastric cancers. This review will focus on the clinical utility and potential use of ramucirumab in advanced gastric cancer. Keywords: ramucirumab, IMC-1121B, gastric cancer, vascular endothelial growth factor receptor-2, angiogenesis, targeted therapy

Machine learning with R cookbook

CERN Document Server

Chiu, Yu-Wei

2015-01-01

If you want to learn how to use R for machine learning and gain insights from your data, then this book is ideal for you. Regardless of your level of experience, this book covers the basics of applying R to machine learning through to advanced techniques. While it is helpful if you are familiar with basic programming or machine learning concepts, you do not require prior experience to benefit from this book.

Machining of Metal Matrix Composites

CERN Document Server

2012-01-01

Machining of Metal Matrix Composites provides the fundamentals and recent advances in the study of machining of metal matrix composites (MMCs). Each chapter is written by an international expert in this important field of research. Machining of Metal Matrix Composites gives the reader information on machining of MMCs with a special emphasis on aluminium matrix composites. Chapter 1 provides the mechanics and modelling of chip formation for traditional machining processes. Chapter 2 is dedicated to surface integrity when machining MMCs. Chapter 3 describes the machinability aspects of MMCs. Chapter 4 contains information on traditional machining processes and Chapter 5 is dedicated to the grinding of MMCs. Chapter 6 describes the dry cutting of MMCs with SiC particulate reinforcement. Finally, Chapter 7 is dedicated to computational methods and optimization in the machining of MMCs. Machining of Metal Matrix Composites can serve as a useful reference for academics, manufacturing and materials researchers, manu...

Finding New Perovskite Halides via Machine learning

Directory of Open Access Journals (Sweden)

Ghanshyam ePilania

2016-04-01

Full Text Available Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning via building a support vector machine (SVM based classifier that uses elemental features (or descriptors to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

Finding New Perovskite Halides via Machine learning

Science.gov (United States)

Pilania, Ghanshyam; Balachandran, Prasanna V.; Kim, Chiho; Lookman, Turab

2016-04-01

Advanced materials with improved properties have the potential to fuel future technological advancements. However, identification and discovery of these optimal materials for a specific application is a non-trivial task, because of the vastness of the chemical search space with enormous compositional and configurational degrees of freedom. Materials informatics provides an efficient approach towards rational design of new materials, via learning from known data to make decisions on new and previously unexplored compounds in an accelerated manner. Here, we demonstrate the power and utility of such statistical learning (or machine learning) via building a support vector machine (SVM) based classifier that uses elemental features (or descriptors) to predict the formability of a given ABX3 halide composition (where A and B represent monovalent and divalent cations, respectively, and X is F, Cl, Br or I anion) in the perovskite crystal structure. The classification model is built by learning from a dataset of 181 experimentally known ABX3 compounds. After exploring a wide range of features, we identify ionic radii, tolerance factor and octahedral factor to be the most important factors for the classification, suggesting that steric and geometric packing effects govern the stability of these halides. The trained and validated models then predict, with a high degree of confidence, several novel ABX3 compositions with perovskite crystal structure.

Fault Tolerance Automotive Air-Ratio Control Using Extreme Learning Machine Model Predictive Controller

OpenAIRE

Pak Kin Wong; Hang Cheong Wong; Chi Man Vong; Tong Meng Iong; Ka In Wong; Xianghui Gao

2015-01-01

Effective air-ratio control is desirable to maintain the best engine performance. However, traditional air-ratio control assumes the lambda sensor located at the tail pipe works properly and relies strongly on the air-ratio feedback signal measured by the lambda sensor. When the sensor is warming up during cold start or under failure, the traditional air-ratio control no longer works. To address this issue, this paper utilizes an advanced modelling technique, kernel extreme learning machine (...

Theory and practice in machining systems

CERN Document Server

Ito, Yoshimi

2017-01-01

This book describes machining technology from a wider perspective by considering it within the machining space. Machining technology is one of the metal removal activities that occur at the machining point within the machining space. The machining space consists of structural configuration entities, e.g., the main spindle, the turret head and attachments such the chuck and mandrel, and also the form-generating movement of the machine tool itself. The book describes fundamental topics, including the form-generating movement of the machine tool and the important roles of the attachments, before moving on to consider the supply of raw materials into the machining space, and the discharge of swarf from it, and then machining technology itself. Building on the latest research findings “Theory and Practice in Machining System” discusses current challenges in machining. Thus, with the inclusion of introductory and advanced topics, the book can be used as a guide and survey of machining technology for students an...

Advanced rotary engine components utilizing fiber reinforced Mg castings

Science.gov (United States)

Goddard, D.; Whitman, W.; Pumphrey, R.; Lee, C.-M.

1986-01-01

Under a two-phase program sponsored by NASA, the technology for producing advanced rotary engine components utilizing graphite fiber-reinforced magnesium alloy casting is being developed. In Phase I, the successful casting of a simulated intermediate housing was demonstrated. In Phase II, the goal is to produce an operating rotor housing. The effort involves generation of a material property data base, optimization of parameters, and development of wear- and corrosion-resistant cast surfaces and surface coatings. Results to date are described.

Research opportunities to advance solar energy utilization.

Science.gov (United States)

Lewis, Nathan S

2016-01-22

Major developments, as well as remaining challenges and the associated research opportunities, are evaluated for three technologically distinct approaches to solar energy utilization: solar electricity, solar thermal, and solar fuels technologies. Much progress has been made, but research opportunities are still present for all approaches. Both evolutionary and revolutionary technology development, involving foundational research, applied research, learning by doing, demonstration projects, and deployment at scale will be needed to continue this technology-innovation ecosystem. Most of the approaches still offer the potential to provide much higher efficiencies, much lower costs, improved scalability, and new functionality, relative to the embodiments of solar energy-conversion systems that have been developed to date. Copyright © 2016, American Association for the Advancement of Science.

Virtual Machine Language 2.1

Science.gov (United States)

Riedel, Joseph E.; Grasso, Christopher A.

2012-01-01

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

Man Machine Systems in Education.

Science.gov (United States)

Sall, Malkit S.

This review of the research literature on the interaction between humans and computers discusses how man machine systems can be utilized effectively in the learning-teaching process, especially in secondary education. Beginning with a definition of man machine systems and comments on the poor quality of much of the computer-based learning material…

Prediction of tunnel boring machine performance using machine and rock mass data

International Nuclear Information System (INIS)

Dastgir, G.

2012-01-01

Performance of the tunnel boring machine and its prediction by different methods has been a hot issue since the first TBM came into being. For the sake of safe and sound transport, improvement of hydro-power, mining, civil and many other tunneling projects that cannot be driven efficiently and economically by conventional drill and blast, TBMs are quite frequently used. TBM parameters and rock mass properties, which heavily influence machine performance, should be estimated or known before choice of TBM-type and start of excavation. By applying linear regression analysis (SPSS19), fuzzy logic tools and a special Math-Lab code on actual field data collected from seven TBM driven tunnels (Hieflau expansion, Queen water tunnel, Vereina, Hemerwald, Maen, Pieve and Varzo tunnel), an attempt was made to provide prediction of rock mass class (RMC), rock fracture class (RFC), penetration rate (PR) and advance rate (AR). For detailed analysis of TBM performance, machine parameters (thrust, machine rpm, torque, power etc.), machine types and specification and rock mass properties (UCS, discontinuity in rock mass, RMC, RFC, RMR, etc.) were analyzed by 3-D surface plotting using statistical software R. Correlations between machine parameters and rock mass properties which effectively influence prediction models, are presented as well. In Hieflau expansion tunnel AR linearly decreases with increase of thrust due to high dependence of machine advance rate upon rock strength. For Hieflau expansion tunnel three types of data (TBM, rock mass and seismic data e.g. amplitude, pseudo velocity etc.) were coupled and simultaneously analyzed by plotting 3-D surfaces. No appreciable correlation between seismic data (Amplitude and Pseudo velocity) and rock mass properties and machine parameters could be found. Tool wear as a function of TBM operational parameters was analyzed which revealed that tool wear is minimum if applied thrust is moderate and that tool wear is high when thrust is

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning.

Science.gov (United States)

Boissoneault, Jeff; Sevel, Landrew; Letzen, Janelle; Robinson, Michael; Staud, Roland

2017-01-01

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

High pressure water jet mining machine

Science.gov (United States)

Barker, Clark R.

1981-05-05

A high pressure water jet mining machine for the longwall mining of coal is described. The machine is generally in the shape of a plowshare and is advanced in the direction in which the coal is cut. The machine has mounted thereon a plurality of nozzle modules each containing a high pressure water jet nozzle disposed to oscillate in a particular plane. The nozzle modules are oriented to cut in vertical and horizontal planes on the leading edge of the machine and the coal so cut is cleaved off by the wedge-shaped body.

Technology Roadmap Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs

Energy Technology Data Exchange (ETDEWEB)

Donald D Dudenhoeffer; Burce P Hallbert

2007-03-01

Instrumentation, Controls, and Human-Machine Interface (ICHMI) technologies are essential to ensuring delivery and effective operation of optimized advanced Generation IV (Gen IV) nuclear energy systems. In 1996, the Watts Bar I nuclear power plant in Tennessee was the last U.S. nuclear power plant to go on line. It was, in fact, built based on pre-1990 technology. Since this last U.S. nuclear power plant was designed, there have been major advances in the field of ICHMI systems. Computer technology employed in other industries has advanced dramatically, and computing systems are now replaced every few years as they become functionally obsolete. Functional obsolescence occurs when newer, more functional technology replaces or supersedes an existing technology, even though an existing technology may well be in working order.Although ICHMI architectures are comprised of much of the same technology, they have not been updated nearly as often in the nuclear power industry. For example, some newer Personal Digital Assistants (PDAs) or handheld computers may, in fact, have more functionality than the 1996 computer control system at the Watts Bar I plant. This illustrates the need to transition and upgrade current nuclear power plant ICHMI technologies.

Machine Learning an algorithmic perspective

CERN Document Server

Marsland, Stephen

2009-01-01

Traditional books on machine learning can be divided into two groups - those aimed at advanced undergraduates or early postgraduates with reasonable mathematical knowledge and those that are primers on how to code algorithms. The field is ready for a text that not only demonstrates how to use the algorithms that make up machine learning methods, but also provides the background needed to understand how and why these algorithms work. Machine Learning: An Algorithmic Perspective is that text.Theory Backed up by Practical ExamplesThe book covers neural networks, graphical models, reinforcement le

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

Energy Technology Data Exchange (ETDEWEB)

Reddy, M Mohan; Gorin, Alexander [School of Engineering and Science, Curtin University of Technology, Sarawak (Malaysia); Abou-El-Hossein, K A, E-mail: mohan.m@curtin.edu.my [Mechanical and Aeronautical Department, Nelson Mandela Metropolitan University, Port Elegebeth, 6031 (South Africa)

2011-02-15

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Predictive Surface Roughness Model for End Milling of Machinable Glass Ceramic

International Nuclear Information System (INIS)

Reddy, M Mohan; Gorin, Alexander; Abou-El-Hossein, K A

2011-01-01

Advanced ceramics of Machinable glass ceramic is attractive material to produce high accuracy miniaturized components for many applications in various industries such as aerospace, electronics, biomedical, automotive and environmental communications due to their wear resistance, high hardness, high compressive strength, good corrosion resistance and excellent high temperature properties. Many research works have been conducted in the last few years to investigate the performance of different machining operations when processing various advanced ceramics. Micro end-milling is one of the machining methods to meet the demand of micro parts. Selecting proper machining parameters are important to obtain good surface finish during machining of Machinable glass ceramic. Therefore, this paper describes the development of predictive model for the surface roughness of Machinable glass ceramic in terms of speed, feed rate by using micro end-milling operation.

Human-machine interaction in nuclear power plants

International Nuclear Information System (INIS)

Yoshikawa, Hidekazu

2005-01-01

Advanced nuclear power plants are generally large complex systems automated by computers. Whenever a rate plant emergency occurs the plant operators must cope with the emergency under severe mental stress without committing any fatal errors. Furthermore, the operators must train to improve and maintain their ability to cope with every conceivable situation, though it is almost impossible to be fully prepared for an infinite variety of situations. In view of the limited capability of operators in emergency situations, there has been a new approach to preventing the human error caused by improper human-machine interaction. The new approach has been triggered by the introduction of advanced information systems that help operators recognize and counteract plant emergencies. In this paper, the adverse effect of automation in human-machine systems is explained. The discussion then focuses on how to configure a joint human-machine system for ideal human-machine interaction. Finally, there is a new proposal on how to organize technologies that recognize the different states of such a joint human-machine system

Machining and characterization of self-reinforced polymers

Science.gov (United States)

Deepa, A.; Padmanabhan, K.; Kuppan, P.

2017-11-01

This Paper focuses on obtaining the mechanical properties and the effect of the different machining techniques on self-reinforced composites sample and to derive the best machining method with remarkable properties. Each sample was tested by the Tensile and Flexural tests, fabricated using hot compaction test and those loads were calculated. These composites are machined using conventional methods because of lack of advanced machinery in most of the industries. The advanced non-conventional methods like Abrasive water jet machining were used. These machining techniques are used to get the better output for the composite materials with good mechanical properties compared to conventional methods. But the use of non-conventional methods causes the changes in the work piece, tool properties and more economical compared to the conventional methods. Finding out the best method ideal for the designing of these Self Reinforced Composites with and without defects and the use of Scanning Electron Microscope (SEM) analysis for the comparing the microstructure of the PP and PE samples concludes our process.

Development of Web-based Virtual Training Environment for Machining

Science.gov (United States)

Yang, Zhixin; Wong, S. F.

2010-05-01

With the booming in the manufacturing sector of shoe, garments and toy, etc. in pearl region, training the usage of various facilities and design the facility layout become crucial for the success of industry companies. There is evidence that the use of virtual training may provide benefits in improving the effect of learning and reducing risk in the physical work environment. This paper proposed an advanced web-based training environment that could demonstrate the usage of a CNC machine in terms of working condition and parameters selection. The developed virtual environment could provide training at junior level and advanced level. Junior level training is to explain machining knowledge including safety factors, machine parameters (ex. material, speed, feed rate). Advanced level training enables interactive programming of NG coding and effect simulation. Operation sequence was used to assist the user to choose the appropriate machining condition. Several case studies were also carried out with animation of milling and turning operations.

Machine learning methods without tears: a primer for ecologists.

Science.gov (United States)

Olden, Julian D; Lawler, Joshua J; Poff, N LeRoy

2008-06-01

Machine learning methods, a family of statistical techniques with origins in the field of artificial intelligence, are recognized as holding great promise for the advancement of understanding and prediction about ecological phenomena. These modeling techniques are flexible enough to handle complex problems with multiple interacting elements and typically outcompete traditional approaches (e.g., generalized linear models), making them ideal for modeling ecological systems. Despite their inherent advantages, a review of the literature reveals only a modest use of these approaches in ecology as compared to other disciplines. One potential explanation for this lack of interest is that machine learning techniques do not fall neatly into the class of statistical modeling approaches with which most ecologists are familiar. In this paper, we provide an introduction to three machine learning approaches that can be broadly used by ecologists: classification and regression trees, artificial neural networks, and evolutionary computation. For each approach, we provide a brief background to the methodology, give examples of its application in ecology, describe model development and implementation, discuss strengths and weaknesses, explore the availability of statistical software, and provide an illustrative example. Although the ecological application of machine learning approaches has increased, there remains considerable skepticism with respect to the role of these techniques in ecology. Our review encourages a greater understanding of machin learning approaches and promotes their future application and utilization, while also providing a basis from which ecologists can make informed decisions about whether to select or avoid these approaches in their future modeling endeavors.

Utilizing the Human, Machine, and Environment Matrix in investigations

International Nuclear Information System (INIS)

Curry, David; McKinney, John M.

2006-01-01

'How did we get into this situation?' How many times has this question been asked at the outset of an investigation, or more importantly, at the completion of an investigation? If the answer is not readily and thoroughly apparent, the investigation is not complete. Subsequently, those who will have the responsibility for correction of the conditions leading to the incident will not have all the information necessary to properly complete their task. For many years, in many writings, the Human/Machine interaction and its impact on process design has been discussed. The same impact should be examined when performing incident investigations. Consideration of the interaction of human and machine along with the environment in which they are used has long been recommended by the National Safety Council, in both design and investigation

Three-Phase Modulated Pole Machine Topologies Utilizing Mutual Flux Paths

DEFF Research Database (Denmark)

Washington, Jamie G.; Atkinson, Glynn J.; Baker, Nick J.

2012-01-01

This paper discusses three-phase topologies for modulated pole machines (MPMs). The authors introduce a new threephase topology, which takes advantage of mutual flux paths; this is analyzed using 3-D finite-element methods and compared to a three-phase topology using three single-phase units...... stacked axially. The results show that the new “combined-phase MPM” exhibits a greater torque density, while offering a reduction in the number of components. The results obtained from two prototypes are also presented to verify the concept; the results show that the “combined-phase” machine could provide...

Machine learning and medical imaging

CERN Document Server

Shen, Dinggang; Sabuncu, Mert

2016-01-01

Machine Learning and Medical Imaging presents state-of- the-art machine learning methods in medical image analysis. It first summarizes cutting-edge machine learning algorithms in medical imaging, including not only classical probabilistic modeling and learning methods, but also recent breakthroughs in deep learning, sparse representation/coding, and big data hashing. In the second part leading research groups around the world present a wide spectrum of machine learning methods with application to different medical imaging modalities, clinical domains, and organs. The biomedical imaging modalities include ultrasound, magnetic resonance imaging (MRI), computed tomography (CT), histology, and microscopy images. The targeted organs span the lung, liver, brain, and prostate, while there is also a treatment of examining genetic associations. Machine Learning and Medical Imaging is an ideal reference for medical imaging researchers, industry scientists and engineers, advanced undergraduate and graduate students, a...

Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.

Science.gov (United States)

Vallmuur, Kirsten; Marucci-Wellman, Helen R; Taylor, Jennifer A; Lehto, Mark; Corns, Helen L; Smith, Gordon S

2016-04-01

Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semiautomatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and PPV and reduced the need for human coding to less than a third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of 'big injury narrative data' opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/

Machine takeover the growing threat to human freedom in a computer-controlled society

CERN Document Server

George, Frank Honywill

1977-01-01

Machine Takeover: The Growing Threat to Human Freedom in a Computer-Controlled Society discusses the implications of technological advancement. The title identifies the changes in society that no one is aware of, along with what this changes entails. The text first covers the information science, particularly the aspect of an automated system for information processing. Next, the selection deals with social implications of information science, such as information pollution. The text also tackles the concerns in the utilization of technology in order to manipulate the lives of people without th

Man/machine interface algorithm for advanced delayed-neutron signal characterization system

International Nuclear Information System (INIS)

Gross, K.C.

1985-01-01

The present failed-element rupture detector (FERD) at Experimental Breeder Reactor II (EBR-II) consists of a single bank of delayed-neutron (DN) detectors at a fixed transit time from the core. Plans are currently under way to upgrade the FERD in 1986 and provide advanced DN signal characterization capability that is embodied in an equivalent-recoil-area (ERA) meter. The new configuration will make available to the operator a wealth of quantitative diagnostic information related to the condition and dynamic evolution of a fuel breach. The diagnostic parameters will include a continuous reading of the ERA value for the breach; the transit time, T/sub tr/, for DN emitters traveling from the core to the FERD; and the isotopic holdup time, T/sub h/, for the source. To enhance the processing, interpretation, and display of these parameters to the reactor operator, a man/machine interface (MMI) algorithm has been developed to run in the background on EBR-II's data acquisition system (DAS). The purpose of this paper is to describe the features and implementation of this newly developed MMI algorithm

Machine learning in the string landscape

Science.gov (United States)

Carifio, Jonathan; Halverson, James; Krioukov, Dmitri; Nelson, Brent D.

2017-09-01

We utilize machine learning to study the string landscape. Deep data dives and conjecture generation are proposed as useful frameworks for utilizing machine learning in the landscape, and examples of each are presented. A decision tree accurately predicts the number of weak Fano toric threefolds arising from reflexive polytopes, each of which determines a smooth F-theory compactification, and linear regression generates a previously proven conjecture for the gauge group rank in an ensemble of 4/3× 2.96× {10}^{755} F-theory compactifications. Logistic regression generates a new conjecture for when E 6 arises in the large ensemble of F-theory compactifications, which is then rigorously proven. This result may be relevant for the appearance of visible sectors in the ensemble. Through conjecture generation, machine learning is useful not only for numerics, but also for rigorous results.

Advances in three-dimensional field analysis and evaluation of performance parameters of electrical machines

Science.gov (United States)

Sivasubramaniam, Kiruba

This thesis makes advances in three dimensional finite element analysis of electrical machines and the quantification of their parameters and performance. The principal objectives of the thesis are: (1)the development of a stable and accurate method of nonlinear three-dimensional field computation and application to electrical machinery and devices; and (2)improvement in the accuracy of determination of performance parameters, particularly forces and torque computed from finite elements. Contributions are made in two general areas: a more efficient formulation for three dimensional finite element analysis which saves time and improves accuracy, and new post-processing techniques to calculate flux density values from a given finite element solution. A novel three-dimensional magnetostatic solution based on a modified scalar potential method is implemented. This method has significant advantages over the traditional total scalar, reduced scalar or vector potential methods. The new method is applied to a 3D geometry of an iron core inductor and a permanent magnet motor. The results obtained are compared with those obtained from traditional methods, in terms of accuracy and speed of computation. A technique which has been observed to improve force computation in two dimensional analysis using a local solution of Laplace's equation in the airgap of machines is investigated and a similar method is implemented in the three dimensional analysis of electromagnetic devices. A new integral formulation to improve force calculation from a smoother flux-density profile is also explored and implemented. Comparisons are made and conclusions drawn as to how much improvement is obtained and at what cost. This thesis also demonstrates the use of finite element analysis to analyze torque ripples due to rotor eccentricity in permanent magnet BLDC motors. A new method for analyzing torque harmonics based on data obtained from a time stepping finite element analysis of the machine is

Technology Roadmap on Instrumentation, Control, and Human-Machine Interface to Support DOE Advanced Nuclear Energy Programs

International Nuclear Information System (INIS)

Donald D Dudenhoeffer; Burce P Hallbert

2007-01-01

Instrumentation, Controls, and Human-Machine Interface (ICHMI) technologies are essential to ensuring delivery and effective operation of optimized advanced Generation IV (Gen IV) nuclear energy systems. In 1996, the Watts Bar I nuclear power plant in Tennessee was the last U.S. nuclear power plant to go on line. It was, in fact, built based on pre-1990 technology. Since this last U.S. nuclear power plant was designed, there have been major advances in the field of ICHMI systems. Computer technology employed in other industries has advanced dramatically, and computing systems are now replaced every few years as they become functionally obsolete. Functional obsolescence occurs when newer, more functional technology replaces or supersedes an existing technology, even though an existing technology may well be in working order. Although ICHMI architectures are comprised of much of the same technology, they have not been updated nearly as often in the nuclear power industry. For example, some newer Personal Digital Assistants (PDAs) or handheld computers may, in fact, have more functionality than the 1996 computer control system at the Watts Bar I plant. This illustrates the need to transition and upgrade current nuclear power plant ICHMI technologies

Boosting Economic Growth Through Advanced Machine Vision

OpenAIRE

MAAD, Soha; GARBAYA, Samir; AYADI, Nizar; BOUAKAZ, Saida

2012-01-01

In this chapter, we overview the potential of machine vision and related technologies in various application domains of critical importance for economic growth and prospect. Considered domains include healthcare, energy and environment, finance, and industrial innovation. Visibility technologies considered encompass augmented and virtual reality, 3D technologies, and media content authoring tools and technologies. We overview the main challenges facing the application domains and discuss the ...

Advanced Machine Learning for Classification, Regression, and Generation in Jet Physics

CERN Multimedia

CERN. Geneva

2017-01-01

There is a deep connection between machine learning and jet physics - after all, jets are defined by unsupervised learning algorithms. Jet physics has been a driving force for studying modern machine learning in high energy physics. Domain specific challenges require new techniques to make full use of the algorithms. A key focus is on understanding how and what the algorithms learn. Modern machine learning techniques for jet physics are demonstrated for classification, regression, and generation. In addition to providing powerful baseline performance, we show how to train complex models directly on data and to generate sparse stacked images with non-uniform granularity.

Chatter and machine tools

CERN Document Server

Stone, Brian

2014-01-01

Focussing on occurrences of unstable vibrations, or Chatter, in machine tools, this book gives important insights into how to eliminate chatter with associated improvements in product quality, surface finish and tool wear. Covering a wide range of machining processes, including turning, drilling, milling and grinding, the author uses his research expertise and practical knowledge of vibration problems to provide solutions supported by experimental evidence of their effectiveness. In addition, this book contains links to supplementary animation programs that help readers to visualise the ideas detailed in the text. Advancing knowledge in chatter avoidance and suggesting areas for new innovations, Chatter and Machine Tools serves as a handbook for those desiring to achieve significant reductions in noise, longer tool and grinding wheel life and improved product finish.

Reconfigurable support vector machine classifier with approximate computing

NARCIS (Netherlands)

van Leussen, M.J.; Huisken, J.; Wang, L.; Jiao, H.; De Gyvez, J.P.

2017-01-01

Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines

International Nuclear Information System (INIS)

Frauenhofer, Joachim; Arndt, Tabea; Grundmann, Joern

2013-01-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO 2 emissions.

Cleaning of aluminum after machining with coolants

International Nuclear Information System (INIS)

Roop, B.

1992-01-01

An x-ray photoemission spectroscopic study was undertaken to compare the cleaning of the Advanced Photon Source (APS) aluminum extrusion storage ring vacuum chambers after machining with and without water soluble coolants. While there was significant contamination left by the coolants, the cleaning process was capable of removing the residue. The variation of the surface and near surface composition of samples machined either dry or with coolants was negligible after cleaning. The use of such coolants in the machining process is therefore recommended

Electrical machining method of insulating ceramics

International Nuclear Information System (INIS)

Fukuzawa, Y.; Mohri, N.; Tani, T.

1999-01-01

This paper describes a new electrical discharge machining method for insulating ceramics using an assisting electrode with either a sinking electrical discharge machine or a wire electrical discharge machine. In this method, the metal sheet or mesh is attached to the ceramic surface as an assisting material for the discharge generation around the insulator surface. When the machining condition changes from the attached material to the workpiece, a cracked carbon layer is formed on the workpiece surface. As this layer has an electrical conductivity, electrical discharge occurs in working oil between the tool electrode and the surface of the workpiece. The carbon is formed from the working oil during this electrical discharge. Even after the material is machined, an electrical discharge occurs in the gap region between the tool electrode and the ceramic because an electrically conductive layer is generated continuously. Insulating ceramics can be machined by the electrical discharge machining method using the above mentioned surface modification phenomenon. In this paper the authors show a machined example demonstrating that the proposed method is available for machining a complex shape on insulating ceramics. Copyright (1999) AD-TECH - International Foundation for the Advancement of Technology Ltd

Can Utilities Realize the Benefits of Advanced Metering Infrastructure? : Lessons from the World Bank’s Portfolio

OpenAIRE

Nangia, Varun; Oguah, Samuel; Gaba, Kwawu

2016-01-01

Advanced metering infrastructure (AMI) provides significant benefits to utilities around the world. Although it is entering the mainstream, technical concerns, policy challenges, capacity, and will in the Bank’s client countries hinder wider adoption. Starting out with smaller AMI deployments aimed at addressing revenue constraints seems to offer the best chance of success at utilities sup...

Advanced 3D inverse method for designing turbomachine blades

Energy Technology Data Exchange (ETDEWEB)

Dang, T. [Syracuse Univ., NY (United States)

1995-10-01

To meet the goal of 60% plant-cycle efficiency or better set in the ATS Program for baseload utility scale power generation, several critical technologies need to be developed. One such need is the improvement of component efficiencies. This work addresses the issue of improving the performance of turbo-machine components in gas turbines through the development of an advanced three-dimensional and viscous blade design system. This technology is needed to replace some elements in current design systems that are based on outdated technology.

On-machine measurement of a slow slide servo diamond-machined 3D microstructure with a curved substrate

International Nuclear Information System (INIS)

Zhu, Wu-Le; Yang, Shunyao; Ju, Bing-Feng; Jiang, Jiacheng; Sun, Anyu

2015-01-01

A scanning tunneling microscope-based multi-axis measuring system is specially developed for the on-machine measurement of three-dimensional (3D) microstructures, to address the quality control difficulty with the traditional off-line measurement process. A typical 3D microstructure of the curved compound eye was diamond-machined by the slow slide servo technique, and then the whole surface was on-machine scanned three-dimensionally based on the tip-tracking strategy by utilizing a spindle, two linear motion stages, and an additional rotary stage. The machined surface profile and its shape deviation were accurately measured on-machine. The distortion of imaged ommatidia on the curved substrate was distinctively evaluated based on the characterized points extracted from the measured surface. Furthermore, the machining errors were investigated in connection with the on-machine measured surface and its characteristic parameters. Through experiments, the proposed measurement system is demonstrated to feature versatile on-machine measurement of 3D microstructures with a curved substrate, which is highly meaningful for quality control in the fabrication field. (paper)

A structured review of health utility measures and elicitation in advanced/metastatic breast cancer

Directory of Open Access Journals (Sweden)

Hao Y

2016-06-01

Full Text Available Yanni Hao,1 Verena Wolfram,2 Jennifer Cook2 1Novartis Pharmaceuticals, East Hanover, NJ, USA; 2Adelphi Values, Bollington, UK Background: Health utilities are increasingly incorporated in health economic evaluations. Different elicitation methods, direct and indirect, have been established in the past. This study examined the evidence on health utility elicitation previously reported in advanced/metastatic breast cancer and aimed to link these results to requirements of reimbursement bodies. Methods: Searches were conducted using a detailed search strategy across several electronic databases (MEDLINE, EMBASE, Cochrane Library, and EconLit databases, online sources (Cost-effectiveness Analysis Registry and the Health Economics Research Center, and web sites of health technology assessment (HTA bodies. Publications were selected based on the search strategy and the overall study objectives. Results: A total of 768 publications were identified in the searches, and 26 publications, comprising 18 journal articles and eight submissions to HTA bodies, were included in the evidence review. Most journal articles derived utilities from the European Quality of Life Five-Dimensions questionnaire (EQ-5D. Other utility measures, such as the direct methods standard gamble (SG, time trade-off (TTO, and visual analog scale (VAS, were less frequently used. Several studies described mapping algorithms to generate utilities from disease-specific health-related quality of life (HRQOL instruments such as European Organization for Research and Treatment of Cancer Quality of Life Questionnaire – Core 30 (EORTC QLQ-C30, European Organization for Research and Treatment of Cancer Quality of Life Questionnaire – Breast Cancer 23 (EORTC QLQ-BR23, Functional Assessment of Cancer Therapy – General questionnaire (FACT-G, and Utility-Based Questionnaire-Cancer (UBQ-C; most used EQ-5D as the reference. Sociodemographic factors that affect health utilities, such as age, sex

Probabilistic machine learning and artificial intelligence.

Science.gov (United States)

Ghahramani, Zoubin

2015-05-28

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

Probabilistic machine learning and artificial intelligence

Science.gov (United States)

Ghahramani, Zoubin

2015-05-01

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

Advances Towards Synthetic Machines at the Molecular and Nanoscale Level

Directory of Open Access Journals (Sweden)

Kristina Konstas

2010-06-01

Full Text Available The fabrication of increasingly smaller machines to the nanometer scale can be achieved by either a “top-down” or “bottom-up” approach. While the former is reaching its limits of resolution, the latter is showing promise for the assembly of molecular components, in a comparable approach to natural systems, to produce functioning ensembles in a controlled and predetermined manner. In this review we focus on recent progress in molecular systems that act as molecular machine prototypes such as switches, motors, vehicles and logic operators.

Advanced Electrochemical Machining (ECM) for tungsten surface micro-structuring in blanket applications

International Nuclear Information System (INIS)

Holstein, Nils; Krauss, Wolfgang; Konys, Jürgen; Heuer, Simon; Weber, Thomas

2016-01-01

Highlights: • Electrochemical Machining is an appropriate tool for tungsten shaping. • Progress in shaping achieved by combination of ECM with advanced micro-lithography. • Application in First Wall for connection of plasma facing material to breeder blanket. • Successful development of adhesion promotors by ECM for plasma spraying interlayers. • Microstructure electrochemical manufacturing of tungsten in sizes of 100 μm achieved. - Abstract: Plasma facing components for fusion applications must have to exhibit long-term stability under extreme physical conditions, and therefore any material imperfections caused by mechanical and/or thermal stresses in the shaping processes cannot be tolerated due to a high risk of possible technical failures under fusion conditions. To avoid such defects, the method of Electrochemical Machining (ECM) enables a complete defect-free processing of removal of tungsten material during the desired shaping, also for high penetration depths. Furthermore, supported by lithographic mask pretreatment, three-dimensional distinct geometric structures can be positive-imaged via the directional galvanic dissolution applying M-ECM process into the tungsten bulk material. New required applications for tungsten components, e.g. as adhesion promotors in W-surfaces to enable sure grip and bonding of thick plasma-spraying layers for blanket components, will define the way of further miniaturization of well-established millimeter dimensioned M-ECM shaping processes to dimensions of 100 μm and furthermore down to 50 μm. Besides current M-ECM limits the article describes inevitable needs of further developments for mask resists, mask materials and the resulting ECM parameters, to reach the needed accuracy in tungsten microstructure. The achieved progress and observed correlations of processing parameters will be manifested by produced demonstrators made by the new “μM”-ECM process.

Advanced Electrochemical Machining (ECM) for tungsten surface micro-structuring in blanket applications

Energy Technology Data Exchange (ETDEWEB)

Holstein, Nils, E-mail: nils.holstein@kit.edu [Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen, Baden-Württemberg (Germany); Krauss, Wolfgang; Konys, Jürgen [Karlsruhe Institute of Technology, Hermann-von-Helmholtz-Platz 1, D-76344 Eggenstein-Leopoldshafen, Baden-Württemberg (Germany); Heuer, Simon; Weber, Thomas [Research Center Jülich, Institute of Energy- and Climate Research – Plasma Physics (IEK-4), D-52425 Jülich (Germany)

2016-11-01

Highlights: • Electrochemical Machining is an appropriate tool for tungsten shaping. • Progress in shaping achieved by combination of ECM with advanced micro-lithography. • Application in First Wall for connection of plasma facing material to breeder blanket. • Successful development of adhesion promotors by ECM for plasma spraying interlayers. • Microstructure electrochemical manufacturing of tungsten in sizes of 100 μm achieved. - Abstract: Plasma facing components for fusion applications must have to exhibit long-term stability under extreme physical conditions, and therefore any material imperfections caused by mechanical and/or thermal stresses in the shaping processes cannot be tolerated due to a high risk of possible technical failures under fusion conditions. To avoid such defects, the method of Electrochemical Machining (ECM) enables a complete defect-free processing of removal of tungsten material during the desired shaping, also for high penetration depths. Furthermore, supported by lithographic mask pretreatment, three-dimensional distinct geometric structures can be positive-imaged via the directional galvanic dissolution applying M-ECM process into the tungsten bulk material. New required applications for tungsten components, e.g. as adhesion promotors in W-surfaces to enable sure grip and bonding of thick plasma-spraying layers for blanket components, will define the way of further miniaturization of well-established millimeter dimensioned M-ECM shaping processes to dimensions of 100 μm and furthermore down to 50 μm. Besides current M-ECM limits the article describes inevitable needs of further developments for mask resists, mask materials and the resulting ECM parameters, to reach the needed accuracy in tungsten microstructure. The achieved progress and observed correlations of processing parameters will be manifested by produced demonstrators made by the new “μM”-ECM process.

