On the equivalence between small-step and big-step abstract machines: a simple application of lightweight fusion

DEFF Research Database (Denmark)

Danvy, Olivier; Millikin, Kevin

2008-01-01

-step specification. We illustrate this observation here with a recognizer for Dyck words, the CEK machine, and Krivine’s machine with call/cc. The need for such a simple proof is motivated by our current work on small-step abstract machines as obtained by refocusing a function implementing a reduction semantics (a...... syntactic correspondence), and big-step abstract machines as obtained by CPStransforming and then defunctionalizing a function implementing a big-step semantics (a functional correspondence). © 2007 Elsevier B.V. All rights reserved....

A rule-based approach to model checking of UML state machines

Science.gov (United States)

Grobelna, Iwona; Grobelny, Michał; Stefanowicz, Łukasz

2016-12-01

In the paper a new approach to formal verification of control process specification expressed by means of UML state machines in version 2.x is proposed. In contrast to other approaches from the literature, we use the abstract and universal rule-based logical model suitable both for model checking (using the nuXmv model checker), but also for logical synthesis in form of rapid prototyping. Hence, a prototype implementation in hardware description language VHDL can be obtained that fully reflects the primary, already formally verified specification in form of UML state machines. Presented approach allows to increase the assurance that implemented system meets the user-defined requirements.

Autocoding State Machine in Erlang

DEFF Research Database (Denmark)

Guo, Yu; Hoffman, Torben; Gunder, Nicholas

2008-01-01

This paper presents an autocoding tool suit, which supports development of state machine in a model-driven fashion, where models are central to all phases of the development process. The tool suit, which is built on the Eclipse platform, provides facilities for the graphical specification...... of a state machine model. Once the state machine is specified, it is used as input to a code generation engine that generates source code in Erlang....

Collaborative Systems – Finite State Machines

Directory of Open Access Journals (Sweden)

Ion IVAN

2011-01-01

Full Text Available In this paper the finite state machines are defined and formalized. There are presented the collaborative banking systems and their correspondence is done with finite state machines. It highlights the role of finite state machines in the complexity analysis and performs operations on very large virtual databases as finite state machines. It builds the state diagram and presents the commands and documents transition between the collaborative systems states. The paper analyzes the data sets from Collaborative Multicash Servicedesk application and performs a combined analysis in order to determine certain statistics. Indicators are obtained, such as the number of requests by category and the load degree of an agent in the collaborative system.

On the Equivalence between Small-Step and Big-Step Abstract Machines: A Simple Application of Lightweight Fusion

DEFF Research Database (Denmark)

Danvy, Olivier; Millikin, Kevin

2007-01-01

-step specification. We illustrate this observation here with a recognizer for Dyck words, the CEK machine, and Krivine's machine with call/cc. The need for such a simple proof is motivated by our current work on small-step abstract machines as obtained by refocusing a function implementing a reduction semantics (a...

Refining Nodes and Edges of State Machines

DEFF Research Database (Denmark)

Hallerstede, Stefan; Snook, Colin

2011-01-01

State machines are hierarchical automata that are widely used to structure complex behavioural specifications. We develop two notions of refinement of state machines, node refinement and edge refinement. We compare the two notions by means of examples and argue that, by adopting simple conventions...... refinement theory and UML-B state machine refinement influences the style of node refinement. Hence we propose a method with direct proof of state machine refinement avoiding the detour via Event-B that is needed by UML-B....

From Abstract Art to Abstracted Artists

Directory of Open Access Journals (Sweden)

Romi Mikulinsky

2016-11-01

Full Text Available What lineage connects early abstract films and machine-generated YouTube videos? Hans Richter’s famous piece Rhythmus 21 is considered to be the first abstract film in the experimental tradition. The Webdriver Torso YouTube channel is composed of hundreds of thousands of machine-generated test patterns designed to check frequency signals on YouTube. This article discusses geometric abstraction vis-à-vis new vision, conceptual art and algorithmic art. It argues that the Webdriver Torso is an artistic marvel indicative of a form we call mathematical abstraction, which is art performed by computers and, quite possibly, for computers.

SwingStates: adding state machines to the swing toolkit

OpenAIRE

Appert , Caroline; Beaudouin-Lafon , Michel

2006-01-01

International audience; This article describes SwingStates, a library that adds state machines to the Java Swing user interface toolkit. Unlike traditional approaches, which use callbacks or listeners to define interaction, state machines provide a powerful control structure and localize all of the interaction code in one place. SwingStates takes advantage of Java's inner classes, providing programmers with a natural syntax and making it easier to follow and debug the resulting code. SwingSta...

State Machine Framework And Its Use For Driving LHC Operational states

CERN Document Server

Misiowiec, M; Solfaroli Camilloci, M

2011-01-01

The LHC follows a complex operational cycle with 12 major phases that include equipment tests, preparation, beam injection, ramping and squeezing, finally followed by the physics phase. This cycle is modelled and enforced with a state machine, whereby each operational phase is represented by a state. On each transition, before entering the next state, a series of conditions is verified to make sure the LHC is ready to move on. The State Machine framework was developed to cater for building independent or embedded state machines. They safely drive between the states executing tasks bound to transitions and broadcast related information to interested parties. The framework encourages users to program their own actions. Simple configuration management allows the operators to define and maintain complex models themselves. An emphasis was also put on easy interaction with the remote state machine instances through standard communication protocols. On top of its core functionality, the framework offers a transparen...