Advances in satellite communications

CERN Document Server

Minoli, Daniel

2015-01-01

Discussing advances in modulation techniques and HTS spotbeam technologiesSurveying emerging high speed aeronautical mobility services and maritime and other terrestrial mobility servicesAssessing M2M (machine-to-machine) applications, emerging Ultra HD video technologies and new space technology

Advanced Utility Metering; Period of Performance: April 23, 2002 - September 22, 2002

Energy Technology Data Exchange (ETDEWEB)

2003-09-01

In support of federal agencies considering the approach to utility metering appropriate for their facilities, the U.S. Department of Energy Federal Energy Management Program offers this publication as an overview of options in metering technology, system architecture, implementation, and relative costs. It provides advanced metering systems information to help potential users specify, acquire, use, and expand systems. It also addresses basic security issues and provides case studies and information resources.

A smarter way to search, share and utilize open-spatial online data for energy R&D - Custom machine learning and GIS tools in U.S. DOE's virtual data library & laboratory, EDX

Science.gov (United States)

Rose, K.; Bauer, J.; Baker, D.; Barkhurst, A.; Bean, A.; DiGiulio, J.; Jones, K.; Jones, T.; Justman, D.; Miller, R., III; Romeo, L.; Sabbatino, M.; Tong, A.

2017-12-01

As spatial datasets are increasingly accessible through open, online systems, the opportunity to use these resources to address a range of Earth system questions grows. Simultaneously, there is a need for better infrastructure and tools to find and utilize these resources. We will present examples of advanced online computing capabilities, hosted in the U.S. DOE's Energy Data eXchange (EDX), that address these needs for earth-energy research and development. In one study the computing team developed a custom, machine learning, big data computing tool designed to parse the web and return priority datasets to appropriate servers to develop an open-source global oil and gas infrastructure database. The results of this spatial smart search approach were validated against expert-driven, manual search results which required a team of seven spatial scientists three months to produce. The custom machine learning tool parsed online, open systems, including zip files, ftp sites and other web-hosted resources, in a matter of days. The resulting resources were integrated into a geodatabase now hosted for open access via EDX. Beyond identifying and accessing authoritative, open spatial data resources, there is also a need for more efficient tools to ingest, perform, and visualize multi-variate, spatial data analyses. Within the EDX framework, there is a growing suite of processing, analytical and visualization capabilities that allow multi-user teams to work more efficiently in private, virtual workspaces. An example of these capabilities are a set of 5 custom spatio-temporal models and data tools that form NETL's Offshore Risk Modeling suite that can be used to quantify oil spill risks and impacts. Coupling the data and advanced functions from EDX with these advanced spatio-temporal models has culminated with an integrated web-based decision-support tool. This platform has capabilities to identify and combine data across scales and disciplines, evaluate potential environmental

Utility Values for Advanced Soft Tissue Sarcoma Health States from the General Public in the United Kingdom

Directory of Open Access Journals (Sweden)

Julian F. Guest

2013-01-01

Full Text Available Soft tissue sarcomas are a rare type of cancer generally treated with palliative chemotherapy when in the advanced stage. There is a lack of published health utility data for locally advanced “inoperable”/metastatic disease (ASTS, essential for calculating the cost-effectiveness of current and future treatments. This study estimated time trade-off (TTO and standard gamble (SG preference values associated with four ASTS health states (progressive disease, stable disease, partial response, complete response among members of the general public in the UK (n=207. The four health states were associated with decreases in preference values from full health. Complete response was the most preferred health state (mean utility of 0.60 using TTO. The second most preferred health state was partial response followed by stable disease (mean utilities were 0.51 and 0.43, respectively, using TTO. The least preferred health state was progressive disease (mean utility of 0.30 using TTO. The utility value for each state was significantly different from one another (P<0.001. This study demonstrated and quantified the impact that different treatment responses may have on the health-related quality of life of patients with ASTS.

Diamond machining of micro-optical components and structures

Science.gov (United States)

Gläbe, Ralf; Riemer, Oltmann

2010-05-01

Diamond machining originates from the 1950s to 1970s in the USA. This technology was originally designed for machining of metal optics at macroscopic dimensions with so far unreached tolerances. During the following decades the machine tools, the monocrystalline diamond cutting tools, the workpiece materials and the machining processes advanced to even higher precision and flexibility. For this reason also the fabrication of small functional components like micro optics at a large spectrum of geometries became technologically and economically feasible. Today, several kinds of fast tool machining and multi axis machining operations can be applied for diamond machining of micro optical components as well as diffractive optical elements. These parts can either be machined directly as single or individual component or as mold insert for mass production by plastic replication. Examples are multi lens arrays, micro mirror arrays and fiber coupling lenses. This paper will give an overview about the potentials and limits of the current diamond machining technology with respect to micro optical components.

Investigation of roughing machining simulation by using visual basic programming in NX CAM system

Science.gov (United States)

Hafiz Mohamad, Mohamad; Nafis Osman Zahid, Muhammed

2018-03-01

This paper outlines a simulation study to investigate the characteristic of roughing machining simulation in 4th axis milling processes by utilizing visual basic programming in NX CAM systems. The selection and optimization of cutting orientation in rough milling operation is critical in 4th axis machining. The main purpose of roughing operation is to approximately shape the machined parts into finished form by removing the bulk of material from workpieces. In this paper, the simulations are executed by manipulating a set of different cutting orientation to generate estimated volume removed from the machine parts. The cutting orientation with high volume removal is denoted as an optimum value and chosen to execute a roughing operation. In order to run the simulation, customized software is developed to assist the routines. Operations build-up instructions in NX CAM interface are translated into programming codes via advanced tool available in the Visual Basic Studio. The codes is customized and equipped with decision making tools to run and control the simulations. It permits the integration with any independent program files to execute specific operations. This paper aims to discuss about the simulation program and identifies optimum cutting orientations for roughing processes. The output of this study will broaden up the simulation routines performed in NX CAM systems.

The Utilization of Parallel Corpora for the Extension of Machine ...

African Journals Online (AJOL)

grammar rules for the identification of the grammatical category of each .... An example of the first type of corpus-based machine translation is a sub- ..... The MINISTER OF AGRICULTURE: Mr Chairman, while prayers were being read this.

Standardization of advanced light water reactors and progress on achieving utility requirements

International Nuclear Information System (INIS)

Marston, T.U.; Layman, W.H.; Bockhold, G. Jr.

1992-01-01

This paper reports that for a number of years, the U.S. utilities had led an industry-wide effort to establish a technical foundation for the design of the next generation of light water reactors in the United States. Since 1985, this utility initiative has been effected through a major technical program managed by the Electric Power Research Institute (EPRI); the U.S. Advanced Light Water Reactor (ALWR) Program. In addition to the U.S. utility leadership and sponsorship, the ALWR Program also has the participation and sponsorship of a number of international utility companies and close cooperation with the U.S. Department of Energy (DOE). The NPOC Strategic Plan for Building New Nuclear Plants creates a framework within which new standardized nuclear plants may be built. The Strategic Plan is an expression of the nuclear energy industry's serious intent to create the necessary conditions for new plant construction and operation. The industry has assembled a comprehensive, integrated list of actions that must be taken before new plants will be built and assigns responsibility for managing the various issues and sets time-tables and milestones against which we must measure progress

Advanced reactor design study. Assessing nonbackfittable concepts for improving uranium utilization in light water reactors

International Nuclear Information System (INIS)

Fleischman, R.M.; Goldsmith, S.; Newman, D.F.; Trapp, T.J.; Spinrad, B.I.

1981-09-01

The objective of the Advanced Reactor Design Study (ARDS) is to identify and evaluate nonbackfittable concepts for improving uranium utilization in light water reactors (LWRs). The results of this study provide a basis for selecting and demonstrating specific nonbackfittable concepts that have good potential for implementation. Lead responsibility for managing the study was assigned to the Pacific Northwest Laboratory (PNL). Nonbackfittable concepts for improving uranium utilization in LWRs on the once-through fuel cycle were selected separately for PWRs and BWRs due to basic differences in the way specific concepts apply to those plants. Nonbackfittable concepts are those that are too costly to incorporate in existing plants, and thus, could only be economically incorporated in new reactor designs or plants in very early stages of construction. Essential results of the Advanced Reactor Design Study are summarized

Trends and developments in industrial machine vision: 2013

Science.gov (United States)

Niel, Kurt; Heinzl, Christoph

2014-03-01

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

Participation in the ABWR Man-Machine interface design. Applicability to the Spanish Electrical Sector

International Nuclear Information System (INIS)

Rodriguez, C.; Manrique Martin, A.; Nunez, J.

1997-01-01

Project coordinated by DTN within the advanced reactor programme. Participation in the design activities for the Advanced Boiling Water Reactor (ABWR) man-machine interface was divided into two phases: Phase I: Preparation of drawings for designing, developing and assessing the advanced control room Phase II: Application of these drawings in design activities Participation in this programme has led to the following possible future applications to the electrical sector: 1. Design and implementation of man-machine interfaces 2. Human factor criteria 3. Assessment of man-machine interfaces 4. Functional specification, computerised operating procedures 5. Computerised alarm prototypes. (Author)

Virtual medicine: Utilization of the advanced cardiac imaging patient avatar for procedural planning and facilitation.

Science.gov (United States)

Shinbane, Jerold S; Saxon, Leslie A

Advances in imaging technology have led to a paradigm shift from planning of cardiovascular procedures and surgeries requiring the actual patient in a "brick and mortar" hospital to utilization of the digitalized patient in the virtual hospital. Cardiovascular computed tomographic angiography (CCTA) and cardiovascular magnetic resonance (CMR) digitalized 3-D patient representation of individual patient anatomy and physiology serves as an avatar allowing for virtual delineation of the most optimal approaches to cardiovascular procedures and surgeries prior to actual hospitalization. Pre-hospitalization reconstruction and analysis of anatomy and pathophysiology previously only accessible during the actual procedure could potentially limit the intrinsic risks related to time in the operating room, cardiac procedural laboratory and overall hospital environment. Although applications are specific to areas of cardiovascular specialty focus, there are unifying themes related to the utilization of technologies. The virtual patient avatar computer can also be used for procedural planning, computational modeling of anatomy, simulation of predicted therapeutic result, printing of 3-D models, and augmentation of real time procedural performance. Examples of the above techniques are at various stages of development for application to the spectrum of cardiovascular disease processes, including percutaneous, surgical and hybrid minimally invasive interventions. A multidisciplinary approach within medicine and engineering is necessary for creation of robust algorithms for maximal utilization of the virtual patient avatar in the digital medical center. Utilization of the virtual advanced cardiac imaging patient avatar will play an important role in the virtual health care system. Although there has been a rapid proliferation of early data, advanced imaging applications require further assessment and validation of accuracy, reproducibility, standardization, safety, efficacy, quality

Detecting epileptic seizure with different feature extracting strategies using robust machine learning classification techniques by applying advance parameter optimization approach.

Science.gov (United States)

Hussain, Lal

2018-06-01

Epilepsy is a neurological disorder produced due to abnormal excitability of neurons in the brain. The research reveals that brain activity is monitored through electroencephalogram (EEG) of patients suffered from seizure to detect the epileptic seizure. The performance of EEG detection based epilepsy require feature extracting strategies. In this research, we have extracted varying features extracting strategies based on time and frequency domain characteristics, nonlinear, wavelet based entropy and few statistical features. A deeper study was undertaken using novel machine learning classifiers by considering multiple factors. The support vector machine kernels are evaluated based on multiclass kernel and box constraint level. Likewise, for K-nearest neighbors (KNN), we computed the different distance metrics, Neighbor weights and Neighbors. Similarly, the decision trees we tuned the paramours based on maximum splits and split criteria and ensemble classifiers are evaluated based on different ensemble methods and learning rate. For training/testing tenfold Cross validation was employed and performance was evaluated in form of TPR, NPR, PPV, accuracy and AUC. In this research, a deeper analysis approach was performed using diverse features extracting strategies using robust machine learning classifiers with more advanced optimal options. Support Vector Machine linear kernel and KNN with City block distance metric give the overall highest accuracy of 99.5% which was higher than using the default parameters for these classifiers. Moreover, highest separation (AUC = 0.9991, 0.9990) were obtained at different kernel scales using SVM. Additionally, the K-nearest neighbors with inverse squared distance weight give higher performance at different Neighbors. Moreover, to distinguish the postictal heart rate oscillations from epileptic ictal subjects, and highest performance of 100% was obtained using different machine learning classifiers.

High Metal Removal Rate Process for Machining Difficult Materials

Energy Technology Data Exchange (ETDEWEB)

Bates, Robert; McConnell, Elizabeth

2016-06-29

Machining methods across many industries generally require multiple operations to machine and process advanced materials, features with micron precision, and complex shapes. The resulting multiple machining platforms can significantly affect manufacturing cycle time and the precision of the final parts, with a resultant increase in cost and energy consumption. Ultrafast lasers represent a transformative and disruptive technology that removes material with micron precision and in a single step manufacturing process. Such precision results from athermal ablation without modification or damage to the remaining material which is the key differentiator between ultrafast laser technologies and traditional laser technologies or mechanical processes. Athermal ablation without modification or damage to the material eliminates post-processing or multiple manufacturing steps. Combined with the appropriate technology to control the motion of the work piece, ultrafast lasers are excellent candidates to provide breakthrough machining capability for difficult-to-machine materials. At the project onset in early 2012, the project team recognized that substantial effort was necessary to improve the application of ultrafast laser and precise motion control technologies (for micromachining difficult-to-machine materials) to further the aggregate throughput and yield improvements over conventional machining methods. The project described in this report advanced these leading-edge technologies thru the development and verification of two platforms: a hybrid enhanced laser chassis and a multi-application testbed.

Fluid Mechanics, Drag Reduction and Advanced Configuration Aeronautics

Science.gov (United States)

Bushnell, Dennis M.

2000-01-01

This paper discusses Advanced Aircraft configurational approaches across the speed range, which are either enabled, or greatly enhanced, by clever Flow Control. Configurations considered include Channel Wings with circulation control for VTOL (but non-hovering) operation with high cruise speed, strut-braced CTOL transports with wingtip engines and extensive ('natural') laminar flow control, a midwing double fuselage CTOL approach utilizing several synergistic methods for drag-due-to-lift reduction, a supersonic strut-braced configuration with order of twice the L/D of current approaches and a very advanced, highly engine flow-path-integrated hypersonic cruise machine. This paper indicates both the promise of synergistic flow control approaches as enablers for 'Revolutions' in aircraft performance and fluid mechanic 'areas of ignorance' which impede their realization and provide 'target-rich' opportunities for Fluids Research.

Correction magnet power supplies for APS machine

International Nuclear Information System (INIS)

Kang, Y.G.

1991-01-01

The Advanced Photon Source machine requires a number of correction magnets; five kinds for the storage ring, two for the injector synchrotron, and two for the positron accumulator ring. Three types of bipolar power supply will be used for all the correction magnets. This paper describes the design aspects and considerations for correction magnet power supplies for the APS machine. 3 refs., 3 figs., 1 tab

CONCEPTUAL DESIGN FOR A RADICALLY SMALLER, HIGHLY ADAPTIVE AND APPLICATION-FLEXIBLE MINING MACHINE FOR UTILITY AND DEVELOPMENT WORK

Energy Technology Data Exchange (ETDEWEB)

Andrew H. Stern

2004-12-20

The aim of this research project was to develop a preliminary ''conceptual design'' for a radically smaller, highly adaptive and application-flexible underground coal mining machine, for performing non-production utility work and/or also undertake limited production mining for the recovery of reserves that would otherwise be lost. Whereas historically, mining philosophies have reflected a shift to increasing larger mechanized systems [such as the continuous miner (CM)], specific mining operations that do not benefit from the economy of the large mining equipment are often ignored or addressed with significant inefficiencies. Developing this prototype concept will create a new class of equipment that can provide opportunities to re-think the very structure of the mining system across a broad range of possibilities, not able to be met by existing machinery. The approach involved pooling the collective input from mining professionals, using a structured listing of desired inputs in the form of a questionnaire, which was used to define the range of desired design specifications. From these inputs, a conceptual specification was blended, by the author, to embody the general concurrence of mission concepts for this machine.

Making molecular machines work

NARCIS (Netherlands)

Browne, Wesley R.; Feringa, Ben L.

2006-01-01

In this review we chart recent advances in what is at once an old and very new field of endeavour the achievement of control of motion at the molecular level including solid-state and surface-mounted rotors, and its natural progression to the development of synthetic molecular machines. Besides a

Advanced human machine interaction for an image interpretation workstation

Science.gov (United States)

Maier, S.; Martin, M.; van de Camp, F.; Peinsipp-Byma, E.; Beyerer, J.

2016-05-01

In recent years, many new interaction technologies have been developed that enhance the usability of computer systems and allow for novel types of interaction. The areas of application for these technologies have mostly been in gaming and entertainment. However, in professional environments, there are especially demanding tasks that would greatly benefit from improved human machine interfaces as well as an overall improved user experience. We, therefore, envisioned and built an image-interpretation-workstation of the future, a multi-monitor workplace comprised of four screens. Each screen is dedicated to a complex software product such as a geo-information system to provide geographic context, an image annotation tool, software to generate standardized reports and a tool to aid in the identification of objects. Using self-developed systems for hand tracking, pointing gestures and head pose estimation in addition to touchscreens, face identification, and speech recognition systems we created a novel approach to this complex task. For example, head pose information is used to save the position of the mouse cursor on the currently focused screen and to restore it as soon as the same screen is focused again while hand gestures allow for intuitive manipulation of 3d objects in mid-air. While the primary focus is on the task of image interpretation, all of the technologies involved provide generic ways of efficiently interacting with a multi-screen setup and could be utilized in other fields as well. In preliminary experiments, we received promising feedback from users in the military and started to tailor the functionality to their needs

Apollo-L2, an advanced fuel tokamak reactor utilizing direct conversion

International Nuclear Information System (INIS)

Emmert, G.A.; Kulcinski, G.L.; Blanchard, J.P.; El-Guebaly, L.A.; Khater, H.Y.; Santarius, J.F.; Sawan, M.E.; Sviatoslavsky, I.N.; Wittenberg, L.J.; Witt, R.J.

1989-01-01

A scoping study of a tokamak reactor fueled by a D- 3 He plasma is presented. The Apollo D- 3 He tokamak capitalizes on recent advances in high field magnets (20 T) and utilizes rectennas to convert the synchrotron radiation directly to electricity. The low neutron wall loading (0.1 MW/m 2 ) permits a first wall lasting the life of the plant and enables the reactor to be classified as inherently safe. The cost of electricity is less than that from a similar power level DT reactor. 10 refs., 1 fig., 4 tabs

Machinability of titanium metal matrix composites (Ti-MMCs)

Science.gov (United States)

Aramesh, Maryam

Titanium metal matrix composites (Ti-MMCs), as a new generation of materials, have various potential applications in aerospace and automotive industries. The presence of ceramic particles enhances the physical and mechanical properties of the alloy matrix. However, the hard and abrasive nature of these particles causes various issues in the field of their machinability. Severe tool wear and short tool life are the most important drawbacks of machining this class of materials. There is very limited work in the literature regarding the machinability of this class of materials especially in the area of tool life estimation and tool wear. By far, polycrystalline diamond (PCD) tools appear to be the best choice for machining MMCs from researchers' point of view. However, due to their high cost, economical alternatives are sought. Cubic boron nitride (CBN) inserts, as the second hardest available tools, show superior characteristics such as great wear resistance, high hardness at elevated temperatures, a low coefficient of friction and a high melting point. Yet, so far CBN tools have not been studied during machining of Ti-MMCs. In this study, a comprehensive study has been performed to explore the tool wear mechanisms of CBN inserts during turning of Ti-MMCs. The unique morphology of the worn faces of the tools was investigated for the first time, which led to new insights in the identification of chemical wear mechanisms during machining of Ti-MMCs. Utilizing the full tool life capacity of cutting tools is also very crucial, due to the considerable costs associated with suboptimal replacement of tools. This strongly motivates development of a reliable model for tool life estimation under any cutting conditions. In this study, a novel model based on the survival analysis methodology is developed to estimate the progressive states of tool wear under any cutting conditions during machining of Ti-MMCs. This statistical model takes into account the machining time in

NRC review of Electric Power Research Institute's Advanced Light Reactor Utility Requirements Document - Program summary, Project No. 669

International Nuclear Information System (INIS)

1992-08-01

The staff of the US Nuclear Regulatory Commission has prepared Volume 1 of a safety evaluation report (SER), ''NRC Review of Electric Power Research Institute's Advanced Light Water Reactor Utility Requirements Document -- Program Summary,'' to document the results of its review of the Electric Power Research Institute's ''Advanced Light Water Reactor Utility Requirements Document.'' This SER provides a discussion of the overall purpose and scope of the Requirements Document, the background of the staff's review, the review approach used by the staff, and a summary of the policy and technical issues raised by the staff during its review

Man--machine interface issues for space nuclear power systems

International Nuclear Information System (INIS)

Nelson, W.R.; Haugset, K.

1991-01-01

The deployment of nuclear reactors in space necessitates an entirely new set of guidelines for the design of the man--machine interface (MMI) when compared to earth-based applications such as commerical nuclear power plants. Although the design objectives of earth- and space-based nuclear power systems are the same, that is, to produce electrical power, the differences in the application environments mean that the operator's role will be significantly different for space-based systems. This paper explores the issues associated with establishing the necessary MMI guidelines for space nuclear power systems. The generic human performance requirements for space-based systems are described, and the operator roles that are utilized for the operation of current and advanced earth-based reactors are briefly summarized. The development of a prototype advanced control room, the Integrated Surveillance and Control System (ISACS) at the Organization for Economic Cooperation and Development (OECD) Halden Reactor Project is introduced. Finally, preliminary ideas for the use of the ISACS system as a test bed for establishing MMI guidelines for space nuclear systems are presented

Advanced Grid-Friendly Controls Demonstration Project for Utility-Scale PV Power Plants

Energy Technology Data Exchange (ETDEWEB)

Gevorgian, Vahan; O' Neill, Barbara

2016-01-21

A typical photovoltaic (PV) power plant consists of multiple power electronic inverters and can contribute to grid stability and reliability through sophisticated 'grid-friendly' controls. The availability and dissemination of actual test data showing the viability of advanced utility-scale PV controls among all industry stakeholders can leverage PV's value from being simply an energy resource to providing additional ancillary services that range from variability smoothing and frequency regulation to power quality. Strategically partnering with a selected utility and/or PV power plant operator is a key condition for a successful demonstration project. The U.S. Department of Energy's (DOE's) Solar Energy Technologies Office selected the National Renewable Energy Laboratory (NREL) to be a principal investigator in a two-year project with goals to (1) identify a potential partner(s), (2) develop a detailed scope of work and test plan for a field project to demonstrate the gird-friendly capabilities of utility-scale PV power plants, (3) facilitate conducting actual demonstration tests, and (4) disseminate test results among industry stakeholders via a joint NREL/DOE publication and participation in relevant technical conferences. The project implementation took place in FY 2014 and FY 2015. In FY14, NREL established collaborations with AES and First Solar Electric, LLC, to conduct demonstration testing on their utility-scale PV power plants in Puerto Rico and Texas, respectively, and developed test plans for each partner. Both Puerto Rico Electric Power Authority and the Electric Reliability Council of Texas expressed interest in this project because of the importance of such advanced controls for the reliable operation of their power systems under high penetration levels of variable renewable generation. During FY15, testing was completed on both plants, and a large amount of test data was produced and analyzed that demonstrates the ability of

The relationship between physical and psychological symptoms and health care utilization in hospitalized patients with advanced cancer.

Science.gov (United States)

Nipp, Ryan D; El-Jawahri, Areej; Moran, Samantha M; D'Arpino, Sara M; Johnson, P Connor; Lage, Daniel E; Wong, Risa L; Pirl, William F; Traeger, Lara; Lennes, Inga T; Cashavelly, Barbara J; Jackson, Vicki A; Greer, Joseph A; Ryan, David P; Hochberg, Ephraim P; Temel, Jennifer S

2017-12-01

Patients with advanced cancer often experience frequent and prolonged hospitalizations; however, the factors associated with greater health care utilization have not been described. We sought to investigate the relation between patients' physical and psychological symptom burden and health care utilization. We enrolled patients with advanced cancer and unplanned hospitalizations from September 2014-May 2016. Upon admission, we assessed physical (Edmonton Symptom Assessment System [ESAS]) and psychological symptoms (Patient Health Questionnaire 4 [PHQ-4]). We examined the relationship between symptom burden and healthcare utilization using linear regression for hospital length of stay (LOS) and Cox regression for time to first unplanned readmission within 90 days. We adjusted all models for age, sex, marital status, comorbidity, education, time since advanced cancer diagnosis, and cancer type. We enrolled 1,036 of 1,152 (89.9%) consecutive patients approached. Over one-half reported moderate/severe fatigue, poor well being, drowsiness, pain, and lack of appetite. PHQ-4 scores indicated that 28.8% and 28.0% of patients had depression and anxiety symptoms, respectively. The mean hospital LOS was 6.3 days, and the 90-day readmission rate was 43.1%. Physical symptoms (ESAS: unstandardized coefficient [B], 0.06; P cancer experience a high symptom burden, which is significantly associated with prolonged hospitalizations and readmissions. Interventions are needed to address the symptom burden of this population to improve health care delivery and utilization. Cancer 2017;123:4720-4727. © 2017 American Cancer Society. © 2017 American Cancer Society.

Machine Learning wins the Higgs Challenge

CERN Multimedia

Abha Eli Phoboo

2014-01-01

The winner of the four-month-long Higgs Machine Learning Challenge, launched on 12 May, is Gábor Melis from Hungary, followed closely by Tim Salimans from the Netherlands and Pierre Courtiol from France. The challenge explored the potential of advanced machine learning methods to improve the significance of the Higgs discovery.   Winners of the Higgs Machine Learning Challenge: Gábor Melis and Tim Salimans (top row), Tianqi Chen and Tong He (bottom row). Participants in the Higgs Machine Learning Challenge were tasked with developing an algorithm to improve the detection of Higgs boson signal events decaying into two tau particles in a sample of simulated ATLAS data* that contains few signal and a majority of non-Higgs boson “background” events. No knowledge of particle physics was required for the challenge but skills in machine learning - the training of computers to recognise patterns in data – were essential. The Challenge, hosted by Ka...

Electrochemical advanced oxidation processes as decentralized water treatment technologies to remediate domestic washing machine effluents.

Science.gov (United States)

Dos Santos, Alexsandro Jhones; Costa, Emily Cintia Tossi de Araújo; da Silva, Djalma Ribeiro; Garcia-Segura, Sergi; Martínez-Huitle, Carlos Alberto

2018-03-01

Water scarcity is one of the major concerns worldwide. In order to secure this appreciated natural resource, management and development of water treatment technologies are mandatory. One feasible alternative is the consideration of water recycling/reuse at the household scale. Here, the treatment of actual washing machine effluent by electrochemical advanced oxidation processes was considered. Electrochemical oxidation and electro-Fenton technologies can be applied as decentralized small-scale water treatment devices. Therefore, efficient decolorization and total organic abatement have been followed. The results demonstrate the promising performance of solar photoelectro-Fenton process, where complete color and organic removal was attained after 240 min of treatment under optimum conditions by applying a current density of 66.6 mA cm -2 . Thus, electrochemical technologies emerge as promising water-sustainable approaches.

Support Vector Machine and Application in Seizure Prediction

KAUST Repository

Qiu, Simeng

2018-04-01

Nowadays, Machine learning (ML) has been utilized in various kinds of area which across the range from engineering field to business area. In this paper, we first present several kernel machine learning methods of solving classification, regression and clustering problems. These have good performance but also have some limitations. We present examples to each method and analyze the advantages and disadvantages for solving different scenarios. Then we focus on one of the most popular classification methods, Support Vectors Machine (SVM). In addition, we introduce the basic theory, advantages and scenarios of using Support Vector Machine (SVM) deal with classification problems. We also explain a convenient approach of tacking SVM problems which are called Sequential Minimal Optimization (SMO). Moreover, one class SVM can be understood in a different way which is called Support Vector Data Description (SVDD). This is a famous non-linear model problem compared with SVM problems, SVDD can be solved by utilizing Gaussian RBF kernel function combined with SMO. At last, we compared the difference and performance of SVM-SMO implementation and SVM-SVDD implementation. About the application part, we utilized SVM method to handle seizure forecasting in canine epilepsy, after comparing the results from different methods such as random forest, extremely randomized tree, and SVM to classify preictal (pre-seizure) and interictal (interval-seizure) binary data. We draw the conclusion that SVM has the best performance.

Marketing and vending machine; Marketing to jido hanbaiki

Energy Technology Data Exchange (ETDEWEB)

Onzo, N. [Waseda University, Tokyo (Japan)

1999-08-10

Vending machines in Japan have made original progress and have developed into big business. Annual sales by vending machines are 6 trillion 700 billion yen, which exceeds 6 trillion 100 billion yen sales by convenience stores. Research on vending machines may have advanced on the technical side but almost none on the marketing. In a vending machine that made an appearance in 1980 with the feature of a lottery, the winning probability was approximately one in fifty. In addition to a simple vending function, these machines have a promotion function. Some other machines have an electrical display of a commercial for products inside the machine for the purpose of attracting attention of passersby. This is an advertising function of the machines. In other words, one vending machine is capable of various marketing functions. This precisely means the subjects are numerous in the marketing research on vending machines. In contrast to the present century in which technical innovations have been made for vending machines, the coming 21st century may turn out to be the one in which marketing innovations are the mainstream for them. (NEDO)

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

International Nuclear Information System (INIS)

Rollins, Jameson Graef

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org [LIGO Laboratory, California Institute of Technology, Pasadena, California 91125 (United States)

2016-09-15

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

Contemporary machine learning: techniques for practitioners in the physical sciences

Science.gov (United States)

Spears, Brian

2017-10-01

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

Advanced methods in NDE using machine learning approaches

Science.gov (United States)

Wunderlich, Christian; Tschöpe, Constanze; Duckhorn, Frank

2018-04-01

Machine learning (ML) methods and algorithms have been applied recently with great success in quality control and predictive maintenance. Its goal to build new and/or leverage existing algorithms to learn from training data and give accurate predictions, or to find patterns, particularly with new and unseen similar data, fits perfectly to Non-Destructive Evaluation. The advantages of ML in NDE are obvious in such tasks as pattern recognition in acoustic signals or automated processing of images from X-ray, Ultrasonics or optical methods. Fraunhofer IKTS is using machine learning algorithms in acoustic signal analysis. The approach had been applied to such a variety of tasks in quality assessment. The principal approach is based on acoustic signal processing with a primary and secondary analysis step followed by a cognitive system to create model data. Already in the second analysis steps unsupervised learning algorithms as principal component analysis are used to simplify data structures. In the cognitive part of the software further unsupervised and supervised learning algorithms will be trained. Later the sensor signals from unknown samples can be recognized and classified automatically by the algorithms trained before. Recently the IKTS team was able to transfer the software for signal processing and pattern recognition to a small printed circuit board (PCB). Still, algorithms will be trained on an ordinary PC; however, trained algorithms run on the Digital Signal Processor and the FPGA chip. The identical approach will be used for pattern recognition in image analysis of OCT pictures. Some key requirements have to be fulfilled, however. A sufficiently large set of training data, a high signal-to-noise ratio, and an optimized and exact fixation of components are required. The automated testing can be done subsequently by the machine. By integrating the test data of many components along the value chain further optimization including lifetime and durability

Construction machine control guidance implementation strategy.

Science.gov (United States)

2010-07-01

Machine Controlled Guidance (MCG) technology may be used in roadway and bridge construction to improve construction efficiencies, potentially resulting in reduced project costs and accelerated schedules. The technology utilizes a Global Positioning S...

41 CFR 109-25.104 - Acquisition of office furniture and office machines.

Science.gov (United States)

2010-07-01

... furniture and office machines. 109-25.104 Section 109-25.104 Public Contracts and Property Management... furniture and office machines. DOE offices and designated contractors shall make the determination as to whether requirements can be met through the utilization of DOE owned furniture and office machines. ...

Comparison of Advanced Machine Learning Tools for Disruption Prediction and Disruption Studies

Czech Academy of Sciences Publication Activity Database

Odstrčil, Michal; Murari, A.; Mlynář, Jan

2013-01-01

Roč. 41, č. 7 (2013), s. 1751-1759 ISSN 0093-3813 R&D Projects: GA ČR GAP205/10/2055 Institutional support: RVO:61389021 Keywords : Learning Machines * Support Vector Machines * Neural Network * ASDEX Upgrade * JET * Disruption mitigation * Tokamaks * ITER Subject RIV: BL - Plasma and Gas Discharge Physics Impact factor: 0.950, year: 2013

Machine Learning Algorithms Utilizing Quantitative CT Features May Predict Eventual Onset of Bronchiolitis Obliterans Syndrome After Lung Transplantation.

Science.gov (United States)

Barbosa, Eduardo J Mortani; Lanclus, Maarten; Vos, Wim; Van Holsbeke, Cedric; De Backer, William; De Backer, Jan; Lee, James

2018-02-19

Long-term survival after lung transplantation (LTx) is limited by bronchiolitis obliterans syndrome (BOS), defined as a sustained decline in forced expiratory volume in the first second (FEV 1 ) not explained by other causes. We assessed whether machine learning (ML) utilizing quantitative computed tomography (qCT) metrics can predict eventual development of BOS. Paired inspiratory-expiratory CT scans of 71 patients who underwent LTx were analyzed retrospectively (BOS [n = 41] versus non-BOS [n = 30]), using at least two different time points. The BOS cohort experienced a reduction in FEV 1 of >10% compared to baseline FEV 1 post LTx. Multifactor analysis correlated declining FEV 1 with qCT features linked to acute inflammation or BOS onset. Student t test and ML were applied on baseline qCT features to identify lung transplant patients at baseline that eventually developed BOS. The FEV 1 decline in the BOS cohort correlated with an increase in the lung volume (P = .027) and in the central airway volume at functional residual capacity (P = .018), not observed in non-BOS patients, whereas the non-BOS cohort experienced a decrease in the central airway volume at total lung capacity with declining FEV 1 (P = .039). Twenty-three baseline qCT parameters could significantly distinguish between non-BOS patients and eventual BOS developers (P machine), we could identify BOS developers at baseline with an accuracy of 85%, using only three qCT parameters. ML utilizing qCT could discern distinct mechanisms driving FEV 1 decline in BOS and non-BOS LTx patients and predict eventual onset of BOS. This approach may become useful to optimize management of LTx patients. Copyright © 2018 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.

Block-Module Electric Machines of Alternating Current

Science.gov (United States)

Zabora, I.

2018-03-01

The paper deals with electric machines having active zone based on uniform elements. It presents data on disk-type asynchronous electric motors with short-circuited rotors, where active elements are made by integrated technique that forms modular elements. Photolithography, spraying, stamping of windings, pressing of core and combined methods are utilized as the basic technological approaches of production. The constructions and features of operation for new electric machine - compatible electric machines-transformers are considered. Induction motors are intended for operation in hermetic plants with extreme conditions surrounding gas, steam-to-gas and liquid environment at a high temperature (to several hundred of degrees).

Design of instrumentation and software for precise laser machining

Science.gov (United States)

Wyszyński, D.; Grabowski, Marcin; Lipiec, Piotr

2017-10-01

The paper concerns the design of instrumentation and software for precise laser machining. Application of advanced laser beam manipulation instrumentation enables noticeable improvement of cut quality and material loss. This factors have significant impact on process efficiency and cutting edge quality by means of machined part size and shape accuracy, wall taper, material loss reduction (e.g. diamond) and time effectiveness. The goal can be reached by integration of laser drive, observation and optical measurement system, beam manipulation system and five axis mechanical instrumentation with use of advanced tailored software enabling full laser cutting process control and monitoring.

Automatic welding machine for piping

International Nuclear Information System (INIS)

Yoshida, Kazuhiro; Koyama, Takaichi; Iizuka, Tomio; Ito, Yoshitoshi; Takami, Katsumi.

1978-01-01

A remotely controlled automatic special welding machine for piping was developed. This machine is utilized for long distance pipe lines, chemical plants, thermal power generating plants and nuclear power plants effectively from the viewpoint of good quality control, reduction of labor and good controllability. The function of this welding machine is to inspect the shape and dimensions of edge preparation before welding work by the sense of touch, to detect the temperature of melt pool, inspect the bead form by the sense of touch, and check the welding state by ITV during welding work, and to grind the bead surface and inspect the weld metal by ultrasonic test automatically after welding work. The construction of this welding system, the main specification of the apparatus, the welding procedure in detail, the electrical source of this welding machine, the cooling system, the structure and handling of guide ring, the central control system and the operating characteristics are explained. The working procedure and the effect by using this welding machine, and the application to nuclear power plants and the other industrial field are outlined. The HIDIC 08 is used as the controlling computer. This welding machine is useful for welding SUS piping as well as carbon steel piping. (Nakai, Y.)

Management system of ELHEP cluster machine for FEL photonics design

Science.gov (United States)

Zysik, Jacek; Poźniak, Krzysztof; Romaniuk, Ryszard

2006-10-01

A multipurpose, distributed MatLab calculations oriented, cluster machine was assembled in PERG/ELHEP laboratory at ISE/WUT. It is predicted mainly for advanced photonics and FPGA/DSP based systems design for Free Electron Laser. It will be used also for student projects for superconducting accelerator and FEL. Here we present one specific side of cluster design. For an intense, distributed daily work with the cluster, it is important to have a good interface and practical access to all machine resources. A complex management system was implemented in PERG laboratory. It helps all registered users to work using all necessary applications, communicate with other logged in people, check all the news and gather all necessary information about what is going on in the system, how it is utilized, etc. The system is also very practical for administrator purposes, it helps to keep controlling who is using the resources and for how long. It provides different privileges for different applications and many more. The system is introduced as a freeware, using open source code and can be modified by system operators or super-users who are interested in nonstandard system configuration.

Optimal Placement Algorithms for Virtual Machines

OpenAIRE

Bellur, Umesh; Rao, Chetan S; SD, Madhu Kumar

2010-01-01

Cloud computing provides a computing platform for the users to meet their demands in an efficient, cost-effective way. Virtualization technologies are used in the clouds to aid the efficient usage of hardware. Virtual machines (VMs) are utilized to satisfy the user needs and are placed on physical machines (PMs) of the cloud for effective usage of hardware resources and electricity in the cloud. Optimizing the number of PMs used helps in cutting down the power consumption by a substantial amo...

Quantum machine learning what quantum computing means to data mining

CERN Document Server

Wittek, Peter

2014-01-01

Quantum Machine Learning bridges the gap between abstract developments in quantum computing and the applied research on machine learning. Paring down the complexity of the disciplines involved, it focuses on providing a synthesis that explains the most important machine learning algorithms in a quantum framework. Theoretical advances in quantum computing are hard to follow for computer scientists, and sometimes even for researchers involved in the field. The lack of a step-by-step guide hampers the broader understanding of this emergent interdisciplinary body of research. Quantum Machine L

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

Science.gov (United States)

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.

Tool path strategy and cutting process monitoring in intelligent machining

Science.gov (United States)

Chen, Ming; Wang, Chengdong; An, Qinglong; Ming, Weiwei

2018-06-01

Intelligent machining is a current focus in advanced manufacturing technology, and is characterized by high accuracy and efficiency. A central technology of intelligent machining—the cutting process online monitoring and optimization—is urgently needed for mass production. In this research, the cutting process online monitoring and optimization in jet engine impeller machining, cranio-maxillofacial surgery, and hydraulic servo valve deburring are introduced as examples of intelligent machining. Results show that intelligent tool path optimization and cutting process online monitoring are efficient techniques for improving the efficiency, quality, and reliability of machining.

Equipment system for advanced nuclear fuel development

International Nuclear Information System (INIS)

Kwon, Hyuk Il; Ji, C. G.; Bae, S. O.

2002-11-01

The purpose of the settlement of equipment system for nuclear Fuel Technology Development Facility(FTDF) is to build a seismic designed facility that can accommodate handling of nuclear materials including <20% enriched Uranium and produce HANARO fuel commercially, and also to establish the advanced common research equipment essential for the research on advanced fuel development. For this purpose, this research works were performed for the settlement of radiation protection system and facility special equipment for the FTDF, and the advanced common research equipment for the fuel fabrication and research. As a result, 11 kinds of radiation protection systems such as criticality detection and alarm system, 5 kinds of facility special equipment such as environmental pollution protection system and 5 kinds of common research equipment such as electron-beam welding machine were established. By the settlement of exclusive domestic facility for the research of advanced fuel, the fabrication and supply of HANARO fuel is possible and also can export KAERI-invented centrifugal dispersion fuel materials and its technology to the nations having research reactors in operation. For the future, the utilization of the facility will be expanded to universities, industries and other research institutes

Annual meeting of the Advanced Light Source Users' Association

International Nuclear Information System (INIS)

1995-02-01

This report contains papers on the following topics: ALS Director's Report; ALS Operations Update; Recent Results in Machine Physics; Progress in Beamline Commissioning and Overview of New Projects; The ALS Scientific Program; First Results from the SpectroMicroscopy Beamline; Soft X-ray Fluorescence Spectroscopy of Solids; Soft X-Ray Fluorescence Spectroscopy of Molecules; Microstructures and Micromachining at the ALS; High-Resolution Photoemission from Simple Atoms and Molecules; X-Ray Diffraction at the ALS; Utilizing Synchrotron Radiation in Advanced Materials Industries; Polymer Microscopy: About Balls, Rocks and Other ''Stuff''; Infrared Research and Applications; and ALS User Program

Annual meeting of the Advanced Light Source Users` Association

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-02-01

This report contains papers on the following topics: ALS Director`s Report; ALS Operations Update; Recent Results in Machine Physics; Progress in Beamline Commissioning and Overview of New Projects; The ALS Scientific Program; First Results from the SpectroMicroscopy Beamline; Soft X-ray Fluorescence Spectroscopy of Solids; Soft X-Ray Fluorescence Spectroscopy of Molecules; Microstructures and Micromachining at the ALS; High-Resolution Photoemission from Simple Atoms and Molecules; X-Ray Diffraction at the ALS; Utilizing Synchrotron Radiation in Advanced Materials Industries; Polymer Microscopy: About Balls, Rocks and Other ``Stuff``; Infrared Research and Applications; and ALS User Program.

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

Science.gov (United States)

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.

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

Science.gov (United States)

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

Mechanical design of walking machines.

Science.gov (United States)

Arikawa, Keisuke; Hirose, Shigeo

2007-01-15

The performance of existing actuators, such as electric motors, is very limited, be it power-weight ratio or energy efficiency. In this paper, we discuss the method to design a practical walking machine under this severe constraint with focus on two concepts, the gravitationally decoupled actuation (GDA) and the coupled drive. The GDA decouples the driving system against the gravitational field to suppress generation of negative power and improve energy efficiency. On the other hand, the coupled drive couples the driving system to distribute the output power equally among actuators and maximize the utilization of installed actuator power. First, we depict the GDA and coupled drive in detail. Then, we present actual machines, TITAN-III and VIII, quadruped walking machines designed on the basis of the GDA, and NINJA-I and II, quadruped wall walking machines designed on the basis of the coupled drive. Finally, we discuss walking machines that travel on three-dimensional terrain (3D terrain), which includes the ground, walls and ceiling. Then, we demonstrate with computer simulation that we can selectively leverage GDA and coupled drive by walking posture control.

Micro-machined resonator oscillator

Science.gov (United States)

Koehler, Dale R.; Sniegowski, Jeffry J.; Bivens, Hugh M.; Wessendorf, Kurt O.

1994-01-01

A micro-miniature resonator-oscillator is disclosed. Due to the miniaturization of the resonator-oscillator, oscillation frequencies of one MHz and higher are utilized. A thickness-mode quartz resonator housed in a micro-machined silicon package and operated as a "telemetered sensor beacon" that is, a digital, self-powered, remote, parameter measuring-transmitter in the FM-band. The resonator design uses trapped energy principles and temperature dependence methodology through crystal orientation control, with operation in the 20-100 MHz range. High volume batch-processing manufacturing is utilized, with package and resonator assembly at the wafer level. Unique design features include squeeze-film damping for robust vibration and shock performance, capacitive coupling through micro-machined diaphragms allowing resonator excitation at the package exterior, circuit integration and extremely small (0.1 in. square) dimensioning. A family of micro-miniature sensor beacons is also disclosed with widespread applications as bio-medical sensors, vehicle status monitors and high-volume animal identification and health sensors. The sensor family allows measurement of temperatures, chemicals, acceleration and pressure. A microphone and clock realization is also available.

Expected utility without utility

OpenAIRE

Castagnoli, E.; Licalzi, M.

1996-01-01

This paper advances an interpretation of Von Neumann–Morgenstern’s expected utility model for preferences over lotteries which does not require the notion of a cardinal utility over prizes and can be phrased entirely in the language of probability. According to it, the expected utility of a lottery can be read as the probability that this lottery outperforms another given independent lottery. The implications of this interpretation for some topics and models in decision theory are considered....

Machine learning: Trends, perspectives, and prospects.

Science.gov (United States)

Jordan, M I; Mitchell, T M

2015-07-17

Machine learning addresses the question of how to build computers that improve automatically through experience. It is one of today's most rapidly growing technical fields, lying at the intersection of computer science and statistics, and at the core of artificial intelligence and data science. Recent progress in machine learning has been driven both by the development of new learning algorithms and theory and by the ongoing explosion in the availability of online data and low-cost computation. The adoption of data-intensive machine-learning methods can be found throughout science, technology and commerce, leading to more evidence-based decision-making across many walks of life, including health care, manufacturing, education, financial modeling, policing, and marketing. Copyright © 2015, American Association for the Advancement of Science.

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid.

Science.gov (United States)

Li, Yuancheng; Qiu, Rixuan; Jing, Sitong

2018-01-01

Advanced Metering Infrastructure (AMI) realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM) is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

International Conference on Extreme Learning Machines 2014

CERN Document Server

Mao, Kezhi; Cambria, Erik; Man, Zhihong; Toh, Kar-Ann

2015-01-01

This book contains some selected papers from the International Conference on Extreme Learning Machine 2014, which was held in Singapore, December 8-10, 2014. This conference brought together the researchers and practitioners of Extreme Learning Machine (ELM) from a variety of fields to promote research and development of “learning without iterative tuning”.  The book covers theories, algorithms and applications of ELM. It gives the readers a glance of the most recent advances of ELM.  

International Conference on Extreme Learning Machine 2015

CERN Document Server

Mao, Kezhi; Wu, Jonathan; Lendasse, Amaury; ELM 2015; Theory, Algorithms and Applications (I); Theory, Algorithms and Applications (II)

2016-01-01

This book contains some selected papers from the International Conference on Extreme Learning Machine 2015, which was held in Hangzhou, China, December 15-17, 2015. This conference brought together researchers and engineers to share and exchange R&D experience on both theoretical studies and practical applications of the Extreme Learning Machine (ELM) technique and brain learning. This book covers theories, algorithms ad applications of ELM. It gives readers a glance of the most recent advances of ELM. .

A Closer Look at the Design of Cutterheads for Hard Rock Tunnel-Boring Machines

Directory of Open Access Journals (Sweden)

Jamal Rostami

2017-12-01

Full Text Available The success of a tunnel-boring machine (TBM in a given project depends on the functionality of all components of the system, from the cutters to the backup system, and on the entire rolling stock. However, no part of the machine plays a more crucial role in the efficient operation of the machine than its cutterhead. The design of the cutterhead impacts the efficiency of cutting, the balance of the head, the life of the cutters, the maintenance of the main bearing/gearbox, and the effectiveness of the mucking along with its effects on the wear of the face and gage cutters/muck buckets. Overall, cutterhead design heavily impacts the rate of penetration (ROP, rate of machine utilization (U, and daily advance rate (AR. Although there has been some discussion in commonly available publications regarding disk cutters, cutting forces, and some design features of the head, there is limited literature on this subject because the design of cutterheads is mainly handled by machine manufacturers. Most of the design process involves proprietary algorithms by the manufacturers, and despite recent attention on the subject, the design of rock TBMs has been somewhat of a mystery to most end-users. This paper is an attempt to demystify the basic concepts in design. Although it may not be sufficient for a full-fledged design by the readers, this paper allows engineers and contractors to understand the thought process in the design steps, what to look for in a proper design, and the implications of the head design on machine operation and life cycle. Keywords: TBM cutterhead design, Cutterhead layout, Disk cutters, Cutting pattern, TBM efficiency

Man-machine design integration

Energy Technology Data Exchange (ETDEWEB)

Carrera, J.P. [Westinghouse Electric Corp., Monroeville, PA (United States). Nuclear Technology Div.; Haentjens, J. [Westinghouse Electric Corp., Brussels (Belgium). Nuclear Technology Div.

1995-12-31

The presentation overviews the bases for Man-Machine Interface (MMI) designs that are part of three other presentations during the same conference: Advanced Alarm Management System, Functional Displays and System for Emergency Procedure Execution Monitoring. The MMD group history, team and goals are summarized to give some context to the core of the MMD philosophy and integration. (10 refs., 5 figs.).

Fully automatic CNC machining production system

Directory of Open Access Journals (Sweden)

Lee Jeng-Dao

2017-01-01

Full Text Available Customized manufacturing is increasing years by years. The consumption habits change has been cause the shorter of product life cycle. Therefore, many countries view industry 4.0 as a target to achieve more efficient and more flexible automated production. To develop an automatic loading and unloading CNC machining system via vision inspection is the first step in industrial upgrading. CNC controller is adopted as the main controller to command to the robot, conveyor, and other equipment in this study. Moreover, machine vision systems are used to detect position of material on the conveyor and the edge of the machining material. In addition, Open CNC and SCADA software will be utilized to make real-time monitor, remote system of control, alarm email notification, and parameters collection. Furthermore, RFID has been added to employee classification and management. The machine handshaking has been successfully proposed to achieve automatic vision detect, edge tracing measurement, machining and system parameters collection for data analysis to accomplish industrial automation system integration with real-time monitor.

Application of Artificial Intelligence Techniques for the Control of the Asynchronous Machine

Directory of Open Access Journals (Sweden)

F. Khammar

2016-01-01

Full Text Available The induction machine is experiencing a growing success for two decades by gradually replacing the DC machines and synchronous in many industrial applications. This paper is devoted to the study of advanced methods applied to the command of the asynchronous machine in order to obtain a system of control of high performance. While the criteria for response time, overtaking, and static error can be assured by the techniques of conventional control, the criterion of robustness remains a challenge for researchers. This criterion can be satisfied only by applying advanced techniques of command. After mathematical modeling of the asynchronous machine, it defines the control strategies based on the orientation of the rotor flux. The results of the different simulation tests highlight the properties of robustness of algorithms proposed and suggested to compare the different control strategies.

NRC review of Electric Power Research Institute's Advanced Light Water Reactor Utility Requirements Document - Evolutionary plant designs, Chapter 1, Project No. 669

International Nuclear Information System (INIS)

1992-08-01

The staff of the US Nuclear Regulatory Commission has prepared Volume 2 (Parts 1 and 2) of a safety evaluation report (SER), ''NRC Review of Electric Power Research Institute's Advanced Light Water Reactor Utility Requirements Document -- Evolutionary Plant Designs,'' to document the results of its review of the Electric Power Research Institute's ''Advanced Light Water Reactor Utility Requirements Document.'' This SER gives the results of the staff's review of Volume II of the Requirements Document for evolutionary plant designs, which consists of 13 chapters and contains utility design requirements for an evolutionary nuclear power plant (approximately 1300 megawatts-electric)

The ATLAS Higgs Machine Learning Challenge

CERN Document Server

Cowan, Glen; The ATLAS collaboration; Bourdarios, Claire

2015-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 1990s with Artificial Neural Net and more recently with Boosted Decision Trees, Random Forest etc. Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, data scientists are developing new Machine Learning algorithms to extract meaning from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, and at the same time data scientists have advanced algorithms: the goal of the HiggsML project was to bring the two together by a “challenge”: participants from all over the world and any scientific background could compete online to obtain the best Higgs to tau tau signal significance on a set of ATLAS fully simulated Monte Carlo signal and background. Instead of HEP physicists browsing through machine learning papers and trying to infer which new algorithms might be useful for HEP, then c...

XII International Conference on the Theory of Machines and Mechanisms

CERN Document Server

Bílek, Martin; Žabka, Petr

2017-01-01

This book presents the most recent advances in the research of machines and mechanisms. It collects 54 reviewed papers presented at the XII International Conference on the Theory of Machines and mechanisms (TMM 2016) held in Liberec, Czech Republic, September 6-8, 2016. This volume offers an international selection of the most important new results and developments, grouped in six different parts, representing a well-balanced overview, and spanning the general theory of machines and mechanisms, through analysis and synthesis of planar and spatial mechanisms, linkages and cams, robots and manipulators, dynamics of machines and mechanisms, rotor dynamics, computational mechanics, vibration and noise in machines, optimization of mechanisms and machines, mechanisms of textile machines, mechatronics to the control and monitoring systems of machines. This conference is traditionally organised every four year under the auspices of the international organisation IFToMM and the Czech Society for Mechanics.

U.S. Department of Energy instrumentation and controls technology research for advanced small modular reactors

International Nuclear Information System (INIS)

Wood, Richard Thomas

2013-01-01

Instrumentation, controls, and human-machine interfaces (ICHMI) are essential enabling technologies that strongly influence nuclear power plant performance and operational costs. The U.S. Department of Energy (DOE) has recognized that ICHMI research, development, and demonstration (RD and D) is needed to resolve the technical challenges that may compromise the effective and efficient utilization of modern ICHMI technology and consequently inhibit realization of the benefits offered by expanded utilization of nuclear power. Consequently, key DOE programs have substantial ICHMI RD and D elements to their respective research portfolio. This article describes current ICHMI research to support the development of advanced small modular reactors. (author)

JAEA key facilities for global advanced fuel cycle R and D

Energy Technology Data Exchange (ETDEWEB)

Nomura, Shigeo; Yamamoto, Ryuichi [Nuclear Fuel Cycle Engineering Labos, JAEA, 4-33 Tokai-mura, Ibaraki, 319-1194 (Japan)

2008-07-01

Advanced fuel cycle will be realized with the mid and long term R and D during the long-term transition period from LWR cycle to advanced reactor fuel cycle. Most of JAEA facilities have been utilized to establish the current LWR and FBR (Fast Breeder Reactor) fuel cycle by implementing evolutionary R and D. An assessment of today's state experimental facilities concerning the following research issues: reprocessing, Mox fuel fabrication, irradiation and post-irradiation examination, waste management and nuclear data measurement, is made. The revolutionary R and D requests new issues to be studied: the TRU multi-recycling, minor actinide recycling, the assessment of proliferation resistance and the assessment of cost reduction. To implement the revolutionary R and D for advanced fuel cycle, however, these facilities should be refurbished to install new machines and process equipment to provide more flexible testing parameters.

Support vector machine for the diagnosis of malignant mesothelioma

Science.gov (United States)

Ushasukhanya, S.; Nithyakalyani, A.; Sivakumar, V.

2018-04-01

Harmful mesothelioma is an illness in which threatening (malignancy) cells shape in the covering of the trunk or stomach area. Being presented to asbestos can influence the danger of threatening mesothelioma. Signs and side effects of threatening mesothelioma incorporate shortness of breath and agony under the rib confine. Tests that inspect within the trunk and belly are utilized to recognize (find) and analyse harmful mesothelioma. Certain elements influence forecast (shot of recuperation) and treatment choices. In this review, Support vector machine (SVM) classifiers were utilized for Mesothelioma sickness conclusion. SVM output is contrasted by concentrating on Mesothelioma’s sickness and findings by utilizing similar information set. The support vector machine algorithm gives 92.5% precision acquired by means of 3-overlap cross-approval. The Mesothelioma illness dataset were taken from an organization reports from Turkey.

Using Machine Learning for Land Suitability Classification

African Journals Online (AJOL)

User

West African Journal of Applied Ecology, vol. ... evidence for the utility of machine learning methods in land suitability classification especially MCS methods. ... Artificial intelligence tools. ..... Numerical values of index for the various classes.

Informatics and machine learning to define the phenotype.

Science.gov (United States)

Basile, Anna Okula; Ritchie, Marylyn DeRiggi

2018-03-01

For the past decade, the focus of complex disease research has been the genotype. From technological advancements to the development of analysis methods, great progress has been made. However, advances in our definition of the phenotype have remained stagnant. Phenotype characterization has recently emerged as an exciting area of informatics and machine learning. The copious amounts of diverse biomedical data that have been collected may be leveraged with data-driven approaches to elucidate trait-related features and patterns. Areas covered: In this review, the authors discuss the phenotype in traditional genetic associations and the challenges this has imposed.Approaches for phenotype refinement that can aid in more accurate characterization of traits are also discussed. Further, the authors highlight promising machine learning approaches for establishing a phenotype and the challenges of electronic health record (EHR)-derived data. Expert commentary: The authors hypothesize that through unsupervised machine learning, data-driven approaches can be used to define phenotypes rather than relying on expert clinician knowledge. Through the use of machine learning and an unbiased set of features extracted from clinical repositories, researchers will have the potential to further understand complex traits and identify patient subgroups. This knowledge may lead to more preventative and precise clinical care.

Tomography and generative training with quantum Boltzmann machines

Science.gov (United States)

Kieferová, Mária; Wiebe, Nathan

2017-12-01

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

Seismic Response Prediction of Buildings with Base Isolation Using Advanced Soft Computing Approaches

Directory of Open Access Journals (Sweden)

Mosbeh R. Kaloop

2017-01-01

Full Text Available Modeling response of structures under seismic loads is an important factor in Civil Engineering as it crucially affects the design and management of structures, especially for the high-risk areas. In this study, novel applications of advanced soft computing techniques are utilized for predicting the behavior of centrically braced frame (CBF buildings with lead-rubber bearing (LRB isolation system under ground motion effects. These techniques include least square support vector machine (LSSVM, wavelet neural networks (WNN, and adaptive neurofuzzy inference system (ANFIS along with wavelet denoising. The simulation of a 2D frame model and eight ground motions are considered in this study to evaluate the prediction models. The comparison results indicate that the least square support vector machine is superior to other techniques in estimating the behavior of smart structures.

Studying depression using imaging and machine learning methods.

Science.gov (United States)

Patel, Meenal J; Khalaf, Alexander; Aizenstein, Howard J

2016-01-01

Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1) presents a background on depression, imaging, and machine learning methodologies; (2) reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3) suggests directions for future depression-related studies.

Shear machines

International Nuclear Information System (INIS)

Astill, M.; Sunderland, A.; Waine, M.G.

1980-01-01

A shear machine for irradiated nuclear fuel elements has a replaceable shear assembly comprising a fuel element support block, a shear blade support and a clamp assembly which hold the fuel element to be sheared in contact with the support block. A first clamp member contacts the fuel element remote from the shear blade and a second clamp member contacts the fuel element adjacent the shear blade and is advanced towards the support block during shearing to compensate for any compression of the fuel element caused by the shear blade (U.K.)

Intelligent Human Machine Interface Design for Advanced Product Life Cycle Management Systems

OpenAIRE

Ahmed, Zeeshan

2010-01-01

Designing and implementing an intelligent and user friendly human machine interface for any kind of software or hardware oriented application is always be a challenging task for the designers and developers because it is very difficult to understand the psychology of the user, nature of the work and best suit of the environment. This research paper is basically about to propose an intelligent, flexible and user friendly machine interface for Product Life Cycle Management products or PDM Syste...

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Directory of Open Access Journals (Sweden)

Changbo Zhao

2015-01-01

data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Development of advanced human-machine system for plant operation and maintenance

International Nuclear Information System (INIS)

Wu, Wei; Ohi, Tadashi; Yoshikawa, Hidekazu; Sawaragi, Tetsuo; Kitamura, Masaharu; Furuta, Kazuo; Gofuku, Akio; Ito, Koji

2004-01-01

With the worldwide deregulation of the power industry, and the aging of the nuclear power plants (NPPs), concerns are growing over the reliability and safety of the NPPs, because the regulation of man power may lower the current high level of reliability and safety. In this paper, a concept of overall integrated plant management mechanism is proposed, in order to meet the requirements of cutting costs of NPPs and the requirements of maintaining or increasing safety and reliability. The concept is called as satellite operation maintenance center (SOMC). SOMC integrates the operation and maintenance activities of several NPP units by utilizing advanced information technologies to support cooperation activities between workers allocated at SOMC and the field workers. As for the operation activities, a framework called as Advanced Operation System (AOS) is proposed in this paper. AOS consists of three support sub-systems: dynamic operation permission system(DyOPS), supervisor information presentation system using interface agent, and crew performance evaluation system. As for the maintenance activities, a framework called as Ubiquitous-Computing-based Maintenance support System (UCMS) is proposed next. Two case studies are described, in order to show the way of how UCMS support field workers to do maintenance tasks efficiently, safely, and infallibly as well. Finally, a prospect of SOMC is shown in order to explain the way of how the technology elements developed in this project could be integrated as a whole one system to support maintenance activities of NPPs in the future. (author)

Health Resource Utilization in Patients with Advanced Non-Small Cell Lung Cancer Receiving Chemotherapy in China.

Science.gov (United States)

Shi, Jing; Zhu, Jun

2016-01-01

Chemotherapy is the preferred treatment regimen for advanced lung cancer patients. This study investigated the health resources utilized by and medical expenses of patients with non-small cell lung cancer (NSCLC) as well as the influence of various chemotherapy regimens on the final medical costs in China. The aim of this study was to provide physicians with a reference to use as the basis for their choice of treatment. Data were collected from the Shanghai Chest Hospital's medical charts and billing database. The collected patient information included the baseline characteristics, medical history, chemotherapy regimens, and medical costs, which were used to estimate the health resources utilized by patients and the cost of treatment. This study included 328 patients, and the average total medical cost was $US14,165. This cost included drugs, which accounted for as much as 78.91% of the total cost, and chemotherapy drugs, which accounted for 51.58% of total drug expenses. The most frequently utilized chemotherapy drug was carboplatin, and the most expensive chemotherapy drug was erlotinib. In drug combinations, gemcitabine was utilized most frequently, the combination of gemcitabine and paclitaxel was the most expensive, and cisplatin was the least expensive drug. Epidermal growth factor receptor-positive patients were treated with targeted drug therapy (icotinib, erlotinib, and gefitinib). The use of recombinant human endostatin was often combined with a vinorelbine plus cisplatin regimen. Traditional Chinese medicines were the most frequently utilized non-chemotherapy drugs, and these drugs were also the most expensive. The final cost significantly depended on the specific chemotherapy regimen; thus, the rationale and cost of the chemotherapy regimen and adjuvant chemotherapy should be considered in patients with advanced NSCLC.

The flotation of Roşia Poieni copper ore in column machine, with non-polar oils addition

Directory of Open Access Journals (Sweden)

Ciocani V.

2005-11-01

Full Text Available The most important natural resource of copper in Romania is the ore deposit of Roşia Poieni. At present, the utilization of Roşia Poieni poorphyry copper ore is possible by extraction in quarry of the mass ore and mineral processing into a technological flux with modest results for the value of metal recovery in concentrate 70-72 % and an average contents of 16,5 % Cu. Our researches were directed to studies regarding test and utilisation of special procedure of flotation – addition of the non-polar oil – applied to advanced grinding ore with column type machines.

"Resuscitation" of marginal liver allografts for transplantation with machine perfusion technology.

Science.gov (United States)

Graham, Jay A; Guarrera, James V

2014-08-01

As the rate of medically suitable donors remains relatively static worldwide, clinicians have looked to novel methods to meet the ever-growing demand of the liver transplant waiting lists worldwide. Accordingly, the transplant community has explored many strategies to offset this deficit. Advances in technology that target the ex vivo "preservation" period may help increase the donor pool by augmenting the utilization and improving the outcomes of marginal livers. Novel ex vivo techniques such as hypothermic, normothermic, and subnormothermic machine perfusion may be useful to "resuscitate" marginal organs by reducing ischemia/reperfusion injury. Moreover, other preservation techniques such as oxygen persufflation are explored as they may also have a role in improving function of "marginal" liver allografts. Currently, marginal livers are frequently discarded or can relegate the patient to early allograft dysfunction and primary non-function. Bench to bedside advances are rapidly emerging and hold promise for expanding liver transplantation access and improving outcomes. Copyright © 2014 European Association for the Study of the Liver. Published by Elsevier B.V. All rights reserved.

Deep learning versus traditional machine learning methods for aggregated energy demand prediction

NARCIS (Netherlands)

Paterakis, N.G.; Mocanu, E.; Gibescu, M.; Stappers, B.; van Alst, W.

2018-01-01

In this paper the more advanced, in comparison with traditional machine learning approaches, deep learning methods are explored with the purpose of accurately predicting the aggregated energy consumption. Despite the fact that a wide range of machine learning methods have been applied to

A Review of Design Optimization Methods for Electrical Machines

Directory of Open Access Journals (Sweden)

Gang Lei

2017-11-01

Full Text Available Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their design optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the design optimization methods for electrical machines, including design analysis methods and models, optimization models, algorithms and methods/strategies. Several efficient optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel optimization methods are introduced for advanced design optimization of electrical machines. First, a system-level design optimization method is introduced for the development of advanced electric drive systems. Second, a robust design optimization method based on the design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust design optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart design optimization method for future intelligent design and production of electrical machines.

Spiritualist Writing Machines: Telegraphy, Typtology, Typewriting

Directory of Open Access Journals (Sweden)

Anthony Enns

2015-09-01

Full Text Available This paper examines how religious concepts both reflected and informed the development of new technologies for encoding, transmitting, and printing written information. While many spiritualist writing machines were based on existing technologies that were repurposed for spirit communication, others prefigured or even inspired more advanced technological innovations. The history of spiritualist writing machines thus not only represents a response to the rise of new media technologies in the nineteenth century, but it also reflects a set of cultural demands that helped to shape the development of new technologies, such as the need to replace handwriting with discrete, uniform lettering, which accelerated the speed of composition; the need to translate written information into codes, which could be transmitted across vast distances; and the need to automate the process of transmitting, translating, and transcribing written information, which seemed to endow the machines themselves with a certain degree of autonomy or even intelligence. While spiritualists and inventors were often (but not always motivated by different goals, the development of spiritualist writing machines and the development of technological writing machines were nevertheless deeply interrelated and interdependent.

Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM in advanced metering infrastructure of smart grid.

Directory of Open Access Journals (Sweden)

Yuancheng Li

Full Text Available Advanced Metering Infrastructure (AMI realizes a two-way communication of electricity data through by interconnecting with a computer network as the core component of the smart grid. Meanwhile, it brings many new security threats and the traditional intrusion detection method can't satisfy the security requirements of AMI. In this paper, an intrusion detection system based on Online Sequence Extreme Learning Machine (OS-ELM is established, which is used to detecting the attack in AMI and carrying out the comparative analysis with other algorithms. Simulation results show that, compared with other intrusion detection methods, intrusion detection method based on OS-ELM is more superior in detection speed and accuracy.

ClearTK 2.0: Design Patterns for Machine Learning in UIMA

OpenAIRE

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-01-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, r...

Remote machine engineering applications for nuclear facilities decommissioning

International Nuclear Information System (INIS)

Toto, G.; Wyle, H.R.

1983-01-01

Decontamination and decommissioning of a nuclear facility require the application of techniques that protect the worker and the enviroment from radiological contamination and radiation. Remotely operated portable robotic arms, machines, and devices can be applied. The use of advanced systems should enhance the productivity, safety, and cost facets of the efforts; remote automatic tooling and systems may be used on any job where job hazard and other factors justify application. Many problems based on costs, enviromental impact, health, waste generation, and political issues may be mitigated by use of remotely operated machines. The work that man can not do or should not do will have to be done by machines

CANDU 9 fuelling machine carriage

Energy Technology Data Exchange (ETDEWEB)

Ullrich, D J; Slavik, J F [Atomic Energy of Canada Ltd., Saskatoon, SK (Canada)

1997-12-31

Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs.

CANDU 9 fuelling machine carriage

International Nuclear Information System (INIS)

Ullrich, D.J.; Slavik, J.F.

1996-01-01

Continuous, on-power refuelling is a key feature of all CANDU reactor designs and is essential to maintaining high station capacity factors. The concept of a fuelling machine carriage can be traced to the early CANDU designs, such as the Douglas Point Nuclear Generating Station. In the CANDU 9 480NU unit, the combination of a mobile carriage and a proven fuelling machine head design comprises an effective means of transporting fuel between the reactor and the fuel transfer ports. It is a suitable alternative to the fuelling machine bridge system that has been utilized in the CANDU 6 reactor units. The CANDU 9 480NU fuel handling system successfully combines features that meet the project requirements with respect to fuelling performance, functionality, seismic qualification and the use of proven components. The design incorporates improvements based on experience and applicable current technologies. (author). 4 figs

Brain-machine and brain-computer interfaces.

Science.gov (United States)

Friehs, Gerhard M; Zerris, Vasilios A; Ojakangas, Catherine L; Fellows, Mathew R; Donoghue, John P

2004-11-01

The idea of connecting the human brain to a computer or machine directly is not novel and its potential has been explored in science fiction. With the rapid advances in the areas of information technology, miniaturization and neurosciences there has been a surge of interest in turning fiction into reality. In this paper the authors review the current state-of-the-art of brain-computer and brain-machine interfaces including neuroprostheses. The general principles and requirements to produce a successful connection between human and artificial intelligence are outlined and the authors' preliminary experience with a prototype brain-computer interface is reported.

Performance evaluation of scientific programs on advanced architecture computers

International Nuclear Information System (INIS)

Walker, D.W.; Messina, P.; Baille, C.F.

1988-01-01

Recently a number of advanced architecture machines have become commercially available. These new machines promise better cost-performance then traditional computers, and some of them have the potential of competing with current supercomputers, such as the Cray X/MP, in terms of maximum performance. This paper describes an on-going project to evaluate a broad range of advanced architecture computers using a number of complete scientific application programs. The computers to be evaluated include distributed- memory machines such as the NCUBE, INTEL and Caltech/JPL hypercubes, and the MEIKO computing surface, shared-memory, bus architecture machines such as the Sequent Balance and the Alliant, very long instruction word machines such as the Multiflow Trace 7/200 computer, traditional supercomputers such as the Cray X.MP and Cray-2, and SIMD machines such as the Connection Machine. Currently 11 application codes from a number of scientific disciplines have been selected, although it is not intended to run all codes on all machines. Results are presented for two of the codes (QCD and missile tracking), and future work is proposed

Advanced chronic kidney disease in non-valvular atrial fibrillation: extending the utility of R2CHADS2 to patients with advanced renal failure.