Machine Control System of Steady State Superconducting Tokamak-1

Energy Technology Data Exchange (ETDEWEB)

Masand, Harish, E-mail: harish@ipr.res.in; Kumar, Aveg; Bhandarkar, M.; Mahajan, K.; Gulati, H.; Dhongde, J.; Patel, K.; Chudasma, H.; Pradhan, S.

2016-11-15

Highlights: • Central Control System. • SST-1. • Machine Control System. - Abstract: Central Control System (CCS) of the Steady State Superconducting Tokamak-1 (SST-1) controls and monitors around 25 plant and experiment subsystems of SST-1 located remotely from the Central-Control room. Machine Control System (MCS) is a supervisory system that sits on the top of the CCS hierarchy and implements the CCS state diagram. MCS ensures the software interlock between the SST-1 subsystems with the CCS, any subsystem communication failure or its local error does not prohibit the execution of the MCS and in-turn the CCS operation. MCS also periodically monitors the subsystem’s status and their vital process parameters throughout the campaign. It also provides the platform for the Central Control operator to visualize and exchange remotely the operational and experimental configuration parameters with the sub-systems. MCS remains operational 24 × 7 from the commencement to the termination of the SST-1 campaign. The developed MCS has performed robustly and flawlessly during all the last campaigns of SST-1 carried out so far. This paper will describe various aspects of the development of MCS.

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...

Dynamic thermal analysis of machines in running state

CERN Document Server

Wang, Lihui

2014-01-01

With the increasing complexity and dynamism in today’s machine design and development, more precise, robust and practical approaches and systems are needed to support machine design. Existing design methods treat the targeted machine as stationery. Analysis and simulation are mostly performed at the component level. Although there are some computer-aided engineering tools capable of motion analysis and vibration simulation etc., the machine itself is in the dry-run state. For effective machine design, understanding its thermal behaviours is crucial in achieving the desired performance in real situation. Dynamic Thermal Analysis of Machines in Running State presents a set of innovative solutions to dynamic thermal analysis of machines when they are put under actual working conditions. The objective is to better understand the thermal behaviours of a machine in real situation while at the design stage. The book has two major sections, with the first section presenting a broad-based review of the key areas of ...

Hierarchical State Machines as Modular Horn Clauses

Directory of Open Access Journals (Sweden)

Pierre-Loïc Garoche

2016-07-01

Full Text Available In model based development, embedded systems are modeled using a mix of dataflow formalism, that capture the flow of computation, and hierarchical state machines, that capture the modal behavior of the system. For safety analysis, existing approaches rely on a compilation scheme that transform the original model (dataflow and state machines into a pure dataflow formalism. Such compilation often result in loss of important structural information that capture the modal behaviour of the system. In previous work we have developed a compilation technique from a dataflow formalism into modular Horn clauses. In this paper, we present a novel technique that faithfully compile hierarchical state machines into modular Horn clauses. Our compilation technique preserves the structural and modal behavior of the system, making the safety analysis of such models more tractable.

State machine operation of the MICE cooling channel

International Nuclear Information System (INIS)

Hanlet, Pierrick

2014-01-01

The Muon Ionization Cooling Experiment (MICE) is a demonstration experiment to prove the feasibility of cooling a beam of muons for use in a Neutrino Factory and/or Muon Collider. The MICE cooling channel is a section of a modified Study II cooling channel which will provide a 10% reduction in beam emittance. In order to ensure a reliable measurement, MICE will measure the beam emittance before and after the cooling channel at the level of 1%, a relative measurement of 0.001. This renders MICE a precision experiment which requires strict controls and monitoring of all experimental parameters in order to control systematic errors. The MICE Controls and Monitoring system is based on EPICS and integrates with the DAQ, Data monitoring systems, and a configuration database. The cooling channel for MICE has between 12 and 18 superconductnig solenoid coils in 3 to 7 magnets, depending on the staged development of the experiment. The magnets are coaxial and in close proximity which requires coordinated operation of the magnets when ramping, responding to quench conditions, and quench recovery. To reliably manage the operation of the magnets, MICE is implementing state machines for each magnet and an over-arching state machine for the magnets integrated in the cooling channel. The state machine transitions and operating parameters are stored/restored to/from the configuration database and coupled with MICE Run Control. Proper implementation of the state machines will not only ensure safe operation of the magnets, but will help ensure reliable data quality. A description of MICE, details of the state machines, and lessons learned from use of the state machines in recent magnet training tests will be discussed.

Machine learning topological states

Science.gov (United States)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Formal modeling of virtual machines

Science.gov (United States)

Cremers, A. B.; Hibbard, T. N.

1978-01-01

Systematic software design can be based on the development of a 'hierarchy of virtual machines', each representing a 'level of abstraction' of the design process. The reported investigation presents the concept of 'data space' as a formal model for virtual machines. The presented model of a data space combines the notions of data type and mathematical machine to express the close interaction between data and control structures which takes place in a virtual machine. One of the main objectives of the investigation is to show that control-independent data type implementation is only of limited usefulness as an isolated tool of program development, and that the representation of data is generally dictated by the control context of a virtual machine. As a second objective, a better understanding is to be developed of virtual machine state structures than was heretofore provided by the view of the state space as a Cartesian product.

Solid-state resistor for pulsed power machines

Science.gov (United States)

Stoltzfus, Brian; Savage, Mark E.; Hutsel, Brian Thomas; Fowler, William E.; MacRunnels, Keven Alan; Justus, David; Stygar, William A.

2016-12-06

A flexible solid-state resistor comprises a string of ceramic resistors that can be used to charge the capacitors of a linear transformer driver (LTD) used in a pulsed power machine. The solid-state resistor is able to absorb the energy of a switch prefire, thereby limiting LTD cavity damage, yet has a sufficiently low RC charge time to allow the capacitor to be recharged without disrupting the operation of the pulsed power machine.

Multivariate Cross-Classification: Applying machine learning techniques to characterize abstraction in neural representations

Directory of Open Access Journals (Sweden)

Jonas eKaplan

2015-03-01

Full Text Available Here we highlight an emerging trend in the use of machine learning classifiers to test for abstraction across patterns of neural activity. When a classifier algorithm is trained on data from one cognitive context, and tested on data from another, conclusions can be drawn about the role of a given brain region in representing information that abstracts across those cognitive contexts. We call this kind of analysis Multivariate Cross-Classification (MVCC, and review several domains where it has recently made an impact. MVCC has been important in establishing correspondences among neural patterns across cognitive domains, including motor-perception matching and cross-sensory matching. It has been used to test for similarity between neural patterns evoked by perception and those generated from memory. Other work has used MVCC to investigate the similarity of representations for semantic categories across different kinds of stimulus presentation, and in the presence of different cognitive demands. We use these examples to demonstrate the power of MVCC as a tool for investigating neural abstraction and discuss some important methodological issues related to its application.

RNA interference and Register Machines (extended abstract

Directory of Open Access Journals (Sweden)

Masahiro Hamano

2012-11-01

Full Text Available RNA interference (RNAi is a mechanism whereby small RNAs (siRNAs directly control gene expression without assistance from proteins. This mechanism consists of interactions between RNAs and small RNAs both of which may be single or double stranded. The target of the mechanism is mRNA to be degraded or aberrated, while the initiator is double stranded RNA (dsRNA to be cleaved into siRNAs. Observing the digital nature of RNAi, we represent RNAi as a Minsky register machine such that (i The two registers hold single and double stranded RNAs respectively, and (ii Machine's instructions are interpreted by interactions of enzyme (Dicer, siRNA (with RISC com- plex and polymerization (RdRp to the appropriate registers. Interpreting RNAi as a computational structure, we can investigate the computational meaning of RNAi, especially its complexity. Initially, the machine is configured as a Chemical Ground Form (CGF, which generates incorrect jumps. To remedy this problem, the system is remodeled as recursive RNAi, in which siRNA targets not only mRNA but also the machine instructional analogues of Dicer and RISC. Finally, probabilistic termination is investigated in the recursive RNAi system.

Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines

Science.gov (United States)

Le, Martin; Zheng, Xin; Katanyoutant, Sunant

2008-01-01

Single-event upsets (SEUs) pose great threats to avionic systems state machine control logic, which are frequently used to control sequence of events and to qualify protocols. The risks of SEUs manifest in two ways: (a) the state machine s state information is changed, causing the state machine to unexpectedly transition to another state; (b) due to the asynchronous nature of SEU, the state machine's state registers become metastable, consequently causing any combinational logic associated with the metastable registers to malfunction temporarily. Effect (a) can be mitigated with methods such as triplemodular redundancy (TMR). However, effect (b) cannot be eliminated and can degrade the effectiveness of any mitigation method of effect (a). Although there is no way to completely eliminate the risk of SEU-induced errors, the risk can be made very small by use of a combination of very fast state-machine logic and error-detection logic. Therefore, one goal of two main elements of the present method is to design the fastest state-machine logic circuitry by basing it on the fastest generic state-machine design, which is that of a one-hot state machine. The other of the two main design elements is to design fast error-detection logic circuitry and to optimize it for implementation in a field-programmable gate array (FPGA) architecture: In the resulting design, the one-hot state machine is fitted with a multiple-input XNOR gate for detection of illegal states. The XNOR gate is implemented with lookup tables and with pipelines for high speed. In this method, the task of designing all the logic must be performed manually because no currently available logic synthesis software tool can produce optimal solutions of design problems of this type. However, some assistance is provided by a script, written for this purpose in the Python language (an object-oriented interpretive computer language) to automatically generate hardware description language (HDL) code from state

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

Directory of Open Access Journals (Sweden)

Cai Wingfield

2017-09-01

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

SPR 2017. Abstracts

Energy Technology Data Exchange (ETDEWEB)

NONE

2017-05-15

The conference proceedings SPR 2017 include abstracts on the following issues: gastrointestinal radiography - inflammatory bowel diseases, cardiovascular CTA, general muscoskeletal radiology, muscoskeletal congenital development diseases, general pediatric radiology - chest, muscoskeletal imaging - marrow and infectious disorders, state-of-the-art body MR imaging, practical pediatric sonography, quality and professionalism, CT imaging in congenital heart diseases, radiographic courses, body MT techniques, contrast enhanced ultrasound, machine learning, forensic imaging, the radiation dos conundrum - reconciling imaging, imagining and managing, the practice of radiology, interventional radiology, neuroradiology, PET/MR.