Science.gov (United States)

Bautista, Josef; Bella, Archie; Chaudhari, Ashok; Pekler, Gerald; Sapra, Katherine J; Carbajal, Roger; Baumstein, Donald

2015-04-01

The R2CHADS2 is a new prediction rule for stroke risk in atrial fibrillation (AF) patients wherein R stands for renal risk. However, it was created from a cohort that excluded patients with advanced renal failure (defined as glomerular filtration rate of advanced renal failure and aims to compare its predictive power against the currently used CHADS and CHA2DS2VaSc. This retrospective cohort study analyzed the 1-year risk for stroke of the 524 patients with AF at Metropolitan Hospital Center. AUC and C statistics were calculated using three groups: (i) the entire cohort including patients with advanced renal failure, (ii) a cohort excluding patients with advanced renal failure and (iii) all patients with GFR statistic was highest in R2CHADS compared with CHADS or CHADSVASC in group 1 (0.718 versus 0.605 versus 0.602) and in group 2 (0.724 versus 0.584 versus 0.579). However, there was no statistically significant difference in group 3 (0.631 versus 0.629 versus 0.623). Our study supports the utility of R2CHADS2 as a clinical prediction rule for stroke risk in patients with advanced renal failure.

Mechanics and model-based control of advanced engineering systems

CERN Document Server

Irschik, Hans; Krommer, Michael

2014-01-01

Mechanics and Model-Based Control of Advanced Engineering Systems collects 32 contributions presented at the International Workshop on Advanced Dynamics and Model Based Control of Structures and Machines, which took place in St. Petersburg, Russia in July 2012. The workshop continued a series of international workshops, which started with a Japan-Austria Joint Workshop on Mechanics and Model Based Control of Smart Materials and Structures and a Russia-Austria Joint Workshop on Advanced Dynamics and Model Based Control of Structures and Machines. In the present volume, 10 full-length papers based on presentations from Russia, 9 from Austria, 8 from Japan, 3 from Italy, one from Germany and one from Taiwan are included, which represent the state of the art in the field of mechanics and model based control, with particular emphasis on the application of advanced structures and machines.

Radiation or chemoradiation: initial utility study of selected therapy for local advanced stadium cervical cancer

Science.gov (United States)

Pramitasari, D. A.; Gondhowiardjo, S.; Nuranna, L.

2017-08-01

This study aimed to compare radiation only or chemo radiation treatment of local advanced cervical cancers by examining the initial response of tumors and acute side effects. An initial assessment employed value based medicine (VBM) by obtaining utility values for both types of therapy. The incidences of acute lower gastrointestinal, genitourinary, and hematology side effects in patients undergoing chemoradiation did not differ significantly from those undergoing radiation alone. Utility values for patients who underwent radiation alone were higher compared to those who underwent chemoradiation. It was concluded that the complete response of patients who underwent chemoradiation did not differ significantly from those who underwent radiation alone.

Revision of Import and Export Requirements for Controlled Substances, Listed Chemicals, and Tableting and Encapsulating Machines, Including Changes To Implement the International Trade Data System (ITDS); Revision of Reporting Requirements for Domestic Transactions in Listed Chemicals and Tableting and Encapsulating Machines; and Technical Amendments. Final rule.

Science.gov (United States)

2016-12-30

The Drug Enforcement Administration is updating its regulations for the import and export of tableting and encapsulating machines, controlled substances, and listed chemicals, and its regulations relating to reports required for domestic transactions in listed chemicals, gamma-hydroxybutyric acid, and tableting and encapsulating machines. In accordance with Executive Order 13563, the Drug Enforcement Administration has reviewed its import and export regulations and reporting requirements for domestic transactions in listed chemicals (and gamma-hydroxybutyric acid) and tableting and encapsulating machines, and evaluated them for clarity, consistency, continued accuracy, and effectiveness. The amendments clarify certain policies and reflect current procedures and technological advancements. The amendments also allow for the implementation, as applicable to tableting and encapsulating machines, controlled substances, and listed chemicals, of the President's Executive Order 13659 on streamlining the export/import process and requiring the government-wide utilization of the International Trade Data System (ITDS). This rule additionally contains amendments that implement recent changes to the Controlled Substances Import and Export Act (CSIEA) for reexportation of controlled substances among members of the European Economic Area made by the Improving Regulatory Transparency for New Medical Therapies Act. The rule also includes additional substantive and technical and stylistic amendments.

Parallel machine scheduling with release dates, due dates and family setup times

NARCIS (Netherlands)

Schutten, Johannes M.J.; Leussink, R.A.M.

1996-01-01

In manufacturing, there is a fundamental conflict between efficient production and delivery performance. Maximizing machine utilization by batching similar jobs may lead to poor delivery performance. Minimizing customers' dissatisfaction may lead to an inefficient use of the machines. In this paper,

Computation of emotions in man and machines.

Science.gov (United States)

Robinson, Peter; el Kaliouby, Rana

2009-12-12

The importance of emotional expression as part of human communication has been understood since Aristotle, and the subject has been explored scientifically since Charles Darwin and others in the nineteenth century. Advances in computer technology now allow machines to recognize and express emotions, paving the way for improved human-computer and human-human communications. Recent advances in psychology have greatly improved our understanding of the role of affect in communication, perception, decision-making, attention and memory. At the same time, advances in technology mean that it is becoming possible for machines to sense, analyse and express emotions. We can now consider how these advances relate to each other and how they can be brought together to influence future research in perception, attention, learning, memory, communication, decision-making and other applications. The computation of emotions includes both recognition and synthesis, using channels such as facial expressions, non-verbal aspects of speech, posture, gestures, physiology, brain imaging and general behaviour. The combination of new results in psychology with new techniques of computation is leading to new technologies with applications in commerce, education, entertainment, security, therapy and everyday life. However, there are important issues of privacy and personal expression that must also be considered.

Ex-vivo machine perfusion for kidney preservation.

Science.gov (United States)

Hamar, Matyas; Selzner, Markus

2018-06-01

Machine perfusion is a novel strategy to decrease preservation injury, improve graft assessment, and increase organ acceptance for transplantation. This review summarizes the current advances in ex-vivo machine-based kidney preservation technologies over the last year. Ex-vivo perfusion technologies, such as hypothermic and normothermic machine perfusion and controlled oxygenated rewarming, have gained high interest in the field of organ preservation. Keeping kidney grafts functionally and metabolically active during the preservation period offers a unique chance for viability assessment, reconditioning, and organ repair. Normothermic ex-vivo kidney perfusion has been recently translated into clinical practice. Preclinical results suggest that prolonged warm perfusion appears superior than a brief end-ischemic reconditioning in terms of renal function and injury. An established standardized protocol for continuous warm perfusion is still not available for human grafts. Ex-vivo machine perfusion represents a superior organ preservation method over static cold storage. There is still an urgent need for the optimization of the perfusion fluid and machine technology and to identify the optimal indication in kidney transplantation. Recent research is focusing on graft assessment and therapeutic strategies.

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

Science.gov (United States)

Zevin, M; Coughlin, S; Bahaadini, S; Besler, E; Rohani, N; Allen, S; Cabero, M; Crowston, K; Katsaggelos, A K; Larson, S L; Lee, T K; Lintott, C; Littenberg, T B; Lundgren, A; Østerlund, C; Smith, J R; Trouille, L; Kalogera, V

2018-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches, which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

International Nuclear Information System (INIS)

Zevin, M; Coughlin, S; Larson, S L; Trouille, L; Kalogera, V; Bahaadini, S; Besler, E; Rohani, N; Katsaggelos, A K; Allen, S; Cabero, M; Lundgren, A; Crowston, K; Østerlund, C; Lee, T K; Lintott, C; Littenberg, T B; Smith, J R

2017-01-01

With the first direct detection of gravitational waves, the advanced laser interferometer gravitational-wave observatory (LIGO) has initiated a new field of astronomy by providing an alternative means of sensing the universe. The extreme sensitivity required to make such detections is achieved through exquisite isolation of all sensitive components of LIGO from non-gravitational-wave disturbances. Nonetheless, LIGO is still susceptible to a variety of instrumental and environmental sources of noise that contaminate the data. Of particular concern are noise features known as glitches , which are transient and non-Gaussian in their nature, and occur at a high enough rate so that accidental coincidence between the two LIGO detectors is non-negligible. Glitches come in a wide range of time-frequency-amplitude morphologies, with new morphologies appearing as the detector evolves. Since they can obscure or mimic true gravitational-wave signals, a robust characterization of glitches is paramount in the effort to achieve the gravitational-wave detection rates that are predicted by the design sensitivity of LIGO. This proves a daunting task for members of the LIGO Scientific Collaboration alone due to the sheer amount of data. In this paper we describe an innovative project that combines crowdsourcing with machine learning to aid in the challenging task of categorizing all of the glitches recorded by the LIGO detectors. Through the Zooniverse platform, we engage and recruit volunteers from the public to categorize images of time-frequency representations of glitches into pre-identified morphological classes and to discover new classes that appear as the detectors evolve. In addition, machine learning algorithms are used to categorize images after being trained on human-classified examples of the morphological classes. Leveraging the strengths of both classification methods, we create a combined method with the aim of improving the efficiency and accuracy of each individual

NRC review of Electric Power Research Institute's Advanced Light Water Reactor Utility Requirements Document - Evolutionary plant designs, Chapters 2--13, Project No. 669

International Nuclear Information System (INIS)

1992-08-01

The staff of the US Nuclear Regulatory Commission has prepared Volume 2 (Parts 1 and 2) of a safety evaluation report (SER), ''NRC Review of Electric Power Research Institute's Advanced Light Water Reactor Utility Requirements Document -- Evolutionary Plant Designs,'' to document the results of its review of the Electric Power Research Institute's ''Advanced Light Water Reactor Utility Requirements Document.'' This SER gives the results of the staff's review of Volume II of the Requirements Document for evolutionary plant designs, which consists of 13 chapters and contains utility design requirements for an evolutionary nuclear power plant (approximately 1300 megawatts-electric)

IoT Security Techniques Based on Machine Learning

OpenAIRE

Xiao, Liang; Wan, Xiaoyue; Lu, Xiaozhen; Zhang, Yanyong; Wu, Di

2018-01-01

Internet of things (IoT) that integrate a variety of devices into networks to provide advanced and intelligent services have to protect user privacy and address attacks such as spoofing attacks, denial of service attacks, jamming and eavesdropping. In this article, we investigate the attack model for IoT systems, and review the IoT security solutions based on machine learning techniques including supervised learning, unsupervised learning and reinforcement learning. We focus on the machine le...

Application of Machine Learning to Predict Dietary Lapses During Weight Loss.

Science.gov (United States)

Goldstein, Stephanie P; Zhang, Fengqing; Thomas, John G; Butryn, Meghan L; Herbert, James D; Forman, Evan M

2018-05-01

Individuals who adhere to dietary guidelines provided during weight loss interventions tend to be more successful with weight control. Any deviation from dietary guidelines can be referred to as a "lapse." There is a growing body of research showing that lapses are predictable using a variety of physiological, environmental, and psychological indicators. With recent technological advancements, it may be possible to assess these triggers and predict dietary lapses in real time. The current study sought to use machine learning techniques to predict lapses and evaluate the utility of combining both group- and individual-level data to enhance lapse prediction. The current study trained and tested a machine learning algorithm capable of predicting dietary lapses from a behavioral weight loss program among adults with overweight/obesity (n = 12). Participants were asked to follow a weight control diet for 6 weeks and complete ecological momentary assessment (EMA; repeated brief surveys delivered via smartphone) regarding dietary lapses and relevant triggers. WEKA decision trees were used to predict lapses with an accuracy of 0.72 for the group of participants. However, generalization of the group algorithm to each individual was poor, and as such, group- and individual-level data were combined to improve prediction. The findings suggest that 4 weeks of individual data collection is recommended to attain optimal model performance. The predictive algorithm could be utilized to provide in-the-moment interventions to prevent dietary lapses and therefore enhance weight losses. Furthermore, methods in the current study could be translated to other types of health behavior lapses.

LHC Report: machine commissioning - drawing to a close

CERN Multimedia

Belen Salvachua Ferrando for the LHC team

2016-01-01

The recommissioning of the LHC is going well: the machine has delivered first pilot Stable Beams collisions.   Some of the first collisions recorded by the experiments during the LHC 2016 commissioning with low-intensity stable beams. (Image: CERN)   TOTEM bump The main goal of the past couple of weeks was to advance with the preparation of collimators settings and protection devices. Over the weekend of 16-17 April, collisions were re-established after setting up a new orbit bump around the Roman Pot locations in IP5 (TOTEM), in order to improve their acceptance. The bump was smoothly incorporated into the machine settings that lead into Stable Beams. The LHC orbit was corrected towards the reference leaving the machine ready for the next steps: aperture measurements and final collimator alignment. Alignment and aperture at 40 cm The aperture is the available space in the transverse plane of the machine. Detailed simulations are used to predict the minimum machine aperture. At ...

Utilization technique for advanced nuclear materials database system Data-Free-Way'

International Nuclear Information System (INIS)

Fujita, Mitsutane; Kurihara, Yutaka; Kinugawa, Junichi; Kitajima, Masahiro; Nagakawa, Josei; Yamamoto, Norikazu; Noda, Tetsuji; Yagi, Koichi; Ono, Akira

2001-01-01

Four organizations the National Research Institute for Metals (NRIM), the Japan Atomic Energy Research Institute (JAERI), the Japan Nuclear Fuel Cycle Development Institute (JNC) and Japan Science and Technology Incorporation (JST), conducted the 2nd period joint research for the purpose of development of utilization techniques for advanced nuclear materials database system named 'Data-Free-Way' (DFW), to make more useful system to support research and development of the nuclear materials, from FY 1995 to FY 1999. NRIM intended to fill a data system on diffusion and nuclear data by developing utilization technique on diffusion informations of steels and aluminum and nuclear data for materials for its independent system together with participating in fulfil of the DFW. And, NRIM has entered to a project on wide area band circuit application agreed at the G7 by using technologies cultivated by NRIM, to investigate network application technology with the Michigan State University over the sea under cooperation assistant business of JST, to make results on CCT diagram for welding and forecasting of welding heat history accumulated at NRIM for a long term, to perform development of a simulator assisting optimum condition decision of welding. (G.K.)

Future vision of advanced telecommunication networks for electric utilities; Denki jigyo ni okeru joho tsushin network no shorai vision

Energy Technology Data Exchange (ETDEWEB)

Tonaru, S.; Ono, K.; Sakai, S.; Kawai, Y.; Tsuboi, A. [Central Research Institute of Electric Power Industry, Tokyo (Japan); Manabe, S. [Shikoku Electric Power Co., Inc., Kagawa (Japan); Miki, Y. [Kansai Electric Power Co. Inc., Osaka (Japan)

1995-06-01

The vision of an advanced information system is proposed to cope with the future social demand and business environmental change in electric utilities. At the large turning point such as drastic reconsideration of Electricity Utilities Industry Law, further improvement of efficiency and cost reduction are requested as well as business innovation such as proposal of a new business policy. For that purpose utilization of information and its technology is indispensable, and use of multimedia and common information in organization are the future direction for improving information basis. Consequently, free information networks without any limitation due to person and media are necessary, and the following are important: high-speed, high-frequency band, digital, easily connectable and multimedia transmission lines, and cost reduction and high reliability of networks. Based on innovation of information networks and the clear principle on advanced information system, development of new applications by multimedia technologies, diffusion of communication terminals, and promotion of standardization are essential. 60 refs., 30 figs., 5 tabs.

Development and utilization of the NRC policy statement on the regulation of advanced nuclear power plants

International Nuclear Information System (INIS)

Williams, P.M.; King, T.L.

1988-06-01

On March 26, 1985, the US Nuclear Regulatory Commission issued for public comment a ''Proposed Policy for Regulation of Advanced Nuclear Power Plants'' (50 FR 11884). This report presents and discusses the Commission's final version of that policy as titled and published on July 8, 1986 ''Regulation of Advanced Nuclear Power Plants, Statement of Policy'' (51 FR 24643). It provides an overview of comments received from the public, of the significant changes from the proposed Policy Statement to the final Policy Statement, and of the Commission's response to six questions contained in the proposed Policy Statement. The report also discusses the definition for advanced reactors, the establishment of an Advanced Reactors Group, the staff review approach and information needs, and the utilization of the Policy Statement in relation to other NRC programs, including the policies for safety goals, severe accidents and standardization. In addition, guidance for advanced reactors with respect to operating experience, technology development, foreign information and data, and prototype testing is provided. Finally, a discussion on the use of less prescriptive and nonprescriptive design criteria for advanced reactors, which the Policy Statement encourages, is presented

Machine Learning Approaches Toward Building Predictive Models for Small Molecule Modulators of miRNA and Its Utility in Virtual Screening of Molecular Databases.

Science.gov (United States)

Periwal, Vinita; Scaria, Vinod

2017-01-01

The ubiquitous role of microRNAs (miRNAs) in a number of pathological processes has suggested that they could act as potential drug targets. RNA-binding small molecules offer an attractive means for modulating miRNA function. The availability of bioassay data sets for a variety of biological assays and molecules in public domain provides a new opportunity toward utilizing them to create models and further utilize them for in silico virtual screening approaches to prioritize or assign potential functions for small molecules. Here, we describe a computational strategy based on machine learning for creation of predictive models from high-throughput biological screens for virtual screening of small molecules with the potential to inhibit microRNAs. Such models could be potentially used for computational prioritization of small molecules before performing high-throughput biological assay.

Utilization of MCNP code in the research and design for China advanced research reactor

International Nuclear Information System (INIS)

Shen Feng

2006-01-01

MCNP, which is the internationalized neutronics code, is used for nuclear research and design in China Advanced Research Reactor (CARR). MCNP is an important neutronics code in the research and design for CARR since many calculation tasks could be undertaken by it. Many nuclear parameters on reactor core, the design and optimization research for many reactor utilizations, much verification for other nuclear calculation code and so on are conducted with help of MCNP. (author)

Studying depression using imaging and machine learning methods

Directory of Open Access Journals (Sweden)

Meenal J. Patel

2016-01-01

Full Text Available Depression is a complex clinical entity that can pose challenges for clinicians regarding both accurate diagnosis and effective timely treatment. These challenges have prompted the development of multiple machine learning methods to help improve the management of this disease. These methods utilize anatomical and physiological data acquired from neuroimaging to create models that can identify depressed patients vs. non-depressed patients and predict treatment outcomes. This article (1 presents a background on depression, imaging, and machine learning methodologies; (2 reviews methodologies of past studies that have used imaging and machine learning to study depression; and (3 suggests directions for future depression-related studies.

The APS intranet as a man-machine interface

International Nuclear Information System (INIS)

Ciarlette, D.; Gerig, R.; McDowell, W.

1997-01-01

The Advanced Photon Source at Argonne National Laboratory has implemented a number of methods for people to interact with the accelerator systems. The accelerator operators use Sun workstations running MEDM and WCL to interface interactively with the accelerator, however, many people need to view information rather than interact with the machine. One of the most common interfaces for viewing information at the Advanced Photon Source is the World Wide Web. Information such as operations logbook entries, machine status updates, and displays of archived and current data are easily available to APS personnel. This interface between people and the accelerator has proven to be quite useful. Because the Intranet is operating-system independent and inherently unidirectional, ensuring the prevention of unauthorized or accidental control of the accelerators is straightforward

An overview of rotating machine systems with high-temperature bulk superconductors

Science.gov (United States)

Zhou, Difan; Izumi, Mitsuru; Miki, Motohiro; Felder, Brice; Ida, Tetsuya; Kitano, Masahiro

2012-10-01

The paper contains a review of recent advancements in rotating machines with bulk high-temperature superconductors (HTS). The high critical current density of bulk HTS enables us to design rotating machines with a compact configuration in a practical scheme. The development of an axial-gap-type trapped flux synchronous rotating machine together with the systematic research works at the Tokyo University of Marine Science and Technology since 2001 are briefly introduced. Developments in bulk HTS rotating machines in other research groups are also summarized. The key issues of bulk HTS machines, including material progress of bulk HTS, in situ magnetization, and cooling together with AC loss at low-temperature operation are discussed.

Tunnel Boring Machine Performance Study. Final Report

Science.gov (United States)

1984-06-01

Full face tunnel boring machine "TBM" performance during the excavation of 6 tunnels in sedimentary rock is considered in terms of utilization, penetration rates and cutter wear. The construction records are analyzed and the results are used to inves...

Advanced intelligent systems

CERN Document Server

Ryoo, Young; Jang, Moon-soo; Bae, Young-Chul

2014-01-01

Intelligent systems have been initiated with the attempt to imitate the human brain. People wish to let machines perform intelligent works. Many techniques of intelligent systems are based on artificial intelligence. According to changing and novel requirements, the advanced intelligent systems cover a wide spectrum: big data processing, intelligent control, advanced robotics, artificial intelligence and machine learning. This book focuses on coordinating intelligent systems with highly integrated and foundationally functional components. The book consists of 19 contributions that features social network-based recommender systems, application of fuzzy enforcement, energy visualization, ultrasonic muscular thickness measurement, regional analysis and predictive modeling, analysis of 3D polygon data, blood pressure estimation system, fuzzy human model, fuzzy ultrasonic imaging method, ultrasonic mobile smart technology, pseudo-normal image synthesis, subspace classifier, mobile object tracking, standing-up moti...

Advanced Model of Squirrel Cage Induction Machine for Broken Rotor Bars Fault Using Multi Indicators

Directory of Open Access Journals (Sweden)

Ilias Ouachtouk

2016-01-01

Full Text Available Squirrel cage induction machine are the most commonly used electrical drives, but like any other machine, they are vulnerable to faults. Among the widespread failures of the induction machine there are rotor faults. This paper focuses on the detection of broken rotor bars fault using multi-indicator. However, diagnostics of asynchronous machine rotor faults can be accomplished by analysing the anomalies of machine local variable such as torque, magnetic flux, stator current and neutral voltage signature analysis. The aim of this research is to summarize the existing models and to develop new models of squirrel cage induction motors with consideration of the neutral voltage and to study the effect of broken rotor bars on the different electrical quantities such as the park currents, torque, stator currents and neutral voltage. The performance of the model was assessed by comparing the simulation and experimental results. The obtained results show the effectiveness of the model, and allow detection and diagnosis of these defects.

Aeromechanics and man-machine integration technology opportunities for rotorcraft of the 1990s and beyond

Science.gov (United States)

Kerr, Andrew W.

1989-01-01

Programs related to rotorcraft aeromechanics and man-machine integration are discussed which will support advanced army rotorcraft design. In aeromechanics, recent advances in computational fluid dynamics will be used to characterize the complex unsteady flowfields of rotorcraft, and a second-generation comprehensive helicopter analysis system will be used along with models of aerodynamics, engines, and control systems to study the structural dynamics of rotor/body configurations. The man-machine integration program includes the development of advanced cockpit design technology and the evaluation of cockpit and mission equipment concepts in a real-time full-combat environment.

Modern machine learning techniques and their applications in cartoon animation research

CERN Document Server

Yu, Jun

2013-01-01

The integration of machine learning techniques and cartoon animation research is fast becoming a hot topic. This book helps readers learn the latest machine learning techniques, including patch alignment framework; spectral clustering, graph cuts, and convex relaxation; ensemble manifold learning; multiple kernel learning; multiview subspace learning; and multiview distance metric learning. It then presents the applications of these modern machine learning techniques in cartoon animation research. With these techniques, users can efficiently utilize the cartoon materials to generate animations

Advanced pressure tube sampling tools

International Nuclear Information System (INIS)

Wittich, K.C.; King, J.M.

2002-01-01

Deuterium concentration is an important parameter that must be assessed to evaluate the Fitness for service of CANDU pressure tubes. In-reactor pressure tube sampling allows accurate deuterium concentration assessment to be made without the expenses associated with fuel channel removal. This technology, which AECL has developed over the past fifteen years, has become the standard method for deuterium concentration assessment. AECL is developing a multi-head tool that would reduce in-reactor handling overhead by allowing one tool to sequentially sample at all four axial pressure tube locations before removal from the reactor. Four sets of independent cutting heads, like those on the existing sampling tools, facilitate this incorporating proven technology demonstrated in over 1400 in-reactor samples taken to date. The multi-head tool is delivered by AECL's Advanced Delivery Machine or other similar delivery machines. Further, AECL has developed an automated sample handling system that receives and processes the tool once out of the reactor. This system retrieves samples from the tool, dries, weighs and places them in labelled vials which are then directed into shielded shipping flasks. The multi-head wet sampling tool and the automated sample handling system are based on proven technology and offer continued savings and dose reduction to utilities in a competitive electricity market. (author)

Application of high speed machining technology in aviation

Science.gov (United States)

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.

Homopolar machine for reversible energy storage and transfer systems

International Nuclear Information System (INIS)

Stillwagon, R.E.

1978-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermonuclear reactor is described. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals

Utility requirements for advanced LWR passive plants

International Nuclear Information System (INIS)

Yedidia, J.M.; Sugnet, W.R.

1992-01-01

LWR Passive Plants are becoming an increasingly attractive and prominent option for future electric generating capacity for U.S. utilities. Conceptual designs for ALWR Passive Plants are currently being developed by U.S. suppliers. EPRI-sponsored work beginning in 1985 developed preliminary conceptual designs for a passive BWR and PWR. DOE-sponsored work from 1986 to the present in conjunction with further EPRI-sponsored studies has continued this development to the point of mature conceptual designs. The success to date in developing the ALWR Passive Plant concepts has substantially increased utility interest. The EPRI ALWR Program has responded by augmenting its initial scope to develop a Utility Requirements Document for ALWR Passive Plants. These requirements will be largely based on the ALWR Utility Requirements Document for Evolutionary Plants, but with significant changes in areas related to the passive safety functions and system configurations. This work was begun in late 1988, and the thirteen-chapter Passive Plant Utility Requirements Document will be completed in 1990. This paper discusses the progress to date in developing the Passive Plant requirements, reviews the top-level requirements, and discusses key issues related to adaptation of the utility requirements to passive safety functions and system configurations. (orig.)

UTILITY ADVANCED TURBINE SYSTEMS(ATS) TECHNOLOGY READINESS TESTING

Energy Technology Data Exchange (ETDEWEB)

Kenneth A. Yackly

2001-06-01

component is optimized for the highest level of performance. The unique feature of an H-technology combined-cycle system is the integrated heat transfer system, which combines both the steam plant reheat process and gas turbine bucket and nozzle cooling. This feature allows the power generator to operate at a higher firing temperature than current technology units, thereby resulting in dramatic improvements in fuel-efficiency. The end result is the generation of electricity at the lowest, most competitive price possible. Also, despite the higher firing temperature of the H System{trademark}, the combustion temperature is kept at levels that minimize emission production. GE has more than 3.6 million fired hours of experience in operating advanced technology gas turbines, more than three times the fired hours of competitors' units combined. The H System{trademark} design incorporates lessons learned from this experience with knowledge gleaned from operating GE aircraft engines. In addition, the 9H gas turbine is the first ever designed using ''Design for Six Sigma'' methodology, which maximizes reliability and availability throughout the entire design process. Both the 7H and 9H gas turbines will achieve the reliability levels of our F-class technology machines. GE has tested its H System{trademark} gas turbine more thoroughly than any previously introduced into commercial service. The H System{trademark} gas turbine has undergone extensive design validation and component testing. Full-speed, no-load testing of the 9H was achieved in May 1998 and pre-shipment testing was completed in November 1999. The 9H will also undergo approximately a half-year of extensive demonstration and characterization testing at the launch site. Testing of the 7H began in December 1999, and full speed, no-load testing was completed in February 2000. The 7H gas turbine will also be subjected to extensive demonstration and characterization testing at the launch site.

Development and operating performance of the refuelling machine of the Fugen

International Nuclear Information System (INIS)

Kaneko, Jun; Kasai, Yoshimitsu; Takeshita, Norito; Ohta, Takeo

1985-01-01

In the advanced thermal reactor ''Fugen'' power station, with the refuelling machine the fuel replacement during operation is made through the reactor bottom. Its design was started in 1967 and up to 1975 various tests were conducted. Fugen's refuelling machine has thus been used from the initial fuel loading in 1978 and handled so far about 1300 fuel assemblies in seven times of the refuelling. In the stage of Fugen operation there occurred failure of the grab drive due to crud, etc. At present, with such troubles all eliminated, the refuelling machine is in steady operation with proper maintenance. The results with Fugen's refuelling machine are reflected in the development of the refuelling machine for the demonstration ATR. (Mori, K.)

1998 annual report of advanced combustion science utilizing microgravity

Energy Technology Data Exchange (ETDEWEB)

NONE

1999-03-01

For the purpose of stabilizing energy supply, diversifying energy supply sources and reducing the worsening of global environment caused by combustion exhaust gases, advanced combustion technology was studied and the FY 1998 results were summarized. Following the previous year, the following were conducted: international research jointly with NASA, experiments using microgravity test facilities of Japan Space Utilization Promotion Center (JSUP), evaluation studies made by universities/national research institutes/private companies, etc. In the FY 1998 joint study, a total of 52 drop experiments were carried out on 4 themes using test facilities of Japan Microgravity Center (JAMIC), and 100 experiments were conducted on one theme using test facilities of NASA. In the study using microgravity test facilities, the following were carried out: study of combustion and evaporation of fuel droplets, study of ignition/combustion of fuel droplets in the suspending state, study of combustion of spherical/cylinder state liquid fuels, study of high pressure combustion of binary fuel spray, study of interaction combustion of fuel droplets in the microgravity field, etc. (NEDO)

Human factor engineering analysis for computerized human machine interface design issues

International Nuclear Information System (INIS)

Wang Zhifang; Gu Pengfei; Zhang Jianbo

2010-01-01

The application of digital I and C technology in nuclear power plants is a significant improvement in terms of functional performances and flexibility, and it also poses a challenge to operation safety. Most of the new NPPs under construction are adopting advanced control room design which utilizes the computerized human machine interface (HMI) as the main operating means. Thus, it greatly changes the way the operators interact with the plant. This paper introduces the main challenges brought out by computerized technology on the human factor engineering aspect and addresses the main issues to be dealt with in the computerized HMI design process. Based on a operator task-resources-cognitive model, it states that the root cause of human errors is the mismatch between resources demand and their supply. And a task-oriented HMI design principle is discussed. (authors)

Job shop scheduling model for non-identic machine with fixed delivery time to minimize tardiness

Science.gov (United States)

Kusuma, K. K.; Maruf, A.

2016-02-01

Scheduling non-identic machines problem with low utilization characteristic and fixed delivery time are frequent in manufacture industry. This paper propose a mathematical model to minimize total tardiness for non-identic machines in job shop environment. This model will be categorized as an integer linier programming model and using branch and bound algorithm as the solver method. We will use fixed delivery time as main constraint and different processing time to process a job. The result of this proposed model shows that the utilization of production machines can be increase with minimal tardiness using fixed delivery time as constraint.

Twin Support Vector Machine: A review from 2007 to 2014

Directory of Open Access Journals (Sweden)

Divya Tomar

2015-03-01

Full Text Available Twin Support Vector Machine (TWSVM is an emerging machine learning method suitable for both classification and regression problems. It utilizes the concept of Generalized Eigen-values Proximal Support Vector Machine (GEPSVM and finds two non-parallel planes for each class by solving a pair of Quadratic Programming Problems. It enhances the computational speed as compared to the traditional Support Vector Machine (SVM. TWSVM was initially constructed to solve binary classification problems; later researchers successfully extended it for multi-class problem domain. TWSVM always gives promising empirical results, due to which it has many attractive features which enhance its applicability. This paper presents the research development of TWSVM in recent years. This study is divided into two main broad categories - variant based and multi-class based TWSVM methods. The paper primarily discusses the basic concept of TWSVM and highlights its applications in recent years. A comparative analysis of various research contributions based on TWSVM is also presented. This is helpful for researchers to effectively utilize the TWSVM as an emergent research methodology and encourage them to work further in the performance enhancement of TWSVM.

Stereoscopic display in a slot machine

Science.gov (United States)

Laakso, M.

2012-03-01

This paper reports the results of a user trial with a slot machine equipped with a stereoscopic display. The main research question was to find out what kind of added value does stereoscopic 3D (S-3D) bring to slot games? After a thorough literature survey, a novel gaming platform was designed and implemented. Existing multi-game slot machine "Nova" was converted to "3DNova" by replacing the monitor with an S-3D display and converting six original games to S-3D format. To evaluate the system, several 3DNova machines were put available for players for four months. Both qualitative and quantitative analysis was carried out from statistical values, questionnaires and observations. According to the results, people find the S-3D concept interesting but the technology is not optimal yet. Young adults and adults were fascinated by the system, older people were more cautious. Especially the need to wear stereoscopic glasses provide a challenge; ultimate system would probably use autostereoscopic technology. Also the games should be designed to utilize its full power. The main contributions of this paper are lessons learned from creating an S-3D slot machine platform and novel information about human factors related to stereoscopic slot machine gaming.

FreedomCAR Advanced Traction Drive Motor Development Phase I

Energy Technology Data Exchange (ETDEWEB)

Ley, Josh (UQM Technologies, Inc.); Lutz, Jon (UQM Technologies, Inc.)

2006-09-01

The overall objective of this program is to design and develop an advanced traction motor that will meet the FreedomCAR and Vehicle Technologies (FCVT) 2010 goals and the traction motor technical targets. The motor specifications are given in Section 1.3. Other goals of the program include providing a cost study to ensure the motor can be developed within the cost targets needed for the automotive industry. The program has focused on using materials that are both high performance and low costs such that the performance can be met and cost targets are achieved. In addition, the motor technologies and machine design features must be compatible with high volume manufacturing and able to provide high reliability, efficiency, and ruggedness while simultaneously reducing weight and volume. Weight and volume reduction will become a major factor in reducing cost, material cost being the most significant part of manufacturing cost at high volume. Many motor technology categories have been considered in the past and present for traction drive applications, including: brushed direct current (DC), PM (PM) brushless dc (BLDC), alternating current (AC) induction, switched reluctance and synchronous reluctance machines. Of these machine technologies, PM BLDC has consistently demonstrated an advantage in terms of power density and efficiency. As rare earth magnet cost has declined, total cost may also be reduced over the other technologies. Of the many different configurations of PM BLDC machines, those which incorporate power production utilizing both magnetic torque as well as reluctance torque appear to have the most promise for traction applications. There are many different PM BLDC machine configurations which employ both of these torque producing mechanisms; however, most would fall into one of two categories--some use weaker magnets and rely more heavily on reluctance torque (reluctance-dominant PM machines), others use strong PMs and supplement with reluctance torque

Live Replication of Paravirtual Machines

OpenAIRE

Stodden, Daniel

2009-01-01

Virtual machines offer a fair degree of system state encapsulation, which promotes practical advances in fault tolerance, system debugging, profiling and security applications. This work investigates deterministic replay and semi-active replication for system paravirtualization, a software discipline trading guest kernel binar compatibility for reduced dependency on costly trap-and-emulate techniques. A primary contribution is evidence that trace capturing under a piecewise deterministic exec...

MLBCD: a machine learning tool for big clinical data.

Science.gov (United States)

Luo, Gang

2015-01-01

Predictive modeling is fundamental for extracting value from large clinical data sets, or "big clinical data," advancing clinical research, and improving healthcare. Machine learning is a powerful approach to predictive modeling. Two factors make machine learning challenging for healthcare researchers. First, before training a machine learning model, the values of one or more model parameters called hyper-parameters must typically be specified. Due to their inexperience with machine learning, it is hard for healthcare researchers to choose an appropriate algorithm and hyper-parameter values. Second, many clinical data are stored in a special format. These data must be iteratively transformed into the relational table format before conducting predictive modeling. This transformation is time-consuming and requires computing expertise. This paper presents our vision for and design of MLBCD (Machine Learning for Big Clinical Data), a new software system aiming to address these challenges and facilitate building machine learning predictive models using big clinical data. The paper describes MLBCD's design in detail. By making machine learning accessible to healthcare researchers, MLBCD will open the use of big clinical data and increase the ability to foster biomedical discovery and improve care.

Paenibacillus motobuensis sp. nov., isolated from a composting machine utilizing soil from Motobu-town, Okinawa, Japan.

Science.gov (United States)

Iida, Ken-ichiro; Ueda, Yasuichi; Kawamura, Yoshiaki; Ezaki, Takayuki; Takade, Akemi; Yoshida, Shin-ichi; Amako, Kazunobu

2005-09-01

A novel bacterial strain, MC10(T), was isolated from a compost sample produced in a composting machine utilizing soil from Motobu-town, Okinawa, Japan. The isolate was Gram-negative, but produced endospores. These conflicting characters prompted a taxonomic study of the isolate. The isolate was examined using a combination of phenotypic characterization, cellular fatty acid analysis, DNA base composition determination and 16S rRNA gene sequence analysis. Phylogenetic analysis, based on 16S rRNA gene sequence comparisons, placed strain MC10(T) within the genus Paenibacillus. As in other Paenibacillus species, the isolate contained anteiso-C(15:0) as the major fatty acid and the DNA G+C content was 47.0 mol%. However, 16S rRNA gene sequence similarity values of less than 95.6% were found between this isolate and all members of the genus Paenibacillus. Based upon these results, strain MC10(T) (=GTC 1835(T)=JCM 12774(T)=CCUG 50090(T)) should be designated as the type strain of a novel species within the genus Paenibacillus, Paenibacillus motobuensis sp. nov.

Machine learning for Big Data analytics in plants.

Science.gov (United States)

Ma, Chuang; Zhang, Hao Helen; Wang, Xiangfeng

2014-12-01

Rapid advances in high-throughput genomic technology have enabled biology to enter the era of 'Big Data' (large datasets). The plant science community not only needs to build its own Big-Data-compatible parallel computing and data management infrastructures, but also to seek novel analytical paradigms to extract information from the overwhelming amounts of data. Machine learning offers promising computational and analytical solutions for the integrative analysis of large, heterogeneous and unstructured datasets on the Big-Data scale, and is gradually gaining popularity in biology. This review introduces the basic concepts and procedures of machine-learning applications and envisages how machine learning could interface with Big Data technology to facilitate basic research and biotechnology in the plant sciences. Copyright © 2014 Elsevier Ltd. All rights reserved.

Mineral mining machines

Energy Technology Data Exchange (ETDEWEB)

Mc Gaw, B H

1984-01-01

A machine for mining minerals is patented. It is a cutter loader with a drum actuating element of the worm type equipped with a multitude of cutting teeth reinforced with tungsten carbide. A feature of the patented machine is that all of the cutting teeth and holders on the drum have the identical design. This is achieved through selecting a slant angle for the cutting teeth which is the mean between the slant angle of the conventional radial teeth and the slant angle of the advance teeth. This, in turn, is provided thanks to the corresponding slant of the holders relative to the drum and (or) the slant of the cutting part of the teeth relative to their stems. Thus, the advance teeth projecting beyond the surface of the drum on the face side and providing upper and lateral clearances have the same angle of attack as the radial teeth, that is, from 20 to 35 degrees. A series of modifications of the cutting teeth is patented. One of the designs allows the cutting tooth to occupy a varying position relative to the drum, from the conventional vertical to an inverted, axially projecting position. In the last case the tooth in the extraction process provides the upper and lateral clearances for the drum on the face side. Among the different modifications of the cutting teeth, a design is proposed which provides for the presence of a stem which is shaped like a truncated cone. This particular stem is designed for use jointly with a wedge which unfastens the teeth and is placed in a holder. The latter is completed in a transverse slot thanks to which the rear end of the stem is compressed, which simplifies replacement of a tooth. Channels are provided in the patented machine for feeding water to the worm spiral, the holders and the cutting teeth themselves in order to deal with dust.

Manufacture of mirrors by NC machining of EEM

International Nuclear Information System (INIS)

Hongo, Toshio; Azuma, Yasuo; Kato, Haruo; Hoshino, Hideo

1981-01-01

In the X-ray optical system for the photon factory facility being constructed now in the National Laboratory for High Energy Physics, total reflection mirrors occupy important position. The shapes of mirrors are both plane and curved surface, and the sizes are various. Especially concerning hard X-ray, the required accuracy of the shapes and surface roughness is high. Thereupon mirrors were machined by elastic emission machining (EEM) developed by Mori et al. of Osaka University, and the flatness and surface roughness were examined. The materials machined were Pyrex and copper, the mirror finish of which is difficult. The results are reported. In this machining method, the liquid in which very fine powder is uniformly dispersed and suspended in water was used. By approaching a rotating urethane ball to a work surface, the gap of about 1 μm was formed between them utilizing fluid bearing-like flow arising there. The machining was carried out by colliding the fine particles in suspension to a minute region of the work surface. In order to obtain an arbitrary curved surface, the numerical control according to the variable controling the amount of machining was made. In the case of glasses, the amount of machining was able to be controlled to about 0.01 μm. As for polycrystalline copper, the machining was difficult, and the suitable conditions must be sought hereafter. (Kako, I.)

Machine Learning Method Applied in Readout System of Superheated Droplet Detector

Science.gov (United States)

Liu, Yi; Sullivan, Clair Julia; d'Errico, Francesco

2017-07-01

Direct readability is one advantage of superheated droplet detectors in neutron dosimetry. Utilizing such a distinct characteristic, an imaging readout system analyzes image of the detector for neutron dose readout. To improve the accuracy and precision of algorithms in the imaging readout system, machine learning algorithms were developed. Deep learning neural network and support vector machine algorithms are applied and compared with generally used Hough transform and curvature analysis methods. The machine learning methods showed a much higher accuracy and better precision in recognizing circular gas bubbles.

Large-scale Machine Learning in High-dimensional Datasets

DEFF Research Database (Denmark)

Hansen, Toke Jansen

Over the last few decades computers have gotten to play an essential role in our daily life, and data is now being collected in various domains at a faster pace than ever before. This dissertation presents research advances in four machine learning fields that all relate to the challenges imposed...... are better at modeling local heterogeneities. In the field of machine learning for neuroimaging, we introduce learning protocols for real-time functional Magnetic Resonance Imaging (fMRI) that allow for dynamic intervention in the human decision process. Specifically, the model exploits the structure of f...

Pre-Operative Prediction of Advanced Prostatic Cancer Using Clinical Decision Support Systems: Accuracy Comparison between Support Vector Machine and Artificial Neural Network

Energy Technology Data Exchange (ETDEWEB)

Kim, Sang Youn; Moon, Sung Kyoung; Hwang, Sung Il; Sung, Chang Kyu; Cho, Jeong Yeon; Kim, Seung Hyup; Lee, Hak Jong [Seoul National University College of Medicine, Seoul (Korea, Republic of); Jung, Dae Chul [National Cancer Center, Ilsan (Korea, Republic of); Lee, Ji Won [Kangwon National University College of Medicine, Chuncheon (Korea, Republic of)

2011-10-15

The purpose of the current study was to develop support vector machine (SVM) and artificial neural network (ANN) models for the pre-operative prediction of advanced prostate cancer by using the parameters acquired from transrectal ultrasound (TRUS)-guided prostate biopsies, and to compare the accuracies between the two models. Five hundred thirty-two consecutive patients who underwent prostate biopsies and prostatectomies for prostate cancer were divided into the training and test groups (n = 300 versus n 232). From the data in the training group, two clinical decision support systems (CDSSs-[SVM and ANN]) were constructed with input (age, prostate specific antigen level, digital rectal examination, and five biopsy parameters) and output data (the probability for advanced prostate cancer [> pT3a]). From the data of the test group, the accuracy of output data was evaluated. The areas under the receiver operating characteristic (ROC) curve (AUC) were calculated to summarize the overall performances, and a comparison of the ROC curves was performed (p < 0.05). The AUC of SVM and ANN is 0.805 and 0.719, respectively (p = 0.020), in the pre-operative prediction of advanced prostate cancer. Te performance of SVM is superior to ANN in the pre-operative prediction of advanced prostate cancer.

Overcoming uncertainty for within-network relational machine learning

OpenAIRE

Pfeiffer, Joseph J.

2015-01-01

People increasingly communicate through email and social networks to maintain friendships and conduct business, as well as share online content such as pictures, videos and products. Relational machine learning (RML) utilizes a set of observed attributes and network structure to predict corresponding labels for items; for example, to predict individuals engaged in securities fraud, we can utilize phone calls and workplace information to make joint predictions over the individuals. However, in...

The ATLAS Higgs machine learning challenge

CERN Document Server

Davey, W; The ATLAS collaboration; Rousseau, D; Cowan, G; Kegl, B; Germain-Renaud, C; Guyon, I

2014-01-01

High Energy Physics has been using Machine Learning techniques (commonly known as Multivariate Analysis) since the 90's with Artificial Neural Net for example, more recently with Boosted Decision Trees, Random Forest etc... Meanwhile, Machine Learning has become a full blown field of computer science. With the emergence of Big Data, Data Scientists are developing new Machine Learning algorithms to extract sense from large heterogeneous data. HEP has exciting and difficult problems like the extraction of the Higgs boson signal, data scientists have advanced algorithms: the goal of the HiggsML project is to bring the two together by a “challenge”: participants from all over the world and any scientific background can compete online ( https://www.kaggle.com/c/higgs-boson ) to obtain the best Higgs to tau tau signal significance on a set of ATLAS full simulated Monte Carlo signal and background. Winners with the best scores will receive money prizes ; authors of the best method (most usable) will be invited t...

Advances in precision machining and moulding technology bring design opportunities.

Science.gov (United States)

Glendening, Paul

2008-09-01

Machining of materials for medical applications has moved to a new level of precision. In parallel with this, moulding technology has improved through the increased use of sensors in moulds, enhanced design simulation and processes such as micromoulding. This article examines the opportunities offered by these developments and includes examples of mass produced parts that demonstrate the new capabilities useful to product designers.

Peak thrust operation of linear induction machines from parameter identification

Energy Technology Data Exchange (ETDEWEB)

Zhang, Z.; Eastham, T.R.; Dawson, G.E. [Queen`s Univ., Kingston, Ontario (Canada). Dept. of Electrical and Computer Engineering

1995-12-31

Various control strategies are being used to achieve high performance operation of linear drives. To maintain minimum volume and weight of the power supply unit on board the transportation vehicle, peak thrust per unit current operation is a desirable objective. True peak thrust per unit current through slip control is difficult to achieve because the parameters of linear induction machines vary during normal operation. This paper first develops a peak thrust per unit current control law based on the per-phase equivalent circuit for linear induction machines. The algorithm for identification of the variable parameters in induction machines is then presented. Application to an operational linear induction machine (LIM) demonstrates the utility of this algorithm. The control strategy is then simulated, based on an operational transit LIM, to show the capability of achieving true peak thrust operation for linear induction machines.

Study on the effect of thermal property of metals in ultrasonic-assisted laser machining

International Nuclear Information System (INIS)

Lee, Hu Seung; Kim, Gun Woo; Park, Jong Eun; Cho, Sung Hak; Yang, Min Yang; Park, Jong Kweon

2015-01-01

The laser machining process has been proposed as an advanced process for the selective fabrication of electrodes without a mask. In this study, we adapt laser machining to metals that have different thermal properties. Based on the results, the metals exhibit a different surface morphology, heat-affected zone (HAZ), and a recast layer around the machined surface according to their thermal conductivity, boiling point, and thermal diffusivity. Then, we apply ultrasonic-assisted laser machining to remove the recast layer. The ultrasonic-assisted laser machining exhibits a better surface quality in metals with higher diffusivity than those having lower diffusivity

Optimization on robot arm machining by using genetic algorithms

Science.gov (United States)

Liu, Tung-Kuan; Chen, Chiu-Hung; Tsai, Shang-En

2007-12-01

In this study, an optimization problem on the robot arm machining is formulated and solved by using genetic algorithms (GAs). The proposed approach adopts direct kinematics model and utilizes GA's global search ability to find the optimum solution. The direct kinematics equations of the robot arm are formulated and can be used to compute the end-effector coordinates. Based on these, the objective of optimum machining along a set of points can be evolutionarily evaluated with the distance between machining points and end-effector positions. Besides, a 3D CAD application, CATIA, is used to build up the 3D models of the robot arm, work-pieces and their components. A simulated experiment in CATIA is used to verify the computation results first and a practical control on the robot arm through the RS232 port is also performed. From the results, this approach is proved to be robust and can be suitable for most machining needs when robot arms are adopted as the machining tools.

Advanced stability control of multi-machine power system by vips apparatus

Energy Technology Data Exchange (ETDEWEB)

Yokoyama, A [Tokyo Univ., Tokyo (Japan). Dept. of Electrical Engineering; Sekine, Y [Science Univ. of Tokyo, Tokyo (Japan). Dept. of Electrical Engineering

1994-12-31

New technology such as synchronized switching and power electronics will make it possible to change the configuration of transmission network, the impedances of transmission lines and the phase angles of voltage in the future power systems. This paper presents a comprehensive power system damping control by power electronics based variable impedance apparatus such as variable series capacitor and high speed phase shifter and also shows a novel switching-over control of transmission lines by synchronized switching for the first awing stability and damping enhancement. The control scheme discussed in this paper is based on an energy function of multi-machine power system and its time derivative. Its effectiveness is demonstrated by digital simulations and eigenvalue analysis in multi-machine test systems. It is demonstrated that multiple switching of transmission lines improves damping in the post-fault conditions. (author) 13 refs., 24 figs., 5 tabs.

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

Science.gov (United States)

Hrubiak, Rostislav; Sinogeikin, Stanislav; Rod, Eric; Shen, Guoyin

2015-07-01

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: amorphous 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.

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

International Nuclear Information System (INIS)

Hrubiak, Rostislav; Sinogeikin, Stanislav; Rod, Eric; Shen, Guoyin

2015-01-01

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: amorphous 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

A Review of Current Machine Learning Techniques Used in Manufacturing Diagnosis

OpenAIRE

Ademujimi , Toyosi ,; Brundage , Michael ,; Prabhu , Vittaldas ,

2017-01-01

Part 6: Intelligent Diagnostics and Maintenance Solutions; International audience; Artificial intelligence applications are increasing due to advances in data collection systems, algorithms, and affordability of computing power. Within the manufacturing industry, machine learning algorithms are often used for improving manufacturing system fault diagnosis. This study focuses on a review of recent fault diagnosis applications in manufacturing that are based on several prominent machine learnin...

Utilization technique for advanced nuclear materials database system Data-Free-Way'

Energy Technology Data Exchange (ETDEWEB)

Fujita, Mitsutane; Kurihara, Yutaka; Kinugawa, Junichi; Kitajima, Masahiro; Nagakawa, Josei; Yamamoto, Norikazu; Noda, Tetsuji; Yagi, Koichi; Ono, Akira [National Research Inst. for Metals, Tsukuba, Ibaraki (Japan)

2001-02-01

Four organizations the National Research Institute for Metals (NRIM), the Japan Atomic Energy Research Institute (JAERI), the Japan Nuclear Fuel Cycle Development Institute (JNC) and Japan Science and Technology Incorporation (JST), conducted the 2nd period joint research for the purpose of development of utilization techniques for advanced nuclear materials database system named 'Data-Free-Way' (DFW), to make more useful system to support research and development of the nuclear materials, from FY 1995 to FY 1999. NRIM intended to fill a data system on diffusion and nuclear data by developing utilization technique on diffusion informations of steels and aluminum and nuclear data for materials for its independent system together with participating in fulfil of the DFW. And, NRIM has entered to a project on wide area band circuit application agreed at the G7 by using technologies cultivated by NRIM, to investigate network application technology with the Michigan State University over the sea under cooperation assistant business of JST, to make results on CCT diagram for welding and forecasting of welding heat history accumulated at NRIM for a long term, to perform development of a simulator assisting optimum condition decision of welding. (G.K.)

Synchronous machines. General principles and structures; Machines synchrones. Principes generaux et structures

Energy Technology Data Exchange (ETDEWEB)

Ben Ahmed, H.; Feld, G.; Multon, B. [Ecole Normale Superieure de Cachan, Lab. SATIE, Systemes et Applications des Technologies de l' Information et de l' Energie, UMR CNRS 8029, 94 (France); Bernard, N. [Institut Universitaire de Saint-Nazaire, Institut de Recherche en Electrotechnique et Electronique de Nantes Atlantique (IREENA), 44 - Nantes (France)

2005-10-01

Power generation is mainly performed by synchronous rotating machines which consume about a third of the world primary energy. Electric motors used in industrial applications convert about two thirds of this electricity. Therefore, synchronous machines are present everywhere at different scales, from micro-actuators of few micro-watts to thermo-mechanical production units of more than 1 GW, and represent a large variety of structures which have in common the synchronism between the frequency of the power supply currents and the relative movement of the fixed part with respect to the mobile part. Since several decades, these machines are more and more used as variable speed motors with permanent magnets. The advances in power electronics have contributed to the widening of their use in various applications with a huge range of powers. This article presents the general principle of operation of electromechanical converters of synchronous type: 1 - electromechanical conversion in electromagnetic systems: basic laws and elementary structures (elementary structure, energy conversion cycle, case of a system working in linear magnetic regime), rotating fields structure (magneto-motive force and Ferraris theorem, superficial air gap permeance, air gap magnetic induction, case of a permanent magnet inductor, magnetic energy and electromagnetic torque, conditions for reaching a non-null average torque, application to common cases); 2 - constitution, operation modes and efficiency: constitution and main types of synchronous machines, efficiency - analysis by similarity laws (other expression of the electromagnetic torque, thermal limitation in permanent regime, scale effects, effect of pole pairs number, examples of efficiencies and domains of use), operation modes. (J.S.)

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

OpenAIRE

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 ...

Recent results relevant to ignition physics and machine design issues

International Nuclear Information System (INIS)

Coppi, B.; Airoldi, A.; Bombarda, F.

2001-01-01

The plasma regimes under which ignition can be achieved involve a characteristic range of parameters and issues on which information has been provided by recent experiments. In particular, these results have motivated a new, in-depth analysis of the expected performance of the Ignitor machine as well as of the plasma processes that it can investigate. The main results and recent advances in the design of key systems of the machine are reported. (author)

Recent results relevant to ignition physics and machine design issues

International Nuclear Information System (INIS)

Coppi, B.; Airoldi, A.; Bombarda, F.

1999-01-01

The plasma regimes under which ignition can be achieved involve a characteristic range of parameters and issues on which information has been provided by recent experiments. In particular, these results have motivated a new, in-depth analysis of the expected performance of the Ignitor machine as well as of the plasma processes that it can investigate. The main results and recent advances in the design of key systems of the machine are reported. (author)

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective

Science.gov (United States)

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification. PMID:26246834

Advances in Patient Classification for Traditional Chinese Medicine: A Machine Learning Perspective.

Science.gov (United States)

Zhao, Changbo; Li, Guo-Zheng; Wang, Chengjun; Niu, Jinling

2015-01-01

As a complementary and alternative medicine in medical field, traditional Chinese medicine (TCM) has drawn great attention in the domestic field and overseas. In practice, TCM provides a quite distinct methodology to patient diagnosis and treatment compared to western medicine (WM). Syndrome (ZHENG or pattern) is differentiated by a set of symptoms and signs examined from an individual by four main diagnostic methods: inspection, auscultation and olfaction, interrogation, and palpation which reflects the pathological and physiological changes of disease occurrence and development. Patient classification is to divide patients into several classes based on different criteria. In this paper, from the machine learning perspective, a survey on patient classification issue will be summarized on three major aspects of TCM: sign classification, syndrome differentiation, and disease classification. With the consideration of different diagnostic data analyzed by different computational methods, we present the overview for four subfields of TCM diagnosis, respectively. For each subfield, we design a rectangular reference list with applications in the horizontal direction and machine learning algorithms in the longitudinal direction. According to the current development of objective TCM diagnosis for patient classification, a discussion of the research issues around machine learning techniques with applications to TCM diagnosis is given to facilitate the further research for TCM patient classification.

Development of the advanced on-line BWR core monitoring system TiARA

International Nuclear Information System (INIS)

Kobayashi, Yoko; Yamazaki, Hiroshi

1996-01-01

Development of an integrated computer environment to support plant operators and station nuclear engineers is a recent activity. In achieving this goal, an advanced on-line boiling water reactor (BWR) core monitoring system: TiARA has been developed by Toden Software. An integrated design approach was performed through the introduction of recent computer technologies, a sophisticated human/machine interface (HMI) and an advanced nodal method. The first prototype of TiARA was ready in early 1996. This prototype is now undergoing a field test at Kashiwazaki-Kariwa unit 6. After successful completion of this test, the authors will have achieved the following goals: (1) consistency between on-line core monitoring system and off-line core management system; (2) an enhanced HMI and database; (3) user-friendly operability and maintainability; (4) system development from the utilities' standpoint to fully satisfy operator needs

Integrated Real-Virtuality System and Environments for Advanced Control System Developers and Machines Builders

OpenAIRE

Hussein, Mohamed

2008-01-01

The pace of technological change is increasing and sophisticated customer driven markets are forcing rapid machine evolution, increasing complexity and quality, and faster response. To survive and thrive in these markets, machine builders/suppliers require absolute customer and market orientation, focusing on .. rapid provision of solutions rather than products. Their production systems will need to accommodate unpredictable changes while maintaining financial and operational efficiency with ...

ClearTK 2.0: Design Patterns for Machine Learning in UIMA.

Science.gov (United States)

Bethard, Steven; Ogren, Philip; Becker, Lee

2014-05-01

ClearTK adds machine learning functionality to the UIMA framework, providing wrappers to popular machine learning libraries, a rich feature extraction library that works across different classifiers, and utilities for applying and evaluating machine learning models. Since its inception in 2008, ClearTK has evolved in response to feedback from developers and the community. This evolution has followed a number of important design principles including: conceptually simple annotator interfaces, readable pipeline descriptions, minimal collection readers, type system agnostic code, modules organized for ease of import, and assisting user comprehension of the complex UIMA framework.

A structured review of health utility measures and elicitation in advanced/metastatic breast cancer.

Science.gov (United States)

Hao, Yanni; Wolfram, Verena; Cook, Jennifer

2016-01-01

Health utilities are increasingly incorporated in health economic evaluations. Different elicitation methods, direct and indirect, have been established in the past. This study examined the evidence on health utility elicitation previously reported in advanced/metastatic breast cancer and aimed to link these results to requirements of reimbursement bodies. Searches were conducted using a detailed search strategy across several electronic databases (MEDLINE, EMBASE, Cochrane Library, and EconLit databases), online sources (Cost-effectiveness Analysis Registry and the Health Economics Research Center), and web sites of health technology assessment (HTA) bodies. Publications were selected based on the search strategy and the overall study objectives. A total of 768 publications were identified in the searches, and 26 publications, comprising 18 journal articles and eight submissions to HTA bodies, were included in the evidence review. Most journal articles derived utilities from the European Quality of Life Five-Dimensions questionnaire (EQ-5D). Other utility measures, such as the direct methods standard gamble (SG), time trade-off (TTO), and visual analog scale (VAS), were less frequently used. Several studies described mapping algorithms to generate utilities from disease-specific health-related quality of life (HRQOL) instruments such as European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Core 30 (EORTC QLQ-C30), European Organization for Research and Treatment of Cancer Quality of Life Questionnaire - Breast Cancer 23 (EORTC QLQ-BR23), Functional Assessment of Cancer Therapy - General questionnaire (FACT-G), and Utility-Based Questionnaire-Cancer (UBQ-C); most used EQ-5D as the reference. Sociodemographic factors that affect health utilities, such as age, sex, income, and education, as well as disease progression, choice of utility elicitation method, and country settings, were identified within the journal articles. Most

Machine rates for selected forest harvesting machines

Science.gov (United States)

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

2002-01-01

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

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning

Directory of Open Access Journals (Sweden)

Fabrizia Caiazzo

2018-03-01

Full Text Available Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Laser Direct Metal Deposition of 2024 Al Alloy: Trace Geometry Prediction via Machine Learning.

Science.gov (United States)

Caiazzo, Fabrizia; Caggiano, Alessandra

2018-03-19

Laser direct metal deposition is an advanced additive manufacturing technology suitably applicable in maintenance, repair, and overhaul of high-cost products, allowing for minimal distortion of the workpiece, reduced heat affected zones, and superior surface quality. Special interest is growing for the repair and coating of 2024 aluminum alloy parts, extensively utilized for a wide range of applications in the automotive, military, and aerospace sectors due to its excellent plasticity, corrosion resistance, electric conductivity, and strength-to-weight ratio. A critical issue in the laser direct metal deposition process is related to the geometrical parameters of the cross-section of the deposited metal trace that should be controlled to meet the part specifications. In this research, a machine learning approach based on artificial neural networks is developed to find the correlation between the laser metal deposition process parameters and the output geometrical parameters of the deposited metal trace produced by laser direct metal deposition on 5-mm-thick 2024 aluminum alloy plates. The results show that the neural network-based machine learning paradigm is able to accurately estimate the appropriate process parameters required to obtain a specified geometry for the deposited metal trace.

Moved range monitor of a refueling machine

International Nuclear Information System (INIS)

Nakajima, Masaaki; Sakanaka, Tadao; Kayano, Hiroyuki.

1976-01-01

Purpose: To incorporate light receiving and emitting elements in a face monitor to thereby increase accuracy and reliability to facilitate handling in the refueling of a BWR power plant. Constitution: In the present invention, a refueling machine and a face monitoring light receiving and emitting elements are analogously coupled whereby the face monitoring light receiving and emitting elements may be moved so as to be analogous to a route along which the refueling machine has moved. A shielding plate is positioned in the middle of the light receiving and emitting elements, and the shielding plate is machined so as to be outside of action. The range of action of the refueling machine may be monitored depending on the light receiving state of the light receiving element. Since the present invention utilizes the permeating light as described above, it is possible to detect positions more accurately than the mechanical switch. In addition, the detection section is of the non-contact system and the light receiving element comprises a hot cell, and therefore the service life is extended and the reliability is high. (Nakamura, S.)

Safety Aspects of EPS-3000 Electron Beam Machine

International Nuclear Information System (INIS)

Siti Aiasah Hashim; Shari Jahar; Ayub Muhamad; Sarada Idris

2011-01-01

The EPS-3000 electron beam machine was installed and commission in 1991 at the Alurtron Electron Beam Irradiation Centre. It is utilized as a tool to enhance finished products through electron beam irradiation. The machine and its auxiliary systems were built with highest safety in mind due to the possible dangers that it can cause during the irradiation activities. Automatic stops may be activated via various interlocks to protect the integrity of the machine. This type of interlocks are controlled by the set upper and lower limits, mostly related to the machine high voltage (and beam) generation and cooling systems. Radiation safety is also taken care of by provision of shielding and area monitoring. Other potential hazards include ozone poisoning and electromagnetic field (EMF) could be generated by the high voltage. This paper describes the safety and security systems installed within the facility as measures to protect the workers and general public from radiation and other physical threats. (author)

Multiphysics simulation by design for electrical machines, power electronics and drives

CERN Document Server

Rosu, Marius; Lin, Dingsheng; Ionel, Dan M; Popescu, Mircea; Blaabjerg, Frede; Rallabandi, Vandana; Staton, David

2018-01-01

This book combines the knowledge of experts from both academia and the software industry to present theories of multiphysics simulation by design for electrical machines, power electronics, and drives. The comprehensive design approach described within supports new applications required by technologies sustaining high drive efficiency. The highlighted framework considers the electric machine at the heart of the entire electric drive. The book also emphasizes the simulation by design concept--a concept that frames the entire highlighted design methodology, which is described and illustrated by various advanced simulation technologies. Multiphysics Simulation by Design for Electrical Machines, Power Electronics and Drives begins with the basics of electrical machine design and manufacturing tolerances. It also discusses fundamental aspects of the state of the art design process and includes examples from industrial practice. It explains FEM-based analysis techniques for electrical machine design--providing deta...

Dynamic Resource Allocation and Access Class Barring Scheme for Delay-Sensitive Devices in Machine to Machine (M2M) Communications.

Science.gov (United States)

Li, Ning; Cao, Chao; Wang, Cong

2017-06-15

Supporting simultaneous access of machine-type devices is a critical challenge in machine-to-machine (M2M) communications. In this paper, we propose an optimal scheme to dynamically adjust the Access Class Barring (ACB) factor and the number of random access channel (RACH) resources for clustered machine-to-machine (M2M) communications, in which Delay-Sensitive (DS) devices coexist with Delay-Tolerant (DT) ones. In M2M communications, since delay-sensitive devices share random access resources with delay-tolerant devices, reducing the resources consumed by delay-sensitive devices means that there will be more resources available to delay-tolerant ones. Our goal is to optimize the random access scheme, which can not only satisfy the requirements of delay-sensitive devices, but also take the communication quality of delay-tolerant ones into consideration. We discuss this problem from the perspective of delay-sensitive services by adjusting the resource allocation and ACB scheme for these devices dynamically. Simulation results show that our proposed scheme realizes good performance in satisfying the delay-sensitive services as well as increasing the utilization rate of the random access resources allocated to them.

Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks

OpenAIRE

Rui Zhao; Ruqiang Yan; Jinjiang Wang; Kezhi Mao

2017-01-01

In modern manufacturing systems and industries, more and more research efforts have been made in developing effective machine health monitoring systems. Among various machine health monitoring approaches, data-driven methods are gaining in popularity due to the development of advanced sensing and data analytic techniques. However, considering the noise, varying length and irregular sampling behind sensory data, this kind of sequential data cannot be fed into classification and regression mode...

CrN-based wear resistant hard coatings for machining and forming tools

Energy Technology Data Exchange (ETDEWEB)

Yang, S; Cooke, K E; Teer, D G [Teer Coatings Ltd, West Stone House, Berry Hill Industrial Estate, Droitwich, Worcestershire WR9 9AS (United Kingdom); Li, X [School of Metallurgy and Materials, University of Birmingham, Birmingham B15 2TT (United Kingdom); McIntosh, F [Rolls-Royce plc, Inchinnan, Renfrewshire PA4 9AF, Scotland (United Kingdom)

2009-05-21

Highly wear resistant multicomponent or multilayer hard coatings, based on CrN but incorporating other metals, have been developed using closed field unbalanced magnetron sputter ion plating technology. They are exploited in coated machining and forming tools cutting and forming of a wide range of materials in various application environments. These coatings are characterized by desirable properties including good adhesion, high hardness, high toughness, high wear resistance, high thermal stability and high machining capability for steel. The coatings appear to show almost universal working characteristics under operating conditions of low and high temperature, low and high machining speed, machining of ordinary materials and difficult to machine materials, and machining under lubricated and under minimum lubricant quantity or even dry conditions. These coatings can be used for cutting and for forming tools, for conventional (macro-) machining tools as well as for micromachining tools, either as a single coating or in combination with an advanced, self-lubricating topcoat.

Integrating Heuristic and Machine-Learning Methods for Efficient Virtual Machine Allocation in Data Centers

OpenAIRE

Pahlevan, Ali; Qu, Xiaoyu; Zapater Sancho, Marina; Atienza Alonso, David

2017-01-01

Modern cloud data centers (DCs) need to tackle efficiently the increasing demand for computing resources and address the energy efficiency challenge. Therefore, it is essential to develop resource provisioning policies that are aware of virtual machine (VM) characteristics, such as CPU utilization and data communication, and applicable in dynamic scenarios. Traditional approaches fall short in terms of flexibility and applicability for large-scale DC scenarios. In this paper we propose a heur...

Parameter optimization of electrochemical machining process using black hole algorithm

Science.gov (United States)

Singh, Dinesh; Shukla, Rajkamal

2017-12-01

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

13th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2014-01-01

The integrated and advanced science research topic Man-Machine-Environment System Engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of Man-Machine-Environment system...

14th International Conference on Man-Machine-Environment System Engineering

CERN Document Server

Dhillon, Balbir

2015-01-01

The integrated and advanced science research topic man-machine-environment system engineering (MMESE) was first established in China by Professor Shengzhao Long in 1981, with direct support from one of the greatest modern Chinese scientists, Xuesen Qian. In a letter to Shengzhao Long from October 22nd, 1993, Xuesen Qian wrote: “You have created a very important modern science and technology in China!”   MMESE primarily focuses on the relationship between man, machines and the environment, studying the optimum combination of man-machine-environment systems. In this system, “man” refers to people in the workplace (e.g. operators, decision-makers); “ machine” is the general name for any object controlled by man (including tools, machinery, computers, systems and technologies), and “environment” describes the specific working conditions under which man and machine interact (e.g. temperature, noise, vibration, hazardous gases etc.). The three goals of optimization of man-machine-environment system...

Social Media, Big Data, and Mental Health: Current Advances and Ethical Implications.

Science.gov (United States)

Conway, Mike; O'Connor, Daniel

2016-06-01

Mental health (including substance abuse) is the fifth greatest contributor to the global burden of disease, with an economic cost estimated to be US $2.5 trillion in 2010, and expected to double by 2030. Developing information systems to support and strengthen population-level mental health monitoring forms a core part of the World Health Organization's Comprehensive Action Plan 2013-2020. In this paper, we review recent work that utilizes social media "big data" in conjunction with associated technologies like natural language processing and machine learning to address pressing problems in population-level mental health surveillance and research, focusing both on technological advances and core ethical challenges.

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

Energy Technology Data Exchange (ETDEWEB)

Husain, Tausif; Sozer, Yilmaz; Husain, Iqbal; Muljadi, Eduard

2015-08-24

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

Near Real-Time Nondestructive Active Inspection Technologies Utilizing Delayed γ-Rays and Neutrons for Advanced Safeguards

International Nuclear Information System (INIS)

Hunt, Alan; Tobin, S. J.

2015-01-01

In this two year project, the research team investigated how delayed γ-rays from short-lived fission fragments detected in the short interval between irradiating pulses can be exploited for advanced safeguards technologies. This program contained experimental and modeling efforts. The experimental effort measured the emitted spectra, time histories and correlations of the delayed γ-rays from aqueous solutions and solid targets containing fissionable isotopes. The modeling effort first developed and benchmarked a hybrid Monte Carlo simulation technique based on these experiments. The benchmarked simulations were then extended to other safeguards scenarios, allowing comparisons to other advanced safeguards technologies and to investigate combined techniques. Ultimately, the experiments demonstrated the possible utility of actively induced delayed γ-ray spectroscopy for fissionable material assay.