Automata Learning through Counterexample Guided Abstraction Refinement

DEFF Research Database (Denmark)

Aarts, Fides; Heidarian, Faranak; Kuppens, Harco

2012-01-01

to a small set of abstract events that can be handled by automata learning tools. In this article, we show how such abstractions can be constructed fully automatically for a restricted class of extended finite state machines in which one can test for equality of data parameters, but no operations on data...... are allowed. Our approach uses counterexample-guided abstraction refinement: whenever the current abstraction is too coarse and induces nondeterministic behavior, the abstraction is refined automatically. Using Tomte, a prototype tool implementing our algorithm, we have succeeded to learn – fully......Abstraction is the key when learning behavioral models of realistic systems. Hence, in most practical applications where automata learning is used to construct models of software components, researchers manually define abstractions which, depending on the history, map a large set of concrete events...

Towards Compatible and Interderivable Semantic Specifications for the Scheme Programming Language, Part II: Reduction Semantics and Abstract Machines

DEFF Research Database (Denmark)

Biernacka, Malgorzata; Danvy, Olivier

2009-01-01

We present a context-sensitive reduction semantics for a lambmda-calculus with explicit substitutions and we show that the functional implementation of this small-step semantics mechanically corresponds to that of the abstract machine for Core Scheme presented by Clinger at PLDI'98, including fir...

Artificial emotional model based on finite state machine

Institute of Scientific and Technical Information of China (English)

MENG Qing-mei; WU Wei-guo

2008-01-01

According to the basic emotional theory, the artificial emotional model based on the finite state machine(FSM) was presented. In finite state machine model of emotion, the emotional space included the basic emotional space and the multiple emotional spaces. The emotion-switching diagram was defined and transition function was developed using Markov chain and linear interpolation algorithm. The simulation model was built using Stateflow toolbox and Simulink toolbox based on the Matlab platform.And the model included three subsystems: the input one, the emotion one and the behavior one. In the emotional subsystem, the responses of different personalities to the external stimuli were described by defining personal space. This model takes states from an emotional space and updates its state depending on its current state and a state of its input (also a state-emotion). The simulation model realizes the process of switching the emotion from the neutral state to other basic emotions. The simulation result is proved to correspond to emotion-switching law of human beings.

An Approach for Implementing State Machines with Online Testability

Directory of Open Access Journals (Sweden)

P. K. Lala

2010-01-01

Full Text Available During the last two decades, significant amount of research has been performed to simplify the detection of transient or soft errors in VLSI-based digital systems. This paper proposes an approach for implementing state machines that uses 2-hot code for state encoding. State machines designed using this approach allow online detection of soft errors in registers and output logic. The 2-hot code considerably reduces the number of required flip-flops and leads to relatively straightforward implementation of next state and output logic. A new way of designing output logic for online fault detection has also been presented.

The Design of Finite State Machine for Asynchronous Replication Protocol

Science.gov (United States)

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

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

A Concrete Framework for Environment Machines

DEFF Research Database (Denmark)

Biernacka, Malgorzata; Danvy, Olivier

2007-01-01

calculus with explicit substitutions), we extend it minimally so that it can also express one-step reduction strategies, and we methodically derive a series of environment machines from the specification of two one-step reduction strategies for the lambda-calculus: normal order and applicative order....... The derivation extends Danvy and Nielsen’s refocusing-based construction of abstract machines with two new steps: one for coalescing two successive transitions into one, and the other for unfolding a closure into a term and an environment in the resulting abstract machine. The resulting environment machines...... include both the Krivine machine and the original version of Krivine’s machine, Felleisen et al.’s CEK machine, and Leroy’s Zinc abstract machine....

Developing a PLC-friendly state machine model: lessons learned

Science.gov (United States)

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

2014-07-01

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

State Machine Modeling of the Space Launch System Solid Rocket Boosters

Science.gov (United States)

Harris, Joshua A.; Patterson-Hine, Ann

2013-01-01

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

Rethinking State Politics: The Withering of State Dominant Machines in Brazil

Directory of Open Access Journals (Sweden)

André Borges

2007-03-01

Full Text Available Research on Brazilian federalism and state politics has focused mainly on the impact of federal arrangements on national political systems, whereas comparative analyses of the workings of state political institutions and patterns of political competition and decision-making have often been neglected. The article contributes to an emerging comparative literature on state politics by developing a typology that systematizes the variation in political competitiveness and the extent of state elites’ control over the electoral arena across Brazilian states. It relies on factor analysis to create an index of “electoral dominance”, comprised of a set of indicators of party and electoral competitiveness at the state level, which measures state elites’ capacity to control the state electoral arena over time. Based on this composite index and on available case-study evidence, the article applies the typological classificatory scheme to all 27 Brazilian states. Further, the article relies on the typological classification to assess the recent evolution of state-level political competitiveness. The empirical analysis demonstrates that state politics is becoming more competitive and fragmented, including in those states that have been characterized as bastions of oligarchism and political bossism. In view of these findings, the article argues that the power of state political machines rests on fragile foundations: in Brazil’s multiparty federalism, vertical competition between the federal and state governments in the provision of social policies works as a constraint on state bosses’ machine-building strategies. It is concluded that our previous views on state political dynamics are in serious need of re-evaluation.

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.

Removing the Restrictions Imposed on Finite State Machines ...

African Journals Online (AJOL)

This study determines an effective method of removing the fixed and finite state amount of memory that restricts finite state machines from carrying out compilation jobs that require larger amount of memory. The study is ... The conclusion reviewed the various steps followed and made projections for further reading. Keyword: ...