Near Real-Time Nondestructive Active Inspection Technologies Utilizing Delayed γ-Rays and Neutrons for Advanced Safeguards

Energy Technology Data Exchange (ETDEWEB)

Hunt, Alan [Idaho State Univ., Pocatello, ID (United States). Idaho Accelerator Center, Dept. of Physics; Reedy, E. T.E. [Idaho State Univ., Pocatello, ID (United States). Dept. of Phyics, Idaho Accelerator Center; Mozin, V. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Tobin, S. J. [Los Alamos National Lab. (LANL), Los Alamos, NM (United States). Nuclear Nonproliferation

2015-02-12

In this two year project, the research team investigated how delayed γ-rays from short-lived fission fragments detected in the short interval between irradiating pulses can be exploited for advanced safeguards technologies. This program contained experimental and modeling efforts. The experimental effort measured the emitted spectra, time histories and correlations of the delayed γ-rays from aqueous solutions and solid targets containing fissionable isotopes. The modeling effort first developed and benchmarked a hybrid Monte Carlo simulation technique based on these experiments. The benchmarked simulations were then extended to other safeguards scenarios, allowing comparisons to other advanced safeguards technologies and to investigate combined techniques. Ultimately, the experiments demonstrated the possible utility of actively induced delayed γ-ray spectroscopy for fissionable material assay.

Virtual machine migration in an over-committed cloud

KAUST Repository

Zhang, Xiangliang

2012-04-01

While early emphasis of Infrastructure as a Service (IaaS) clouds was on providing resource elasticity to end users, providers are increasingly interested in over-committing their resources to maximize the utilization and returns of their capital investments. In principle, over-committing resources hedges that users - on average - only need a small portion of their leased resources. When such hedge fails (i.e., resource demand far exceeds available physical capacity), providers must mitigate this provider-induced overload, typically by migrating virtual machines (VMs) to underutilized physical machines. Recent works on VM placement and migration assume the availability of target physical machines [1], [2]. However, in an over-committed cloud data center, this is not the case. VM migration can even trigger cascading overloads if performed haphazardly. In this paper, we design a new VM migration algorithm (called Scattered) that minimizes VM migrations in over-committed data centers. Compared to a traditional implementation, our algorithm can balance host utilization across all time epochs. Using real-world data traces from an enterprise cloud, we show that our migration algorithm reduces the risk of overload, minimizes the number of needed migrations, and has minimal impact on communication cost between VMs. © 2012 IEEE.

Efficiency trends in electric machines and drives

International Nuclear Information System (INIS)

Mecrow, B.C.; Jack, A.G.

2008-01-01

Almost all electricity in the UK is generated by rotating electrical generators, and approximately half of it is used to drive electrical motors. This means that efficiency improvements to electrical machines can have a very large impact on energy consumption. The key challenges to increased efficiency in systems driven by electrical machines lie in three areas: to extend the application of variable-speed electric drives into new areas through reduction of power electronic and control costs; to integrate the drive and the driven load to maximise system efficiency; and to increase the efficiency of the electrical drive itself. In the short to medium term, efficiency gains within electrical machines will result from the development of new materials and construction techniques. Approximately a quarter of new electrical machines are driven by variable-speed drives. These are a less mature product than electrical machines and should see larger efficiency gains over the next 50 years. Advances will occur, with new types of power electronic devices that reduce switching and conduction loss. With variable-speed drives, there is complete freedom to vary the speed of the driven load. Replacing fixed-speed machines with variable-speed drives for a high proportion of industrial loads could mean a 15-30% energy saving. This could save the UK 15 billion kWh of electricity per year which, when combined with motor and drive efficiency gains, would amount to a total annual saving of 24 billion kWh

Machine learning for micro-tomography

Science.gov (United States)

Parkinson, Dilworth Y.; Pelt, Daniël. M.; Perciano, Talita; Ushizima, Daniela; Krishnan, Harinarayan; Barnard, Harold S.; MacDowell, Alastair A.; Sethian, James

2017-09-01

Machine learning has revolutionized a number of fields, but many micro-tomography users have never used it for their work. The micro-tomography beamline at the Advanced Light Source (ALS), in collaboration with the Center for Applied Mathematics for Energy Research Applications (CAMERA) at Lawrence Berkeley National Laboratory, has now deployed a series of tools to automate data processing for ALS users using machine learning. This includes new reconstruction algorithms, feature extraction tools, and image classification and recommen- dation systems for scientific image. Some of these tools are either in automated pipelines that operate on data as it is collected or as stand-alone software. Others are deployed on computing resources at Berkeley Lab-from workstations to supercomputers-and made accessible to users through either scripting or easy-to-use graphical interfaces. This paper presents a progress report on this work.

A primer on brain-machine interfaces, concepts, and technology: a key element in the future of functional neurorestoration.

Science.gov (United States)

Lee, Brian; Liu, Charles Y; Apuzzo, Michael L J

2013-01-01

Conventionally, the practice of neurosurgery has been characterized by the removal of pathology, congenital or acquired. The emerging complement to the removal of pathology is surgery for the specific purpose of restoration of function. Advents in neuroscience, technology, and the understanding of neural circuitry are creating opportunities to intervene in disease processes in a reparative manner, thereby advancing toward the long-sought-after concept of neurorestoration. Approaching the issue of neurorestoration from a biomedical engineering perspective is the rapidly growing arena of implantable devices. Implantable devices are becoming more common in medicine and are making significant advancements to improve a patient's functional outcome. Devices such as deep brain stimulators, vagus nerve stimulators, and spinal cord stimulators are now becoming more commonplace in neurosurgery as we utilize our understanding of the nervous system to interpret neural activity and restore function. One of the most exciting prospects in neurosurgery is the technologically driven field of brain-machine interface, also known as brain-computer interface, or neuroprosthetics. The successful development of this technology will have far-reaching implications for patients suffering from a great number of diseases, including but not limited to spinal cord injury, paralysis, stroke, or loss of limb. This article provides an overview of the issues related to neurorestoration using implantable devices with a specific focus on brain-machine interface technology. Copyright © 2013 Elsevier Inc. All rights reserved.

Automatic testing in the integration phase of mobile work machine (TINAT) - MASIT31

Energy Technology Data Exchange (ETDEWEB)

Multanen, P.; Hyvoenen, M. (Tampere University of Technology, Department of Intelligent Hydraulics and Automation, Tampere (Finland)); Ellman, A. (Tampere University of Technology, Department of Mechanics and Design, Tampere (Finland)); Rantala, S.; Alanen, J. (VTT Technical Research Centre of Finland, Espoo (Finland))

2008-07-01

Abstract The performance and reliability of mobile work machines are significantly affected by control systems of machines and their characteristics. Currently the testing of control systems and verification of their properties is often carried out just in the integration phase of controls and mechanical structure of machine. This is very time consuming, requires a lot of test personnel and is not extensive enough. In TINAT project a test concept will be developed for the testing of entire control systems of work machines without real the mechanical structures of the machines by utilizing modelling and real-time hardware-in-the-loop simulation. The simulator system enables automatic generation of test scenarios and automatic analysis and reporting of test results. (orig.)

Advanced Agriculture system

Directory of Open Access Journals (Sweden)

Shrinivas R. Zanwar

2012-05-01

Full Text Available This article addresses the advanced system which improves agriculture processes like cultivation on ploughed land, based on robotic platform. We have developed a robotic vehicle having four wheels and steered by DC motor. The advanced autonomous system architecture gives us the opportunity to develop a complete new range of agricultural equipment based on small smart machines. The machine will cultivate the farm by considering particular rows and specific column at fixed distance depending on crop. The obstacle detection problem will also be considered, sensed by infrared sensor. The whole algorithm, calculation, processing, monitoring are designed with motors & sensor interfaced with microcontroller. The result obtained through example activation unit is also presented. The dc motor simulation with feedforward and feedback technique shows precise output. With the help of two examples, a DC motor and a magnetic levitation system, the use of MATLAB and Simulink for modeling, analysis and control is designed.

Utility values associated with advanced or metastatic non-small cell lung cancer: data needs for economic modeling.

Science.gov (United States)

Brown, Jacqueline; Cook, Keziah; Adamski, Kelly; Lau, Jocelyn; Bargo, Danielle; Breen, Sarah; Chawla, Anita

2017-04-01

Cost-effectiveness analyses often inform healthcare reimbursement decisions. The preferred measure of effectiveness is the quality adjusted life year (QALY) gained, where the quality of life adjustment is measured in terms of utility. Areas covered: We assessed the availability and variation of utility values for health states associated with advanced or metastatic non-small cell lung cancer (NSCLC) to identify values appropriate for cost-effectiveness models assessing alternative treatments. Our systematic search of six electronic databases (January 2000 to August 2015) found the current literature to be sparse in terms of utility values associated with NSCLC, identifying 27 studies. Utility values were most frequently reported over time and by treatment type, and less frequently by disease response, stage of disease, adverse events or disease comorbidities. Expert commentary: In response to rising healthcare costs, payers increasingly consider the cost-effectiveness of novel treatments in reimbursement decisions, especially in oncology. As the number of therapies available to treat NSCLC increases, cost-effectiveness analyses will play a key role in reimbursement decisions in this area. Quantifying the relationship between health and quality of life for NSCLC patients via utility values is an important component of assessing the cost effectiveness of novel treatments.

Experimental analysis of electro-pneumatic optimization of hot stamping machine control systems with on-delay timer

OpenAIRE

Bankole I. Oladapo; Vincent A. Balogun; Adeyinka O.M. Adeoye; Ige E. Olubunmi; Samuel O. Afolabi

2017-01-01

The sustainability criterion in the manufacturing industries is imperative, especially in the automobile industries. Currently, efforts are being made by the industries to mitigate CO2 emission by the total vehicle weight optimization, machine utilization and resource efficiency. In lieu of this, it is important to understudy the manufacturing machines adopted in the automobile industries. One of such machine is the hot stamping machine that is used for about 35% of the manufacturing operatio...

Man-machine cooperation in remote handling for fusion plants

International Nuclear Information System (INIS)

Leinemann, K.

1984-01-01

Man-machine cooperation in remote handling for fusion plants comprises cooperation for design of equipment and planning of procedures using a CAD system, and cooperation during operation of the equipment with computer aided telemanipulation systems (CAT). This concept is presently being implemented for support of slave positioning, camera tracking, and camera alignment in the KfK manipulator test facility. The pilot implementation will be used to test various man-machine interface layouts, and to establish a set of basic buildings blocks for future implementations of advanced remote handling control systems. (author)

CHISSL: A Human-Machine Collaboration Space for Unsupervised Learning

Energy Technology Data Exchange (ETDEWEB)

Arendt, Dustin L.; Komurlu, Caner; Blaha, Leslie M.

2017-07-14

We developed CHISSL, a human-machine interface that utilizes supervised machine learning in an unsupervised context to help the user group unlabeled instances by her own mental model. The user primarily interacts via correction (moving a misplaced instance into its correct group) or confirmation (accepting that an instance is placed in its correct group). Concurrent with the user's interactions, CHISSL trains a classification model guided by the user's grouping of the data. It then predicts the group of unlabeled instances and arranges some of these alongside the instances manually organized by the user. We hypothesize that this mode of human and machine collaboration is more effective than Active Learning, wherein the machine decides for itself which instances should be labeled by the user. We found supporting evidence for this hypothesis in a pilot study where we applied CHISSL to organize a collection of handwritten digits.

Physics-based simulation modeling and optimization of microstructural changes induced by machining and selective laser melting processes in titanium and nickel based alloys

Science.gov (United States)

Arisoy, Yigit Muzaffer

Manufacturing processes may significantly affect the quality of resultant surfaces and structural integrity of the metal end products. Controlling manufacturing process induced changes to the product's surface integrity may improve the fatigue life and overall reliability of the end product. The goal of this study is to model the phenomena that result in microstructural alterations and improve the surface integrity of the manufactured parts by utilizing physics-based process simulations and other computational methods. Two different (both conventional and advanced) manufacturing processes; i.e. machining of Titanium and Nickel-based alloys and selective laser melting of Nickel-based powder alloys are studied. 3D Finite Element (FE) process simulations are developed and experimental data that validates these process simulation models are generated to compare against predictions. Computational process modeling and optimization have been performed for machining induced microstructure that includes; i) predicting recrystallization and grain size using FE simulations and the Johnson-Mehl-Avrami-Kolmogorov (JMAK) model, ii) predicting microhardness using non-linear regression models and the Random Forests method, and iii) multi-objective machining optimization for minimizing microstructural changes. Experimental analysis and computational process modeling of selective laser melting have been also conducted including; i) microstructural analysis of grain sizes and growth directions using SEM imaging and machine learning algorithms, ii) analysis of thermal imaging for spattering, heating/cooling rates and meltpool size, iii) predicting thermal field, meltpool size, and growth directions via thermal gradients using 3D FE simulations, iv) predicting localized solidification using the Phase Field method. These computational process models and predictive models, once utilized by industry to optimize process parameters, have the ultimate potential to improve performance of

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

Science.gov (United States)

Iwasaki, N

2001-06-01

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

Simulation-driven machine learning: Bearing fault classification

Science.gov (United States)

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.

Virtual C Machine and Integrated Development Environment for ATMS Controllers.

Science.gov (United States)

2000-04-01

The overall objective of this project is to develop a prototype virtual machine that fits on current Advanced Traffic Management Systems (ATMS) controllers and provides functionality for complex traffic operations.;Prepared in cooperation with Utah S...

Overall design concepts for the APS storage ring machine protection system

International Nuclear Information System (INIS)

Lumpkin, A.; Fuja, R.; Votaw, A.; Wang, X.; Shu, D.; Stepp, J.; Arnold, N.; Nawrocki, G.; Decker, G.; Chung, Y.

1995-01-01

The basic design and status of the machine protection system for the Advanced Photon Source (APS) storage ring are discussed. The machine is passively safe to the bending magnet sources, but the high power of the insertion devices requires missteering conditions to be identified and the beam aborted in less than one millisecond. The basic aspects of waterflow, temperature, beam position, etc. monitoring are addressed. Initial commissioning of subsystems and sensors is statused

A new accurate curvature matching and optimal tool based five-axis machining algorithm

International Nuclear Information System (INIS)

Lin, Than; Lee, Jae Woo; Bohez, Erik L. J.

2009-01-01

Free-form surfaces are widely used in CAD systems to describe the part surface. Today, the most advanced machining of free from surfaces is done in five-axis machining using a flat end mill cutter. However, five-axis machining requires complex algorithms for gouging avoidance, collision detection and powerful computer-aided manufacturing (CAM) systems to support various operations. An accurate and efficient method is proposed for five-axis CNC machining of free-form surfaces. The proposed algorithm selects the best tool and plans the tool path autonomously using curvature matching and integrated inverse kinematics of the machine tool. The new algorithm uses the real cutter contact tool path generated by the inverse kinematics and not the linearized piecewise real cutter location tool path

Vending machine assessment methodology. A systematic review.

Science.gov (United States)

Matthews, Melissa A; Horacek, Tanya M

2015-07-01

The nutritional quality of food and beverage products sold in vending machines has been implicated as a contributing factor to the development of an obesogenic food environment. How comprehensive, reliable, and valid are the current assessment tools for vending machines to support or refute these claims? A systematic review was conducted to summarize, compare, and evaluate the current methodologies and available tools for vending machine assessment. A total of 24 relevant research studies published between 1981 and 2013 met inclusion criteria for this review. The methodological variables reviewed in this study include assessment tool type, study location, machine accessibility, product availability, healthfulness criteria, portion size, price, product promotion, and quality of scientific practice. There were wide variations in the depth of the assessment methodologies and product healthfulness criteria utilized among the reviewed studies. Of the reviewed studies, 39% evaluated machine accessibility, 91% evaluated product availability, 96% established healthfulness criteria, 70% evaluated portion size, 48% evaluated price, 52% evaluated product promotion, and 22% evaluated the quality of scientific practice. Of all reviewed articles, 87% reached conclusions that provided insight into the healthfulness of vended products and/or vending environment. Product healthfulness criteria and complexity for snack and beverage products was also found to be variable between the reviewed studies. These findings make it difficult to compare results between studies. A universal, valid, and reliable vending machine assessment tool that is comprehensive yet user-friendly is recommended. Copyright © 2015 Elsevier Ltd. All rights reserved.

2015 International Conference on Machine Learning and Signal Processing

CERN Document Server

Woo, Wai; Sulaiman, Hamzah; Othman, Mohd; Saat, Mohd

2016-01-01

This book presents important research findings and recent innovations in the field of machine learning and signal processing. A wide range of topics relating to machine learning and signal processing techniques and their applications are addressed in order to provide both researchers and practitioners with a valuable resource documenting the latest advances and trends. The book comprises a careful selection of the papers submitted to the 2015 International Conference on Machine Learning and Signal Processing (MALSIP 2015), which was held on 15–17 December 2015 in Ho Chi Minh City, Vietnam with the aim of offering researchers, academicians, and practitioners an ideal opportunity to disseminate their findings and achievements. All of the included contributions were chosen by expert peer reviewers from across the world on the basis of their interest to the community. In addition to presenting the latest in design, development, and research, the book provides access to numerous new algorithms for machine learni...

Modelling machine ensembles with discrete event dynamical system theory

Science.gov (United States)

Hunter, Dan

1990-01-01

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

What is the machine learning?

Science.gov (United States)

Chang, Spencer; Cohen, Timothy; Ostdiek, Bryan

2018-03-01

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

Wind energy utilization: A bibliography

Science.gov (United States)

1975-01-01

Bibliography cites documents published to and including 1974 with abstracts and references, and is indexed by topic, author, organization, title, and keywords. Topics include: Wind Energy Potential and Economic Feasibility, Utilization, Wind Power Plants and Generators, Wind Machines, Wind Data and Properties, Energy Storage, and related topics.

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

Science.gov (United States)

El-Magrabi, M. G.

1982-01-01

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

Multi-functional dielectric elastomer artificial muscles for soft and smart machines

Science.gov (United States)

Anderson, Iain A.; Gisby, Todd A.; McKay, Thomas G.; O'Brien, Benjamin M.; Calius, Emilio P.

2012-08-01

Dielectric elastomer (DE) actuators are popularly referred to as artificial muscles because their impressive actuation strain and speed, low density, compliant nature, and silent operation capture many of the desirable physical properties of muscle. Unlike conventional robots and machines, whose mechanisms and drive systems rapidly become very complex as the number of degrees of freedom increases, groups of DE artificial muscles have the potential to generate rich motions combining many translational and rotational degrees of freedom. These artificial muscle systems can mimic the agonist-antagonist approach found in nature, so that active expansion of one artificial muscle is taken up by passive contraction in the other. They can also vary their stiffness. In addition, they have the ability to produce electricity from movement. But departing from the high stiffness paradigm of electromagnetic motors and gearboxes leads to new control challenges, and for soft machines to be truly dexterous like their biological analogues, they need precise control. Humans control their limbs using sensory feedback from strain sensitive cells embedded in muscle. In DE actuators, deformation is inextricably linked to changes in electrical parameters that include capacitance and resistance, so the state of strain can be inferred by sensing these changes, enabling the closed loop control that is critical for a soft machine. But the increased information processing required for a soft machine can impose a substantial burden on a central controller. The natural solution is to distribute control within the mechanism itself. The octopus arm is an example of a soft actuator with a virtually infinite number of degrees of freedom (DOF). The arm utilizes neural ganglia to process sensory data at the local "arm" level and perform complex tasks. Recent advances in soft electronics such as the piezoresistive dielectric elastomer switch (DES) have the potential to be fully integrated with actuators

Factors Affecting the Behavior of Engineering Students toward Safety Practices in the Machine Shop

Directory of Open Access Journals (Sweden)

Jessie Kristian M. Neria

2015-08-01

Full Text Available This study aimed to determine the factors that affect the behavior of engineering student toward safety practices in the machine shop. Descriptive type of research was utilized in the study. Results showed that most of the engineering students clearly understand the signage shown in the machine shop. Students are aware that they should not leave the machines unattended. Most of the engineering students handle and use the machine properly. The respondents have an average extent of safety practices in the machine shop which means that they are applying safety practices in their every activity in machine shop. There is strong relationship between the safety practices and the factors affecting behavior in terms of signage, reminder of teacher and rules and regulation.

Using Overall Equipment Effectiveness indicator to measure the level of planned production time usage of sewing machine

Directory of Open Access Journals (Sweden)

Marek Krynke

2014-12-01

Full Text Available The chapter presents the results of utilization of the OEE indicator to measure the level of operating time usage of sewing machine production of air bags. The idea of an OEE indictor, which is a key metrics in Total Productive Maintenance (TPM program, is presented. The goals and benefits of its calculation are included. The research object – KL 110 air bags sewing machine - what for the machine is used. The calculation of TPM indicators for the analysed machine is presented. The calculation of TPM indicators was undertaken over a period of six months of the machine’s working time. It was indicated that the overall effectiveness of the machine is at a level of 65,7%, the time losses were 34,3%. Most of the losses were related to low performance. Only Availability indicator reaches a word class level, if other indicators such as Performance, Quality and OEE should be improved, their value should be increased. Activities to improve the effectiveness of the machine utilization were determined.

Machine learning in manufacturing: advantages, challenges, and applications

Directory of Open Access Journals (Sweden)

Thorsten Wuest

2016-01-01

Full Text Available The nature of manufacturing systems faces ever more complex, dynamic and at times even chaotic behaviors. In order to being able to satisfy the demand for high-quality products in an efficient manner, it is essential to utilize all means available. One area, which saw fast pace developments in terms of not only promising results but also usability, is machine learning. Promising an answer to many of the old and new challenges of manufacturing, machine learning is widely discussed by researchers and practitioners alike. However, the field is very broad and even confusing which presents a challenge and a barrier hindering wide application. Here, this paper contributes in presenting an overview of available machine learning techniques and structuring this rather complicated area. A special focus is laid on the potential benefit, and examples of successful applications in a manufacturing environment.

Machine Learning in Medicine.

Science.gov (United States)

Deo, Rahul C

2015-11-17

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games - tasks that would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in health care. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades, and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus, part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. © 2015 American Heart Association, Inc.

Machine Learning in Medicine

Science.gov (United States)

Deo, Rahul C.

2015-01-01

Spurred by advances in processing power, memory, storage, and an unprecedented wealth of data, computers are being asked to tackle increasingly complex learning tasks, often with astonishing success. Computers have now mastered a popular variant of poker, learned the laws of physics from experimental data, and become experts in video games – tasks which would have been deemed impossible not too long ago. In parallel, the number of companies centered on applying complex data analysis to varying industries has exploded, and it is thus unsurprising that some analytic companies are turning attention to problems in healthcare. The purpose of this review is to explore what problems in medicine might benefit from such learning approaches and use examples from the literature to introduce basic concepts in machine learning. It is important to note that seemingly large enough medical data sets and adequate learning algorithms have been available for many decades – and yet, although there are thousands of papers applying machine learning algorithms to medical data, very few have contributed meaningfully to clinical care. This lack of impact stands in stark contrast to the enormous relevance of machine learning to many other industries. Thus part of my effort will be to identify what obstacles there may be to changing the practice of medicine through statistical learning approaches, and discuss how these might be overcome. PMID:26572668

A review of machine learning in obesity.

Science.gov (United States)

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

2018-05-01

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

The Machine within the Machine

CERN Multimedia

Katarina Anthony

2014-01-01

Although Virtual Machines are widespread across CERN, you probably won't have heard of them unless you work for an experiment. Virtual machines - known as VMs - allow you to create a separate machine within your own, allowing you to run Linux on your Mac, or Windows on your Linux - whatever combination you need.   Using a CERN Virtual Machine, a Linux analysis software runs on a Macbook. When it comes to LHC data, one of the primary issues collaborations face is the diversity of computing environments among collaborators spread across the world. What if an institute cannot run the analysis software because they use different operating systems? "That's where the CernVM project comes in," says Gerardo Ganis, PH-SFT staff member and leader of the CernVM project. "We were able to respond to experimentalists' concerns by providing a virtual machine package that could be used to run experiment software. This way, no matter what hardware they have ...

Advanced Reactor Technology Options for Utilization and Transmutation of Actinides in Spent Nuclear Fuel

International Nuclear Information System (INIS)

2009-09-01

Renewed interest in the potential of nuclear energy to contribute to a sustainable worldwide energy mix is strengthening the IAEA's statutory role in fostering the peaceful uses of nuclear energy, in particular the need for effective exchanges of information and collaborative research and technology development among Member States on advanced nuclear power technologies (Articles III-A.1 and III-A.3). The major challenges facing the long term development of nuclear energy as a part of the world's energy mix are improvement of the economic competitiveness, meeting increasingly stringent safety requirements, adhering to the criteria of sustainable development, and public acceptability. The concern linked to the long life of many of the radioisotopes generated from fission has led to increased R and D efforts to develop a technology aimed at reducing the amount of long lived radioactive waste through transmutation in fission reactors or accelerator driven hybrids. In recent years, in various countries and at an international level, more and more studies have been carried out on advanced and innovative waste management strategies (i.e. actinide separation and elimination). Within the framework of the Project on Technology Advances in Fast Reactors and Accelerator Driven Systems (http://www.iaea.org/inisnkm/nkm/aws/fnss/index.html), the IAEA initiated a number of activities on utilization of plutonium and transmutation of long lived radioactive waste, accelerator driven systems, thorium fuel options, innovative nuclear reactors and fuel cycles, non-conventional nuclear energy systems, and fusion/fission hybrids. These activities are implemented under the guidance and with the support of the IAEA Nuclear Energy Department's Technical Working Group on Fast Reactors (TWG-FR). This publication compiles the analyses and findings of the Coordinated Research Project (CRP) on Studies of Advanced Reactor Technology Options for Effective Incineration of Radioactive Waste (2002

SwingStates: Adding state machines to Java and the Swing toolkit

OpenAIRE

Appert , Caroline; Beaudouin-Lafon , Michel

2008-01-01

International audience; This article describes SwingStates, a Java toolkit designed to facilitate the development of graphical user interfaces and bring advanced interaction techniques to the Java platform. SwingStates is based on the use of finite-state machines specified directly in Java to describe the behavior of interactive systems. State machines can be used to redefine the behavior of existing Swing widgets or, in combination with a new canvas widget that features a rich graphical mode...

Target fabrication using laser and spark erosion machining

International Nuclear Information System (INIS)

Clement, X.; Coudeville, A.; Eyharts, P.; Perrine, J.P.; Rouillard, R.

1982-01-01

Fabrication of laser fusion targets requires a number of special techniques. We have developed both laser and spark erosion machining to produce minute parts of complex targets. A high repetition rate YAG laser at double frequency is used to etch various materials. For example, marks or patterns are often necessary on structured or advanced targets. The laser is also used to thin down plastic coated stalks. A spark erosion system has proved to be a versatile tool and we describe current fabrication processes like cutting, drilling, and ultra precise machining. Spark erosion has interesting features for target fabrication: it is a highly controllable and reproducible technique as well as relatively inexpensive

Experimental investigation of the tip based micro/nano machining

Science.gov (United States)

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.

Development of a machine treating removed shells and others in thermal and nuclear power stations

International Nuclear Information System (INIS)

Daiho, Koichi; Iwao, Takenobu

1981-01-01

The living things removed form the cooling water systems in thermal and nuclear power stations, such as shells and jelly fish, have been disposed by burying in the premises, but it is the actual situation that the occurrence of bad smell and the securing of land for burying are the worries. Accordingly, a machine for deodorizing the removed living things was manufactured for trial, and the treatment experiment was carried out in Chita Power Station. This treating machine dries the removed living things around 200 deg C, and makes the deodorizing treatment. The treated products can be utilized effectively as fertilizer, and the prospect to put this machine in practical use as a waste treatment machine of resource re-utilization type was obtained. General Technical Research Institute, Chubu Electric Power Co., Inc., has developed a machine treating abandoned fish for making organic fertilizer, and its principle was applied to the development of this treating machine. The treating capacity of this machine is 1 t/day, and the power consumption is 9.3 kW. The waste oil from power stations of about 15 l/h is used as the fuel. A crusher, a constant feed screw conveyer and a rotary kiln for drying are used. In the treating experiment, about 30 t of shells and others were treated during 51 days. The results are reported. (Kako, I.)

Pathogenesis-based treatments in primary Sjogren's syndrome using artificial intelligence and advanced machine learning techniques: a systematic literature review.

Science.gov (United States)

Foulquier, Nathan; Redou, Pascal; Le Gal, Christophe; Rouvière, Bénédicte; Pers, Jacques-Olivier; Saraux, Alain

2018-05-17

Big data analysis has become a common way to extract information from complex and large datasets among most scientific domains. This approach is now used to study large cohorts of patients in medicine. This work is a review of publications that have used artificial intelligence and advanced machine learning techniques to study physio pathogenesis-based treatments in pSS. A systematic literature review retrieved all articles reporting on the use of advanced statistical analysis applied to the study of systemic autoimmune diseases (SADs) over the last decade. An automatic bibliography screening method has been developed to perform this task. The program called BIBOT was designed to fetch and analyze articles from the pubmed database using a list of keywords and Natural Language Processing approaches. The evolution of trends in statistical approaches, sizes of cohorts and number of publications over this period were also computed in the process. In all, 44077 abstracts were screened and 1017 publications were analyzed. The mean number of selected articles was 101.0 (S.D. 19.16) by year, but increased significantly over the time (from 74 articles in 2008 to 138 in 2017). Among them only 12 focused on pSS but none of them emphasized on the aspect of pathogenesis-based treatments. To conclude, medicine progressively enters the era of big data analysis and artificial intelligence, but these approaches are not yet used to describe pSS-specific pathogenesis-based treatment. Nevertheless, large multicentre studies are investigating this aspect with advanced algorithmic tools on large cohorts of SADs patients.

The effect of automated teller machines on banks' services in Nigeria

African Journals Online (AJOL)

AFRICAN JOURNALS ONLINE (AJOL) · Journals · Advanced Search ... work is to find out the effects of Automated Teller Machines (ATM) on Bank's services. ... used by successful organizations for gaining competitive advantage over others.

Innovative grinding wheel design for cost-effective machining of advanced ceramics. Phase I, final report

Energy Technology Data Exchange (ETDEWEB)

Licht, R.H.; Ramanath, S.; Simpson, M.; Lilley, E.

1996-02-01

Norton Company successfully completed the 16-month Phase I technical effort to define requirements, design, develop, and evaluate a next-generation grinding wheel for cost-effective cylindrical grinding of advanced ceramics. This program was a cooperative effort involving three Norton groups representing a superabrasive grinding wheel manufacturer, a diamond film manufacturing division and a ceramic research center. The program was divided into two technical tasks, Task 1, Analysis of Required Grinding Wheel Characteristics, and Task 2, Design and Prototype Development. In Task 1 we performed a parallel path approach with Superabrasive metal-bond development and the higher technical risk, CVD diamond wheel development. For the Superabrasive approach, Task 1 included bond wear and strength tests to engineer bond-wear characteristics. This task culminated in a small-wheel screening test plunge grinding sialon disks. In Task 2, an improved Superabrasive metal-bond specification for low-cost machining of ceramics in external cylindrical grinding mode was identified. The experimental wheel successfully ground three types of advanced ceramics without the need for wheel dressing. The spindle power consumed by this wheel during test grinding of NC-520 sialon is as much as to 30% lower compared to a standard resin bonded wheel with 100 diamond concentration. The wheel wear with this improved metal bond was an order of magnitude lower than the resin-bonded wheel, which would significantly reduce ceramic grinding costs through fewer wheel changes for retruing and replacements. Evaluation of ceramic specimens from both Tasks 1 and 2 tests for all three ceramic materials did not show evidence of unusual grinding damage. The novel CVD-diamond-wheel approach was incorporated in this program as part of Task 1. The important factors affecting the grinding performance of diamond wheels made by CVD coating preforms were determined.

Design of an ARM-based Automatic Rice-Selling Machine for Cafeterias

Directory of Open Access Journals (Sweden)

Zhiliang Kang

2016-02-01

Full Text Available To address the problems of low selling efficiency, poor sanitation conditions, labor-intensive requirement, and quick rice cooling speed in manual rice selling in cafeterias, especially in colleges and secondary schools, this paper presented an Advanced RISC Machines (ARM microprocessor-based rice-selling machine for cafeterias. The machines consisted of a funnel-shaped rice bin, a thermal insulation box, and a conveying and scattering mechanism. Moreover, this machine exerts fuzzy control over stepper motor rpm, and the motor drives the conveyor belt with a scraper to scatter rice, deliver it, and keep it warm. Apart from an external 4*4 keyboard, a point of sale (POS machine, an ARM process and a pressure sensor, the machine is also equipped with card swiping and weighting mechanisms to achieve functions of card swiping payment and precise measurement, respectively. In addition, detection of the right amount of rice and the alarm function are achieved using an ultrasonic sensor and a beeper, respectively. The presence of the rice container on the rice outlet is detected by an optoelectronic switch. Results show that this rice-selling machine achieves precise measurement, quick card swiping, fast rice selling, stable operation, and good rice heat preservation. Therefore, the mechanical design enables the machine to achieve its goals.

Energy-efficient electrical machines by new materials. Superconductivity in large electrical machines; Energieeffiziente elektrische Maschinen durch neue Materialien. Supraleitung in grossen elektrischen Maschinen

Energy Technology Data Exchange (ETDEWEB)

Frauenhofer, Joachim [Siemens, Nuernberg (Germany); Arndt, Tabea; Grundmann, Joern [Siemens, Erlangen (Germany)

2013-07-01

The implementation of superconducting materials in high-power electrical machines results in significant advantages regarding efficiency, size and dynamic behavior when compared to conventional machines. The application of HTS (high-temperature superconductors) in electrical machines allows significantly higher power densities to be achieved for synchronous machines. In order to gain experience with the new technology, Siemens carried out a series of development projects. A 400 kW model motor for the verification of a concept for the new technology was followed by a 4000 kV A generator as highspeed machine - as well as a low-speed 4000 kW propeller motor with high torque. The 4000 kVA generator is still employed to carry out long-term tests and to check components. Superconducting machines have significantly lower weight and envelope dimensions compared to conventional machines, and for this reason alone, they utilize resources better. At the same time, operating losses are slashed to about half and the efficiency increases. Beyond this, they set themselves apart as a result of their special features in operation, such as high overload capability, stiff alternating load behavior and low noise. HTS machines provide significant advantages where the reduction of footprint, weight and losses or the improved dynamic behavior results in significant improvements of the overall system. Propeller motors and generators,for ships, offshore plants, in wind turbine and hydroelectric plants and in large power stations are just some examples. HTS machines can therefore play a significant role when it comes to efficiently using resources and energy as well as reducing the CO{sub 2} emissions.