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.

PLA realizations for VLSI state machines

Science.gov (United States)

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

1990-01-01

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

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.

Implementing finite state machines in a computer-based teaching system

Science.gov (United States)

Hacker, Charles H.; Sitte, Renate

1999-09-01

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

Reverse Engineering Integrated Circuits Using Finite State Machine Analysis

Energy Technology Data Exchange (ETDEWEB)

Oler, Kiri J. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Miller, Carl H. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

2016-04-12

In this paper, we present a methodology for reverse engineering integrated circuits, including a mathematical verification of a scalable algorithm used to generate minimal finite state machine representations of integrated circuits.

Twentieth Century evolution of machining in the United States – An ...

Indian Academy of Sciences (India)

R. Narasimhan (Krishtel eMaging) 1461 1996 Oct 15 13:05:22

beginning of the Industrial Revolution in the late 1700's, virtually no ... expected that, by the middle of the 19th Century, as machine tools began to be manufactured .... Twentieth Century evolution of machining in the United States. 873. DESIGN ... Merchant M E 1961 The manufacturing system concept in production ...

Constraint-Based Abstraction of a Model Checker for Infinite State Systems

DEFF Research Database (Denmark)

Banda, Gourinath; Gallagher, John Patrick

Abstract interpretation-based model checking provides an approach to verifying properties of infinite-state systems. In practice, most previous work on abstract model checking is either restricted to verifying universal properties, or develops special techniques for temporal logics such as modal t...... to implementation of abstract model checking algorithms for abstract domains based on constraints, making use of an SMT solver....

An Embeddable Virtual Machine for State Space Generation

NARCIS (Netherlands)

Weber, M.; Bosnacki, D.; Edelkamp, S.

2007-01-01

The semantics of modelling languages are not always specified in a precise and formal way, and their rather complex underlying models make it a non-trivial exercise to reuse them in newly developed tools. We report on experiments with a virtual machine-based approach for state space generation. The

Implementation of a Microcode-controlled State Machine and Simulator in AVR Microcontrollers (MICoSS

Directory of Open Access Journals (Sweden)

S. Korbel

2005-01-01

Full Text Available This paper describes the design of a microcode-controlled state machine and its software implementation in Atmel AVR microcontrollers. In particular, ATmega103 and ATmega128 microcontrollers are used. This design is closely related to the software implementation of a simulator in AVR microcontrollers. This simulator communicates with the designed state machine and presents a complete design environment for microcode development and debugging. These two devices can be interconnected by a flat cable and linked to a computer through a serial or USB interface.Both devices share the control software that allows us to create and edit microprograms and to control the whole state machine. It is possible to start, cancel or step through the execution of the microprograms. The operator can also observe the current state of the state machine. The second part of the control software enables the operator to create and compile simulating programs. The control software communicates with both devices using commands. All the results of this communication are well arranged in dialog boxes and windows. 

A topological insight into restricted Boltzmann machines (extented abstract)

NARCIS (Netherlands)

Mocanu, D.C.; Mocanu, E.; Nguyen, H.P.; Gibescu, M.; Liotta, A.

2016-01-01

Restricted Boltzmann Machines (RBMs) and models derived from them have been successfully used as basic building blocks in deep neural networks for automatic features extraction, unsupervised weights initialization, but also as standalone models for density estimation, activity recognition and so on.

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

Science.gov (United States)

Mandrà, Salvatore; Zhu, Zheng; Katzgraber, Helmut G

2017-02-17

We study the performance of the D-Wave 2X quantum annealing machine on systems with well-controlled ground-state degeneracy. While obtaining the ground state of a spin-glass benchmark instance represents a difficult task, the gold standard for any optimization algorithm or machine is to sample all solutions that minimize the Hamiltonian with more or less equal probability. Our results show that while naive transverse-field quantum annealing on the D-Wave 2X device can find the ground-state energy of the problems, it is not well suited in identifying all degenerate ground-state configurations associated with a particular instance. Even worse, some states are exponentially suppressed, in agreement with previous studies on toy model problems [New J. Phys. 11, 073021 (2009)NJOPFM1367-263010.1088/1367-2630/11/7/073021]. These results suggest that more complex driving Hamiltonians are needed in future quantum annealing machines to ensure a fair sampling of the ground-state manifold.

The application of state machine based on labview for solid target transfer control system at BATAN’s cyclotron

International Nuclear Information System (INIS)

Heranudin; Rajiman; Parwanto; Edy Slamet R

2015-01-01

Software programming for the new solid target transfer control system referred to the working principle of the whole each sub system. System modeling with state machine diagram was chosen because this simplified a complex design of the control system. State machine implementation of this system was performed by creating basic state drawn from the working system of each sub system. All states with their described inputs, outputs and algorithms were compiled in the sequential state machine diagram. In order to ease the operation, three modes namely automatic, major states and micro states were created. Testing of the system has been conducted and as a result, the system worked properly. The implementation of State machine based on LabView has several advantages such as faster, easier programming and the capability for further developments. (author)

Learning Extended Finite State Machines

Science.gov (United States)

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

2014-01-01

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

Static Object Detection Based on a Dual Background Model and a Finite-State Machine

Directory of Open Access Journals (Sweden)

Heras Evangelio Rubén

2011-01-01

Full Text Available Detecting static objects in video sequences has a high relevance in many surveillance applications, such as the detection of abandoned objects in public areas. In this paper, we present a system for the detection of static objects in crowded scenes. Based on the detection of two background models learning at different rates, pixels are classified with the help of a finite-state machine. The background is modelled by two mixtures of Gaussians with identical parameters except for the learning rate. The state machine provides the meaning for the interpretation of the results obtained from background subtraction; it can be implemented as a look-up table with negligible computational cost and it can be easily extended. Due to the definition of the states in the state machine, the system can be used either full automatically or interactively, making it extremely suitable for real-life surveillance applications. The system was successfully validated with several public datasets.