Innovation of High Voltage Supply Adjustment Device on Diagnostic X-Ray Machine

International Nuclear Information System (INIS)

Sujatno; Wiranto Budi Santoso

2010-01-01

Innovation of high voltage supply adjustment device on diagnostic x-ray machine has been carried out. The innovation is conducted by utilizing an electronic circuit as a high voltage adjustment device. Usually a diagnostic x-ray machine utilizes a transformer or an auto-transformer as a high voltage supply adjustment device. A high power diagnostic x-ray machine needs a high power transformer which has big physical dimension. Therefore a box control where the transformer is located has to have big physical dimension. Besides, the price of the transformer is expensive and hardly found in local markets. In this innovation, the transformer is replaced by an electronic circuit. The main component of the electronic circuit is Triac BTA-40. As adjustment device, the triac is controlled by a variable resistor which is coupled by a stepper motor. A step movement of stepper motor varies a value of resistor. The resistor value determines the triac gate voltage. Furthermore the triac will open according to the value of electrical current flowing to the gate. When the gate is open, electrical voltage and current will flow from cathode to anode of the triac. The value of these electrical voltage and current depend on gate open condition. Then this triac output voltage is feed to diagnostic x-ray machine high voltage supply. Therefore the high voltage value of diagnostic x-ray machine is adjusted by the output voltage of the electronic circuit. By using this electronic circuit, the physical dimension of diagnostic x-ray machine box control and the price of the equipment can be reduced. (author)

Instrumentation and Control and Human Machine Interface Science and Technology Roadmap in Support of Advanced Reactors and Fuel Programs in the U.S

International Nuclear Information System (INIS)

Miller, Don W.; Arndt, Steven A.; Dudenhoeffer, Donald D.; Hallbert, Bruce P.; Bond, Leonard J.; Holcomb, David E.; Wood, Richard T.; Naser, Joseph A.; O'Hara, John M.; Quinn, Edward L.

2008-01-01

The purpose of this paper is to provide an overview of the current status of the Instrumentation, Control and Human Machine Interface (ICHMI) Science and Technology Roadmap (Reference xi) that was developed to address the major challenges in this technical area for the Gen IV and other U.S. Department of Energy (DOE) initiatives that support future deployments of nuclear energy systems. Reliable, capable ICHMI systems will be necessary for the advanced nuclear plants to be economically competitive. ICHMI enables measurement, control, protection, monitoring, and maintenance for processes and components. Through improvements in the technologies and demonstration of their use to facilitate licensing, ICHMI can contribute to the reduction of plant operations and maintenance costs while helping to ensure high plant availability. The impact of ICHMI can be achieved through effective use of the technologies to improve operational efficiency and optimize use of human resources. However, current licensing experience with digital I and C systems has provided lessons learned concerning the difficulties that can be encountered when introducing advanced technologies with expanded capabilities. Thus, in the development of advanced nuclear power designs, it will be important to address both the technical foundations of ICHMI systems and their licensing considerations. The ICHMI roadmap will identify the necessary research, development and demonstration activities that are essential to facilitate necessary technology advancement and resolve outstanding issues

The systematic development of a machine vision based milking robot

NARCIS (Netherlands)

Gouws, J.

1993-01-01

Agriculture involves unique interactions between man, machines, and various elements from nature. Therefore the implementation of advanced technology in agriculture holds different challenges than in other sectors of the economy. This dissertation stems from research into the application of

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces

Directory of Open Access Journals (Sweden)

Yanjiao Li

2017-08-01

Full Text Available Gas utilization ratio (GUR is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs. In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF, depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

A Novel Online Sequential Extreme Learning Machine for Gas Utilization Ratio Prediction in Blast Furnaces.

Science.gov (United States)

Li, Yanjiao; Zhang, Sen; Yin, Yixin; Xiao, Wendong; Zhang, Jie

2017-08-10

Gas utilization ratio (GUR) is an important indicator used to measure the operating status and energy consumption of blast furnaces (BFs). In this paper, we present a soft-sensor approach, i.e., a novel online sequential extreme learning machine (OS-ELM) named DU-OS-ELM, to establish a data-driven model for GUR prediction. In DU-OS-ELM, firstly, the old collected data are discarded gradually and the newly acquired data are given more attention through a novel dynamic forgetting factor (DFF), depending on the estimation errors to enhance the dynamic tracking ability. Furthermore, we develop an updated selection strategy (USS) to judge whether the model needs to be updated with the newly coming data, so that the proposed approach is more in line with the actual production situation. Then, the convergence analysis of the proposed DU-OS-ELM is presented to ensure the estimation of output weight converge to the true value with the new data arriving. Meanwhile, the proposed DU-OS-ELM is applied to build a soft-sensor model to predict GUR. Experimental results demonstrate that the proposed DU-OS-ELM obtains better generalization performance and higher prediction accuracy compared with a number of existing related approaches using the real production data from a BF and the created GUR prediction model can provide an effective guidance for further optimization operation.

Study on the machinability characteristics of superalloy Inconel 718 during high speed turning

International Nuclear Information System (INIS)

Thakur, D.G.; Ramamoorthy, B.; Vijayaraghavan, L.

2009-01-01

The present paper is an attempt of an experimental investigation on the machinability of superalloy, Inconel 718 during high speed turning using tungsten carbide insert (K20) tool. The effect of machining parameters on the cutting force, specific cutting pressure, cutting temperature, tool wear and surface finish criteria were investigated during the experimentation. The machining parameters have been optimized by measuring forces. The effect of machining parameters on the tool wear was examined through SEM micrographs. During high speed turning acoustic emission signal were collected and analyzed to understand the effect of cutting parameters during online. The research work findings will also provide useful economic machining solution by utilizing economical tungsten carbide tooling during high speed processing of Inconel 718, which is otherwise usually machined by costly PCD or CBN tools. The present approach and results will be helpful for understanding the machinability of Inconel 718 during high speed turning for the manufacturing engineers

Novel Transverse Flux Machine for Vehicle Traction Applications: Preprint

Energy Technology Data Exchange (ETDEWEB)

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

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 achieved 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.

Shaft Boring Machine: A method of mechanized vertical shaft excavation

International Nuclear Information System (INIS)

Goodell, T.M.

1991-01-01

The Shaft Boring Machine (SBM) is a vertical application of proven rock boring technology. The machine applies a rotating cutter wheel with disk cutters for shaft excavation. The wheel is thrust against the rock by hydraulic cylinders and slews about the shaft bottom as it rotates. Cuttings are removed by a clam shell device similar to conventional shaft mucking and the muck is hoisted by buckets. The entire machine moves down (and up) the shaft through the use of a system of grippers thrust against the shaft wall. These grippers and their associated cylinders also provide the means to maintain verticality and stability of the machine. The machine applies the same principles as tunnel boring machines but in a vertical mode. Other shaft construction activities such as rock bolting, utility installation and shaft concrete lining can be accomplished concurrent with shaft boring. The method is comparable in cost to conventional sinking to a depth of about 460 meters (1500 feet) beyond which the SBM has a clear host advantage. The SBM has a greater advantage in productivity in that it can excavate significantly faster than drill and blast methods

Effect of Machining Velocity in Nanoscale Machining Operations

International Nuclear Information System (INIS)

Islam, Sumaiya; Khondoker, Noman; Ibrahim, Raafat

2015-01-01

The aim of this study is to investigate the generated forces and deformations of single crystal Cu with (100), (110) and (111) crystallographic orientations at nanoscale machining operation. A nanoindenter equipped with nanoscratching attachment was used for machining operations and in-situ observation of a nano scale groove. As a machining parameter, the machining velocity was varied to measure the normal and cutting forces. At a fixed machining velocity, different levels of normal and cutting forces were generated due to different crystallographic orientations of the specimens. Moreover, after machining operation percentage of elastic recovery was measured and it was found that both the elastic and plastic deformations were responsible for producing a nano scale groove within the range of machining velocities from 250-1000 nm/s. (paper)

Machine Learning in Computer-Aided Synthesis Planning.

Science.gov (United States)

Coley, Connor W; Green, William H; Jensen, Klavs F

2018-05-15

Computer-aided synthesis planning (CASP) is focused on the goal of accelerating the process by which chemists decide how to synthesize small molecule compounds. The ideal CASP program would take a molecular structure as input and output a sorted list of detailed reaction schemes that each connect that target to purchasable starting materials via a series of chemically feasible reaction steps. Early work in this field relied on expert-crafted reaction rules and heuristics to describe possible retrosynthetic disconnections and selectivity rules but suffered from incompleteness, infeasible suggestions, and human bias. With the relatively recent availability of large reaction corpora (such as the United States Patent and Trademark Office (USPTO), Reaxys, and SciFinder databases), consisting of millions of tabulated reaction examples, it is now possible to construct and validate purely data-driven approaches to synthesis planning. As a result, synthesis planning has been opened to machine learning techniques, and the field is advancing rapidly. In this Account, we focus on two critical aspects of CASP and recent machine learning approaches to both challenges. First, we discuss the problem of retrosynthetic planning, which requires a recommender system to propose synthetic disconnections starting from a target molecule. We describe how the search strategy, necessary to overcome the exponential growth of the search space with increasing number of reaction steps, can be assisted through a learned synthetic complexity metric. We also describe how the recursive expansion can be performed by a straightforward nearest neighbor model that makes clever use of reaction data to generate high quality retrosynthetic disconnections. Second, we discuss the problem of anticipating the products of chemical reactions, which can be used to validate proposed reactions in a computer-generated synthesis plan (i.e., reduce false positives) to increase the likelihood of experimental success

Analysis of an environmental exposure health questionnaire in a metropolitan minority population utilizing logistic regression and Support Vector Machines.

Science.gov (United States)

Chen, Chau-Kuang; Bruce, Michelle; Tyler, Lauren; Brown, Claudine; Garrett, Angelica; Goggins, Susan; Lewis-Polite, Brandy; Weriwoh, Mirabel L; Juarez, Paul D; Hood, Darryl B; Skelton, Tyler

2013-02-01

The goal of this study was to analyze a 54-item instrument for assessment of perception of exposure to environmental contaminants within the context of the built environment, or exposome. This exposome was defined in five domains to include 1) home and hobby, 2) school, 3) community, 4) occupation, and 5) exposure history. Interviews were conducted with child-bearing-age minority women at Metro Nashville General Hospital at Meharry Medical College. Data were analyzed utilizing DTReg software for Support Vector Machine (SVM) modeling followed by an SPSS package for a logistic regression model. The target (outcome) variable of interest was respondent's residence by ZIP code. The results demonstrate that the rank order of important variables with respect to SVM modeling versus traditional logistic regression models is almost identical. This is the first study documenting that SVM analysis has discriminate power for determination of higher-ordered spatial relationships on an environmental exposure history questionnaire.

Machine learning for epigenetics and future medical applications.

Science.gov (United States)

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.

The development of damage identification methods for buildings with image recognition and machine learning techniques utilizing aerial photographs of the 2016 Kumamoto earthquake

Science.gov (United States)

Shohei, N.; Nakamura, H.; Fujiwara, H.; Naoichi, M.; Hiromitsu, T.

2017-12-01

It is important to get schematic information of the damage situation immediately after the earthquake utilizing photographs shot from an airplane in terms of the investigation and the decision-making for authorities. In case of the 2016 Kumamoto earthquake, we have acquired more than 1,800 orthographic projection photographs adjacent to damaged areas. These photos have taken between April 16th and 19th by airplanes, then we have distinguished damages of all buildings with 4 levels, and organized as approximately 296,000 GIS data corresponding to the fundamental Geospatial data published by Geospatial Information Authority of Japan. These data have organized by effort of hundreds of engineers. However, it is not considered practical for more extensive disasters like the Nankai Trough earthquake by only human powers. So, we have been developing the automatic damage identification method utilizing image recognition and machine learning techniques. First, we have extracted training data of more than 10,000 buildings which have equally damage levels divided in 4 grades. With these training data, we have been raster scanning in each scanning ranges of entire images, then clipping patch images which represents damage levels each. By utilizing these patch images, we have been developing discriminant models by two ways. One is a model using the Support Vector Machine (SVM). First, extract a feature quantity of each patch images. Then, with these vector values, calculate the histogram density as a method of Bag of Visual Words (BoVW), then classify borders with each damage grades by SVM. The other one is a model using the multi-layered Neural Network. First, design a multi-layered Neural Network. Second, input patch images and damage levels based on a visual judgement, and then, optimize learning parameters with error backpropagation method. By use of both discriminant models, we are going to discriminate damage levels in each patches, then create the image that shows

Proofpoint unveils the industry's most advanced anti-spam laboratory

CERN Multimedia

2003-01-01

"Proofpoint, Inc., the leader in large enterprise anti-spam solutions, today unveiled its Anti-Spam Laboratory, the world's most advanced center for spam research and analysis, and the first to be based on advanced Machine Learning science" (1 page).

A Parameter Communication Optimization Strategy for Distributed Machine Learning in Sensors.

Science.gov (United States)

Zhang, Jilin; Tu, Hangdi; Ren, Yongjian; Wan, Jian; Zhou, Li; Li, Mingwei; Wang, Jue; Yu, Lifeng; Zhao, Chang; Zhang, Lei

2017-09-21

In order to utilize the distributed characteristic of sensors, distributed machine learning has become the mainstream approach, but the different computing capability of sensors and network delays greatly influence the accuracy and the convergence rate of the machine learning model. Our paper describes a reasonable parameter communication optimization strategy to balance the training overhead and the communication overhead. We extend the fault tolerance of iterative-convergent machine learning algorithms and propose the Dynamic Finite Fault Tolerance (DFFT). Based on the DFFT, we implement a parameter communication optimization strategy for distributed machine learning, named Dynamic Synchronous Parallel Strategy (DSP), which uses the performance monitoring model to dynamically adjust the parameter synchronization strategy between worker nodes and the Parameter Server (PS). This strategy makes full use of the computing power of each sensor, ensures the accuracy of the machine learning model, and avoids the situation that the model training is disturbed by any tasks unrelated to the sensors.

Advancing Research in Second Language Writing through Computational Tools and Machine Learning Techniques: A Research Agenda

Science.gov (United States)

Crossley, Scott A.

2013-01-01

This paper provides an agenda for replication studies focusing on second language (L2) writing and the use of natural language processing (NLP) tools and machine learning algorithms. Specifically, it introduces a range of the available NLP tools and machine learning algorithms and demonstrates how these could be used to replicate seminal studies…

Advanced Ground Systems Maintenance Anomaly Detection

Data.gov (United States)

National Aeronautics and Space Administration — The Inductive Monitoring System (IMS) software utilizes techniques from the fields of model-based reasoning, machine learning, and data mining to build system...

Recent trend in coal utilization technology. Coal utilization workshop

Energy Technology Data Exchange (ETDEWEB)

Kim, Chon Ho; Son, Ja Ek; Lee, In Chul; Jin, Kyung Tae; Kim, Seong Soo [Korea Inst. of Energy Research, Taejon (Korea, Republic of)

1995-12-01

The 11th Korea-U.S.A. joint workshop on coal utilization technology was held in somerset, Pennsylvania, U.S.A. from october 2 to 3, 1995. In the opening ceremony, Dr.C. Low-el Miller, associate deputy assistant secretary of office of clean coal technology, U.S.DOE, gave congratulatory remarks and Dr. Young Mok Son, president of KIER, made a keynote address. In this workshop, 30 papers were presented in the fields of emission control technology, advanced power generation systems, and advanced coal cleaning and liquid fuels. Especially, from the Korean side, not only KIER but also other private research institutes and major engineering companies including KEPCO, Daewoo Institute of Construction Technology, Jindo Engineering and Construction Co. Daewoo Institute for Advanced Engineering and universities participated in this workshop, reflecting their great interests. Attendants actively discussed about various coal utilization technologies and exchanged scientific and technical information on the state-of-art clean coal technologies under development. (author)

Mechanical properties of JT-60 tokamak machine in power tests

International Nuclear Information System (INIS)

Takatsu, Hideyuki; Ohkubo, Minoru; Yamamoto, Masahiro; Ohta, Mitsuru

1986-01-01

JT-60 power tests were carried out from Dec. 10, 1984 to Feb. 20, 1985 to demonstrate, in advance of actual plasma operation, satisfactory performance of tokamak machine, power suppliers and control system in combination. The tests began with low power test of individual coil systems and progressed to full power tests. The coil current was raised step by step, monitoring the mechanical, thermal, electrical and vacuum data. Power tests were concluded with successful results. All of the coil systems were raised up to full power operation in combination and system performance was verified including the structural integrity of tokamak machine. Measured strain and deflection showed good agreements with those predicted in the design, which was an evidence that electromagnetic forces were supported as expected in the design. A few limitations to machine operation was made clear quantitatively. And it was found that existing detectors were insufficient to monitor machine integrity and two kinds of detector were proposed to be installed. (author)

Prediction of Machine Tool Condition Using Support Vector Machine

International Nuclear Information System (INIS)

Wang Peigong; Meng Qingfeng; Zhao Jian; Li Junjie; Wang Xiufeng

2011-01-01

Condition monitoring and predicting of CNC machine tools are investigated in this paper. Considering the CNC machine tools are often small numbers of samples, a condition predicting method for CNC machine tools based on support vector machines (SVMs) is proposed, then one-step and multi-step condition prediction models are constructed. The support vector machines prediction models are used to predict the trends of working condition of a certain type of CNC worm wheel and gear grinding machine by applying sequence data of vibration signal, which is collected during machine processing. And the relationship between different eigenvalue in CNC vibration signal and machining quality is discussed. The test result shows that the trend of vibration signal Peak-to-peak value in surface normal direction is most relevant to the trend of surface roughness value. In trends prediction of working condition, support vector machine has higher prediction accuracy both in the short term ('One-step') and long term (multi-step) prediction compared to autoregressive (AR) model and the RBF neural network. Experimental results show that it is feasible to apply support vector machine to CNC machine tool condition prediction.

Financial signal processing and machine learning

CERN Document Server

Kulkarni,Sanjeev R; Dmitry M. Malioutov

2016-01-01

The modern financial industry has been required to deal with large and diverse portfolios in a variety of asset classes often with limited market data available. Financial Signal Processing and Machine Learning unifies a number of recent advances made in signal processing and machine learning for the design and management of investment portfolios and financial engineering. This book bridges the gap between these disciplines, offering the latest information on key topics including characterizing statistical dependence and correlation in high dimensions, constructing effective and robust risk measures, and their use in portfolio optimization and rebalancing. The book focuses on signal processing approaches to model return, momentum, and mean reversion, addressing theoretical and implementation aspects. It highlights the connections between portfolio theory, sparse learning and compressed sensing, sparse eigen-portfolios, robust optimization, non-Gaussian data-driven risk measures, graphical models, causal analy...

Design and fabrication of a unique electromechanical machine for long-term fatigue testing

International Nuclear Information System (INIS)

Boling, K.W.

1984-12-01

An electromechanical machine has been designed and fabricated for performing long-term fatigue tests under conditions that simulate those in modern plants. The machine is now commercially available. Its advantages over current electrohydraulic machines are lower initial cost, minimum maintenance requirements, and greater reliability especially when performing long tests. The machine operates in closed-loop fashion by utilizing continuous feedback signals from the specimen extensometer or load cell, it is programmable for testing in strain or load control. The maximum ram rate is 0.056 mm/s (0.134 in./min), maximum ram travel is 102 mm (4 in.) and load capacity is +-44 (+-10 kips). Induction heating controls speciment temperatures to 1000 0 C

Report on promotion of utilization of radiation

International Nuclear Information System (INIS)

1987-01-01

This report presents results of studies made by the Atomic Energy Commission concerning research and development activities to be carried out in future to promote the utilization of radiation. First, the current state of radiation utilization is described, centering on practical applications, research and development activities (for practical and advanced applications), and international cooperation (with developing and advanced nations). The second part deals with activities required in future to promote practical utilization of radiation, research and development of advanced techniques, and cooperation to be offered to developing countries. Third, specific measures to be carried out for effective radiation utilization are described. For γ-rays and electron beams, which are widely in use at present, there are some economic and social problems remaining to be solved. For advanced utilization of radiation beams, further efforts should be focused on basic research on π-meson and μ-particle beams and their application to canser treatment and nuclear fusion; research on monochromatic neutron beams; application of RI beams to the development of new materials and new analysis techniques, application of epithermal neutron beams to elementary radiography and CT; etc. The utilization, application and development of tracers are also described. (Nogami, K.)

Sample preparation of metal alloys by electric discharge machining

Science.gov (United States)

Chapman, G. B., II; Gordon, W. A.

1976-01-01

Electric discharge machining was investigated as a noncontaminating method of comminuting alloys for subsequent chemical analysis. Particulate dispersions in water were produced from bulk alloys at a rate of about 5 mg/min by using a commercially available machining instrument. The utility of this approach was demonstrated by results obtained when acidified dispersions were substituted for true acid solutions in an established spectrochemical method. The analysis results were not significantly different for the two sample forms. Particle size measurements and preliminary results from other spectrochemical methods which require direct aspiration of liquid into flame or plasma sources are reported.

Machine Learning-Empowered Biometric Methods for Biomedicine Applications

Directory of Open Access Journals (Sweden)

Qingxue Zhang

2017-07-01

Full Text Available Nowadays, pervasive computing technologies are paving a promising way for advanced smart health applications. However, a key impediment faced by wide deployment of these assistive smart devices, is the increasing privacy and security issue, such as how to protect access to sensitive patient data in the health record. Focusing on this challenge, biometrics are attracting intense attention in terms of effective user identification to enable confidential health applications. In this paper, we take special interest in two bio-potential-based biometric modalities, electrocardiogram (ECG and electroencephalogram (EEG, considering that they are both unique to individuals, and more reliable than token (identity card and knowledge-based (username/password methods. After extracting effective features in multiple domains from ECG/EEG signals, several advanced machine learning algorithms are introduced to perform the user identification task, including Neural Network, K-nearest Neighbor, Bagging, Random Forest and AdaBoost. Experimental results on two public ECG and EEG datasets show that ECG is a more robust biometric modality compared to EEG, leveraging a higher signal to noise ratio and also more distinguishable morphological patterns. Among different machine learning classifiers, the random forest greatly outperforms the others and owns an identification rate as high as 98%. This study is expected to demonstrate that properly selected biometric empowered by an effective machine learner owns a great potential, to enable confidential biomedicine applications in the era of smart digital health.

Recent advances in the utilization and the irradiation technology of the refurbished BR2 reactor

International Nuclear Information System (INIS)

Dekeyser, J.; Benoit, P.; Decloedt, C.; Pouleur, Y.; Verwimp, A.; Weber, M.; Vankeerberghen, M.; Ponsard, B.

1999-01-01

Operation and utilization of the materials testing reactor BR2 at the Belgian Nuclear Research Centre (SCK·CEN) has since its start in 1963 always followed closely the needs and developments of nuclear technology. In particular, a multitude of irradiation experiments have been carried out for most types of nuclear power reactors, existing or under design. Since the early 1990s and increased focus was directed towards more specific irradiation testing needs for light water reactor fuels and materials, although other areas of utilization continued as well (e.g. fusion reactor materials, safety research, ...), including also the growing activities of radioisotope production and silicon doping. An important milestone was the decision in 1994 to implement a comprehensive refurbishment programme for the BR2 reactor and plant installations. The scope of this programme comprised very substantial studies and hardware interventions, which have been completed in early 1997 within planning and budget. Directly connected to this strategic decision for reactor refurbishment was the reinforcement of our efforts to requalify and upgrade the existing irradiation facilities and to develop advanced devices in BR2 to support emerging programs in the following fields: - LWR pressure vessel steel, - LWR irradiation assisted stress corrosion cracking (IASCC), - reliability and safety of high-burnup LWR fuel, - fusion reactor materials and blanket components, - fast neutron reactor fuels and actinide burning, - extension and diversification of radioisotope production. The paper highlights these advances in the areas of BR2 utilisation and the ongoing development activities for the required new generation of irradiations devices. (author)

Implementation of machine learning for high-volume manufacturing metrology challenges (Conference Presentation)

Science.gov (United States)

Timoney, Padraig; Kagalwala, Taher; Reis, Edward; Lazkani, Houssam; Hurley, Jonathan; Liu, Haibo; Kang, Charles; Isbester, Paul; Yellai, Naren; Shifrin, Michael; Etzioni, Yoav

2018-03-01

In recent years, the combination of device scaling, complex 3D device architecture and tightening process tolerances have strained the capabilities of optical metrology tools to meet process needs. Two main categories of approaches have been taken to address the evolving process needs. In the first category, new hardware configurations are developed to provide more spectral sensitivity. Most of this category of work will enable next generation optical metrology tools to try to maintain pace with next generation process needs. In the second category, new innovative algorithms have been pursued to increase the value of the existing measurement signal. These algorithms aim to boost sensitivity to the measurement parameter of interest, while reducing the impact of other factors that contribute to signal variability but are not influenced by the process of interest. This paper will evaluate the suitability of machine learning to address high volume manufacturing metrology requirements in both front end of line (FEOL) and back end of line (BEOL) sectors from advanced technology nodes. In the FEOL sector, initial feasibility has been demonstrated to predict the fin CD values from an inline measurement using machine learning. In this study, OCD spectra were acquired after an etch process that occurs earlier in the process flow than where the inline CD is measured. The fin hard mask etch process is known to impact the downstream inline CD value. Figure 1 shows the correlation of predicted CD vs downstream inline CD measurement obtained after the training of the machine learning algorithm. For BEOL, machine learning is shown to provide an additional source of information in prediction of electrical resistance from structures that are not compatible for direct copper height measurement. Figure 2 compares the trench height correlation to electrical resistance (Rs) and the correlation of predicted Rs to the e-test Rs value for a far back end of line (FBEOL) metallization level

Facial Expression Recognition Through Machine Learning

Directory of Open Access Journals (Sweden)

Nazia Perveen

2015-08-01

Full Text Available Facial expressions communicate non-verbal cues which play an important role in interpersonal relations. Automatic recognition of facial expressions can be an important element of normal human-machine interfaces it might likewise be utilized as a part of behavioral science and in clinical practice. In spite of the fact that people perceive facial expressions for all intents and purposes immediately solid expression recognition by machine is still a challenge. From the point of view of automatic recognition a facial expression can be considered to comprise of disfigurements of the facial parts and their spatial relations or changes in the faces pigmentation. Research into automatic recognition of the facial expressions addresses the issues encompassing the representation and arrangement of static or dynamic qualities of these distortions or face pigmentation. We get results by utilizing the CVIPtools. We have taken train data set of six facial expressions of three persons and for train data set purpose we have total border mask sample 90 and 30 border mask sample for test data set purpose and we use RST- Invariant features and texture features for feature analysis and then classified them by using k- Nearest Neighbor classification algorithm. The maximum accuracy is 90.

Active machine learning-driven experimentation to determine compound effects on protein patterns.

Science.gov (United States)

Naik, Armaghan W; Kangas, Joshua D; Sullivan, Devin P; Murphy, Robert F

2016-02-03

High throughput screening determines the effects of many conditions on a given biological target. Currently, to estimate the effects of those conditions on other targets requires either strong modeling assumptions (e.g. similarities among targets) or separate screens. Ideally, data-driven experimentation could be used to learn accurate models for many conditions and targets without doing all possible experiments. We have previously described an active machine learning algorithm that can iteratively choose small sets of experiments to learn models of multiple effects. We now show that, with no prior knowledge and with liquid handling robotics and automated microscopy under its control, this learner accurately learned the effects of 48 chemical compounds on the subcellular localization of 48 proteins while performing only 29% of all possible experiments. The results represent the first practical demonstration of the utility of active learning-driven biological experimentation in which the set of possible phenotypes is unknown in advance.

Control of discrete event systems modeled as hierarchical state machines

Science.gov (United States)

Brave, Y.; Heymann, M.

1991-01-01

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

Permanent magnet machines with air gap windings and integrated teeth windings

Energy Technology Data Exchange (ETDEWEB)

Alatalo, M [Chalmers Univ. of Technology, Goeteborg (Sweden). School of Electrical and Computer Engineering

1996-06-01

The Thesis deals with axial and radial flux permanent magnet machines with air gap windings and an integrated teeth winding. The aim is to develop a machine that produces a high torque per unit volume with as low losses as possible. The hypothesis is that an advanced three-phase winding, magnetized by a permanent magnet rotor should be better than other machine topologies. The finite element method is used to find favourable dimensions of the slotless winding, the integrated teeth winding and the permanent magnet rotor. Three machines were built and tested in order to verify calculations. It can be concluded that the analysis method shows good agreement with the calculated and the measured values of induced voltage and torque. The experiments showed that the slotless machine with NdFeB-magnets performs approximately like the slotted machine. A theoretical comparison of axial flux topology to radial flux topology showed that the torque production of the inner rotor radial flux machine is superior to that of the axial flux machine. An integrated teeth winding based on iron powder teeth glued to the winding was studied. The force density of a pole with integrated teeth is around three times the force density of a slotless pole. A direct drive wind power generator of 6.4 kW with integrated teeth can have the same power losses and magnet weight as a transversal flux machine. Compared to a standard induction machine the integrated teeth machine had approximately 2.5 times the power capacity of the induction machine with the same power losses and outer volume. 39 refs

The Relevance Voxel Machine (RVoxM): A Self-Tuning Bayesian Model for Informative Image-Based Prediction

DEFF Research Database (Denmark)

Sabuncu, Mert R.; Van Leemput, Koen

2012-01-01

This paper presents the relevance voxel machine (RVoxM), a dedicated Bayesian model for making predictions based on medical imaging data. In contrast to the generic machine learning algorithms that have often been used for this purpose, the method is designed to utilize a small number of spatially...

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation

Directory of Open Access Journals (Sweden)

Tiziana Segreto

2017-12-01

Full Text Available Nickel-Titanium (Ni-Ti alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT. The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Vibration Sensor Monitoring of Nickel-Titanium Alloy Turning for Machinability Evaluation.

Science.gov (United States)

Segreto, Tiziana; Caggiano, Alessandra; Karam, Sara; Teti, Roberto

2017-12-12

Nickel-Titanium (Ni-Ti) alloys are very difficult-to-machine materials causing notable manufacturing problems due to their unique mechanical properties, including superelasticity, high ductility, and severe strain-hardening. In this framework, the aim of this paper is to assess the machinability of Ni-Ti alloys with reference to turning processes in order to realize a reliable and robust in-process identification of machinability conditions. An on-line sensor monitoring procedure based on the acquisition of vibration signals was implemented during the experimental turning tests. The detected vibration sensorial data were processed through an advanced signal processing method in time-frequency domain based on wavelet packet transform (WPT). The extracted sensorial features were used to construct WPT pattern feature vectors to send as input to suitably configured neural networks (NNs) for cognitive pattern recognition in order to evaluate the correlation between input sensorial information and output machinability conditions.

Novel jet observables from machine learning

Science.gov (United States)

Datta, Kaustuv; Larkoski, Andrew J.

2018-03-01

Previous studies have demonstrated the utility and applicability of machine learning techniques to jet physics. In this paper, we construct new observables for the discrimination of jets from different originating particles exclusively from information identified by the machine. The approach we propose is to first organize information in the jet by resolved phase space and determine the effective N -body phase space at which discrimination power saturates. This then allows for the construction of a discrimination observable from the N -body phase space coordinates. A general form of this observable can be expressed with numerous parameters that are chosen so that the observable maximizes the signal vs. background likelihood. Here, we illustrate this technique applied to discrimination of H\\to b\\overline{b} decays from massive g\\to b\\overline{b} splittings. We show that for a simple parametrization, we can construct an observable that has discrimination power comparable to, or better than, widely-used observables motivated from theory considerations. For the case of jets on which modified mass-drop tagger grooming is applied, the observable that the machine learns is essentially the angle of the dominant gluon emission off of the b\\overline{b} pair.

Homopolar machine for reversible energy storage and transfer systems

Science.gov (United States)

Stillwagon, Roy E.

1978-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine.

Homopolar machine for reversible energy storage and transfer systems

International Nuclear Information System (INIS)

Stillwagon, R.E.

1981-01-01

A homopolar machine designed to operate as a generator and motor in reversibly storing and transferring energy between the machine and a magnetic load coil for a thermo-nuclear reactor. The machine rotor comprises hollow thin-walled cylinders or sleeves which form the basis of the system by utilizing substantially all of the rotor mass as a conductor thus making it possible to transfer substantially all the rotor kinetic energy electrically to the load coil in a highly economical and efficient manner. The rotor is divided into multiple separate cylinders or sleeves of modular design, connected in series and arranged to rotate in opposite directions but maintain the supply of current in a single direction to the machine terminals. A stator concentrically disposed around the sleeves consists of a hollow cylinder having a number of excitation coils each located radially outward from the ends of adjacent sleeves. Current collected at an end of each sleeve by sleeve slip rings and brushes is transferred through terminals to the magnetic load coil. Thereafter, electrical energy returned from the coil then flows through the machine which causes the sleeves to motor up to the desired speed in preparation for repetition of the cycle. To eliminate drag on the rotor between current pulses, the brush rigging is designed to lift brushes from all slip rings in the machine

A variable-mode stator consequent pole memory machine

Science.gov (United States)

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

2018-05-01

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

ATCA for Machines-- Advanced Telecommunications Computing Architecture

Energy Technology Data Exchange (ETDEWEB)

Larsen, R.S.; /SLAC

2008-04-22

The Advanced Telecommunications Computing Architecture is a new industry open standard for electronics instrument modules and shelves being evaluated for the International Linear Collider (ILC). It is the first industrial standard designed for High Availability (HA). ILC availability simulations have shown clearly that the capabilities of ATCA are needed in order to achieve acceptable integrated luminosity. The ATCA architecture looks attractive for beam instruments and detector applications as well. This paper provides an overview of ongoing R&D including application of HA principles to power electronics systems.

Instrumentation and control and human machine interface science and technology road-map in support of advanced reactors and fuel programs in the U.S

International Nuclear Information System (INIS)

Miller, D. W.; Arndt, S. A.; Bond, L. J.; Dudenhoeffer, D.; Hallbert, B.; Holcomb, D. E.; Wood, R. T.; Naser, J. A.; O'Hara, J.; Quinn, E. L.

2006-01-01

The purpose of this paper is to provide an overview of the current status of the Instrumentation, Control and Human Machine Interface (ICHMI) Science and Technology road-map being developed to address the major challenges in this technical area for the Gen IV and other U.S. Dept. of Energy (DOE) initiatives that support future deployments of nuclear energy systems. Reliable, capable ICHMI systems will be necessary for the advanced nuclear plants to be economically competitive. ICHMI enables measurement, control, protection, monitoring, and maintenance for processes and components. Through improvements in the technologies and demonstration of their use to facilitate licensing, ICHMI can contribute to the reduction of plant operations and maintenance costs while helping to ensure high plant availability. The impact of ICHMI can be achieved through effective use of the technologies to improve operational efficiency and optimize use of human resources. However, current licensing experience with digital I and C systems has provided lessons learned concerning the difficulties that can be encountered when introducing advanced technologies with expanded capabilities. Thus, in the development of advanced nuclear power designs, it will be important to address both the technical foundations of ICHMI systems as well as their licensing considerations. The ICHMI road-map will identify the necessary research, development and demonstration activities that are essential to facilitate necessary technology advancement and resolve outstanding issues. (authors)

Instrumentation and control and human machine interface science and technology Road-map in support of advanced reactors and fuel programs in the U.S

International Nuclear Information System (INIS)

Miller, D. W.; Arndt, S. A.; Dudenhoeffer, D.; Hallbert, B.; Bond, L. J.; Holcomb, D. E.; Wood, R. T.; Naser, J. A.; O'Hara, J.; Quinn, E. L.