Constrained state-feedback control of an externally excited synchronous machine

NARCIS (Netherlands)

Carpiuc, S.C.; Lazar, M.

2013-01-01

State-feedback control of externally excited synchronous machines employed in applications such as hybrid electric vehicles and full electric vehicles is a challenging problem. Indeed, these applications are characterized by fast dynamics that are subject to hard physical and control constraints.

Machine function based control code algebras

NARCIS (Netherlands)

Bergstra, J.A.

Machine functions have been introduced by Earley and Sturgis in [6] in order to provide a mathematical foundation of the use of the T-diagrams proposed by Bratman in [5]. Machine functions describe the operation of a machine at a very abstract level. A theory of hardware and software based on

Making Sense of the Abstraction Hierarchy in the Power Plant Domain

DEFF Research Database (Denmark)

Lind, Morten

2003-01-01

The paper discusses the abstraction hierarchy proposed by Rasmussen [(1986) Information processing and human-machine interaction, North-Holland] for design of human-machine interfaces for supervisory control. The purpose of the abstraction hierarchy is to represent a work domain by multiple levels...... of means-end and part-whole abstractions. It is argued in the paper that the abstraction hierarchy suffers from both methodological and conceptual problems. A cluster of selected problems are analyzed and illustrated by concrete examples from the power plant domain. It is concluded that the semantics...... in the model-building process. It is also pointed out that attempts to clarify the semantics of the abstraction hierarchy will invariably reduce the range of work domains where it can be applied....

Abstraction in artificial intelligence and complex systems

CERN Document Server

Saitta, Lorenza

2013-01-01

Abstraction is a fundamental mechanism underlying both human and artificial perception, representation of knowledge, reasoning and learning. This mechanism plays a crucial role in many disciplines, notably Computer Programming, Natural and Artificial Vision, Complex Systems, Artificial Intelligence and Machine Learning, Art, and Cognitive Sciences. This book first provides the reader with an overview of the notions of abstraction proposed in various disciplines by comparing both commonalities and differences.  After discussing the characterizing properties of abstraction, a formal model, the K

Control-Flow Analysis of Function Calls and Returns by Abstract Interpretation

DEFF Research Database (Denmark)

Midtgaard, Jan; Jensen, Thomas P.

, effectively approximating where function calls return across optimized tail calls. The analysis is systematically calculated by abstract interpretation of the stack-based CaEK abstract machine of Flanagan et al. using a series of Galois connections. Abstract interpretation provides a unifying setting in which...

Support vector machines for nuclear reactor state estimation

Energy Technology Data Exchange (ETDEWEB)

Zavaljevski, N.; Gross, K. C.

2000-02-14

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

Support vector machines for nuclear reactor state estimation

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K. C.

2000-01-01

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

Complete permutation Gray code implemented by finite state machine

Directory of Open Access Journals (Sweden)

Li Peng

2014-09-01

Full Text Available An enumerating method of complete permutation array is proposed. The list of n! permutations based on Gray code defined over finite symbol set Z(n = {1, 2, …, n} is implemented by finite state machine, named as n-RPGCF. An RPGCF can be used to search permutation code and provide improved lower bounds on the maximum cardinality of a permutation code in some cases.

Underlying finite state machine for the social engineering attack detection model

CSIR Research Space (South Africa)

Mouton, Francois

2017-08-01

Full Text Available one to have a clearer overview of the mental processing performed within the model. While the current model provides a general procedural template for implementing detection mechanisms for social engineering attacks, the finite state machine provides a...

Employing finite-state machines in data integrity problems

Directory of Open Access Journals (Sweden)

Malikov Andrey

2016-01-01

Full Text Available This paper explores the issue of group integrity of tuple subsets regarding corporate integrity constraints in relational databases. A solution may be found by applying the finite-state machine theory to guarantee group integrity of data. We present a practical guide to coding such an automaton. After creating SQL queries to manipulate data and control its integrity for real data domains, we study the issue of query performance, determine the level of transaction isolation, and generate query plans.

State of the Art Review on Theoretical Tribology of Fluid Power Displacement Machines

DEFF Research Database (Denmark)

Cerimagic, Remzija; Johansen, Per; Andersen, Torben O.

2016-01-01

machines, and also the work done to validate the theoretical models. This review is not a complete historical account, but aim to describe current trends in fluid power displacement machine tribology. The review considers the rheological models used in the theoretical approaches, the modeling...... and wear mechanisms in the lubricating gaps in fluid power machines is confined to simulation models, as experimental treatments of these mechanisms are very difficult. The aim of this paper is a state of the art review on the theoretical work for the design and optimization of fluid power displacement...... of elastohydrodynamic effects, the modeling of thermal effects, and finally the experimental validation of the theoretical models....