2008-01-01

The purpose of this paper is to provide an overview of the current status of the Instrumentation, Control and Human Machine Interface (ICHMI) Science and Technology Road-map (Reference xi) that was developed to address the major challenges in this technical area for the Gen IV and other U.S. Department of Energy (DOE) initiatives that support future deployments of nuclear energy systems. Reliable, capable ICHMI systems will be necessary for the advanced nuclear plants to be economically competitive. ICHMI enables measurement, control, protection, monitoring, and maintenance for processes and components. Through improvements in the technologies and demonstration of their use to facilitate licensing, ICHMI can contribute to the reduction of plant operations and maintenance costs while helping to ensure high plant availability. The impact of ICHMI can be achieved through effective use of the technologies to improve operational efficiency and optimize use of human resources. However, current licensing experience with digital I and C systems has provided lessons learned concerning the difficulties that can be encountered when introducing advanced technologies with expanded capabilities. Thus, in the development of advanced nuclear power designs, it will be important to address both the technical foundations of ICHMI systems and their licensing considerations. The ICHMI Road-map will identify the necessary research, development and demonstration activities that are essential to facilitate necessary technology advancement and resolve outstanding issues. (authors)

Operations strategy for workload balancing of crews in an advanced main control room

International Nuclear Information System (INIS)

Kim, Seunghwan; Kim, Yochan; Jung, Wondea

2016-01-01

The advanced main control room (advanced-MCR) is the one that allows for reactor operations based on digital instrumentation and control (I and C) technology. Thus, the operators of an advanced-MCR operate the plant through digital I and C interfaces, and for this purpose, an additional digital manipulation task for the operating equipment should be performed that cannot be observed in a conventional-MCR. As a prior study proposing the cognitive, communicative, and operational activity measurement approach (COCOA), COCOA enables an evaluation of the operator's workload in an advanced-MCR,which includes newly generated tasks for Man-Machine Interface System based secondary operation under a digital environment, which does not exist in a conventional-MCR. As a result of observations on the workload level by utilizing COCOA for a reference plant with an advanced-MCR when conducting an emergency operating procedure, it was observed that the workload of the shift supervisor is about two times greater than that of other operators. This is because operators therein stuck to the old guidelines customized to a conventional-MCR and failed to accomplish load balancing in consideration of the operation environment that an advanced-MCR provides. In this context, it would be imperative to develop and apply an operations strategy for an advanced-MCR operation. This study proposes an operations strategy in an attempt to make a balanced workload of operators in an advanced-MCR. (author)

Ethical, environmental and social issues for machine vision in manufacturing industry

Science.gov (United States)

Batchelor, Bruce G.; Whelan, Paul F.

1995-10-01

Some of the ethical, environmental and social issues relating to the design and use of machine vision systems in manufacturing industry are highlighted. The authors' aim is to emphasize some of the more important issues, and raise general awareness of the need to consider the potential advantages and hazards of machine vision technology. However, in a short article like this, it is impossible to cover the subject comprehensively. This paper should therefore be seen as a discussion document, which it is hoped will provoke more detailed consideration of these very important issues. It follows from an article presented at last year's workshop. Five major topics are discussed: (1) The impact of machine vision systems on the environment; (2) The implications of machine vision for product and factory safety, the health and well-being of employees; (3) The importance of intellectual integrity in a field requiring a careful balance of advanced ideas and technologies; (4) Commercial and managerial integrity; and (5) The impact of machine visions technology on employment prospects, particularly for people with low skill levels.

Binary pressure-sensitive paint measurements using miniaturised, colour, machine vision cameras

Science.gov (United States)

Quinn, Mark Kenneth

2018-05-01

Recent advances in machine vision technology and capability have led to machine vision cameras becoming applicable for scientific imaging. This study aims to demonstrate the applicability of machine vision colour cameras for the measurement of dual-component pressure-sensitive paint (PSP). The presence of a second luminophore component in the PSP mixture significantly reduces its inherent temperature sensitivity, increasing its applicability at low speeds. All of the devices tested are smaller than the cooled CCD cameras traditionally used and most are of significantly lower cost, thereby increasing the accessibility of such technology and techniques. Comparisons between three machine vision cameras, a three CCD camera, and a commercially available specialist PSP camera are made on a range of parameters, and a detailed PSP calibration is conducted in a static calibration chamber. The findings demonstrate that colour machine vision cameras can be used for quantitative, dual-component, pressure measurements. These results give rise to the possibility of performing on-board dual-component PSP measurements in wind tunnels or on real flight/road vehicles.

Micro Machining Enhances Precision Fabrication

Science.gov (United States)

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.

Advanced system demonstration for utilization of biomass as an energy source. Environmental report

Energy Technology Data Exchange (ETDEWEB)

McCollom, M.

1979-01-01

The conclusions and findings of extensive analyses undertaken to assess the environmental impacts and effects of the proposal to assist in an Advanced System Demonstration for Utilization of Biomass as an Energy Source by means of a wood-fueled power plant. Included are a description of the proposed project, a discussion of the existing environment that the project would affect, a summary of the project's impacts on the natural and human environments, a discussion of the project's relationships to other government policies and plans, and an extensive review of the alternatives which were considered in evaluating the proposed action. All findings of the research undertaken are discussed. More extensive presentations of the methods of analysis used to arrive at the various conclusions are available in ten topical technical appendices.

Trial-Based Cost-Utility Analysis of Icotinib versus Gefitinib as Second-Line Therapy for Advanced Non-Small Cell Lung Cancer in China.

Science.gov (United States)

Zhang, Chunxiang; Zhang, Hongmei; Shi, Jinning; Wang, Dong; Zhang, Xiuwei; Yang, Jian; Zhai, Qizhi; Ma, Aixia

2016-01-01

Our objective is to compare the cost-utility of icotinib and gefitinib for the second-line treatment of advanced non-small cell lung cancer (NSCLC) from the perspective of the Chinese healthcare system. Model technology was applied to assess the data of randomized clinical trials and the direct medical costs from the perspective of the Chinese healthcare system. Five-year quality-adjusted life years (QALYs) and incremental cost-utility ratios (ICURs) were calculated. One-way and probabilistic sensitivity analyses (PSA) were performed. Our model suggested that the median progression-free survival (PFS) was 4.2 months in the icotinib group and 3.5 months in the gefitinib group while they were 4.6 months and 3.4 months, respectively, in the trials. The 5-year QALYs was 0.279 in the icotinib group and 0.269 in the gefitinib group, and the according medical costs were $10662.82 and $13127.57. The ICUR/QALY of icotinib versus gefitinib presented negative in this study. The most sensitive parameter to the ICUR was utility of PFS, ranging from $-1,259,991.25 to $-182,296.61; accordingly the icotinib treatment consistently represented a dominant cost-utility strategy. The icotinib strategy, as a second-line therapy for advanced NSCLC patients in China, is the preferred strategy relative to gefitinib because of the dominant cost-utility. In addition, icotinib shows a good curative effect and safety, resulting in a strong demand for the Chinese market.

Trial-Based Cost-Utility Analysis of Icotinib versus Gefitinib as Second-Line Therapy for Advanced Non-Small Cell Lung Cancer in China.

Directory of Open Access Journals (Sweden)

Chunxiang Zhang

Full Text Available Our objective is to compare the cost-utility of icotinib and gefitinib for the second-line treatment of advanced non-small cell lung cancer (NSCLC from the perspective of the Chinese healthcare system.Model technology was applied to assess the data of randomized clinical trials and the direct medical costs from the perspective of the Chinese healthcare system. Five-year quality-adjusted life years (QALYs and incremental cost-utility ratios (ICURs were calculated. One-way and probabilistic sensitivity analyses (PSA were performed.Our model suggested that the median progression-free survival (PFS was 4.2 months in the icotinib group and 3.5 months in the gefitinib group while they were 4.6 months and 3.4 months, respectively, in the trials. The 5-year QALYs was 0.279 in the icotinib group and 0.269 in the gefitinib group, and the according medical costs were $10662.82 and $13127.57. The ICUR/QALY of icotinib versus gefitinib presented negative in this study. The most sensitive parameter to the ICUR was utility of PFS, ranging from $-1,259,991.25 to $-182,296.61; accordingly the icotinib treatment consistently represented a dominant cost-utility strategy.The icotinib strategy, as a second-line therapy for advanced NSCLC patients in China, is the preferred strategy relative to gefitinib because of the dominant cost-utility. In addition, icotinib shows a good curative effect and safety, resulting in a strong demand for the Chinese market.

Hybrid machining processes perspectives on machining and finishing

CERN Document Server

Gupta, Kapil; Laubscher, R F

2016-01-01

This book describes various hybrid machining and finishing processes. It gives a critical review of the past work based on them as well as the current trends and research directions. For each hybrid machining process presented, the authors list the method of material removal, machining system, process variables and applications. This book provides a deep understanding of the need, application and mechanism of hybrid machining processes.

A REVIEW OF VIBRATION MACHINE DIAGNOSTICS BY USING ARTIFICIAL INTELLIGENCE METHODS

Directory of Open Access Journals (Sweden)

Grover Zurita

2016-09-01

Full Text Available In the industry, gears and rolling bearings failures are one of the foremost causes of breakdown in rotating machines, reducing availability time of the production and resulting in costly systems downtime. Therefore, there are growing demands for vibration condition based monitoring of gears and bearings, and any method in order to improve the effectiveness, reliability, and accuracy of the bearing faults diagnosis ought to be evaluated. In order to perform machine diagnosis efficiently, researchers have extensively investigated different advanced digital signal processing techniques and artificial intelligence methods to accurately extract fault characteristics from vibration signals. The main goal of this article is to present the state-of-the-art development in vibration analysis for machine diagnosis based on artificial intelligence methods.

Operating point resolved loss computation in electrical machines

Directory of Open Access Journals (Sweden)

Pfingsten Georg Von

2016-03-01

Full Text Available Magnetic circuits of electromagnetic energy converters, such as electrical machines, are nowadays highly utilized. This proposition is intrinsic for the magnetic as well as the electric circuit and depicts that significant enhancements of electrical machines are difficult to achieve in the absence of a detailed understanding of underlying effects. In order to improve the properties of electrical machines the accurate determination of the locally distributed iron losses based on idealized model assumptions solely is not sufficient. Other loss generating effects have to be considered and the possibility being able to distinguish between the causes of particular loss components is indispensable. Parasitic loss mechanisms additionally contributing to the total losses originating from field harmonics, non-linear material behaviour, rotational magnetizations, and detrimental effects caused by the manufacturing process or temperature, are not explicitly considered in the common iron-loss models, probably even not specifically contained in commonly used calibration factors. This paper presents a methodology being able to distinguish between different loss mechanisms and enables to individually consider particular loss mechanisms in the model of the electric machine. A sensitivity analysis of the model parameters can be performed to obtain information about which decisive loss origin for which working point has to be manipulated by the electromagnetic design or the control of the machine.

Main studies results for introduction of EB machine to Vietnam and for its application

International Nuclear Information System (INIS)

Tran, Khac An; Nguyen, Quoc Hien; Le, Hai

2004-01-01

Upon the national program on utilization of EB machine for research and development purposes and the FNCA project on application of electron accelerator, as a counterpart the Research and Development Center for Radiation Technology (VINAGAMMA) is preparing technical, manpower and financial conditions for introduction of an EB machine for R and D purposes. The paper offers main studied results in the field of Radiation Processing aimed at putting applications of EB technology into Vietnam and studies on selection of EB machine for R and D purposes in Vietnam. (author)

Steady State Advanced Tokamak (SSAT): The mission and the machine

International Nuclear Information System (INIS)

Thomassen, K.; Goldston, R.; Nevins, B.; Neilson, H.; Shannon, T.; Montgomery, B.

1992-03-01

Extending the tokamak concept to the steady state regime and pursuing advances in tokamak physics are important and complementary steps for the magnetic fusion energy program. The required transition away from inductive current drive will provide exciting opportunities for advances in tokamak physics, as well as important impetus to drive advances in fusion technology. Recognizing this, the Fusion Policy Advisory Committee and the US National Energy Strategy identified the development of steady state tokamak physics and technology, and improvements in the tokamak concept, as vital elements in the magnetic fusion energy development plan. Both called for the construction of a steady state tokamak facility to address these plan elements. Advances in physics that produce better confinement and higher pressure limits are required for a similar unit size reactor. Regimes with largely self-driven plasma current are required to permit a steady-state tokamak reactor with acceptable recirculating power. Reliable techniques of disruption control will be needed to achieve the availability goals of an economic reactor. Thus the central role of this new tokamak facility is to point the way to a more attractive demonstration reactor (DEMO) than the present data base would support. To meet the challenges, we propose a new ''Steady State Advanced Tokamak'' (SSAT) facility that would develop and demonstrate optimized steady state tokamak operating mode. While other tokamaks in the world program employ superconducting toroidal field coils, SSAT would be the first major tokamak to operate with a fully superconducting coil set in the elongated, divertor geometry planned for ITER and DEMO

ATCA for Machines-- Advanced Telecommunications Computing Architecture

International Nuclear Information System (INIS)

Larsen, R

2008-01-01

The Advanced Telecommunications Computing Architecture is a new industry open standard for electronics instrument modules and shelves being evaluated for the International Linear Collider (ILC). It is the first industrial standard designed for High Availability (HA). ILC availability simulations have shown clearly that the capabilities of ATCA are needed in order to achieve acceptable integrated luminosity. The ATCA architecture looks attractive for beam instruments and detector applications as well. This paper provides an overview of ongoing R and D including application of HA principles to power electronics systems

Utilization of low temperature geothermal water in traditional and advanced agricultural applications

International Nuclear Information System (INIS)

Rossi, L.; Pacciaroni, F.

1992-01-01

The locations of large amounts of low temperature geothermal sources (30 to 80 degrees C) have been identified in Italy and in many European countries; one of the most interesting utilization of these sources is greenhouse heating. Surplus investment in comparison with conventional heating systems is justified only by the application of low cost technologies for well completion, heating distribution and waste heat treatment. In the last few years, many efforts have been made in the development of these technologies and selection of more profitable crops. Since 1984, ENEA (Italian Agency for Energy, New Technologies and the Environment) has carried out experimental work in two geothermal stations located in Canino (VT) and in Gorgo di Latisana (UD). In these plants, a number of greenhouses enveloped with plastic film are provided with different heating systems; the combination of soil and forced air heating is preferred. Plastic pipes, buried in the soil, are used as soil heating for horticulture and fruit production. For plot plant cultivation, soil heating is obtained by plastic pipes half-buried in a concrete floor. Asparagus cultivation is carried out with buried pipes. No additional heating with conventional fuel is provided in any greenhouse. During these years, ENEA has developed heating and water distribution technologies: current industrial components are generally utilized. Moreover, ENEA has recently completed an advanced automatic control system able to control geothermal greenhouses, manage water distribution, save energy and optimize environmental conditions

Utilization of low temperature geothermal water in traditional and advanced agricultural applications

Energy Technology Data Exchange (ETDEWEB)

Rossi, L.; Pacciaroni, F.

1992-12-31

The locations of large amounts of low temperature geothermal sources (30 to 80 degrees C) have been identified in Italy and in many European countries; one of the most interesting utilization of these sources is greenhouse heating. Surplus investment in comparison with conventional heating systems is justified only by the application of low cost technologies for well completion, heating distribution and waste heat treatment. In the last few years, many efforts have been made in the development of these technologies and selection of more profitable crops. Since 1984, ENEA (Italian Agency for Energy, New Technologies and the Environment) has carried out experimental work in two geothermal stations located in Canino (VT) and in Gorgo di Latisana (UD). In these plants, a number of greenhouses enveloped with plastic film are provided with different heating systems; the combination of soil and forced air heating is preferred. Plastic pipes, buried in the soil, are used as soil heating for horticulture and fruit production. For plot plant cultivation, soil heating is obtained by plastic pipes half-buried in a concrete floor. Asparagus cultivation is carried out with buried pipes. No additional heating with conventional fuel is provided in any greenhouse. During these years, ENEA has developed heating and water distribution technologies: current industrial components are generally utilized. Moreover, ENEA has recently completed an advanced automatic control system able to control geothermal greenhouses, manage water distribution, save energy and optimize environmental conditions.

Advanced Autonomous Systems for Space Operations

Science.gov (United States)

Gross, A. R.; Smith, B. D.; Muscettola, N.; Barrett, A.; Mjolssness, E.; Clancy, D. J.

2002-01-01

New missions of exploration and space operations will require unprecedented levels of autonomy to successfully accomplish their objectives. Inherently high levels of complexity, cost, and communication distances will preclude the degree of human involvement common to current and previous space flight missions. With exponentially increasing capabilities of computer hardware and software, including networks and communication systems, a new balance of work is being developed between humans and machines. This new balance holds the promise of not only meeting the greatly increased space exploration requirements, but simultaneously dramatically reducing the design, development, test, and operating costs. New information technologies, which take advantage of knowledge-based software, model-based reasoning, and high performance computer systems, will enable the development of a new generation of design and development tools, schedulers, and vehicle and system health management capabilities. Such tools will provide a degree of machine intelligence and associated autonomy that has previously been unavailable. These capabilities are critical to the future of advanced space operations, since the science and operational requirements specified by such missions, as well as the budgetary constraints will limit the current practice of monitoring and controlling missions by a standing army of ground-based controllers. System autonomy capabilities have made great strides in recent years, for both ground and space flight applications. Autonomous systems have flown on advanced spacecraft, providing new levels of spacecraft capability and mission safety. Such on-board systems operate by utilizing model-based reasoning that provides the capability to work from high-level mission goals, while deriving the detailed system commands internally, rather than having to have such commands transmitted from Earth. This enables missions of such complexity and communication` distances as are not

Advanced computations in plasma physics

International Nuclear Information System (INIS)

Tang, W.M.

2002-01-01

Scientific simulation in tandem with theory and experiment is an essential tool for understanding complex plasma behavior. In this paper we review recent progress and future directions for advanced simulations in magnetically confined plasmas with illustrative examples chosen from magnetic confinement research areas such as microturbulence, magnetohydrodynamics, magnetic reconnection, and others. Significant recent progress has been made in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics, giving increasingly good agreement between experimental observations and computational modeling. This was made possible by innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning widely disparate temporal and spatial scales together with access to powerful new computational resources. In particular, the fusion energy science community has made excellent progress in developing advanced codes for which computer run-time and problem size scale well with the number of processors on massively parallel machines (MPP's). A good example is the effective usage of the full power of multi-teraflop (multi-trillion floating point computations per second) MPP's to produce three-dimensional, general geometry, nonlinear particle simulations which have accelerated progress in understanding the nature of turbulence self-regulation by zonal flows. It should be emphasized that these calculations, which typically utilized billions of particles for thousands of time-steps, would not have been possible without access to powerful present generation MPP computers and the associated diagnostic and visualization capabilities. In general, results from advanced simulations provide great encouragement for being able to include increasingly realistic dynamics to enable deeper physics insights into plasmas in both natural and laboratory environments. The associated scientific excitement should serve to

A Study on Guidelines for the Utilization of Unproven MMIS Technology In Nuclear Power Plant Application

International Nuclear Information System (INIS)

Kang, Sung Kon; Shin, Yeong Cheol; Bae, Byoung Hwan

2007-01-01

New MMIS (Man Machine Interface System) technology is rapidly advanced as digital technology provides opportunity for more functionality and better cost effectiveness and NPP (Nuclear Power Plant) operators are inclined to use the new technology for the construction of new plant and for the upgrade of existing plants. However, this new technology poses risks to the NPP operators at the same time. These risks are mainly due to the poor reliability of newly developed technology. KHNP's past experiences with the new MMIS equipment shows many cases of reliability problem. And their consequences include unintended plant trips, lowered acceptance of the new digital technology by the plant I and C maintenance crew, and increased licensing burden in answering for questions from the nuclear regulatory body. Considering the fact that the risk of these failures in the nuclear plant operation is far greater than those in other industry, utilities require proven technology for the MMIS in nuclear power plants. So that new MMIS technology might be testified as proven technology, guidelines for the utilization of unproven MMIS technology in nuclear power plant application is required for applying new advanced MMIS technology which is apparently needed to obtain a definite gain in simplicity or performance

SCADA for microtron and beam transport line radio therapy machine subsystem

International Nuclear Information System (INIS)

Deshpande, Praveen; Palod, Shradha; Bhujle, Ashok

2003-01-01

Centre for Advanced Technology is developing a Radio Therapy Machine (RTM) to be used for cancer treatment. The radiotherapy machine has a Microtron consisting of a RF system, main and auxiliary magnets. It has a Beam transport line (BTL) consisting of fourteen magnets. This paper describes a PC based supervisory control and data acquisition system (SCADA) developed for controlling mainly the power supplies for the above sub systems from a remote location. It offers a graphic user interface (GUI) at the control room PC for RTM operation in engineering mode

CMIS arithmetic and multiwire news for QCD on the connection machine

International Nuclear Information System (INIS)

Brickner, R.G.

1991-01-01

Our collaboration has been running Wilson fermion QCD simulations on various Connection Machines for over a year and a half. During this time, we have continually optimized our code for operations found in the fermion matrix inversion. Our current version of the matrix inversion is written almost entirely in CMIS (Connection Machine Instruction Set), and utilizes both high-speed arithmetic and multiwire 'news' (nearest-neighbor communications). We present details of how these and other features of our code are implemented on the CM-2. (orig.)

Machine learning in cardiovascular medicine: are we there yet?

Science.gov (United States)

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

2018-01-19

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

Hybrid forecasting of chaotic processes: Using machine learning in conjunction with a knowledge-based model

Science.gov (United States)

Pathak, Jaideep; Wikner, Alexander; Fussell, Rebeckah; Chandra, Sarthak; Hunt, Brian R.; Girvan, Michelle; Ott, Edward

2018-04-01

A model-based approach to forecasting chaotic dynamical systems utilizes knowledge of the mechanistic processes governing the dynamics to build an approximate mathematical model of the system. In contrast, machine learning techniques have demonstrated promising results for forecasting chaotic systems purely from past time series measurements of system state variables (training data), without prior knowledge of the system dynamics. The motivation for this paper is the potential of machine learning for filling in the gaps in our underlying mechanistic knowledge that cause widely-used knowledge-based models to be inaccurate. Thus, we here propose a general method that leverages the advantages of these two approaches by combining a knowledge-based model and a machine learning technique to build a hybrid forecasting scheme. Potential applications for such an approach are numerous (e.g., improving weather forecasting). We demonstrate and test the utility of this approach using a particular illustrative version of a machine learning known as reservoir computing, and we apply the resulting hybrid forecaster to a low-dimensional chaotic system, as well as to a high-dimensional spatiotemporal chaotic system. These tests yield extremely promising results in that our hybrid technique is able to accurately predict for a much longer period of time than either its machine-learning component or its model-based component alone.

Deriving the expected utility of a predictive model when the utilities are uncertain.

Science.gov (United States)

Cooper, Gregory F; Visweswaran, Shyam

2005-01-01

Predictive models are often constructed from clinical databases with the goal of eventually helping make better clinical decisions. Evaluating models using decision theory is therefore natural. When constructing a model using statistical and machine learning methods, however, we are often uncertain about precisely how the model will be used. Thus, decision-independent measures of classification performance, such as the area under an ROC curve, are popular. As a complementary method of evaluation, we investigate techniques for deriving the expected utility of a model under uncertainty about the model's utilities. We demonstrate an example of the application of this approach to the evaluation of two models that diagnose coronary artery disease.

AAA+ Machines of Protein Destruction in Mycobacteria.

Science.gov (United States)

Alhuwaider, Adnan Ali H; Dougan, David A

2017-01-01

The bacterial cytosol is a complex mixture of macromolecules (proteins, DNA, and RNA), which collectively are responsible for an enormous array of cellular tasks. Proteins are central to most, if not all, of these tasks and as such their maintenance (commonly referred to as protein homeostasis or proteostasis) is vital for cell survival during normal and stressful conditions. The two key aspects of protein homeostasis are, (i) the correct folding and assembly of proteins (coupled with their delivery to the correct cellular location) and (ii) the timely removal of unwanted or damaged proteins from the cell, which are performed by molecular chaperones and proteases, respectively. A major class of proteins that contribute to both of these tasks are the AAA+ (ATPases associated with a variety of cellular activities) protein superfamily. Although much is known about the structure of these machines and how they function in the model Gram-negative bacterium Escherichia coli , we are only just beginning to discover the molecular details of these machines and how they function in mycobacteria. Here we review the different AAA+ machines, that contribute to proteostasis in mycobacteria. Primarily we will focus on the recent advances in the structure and function of AAA+ proteases, the substrates they recognize and the cellular pathways they control. Finally, we will discuss the recent developments related to these machines as novel drug targets.

Abbreviated machining schedule for fabricating beryllium parts free of surface damage

International Nuclear Information System (INIS)

Beitscher, S.; Capes, J.F.; Leslie, W.W.; Luckow, J.R.; Riegel, R.L.

1979-01-01

This study was performed to develop a more economical method of machining damage-free beryllium components at Rocky Flats. The present method involves a 9-pass schedule of lathe turning followed by a chemical etch. Prototype beryllium hemispherical shell parts and cylindrical tensile specimens machined to simulate the parts were utilized in this study. The main investigative methods used to evaluate the amount of machining damage were metallography and tensile tests. It was found that damage-free parts could be produced by carefully controlled machining if the number of machining passes was reduced to 4 or even 3, if followed by the standard etching treatment. These findings were made on Select S-65 grade beryllium, and probably apply to other common grades of powder source beryllium but not necessarily to ingot-source beryllium. It is recommended that the 4-pass schedule becomes the standard method to produce damage-free beryllium derived from powder. Significant savings in time, labor, and equipment can be realized by this change in method without decreasing the quality of the product

Device for delivering cryogen to rotary super-conducting winding of cryogen-cooled electrical machine

International Nuclear Information System (INIS)

Filippov, I.F.; Gorbunov, G.S.; Khutoretsky, G.M.; Popov, J.S.; Skachkov, J.V.; Vinokurov, A.A.

1980-01-01

A device is disclosed for delivering cryogen to a superconducting winding of a cryogen-cooled electrical machine comprising a pipe articulated along the axis of the electrical machine and intended to deliver cryogen. One end of said pipe is located in a rotary chamber which communicates through channels with the space of the electrical machine, and said space accommodating its superconducting winding. The said chamber accommodates a needle installed along the chamber axis, and the length of said needle is of sufficient length such that in the advanced position of said cryogen delivering pipe said needle reaches the end of the pipe. The layout of the electrical machine increases the reliability and effectiveness of the device for delivering cryogen to the superconducting winding, simplifies the design of the device and raises the efficiency of the electrical machine

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

Science.gov (United States)

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

2014-01-01

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

A quantum speedup in machine learning: finding an N-bit Boolean function for a classification

International Nuclear Information System (INIS)

Yoo, Seokwon; Lee, Jinhyoung; Bang, Jeongho; Lee, Changhyoup

2014-01-01

We compare quantum and classical machines designed for learning an N-bit Boolean function in order to address how a quantum system improves the machine learning behavior. The machines of the two types consist of the same number of operations and control parameters, but only the quantum machines utilize the quantum coherence naturally induced by unitary operators. We show that quantum superposition enables quantum learning that is faster than classical learning by expanding the approximate solution regions, i.e., the acceptable regions. This is also demonstrated by means of numerical simulations with a standard feedback model, namely random search, and a practical model, namely differential evolution. (paper)

Scientific Discovery through Advanced Computing in Plasma Science

Science.gov (United States)

Tang, William

2005-03-01

Advanced computing is generally recognized to be an increasingly vital tool for accelerating progress in scientific research during the 21st Century. For example, the Department of Energy's ``Scientific Discovery through Advanced Computing'' (SciDAC) Program was motivated in large measure by the fact that formidable scientific challenges in its research portfolio could best be addressed by utilizing the combination of the rapid advances in super-computing technology together with the emergence of effective new algorithms and computational methodologies. The imperative is to translate such progress into corresponding increases in the performance of the scientific codes used to model complex physical systems such as those encountered in high temperature plasma research. If properly validated against experimental measurements and analytic benchmarks, these codes can provide reliable predictive capability for the behavior of a broad range of complex natural and engineered systems. This talk reviews recent progress and future directions for advanced simulations with some illustrative examples taken from the plasma science applications area. Significant recent progress has been made in both particle and fluid simulations of fine-scale turbulence and large-scale dynamics, giving increasingly good agreement between experimental observations and computational modeling. This was made possible by the combination of access to powerful new computational resources together with innovative advances in analytic and computational methods for developing reduced descriptions of physics phenomena spanning a huge range in time and space scales. In particular, the plasma science community has made excellent progress in developing advanced codes for which computer run-time and problem size scale well with the number of processors on massively parallel machines (MPP's). A good example is the effective usage of the full power of multi-teraflop (multi-trillion floating point computations

Technical diagnostics functioning machines and mechanisms

Science.gov (United States)

Kiselev, M. I.; Pronyakin, V. I.; Tulekbaeva, A. K.

2018-02-01

Article discusses the machines and mechanisms technical state monitoring problem. Approaches for estimating mechanical systems current technical state, defects detection and evaluation of mechanical elements degradation levels are considered. The paper analyzes the traditional methods offered in international and national standards, especially vibrodiagnostics. An advanced phase method is presented which is based on registration the kinematic parameters of the mechanism running cycle. The result of coupling the phase method and mathematical modeling is shown, and simulation comparison with the experimental data is presented.

Building machines that learn and think like people.

Science.gov (United States)

Lake, Brenden M; Ullman, Tomer D; Tenenbaum, Joshua B; Gershman, Samuel J

2017-01-01

Recent progress in artificial intelligence has renewed interest in building systems that learn and think like people. Many advances have come from using deep neural networks trained end-to-end in tasks such as object recognition, video games, and board games, achieving performance that equals or even beats that of humans in some respects. Despite their biological inspiration and performance achievements, these systems differ from human intelligence in crucial ways. We review progress in cognitive science suggesting that truly human-like learning and thinking machines will have to reach beyond current engineering trends in both what they learn and how they learn it. Specifically, we argue that these machines should (1) build causal models of the world that support explanation and understanding, rather than merely solving pattern recognition problems; (2) ground learning in intuitive theories of physics and psychology to support and enrich the knowledge that is learned; and (3) harness compositionality and learning-to-learn to rapidly acquire and generalize knowledge to new tasks and situations. We suggest concrete challenges and promising routes toward these goals that can combine the strengths of recent neural network advances with more structured cognitive models.

Machinability study of steels in precision orthogonal cutting

Directory of Open Access Journals (Sweden)

Leonardo Roberto Silva

2012-08-01

Full Text Available The miniaturization of components and systems is advancing steadily in many areas of engineering. Consequently, micro-machining is becoming an important manufacture technology due to the increasing demand for miniaturized products in recent years. Precision machining aims the production of advanced components with high dimensional accuracy and acceptable surface integrity. This work presents an experimental study based on Merchant and Lee & Shaffer theories applied to precision radial turning of AISI D2 cold work tool and AISI 1045 medium carbon steels with uncoated carbide tools ISO grade K15. The aim of this study is to evaluate the influence of feed rate on chip compression ratio (Rc, chip deformation (ε, friction angle (ρ, shear angle (Φ, normal stress (σ and shear stress (• for both work materials. The results indicated that the shear angle decreased and chip deformation increased as the chip compression ratio was elevated without significant differences between both materials. Additionally, higher cutting and thrust forces and normal and shear stresses were observed for the tool steel. Finally, the Lee & Shaffer model gave shear plane angle values closer to the experimental data.

A Review on Parametric Analysis of Magnetic Abrasive Machining Process

Science.gov (United States)

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.

High Torque Density Transverse Flux Machine without the Need to Use SMC Material for 3D Flux Paths

DEFF Research Database (Denmark)

Lu, Kaiyuan; Wu, Weimin

2015-01-01

This paper presents a new transverse flux permanent magnet machine. In a normal transverse flux machine, complicated 3-D flux paths often exist. Such 3-D flux paths would require the use of soft magnetic composites material instead of laminations for construction of the machine stator. In the new...... machine topology proposed in this paper, by advantageously utilizing the magnetic flux path provided by an additional rotor, use of laminations that allow 2-D flux paths only will be sufficient to accomplish the required 3-D flux paths. The machine also has a high torque density and is therefore...

Classifying smoking urges via machine learning.

Science.gov (United States)

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

Advanced gadolinia core and Toshiba advanced reactor management system

International Nuclear Information System (INIS)

Miyamoto, Toshiki; Yoshioka, Ritsuo; Ebisuya, Mitsuo

1988-01-01

At the Hamaoka Nuclear Power Station, Unit No. 3, advanced core design and core management technology have been adopted, significantly improving plant availability, operability and reliability. The outstanding technologies are the advanced gadolinia core (AGC) which utilizes gadolinium for the axial power distribution control, and Toshiba advanced reactor management system (TARMS) which uses a three-dimensional core physics simulator to calculate the power distribution. Presented here are the effects of these advanced technologies as observed during field testing. (author)

Ultraclean Fuels Production and Utilization for the Twenty-First Century: Advances toward Sustainable Transportation Fuels

Energy Technology Data Exchange (ETDEWEB)

Fox, Elise B.; Liu, Zhong-Wen; Liu, Zhao-Tie

2013-11-21

Ultraclean fuels production has become increasingly important as a method to help decrease emissions and allow the introduction of alternative feed stocks for transportation fuels. Established methods, such as Fischer-Tropsch, have seen a resurgence of interest as natural gas prices drop and existing petroleum resources require more intensive clean-up and purification to meet stringent environmental standards. This review covers some of the advances in deep desulfurization, synthesis gas conversion into fuels and feed stocks that were presented at the 245th American Chemical Society Spring Annual Meeting in New Orleans, LA in the Division of Energy and Fuels symposium on "Ultraclean Fuels Production and Utilization".

Advanced characterization of carrier profiles in germanium using micro-machined contact probes

DEFF Research Database (Denmark)

Clarysse, T.; Konttinen, M.; Parmentier, B.

2012-01-01

of new concepts based on micro machined, closely spaced contact probes (10 μm pitch). When using four probes to perform sheet resistance measurements, a quantitative carrier profile extraction based on the evolution of the sheet resistance versus depth along a beveled surface is obtained. Considering...... the properties of both approaches on Al+ implants in germanium with different anneal treatments....

Proceedings of the advanced research and technology development direct utilization, instrumentation and diagnostics contractors' review meeting

Energy Technology Data Exchange (ETDEWEB)

Geiling, D.W. (USDOE Morgantown Energy Technology Center, WV (USA)); Goldberg, P.M. (eds.) (USDOE Pittsburgh Energy Technology Center, PA (USA))

1990-01-01

The 1990 Advanced Research and Technology Development (AR TD) Direct Utilization, and Instrumentation and Diagnostics Contractors Review Meeting was held September 16--18, 1990, at the Hyatt at Chatham Center in Pittsburgh, PA. The meeting was sponsored by the US Department of Energy (DOE), Office of Fossil Energy, and the Pittsburgh and Morgantown Energy Technology Centers. Each year the meeting provides a forum for the exchange of information among the DOE AR TD contractors and interested parties. This year's meeting was hosted by the Pittsburgh Energy Technology Center and was attended by 120 individuals from industry, academia, national laboratories, and other governmental agencies. Papers were presented on research addressing coal surface, science, devolatilization and combustion, ash behavior, emission controls for gases particulates, fluid bed combustion and utilization in diesels and turbines. Individual reports are processed separately for the data bases.

Machine terms dictionary

Energy Technology Data Exchange (ETDEWEB)

NONE

1979-04-15

This book gives descriptions of machine terms which includes machine design, drawing, the method of machine, machine tools, machine materials, automobile, measuring and controlling, electricity, basic of electron, information technology, quality assurance, Auto CAD and FA terms and important formula of mechanical engineering.