Practical programmable circuits a guide to PLDs, state machines, and microcontrollers

CERN Document Server

Broesch, James D

1991-01-01

This is a practical guide to programmable logic devices. It covers all devices related to PLD: PALs, PGAs, state machines, and microcontrollers. Usefulness is evaluated; support needed in order to effectively use the devices is discussed. All examples are based on real-world circuits.

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

Science.gov (United States)

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

2017-10-25

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

Sixth international conference on electrical machines and drives

International Nuclear Information System (INIS)

1993-01-01

This volume contains 111 papers presented at the Sixth International Conference on Electrical Machines and Drives. The topics covered include: miniature and micro motors; induction motors; DC machines; reluctance motors; condition monitoring; synchronous machines and drives; induction machines; induction generators; simulation; design; and operating experience; linear machines; noise and vibration; special machines. Separate abstracts have been prepared for a paper on linear step motors for control rod drives and for a paper on a motor drive for gas filtration in gas-cooled reactors. (UK)

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

Science.gov (United States)

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

2009-11-01

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

Steady-State Characteristics Analysis of Hybrid-Excited Flux-Switching Machines with Identical Iron Laminations

Directory of Open Access Journals (Sweden)

Gan Zhang

2015-11-01

Full Text Available Since the air-gap field of flux-switching permanent magnet (FSPM machines is difficult to regulate as it is produced by the stator-magnets alone, a type of hybrid-excited flux-switching (HEFS machine is obtained by reducing the magnet length of an original FSPM machine and introducing a set of field windings into the saved space. In this paper, the steady-state characteristics, especially for the loaded performances of four prototyped HEFS machines, namely, PM-top, PM-middle-1, PM-middle-2, and PM-bottom, are comprehensively compared and evaluated based on both 2D and 3D finite element analysis. Also, the influences of PM materials including ferrite and NdFeB, respectively, on the characteristics of HEFS machines are covered. Particularly, the impacts of magnet movement in the corresponding slot on flux-regulating performances are studied in depth. The best overall performances employing NdFeB can be obtained when magnets are located near the air-gap. The FEA predictions are validated by experimental measurements on corresponding machine prototypes.

Laser Beam Machining (LBM), State of the Art and New Opportunities

NARCIS (Netherlands)

Meijer, J.

2004-01-01

An overview is given of the state of the art of laser beam machining in general with special emphasis on applications of short and ultrashort lasers. In laser welding the trend is to apply optical sensors for process control. Laser surface treatment is mostly used to apply corrosion and wear

Superconducting rotating machines

International Nuclear Information System (INIS)

Smith, J.L. Jr.; Kirtley, J.L. Jr.; Thullen, P.

1975-01-01

The opportunities and limitations of the applications of superconductors in rotating electric machines are given. The relevant properties of superconductors and the fundamental requirements for rotating electric machines are discussed. The current state-of-the-art of superconducting machines is reviewed. Key problems, future developments and the long range potential of superconducting machines are assessed

Abstraction and art.

Science.gov (United States)

Gortais, Bernard

2003-07-29

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

Advanced light source: Compendium of user abstracts 1993-1996

International Nuclear Information System (INIS)

1997-04-01

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

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

Science.gov (United States)

Aarts, Fides; Jonsson, Bengt; Uijen, Johan

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

Scaling up liquid state machines to predict over address events from dynamic vision sensors.

Science.gov (United States)

Kaiser, Jacques; Stal, Rainer; Subramoney, Anand; Roennau, Arne; Dillmann, Rüdiger

2017-09-01

Short-term visual prediction is important both in biology and robotics. It allows us to anticipate upcoming states of the environment and therefore plan more efficiently. In theoretical neuroscience, liquid state machines have been proposed as a biologically inspired method to perform asynchronous prediction without a model. However, they have so far only been demonstrated in simulation or small scale pre-processed camera images. In this paper, we use a liquid state machine to predict over the whole  [Formula: see text]  event stream provided by a real dynamic vision sensor (DVS, or silicon retina). Thanks to the event-based nature of the DVS, the liquid is constantly fed with data when an object is in motion, fully embracing the asynchronicity of spiking neural networks. We propose a smooth continuous representation of the event stream for the short-term visual prediction task. Moreover, compared to previous works (2002 Neural Comput. 2525 282-93 and Burgsteiner H et al 2007 Appl. Intell. 26 99-109), we scale the input dimensionality that the liquid operates on by two order of magnitudes. We also expose the current limits of our method by running experiments in a challenging environment where multiple objects are in motion. This paper is a step towards integrating biologically inspired algorithms derived in theoretical neuroscience to real world robotic setups. We believe that liquid state machines could complement current prediction algorithms used in robotics, especially when dealing with asynchronous sensors.

Logic synthesis for FPGA-based finite state machines

CERN Document Server

Barkalov, Alexander; Kolopienczyk, Malgorzata; Mielcarek, Kamil; Bazydlo, Grzegorz

2016-01-01

This book discusses control units represented by the model of a finite state machine (FSM). It contains various original methods and takes into account the peculiarities of field-programmable gate arrays (FPGA) chips and a FSM model. It shows that one of the peculiarities of FPGA chips is the existence of embedded memory blocks (EMB). The book is devoted to the solution of problems of logic synthesis and reduction of hardware amount in control units. The book will be interesting and useful for researchers and PhD students in the area of Electrical Engineering and Computer Science, as well as for designers of modern digital systems.

Parallel algorithms for testing finite state machines:Generating UIO sequences

OpenAIRE

Hierons, RM; Turker, UC

2016-01-01

This paper describes an efficient parallel algorithm that uses many-core GPUs for automatically deriving Unique Input Output sequences (UIOs) from Finite State Machines. The proposed algorithm uses the global scope of the GPU's global memory through coalesced memory access and minimises the transfer between CPU and GPU memory. The results of experiments indicate that the proposed method yields considerably better results compared to a single core UIO construction algorithm. Our algorithm is s...

Control-flow analysis of function calls and returns by abstract interpretation

DEFF Research Database (Denmark)

Midtgaard, Jan; Jensen, Thomas P.

2009-01-01

We derive a control-flow analysis that approximates the interprocedural control-flow of both function calls and returns in the presence of first-class functions and tail-call optimization. In addition to an abstract environment, our analysis computes for each expression an abstract control stack......, effectively approximating where function calls return across optimized tail calls. The analysis is systematically calculated by abstract interpretation of the stack-based CaEK abstract machine of Flanagan et al. using a series of Galois connections. Abstract interpretation provides a unifying setting in which...

Using Expert Systems in Evaluation of the State of High Voltage Machine Insulation Systems

Directory of Open Access Journals (Sweden)

K. Záliš

2000-01-01

Full Text Available Expert systems are used for evaluating the actual state and future behavior of insulating systems of high voltage electrical machines and equipment. Several rule-based expert systems have been developed in cooperation with top diagnostic workplaces in the Czech Republic for this purpose. The IZOLEX expert system evaluates diagnostic measurement data from commonly used offline diagnostic methods for the diagnostic of high voltage insulation of rotating machines, non-rotating machines and insulating oils. The CVEX expert system evaluates the discharge activity on high voltage electrical machines and equipment by means of an off-line measurement. The CVEXON expert system is for evaluating the discharge activity by on-line measurement, and the ALTONEX expert system is the expert system for on-line monitoring of rotating machines. These developed expert systems are also used for educating students (in bachelor, master and post-graduate studies and in courses which are organized for practicing engineers and technicians and for specialists in the electrical power engineering branch. A complex project has recently been set up to evaluate the measurement of partial discharges. Two parallel expert systems for evaluating partial dischatge activity on high voltage electrical machines will work at the same time in this complex evaluating system.

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

Science.gov (United States)

Mugan, Jonathan; Khalili, Aram E.

2014-05-01

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

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

Science.gov (United States)

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

2016-01-01

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

Towards Integration of Object-Oriented Languages and State Machines

DEFF Research Database (Denmark)

Madsen, Ole Lehrmann

1999-01-01

The goal of this paper is to obtain a one-to-one correspondence between state machines as e.g. used in UML and object-oriented programming languages. A proposal is made for a language mechanism that makes it possible for an object to change its virtual bindings at run-time. A state of an object may...... then be represented as a set of virtual bindings.One advantage of object-orientation is that it provides an integrating perspective on many phases of software development, including analysis, design and implementation. For the static set of OO language constructs there is almost a one-to-one correspondence between...... analysis/design notations and OO programming languages. No such correspondence exists for the dynamic aspects, but the proposed state-mechanism is a contribution to a better cor respondence. The proposal is based on previous work by Antero Taivalsaari and compared to the more complex features for changing...

Towards an automatic model transformation mechanism from UML state machines to DEVS models

Directory of Open Access Journals (Sweden)

Ariel González

2015-08-01

Full Text Available The development of complex event-driven systems requires studies and analysis prior to deployment with the goal of detecting unwanted behavior. UML is a language widely used by the software engineering community for modeling these systems through state machines, among other mechanisms. Currently, these models do not have appropriate execution and simulation tools to analyze the real behavior of systems. Existing tools do not provide appropriate libraries (sampling from a probability distribution, plotting, etc. both to build and to analyze models. Modeling and simulation for design and prototyping of systems are widely used techniques to predict, investigate and compare the performance of systems. In particular, the Discrete Event System Specification (DEVS formalism separates the modeling and simulation; there are several tools available on the market that run and collect information from DEVS models. This paper proposes a model transformation mechanism from UML state machines to DEVS models in the Model-Driven Development (MDD context, through the declarative QVT Relations language, in order to perform simulations using tools, such as PowerDEVS. A mechanism to validate the transformation is proposed. Moreover, examples of application to analyze the behavior of an automatic banking machine and a control system of an elevator are presented.

A Finite State Machine Approach to Algorithmic Lateral Inhibition for Real-Time Motion Detection †

Directory of Open Access Journals (Sweden)

María T. López

2018-05-01

Full Text Available Many researchers have explored the relationship between recurrent neural networks and finite state machines. Finite state machines constitute the best-characterized computational model, whereas artificial neural networks have become a very successful tool for modeling and problem solving. The neurally-inspired lateral inhibition method, and its application to motion detection tasks, have been successfully implemented in recent years. In this paper, control knowledge of the algorithmic lateral inhibition (ALI method is described and applied by means of finite state machines, in which the state space is constituted from the set of distinguishable cases of accumulated charge in a local memory. The article describes an ALI implementation for a motion detection task. For the implementation, we have chosen to use one of the members of the 16-nm Kintex UltraScale+ family of Xilinx FPGAs. FPGAs provide the necessary accuracy, resolution, and precision to run neural algorithms alongside current sensor technologies. The results offered in this paper demonstrate that this implementation provides accurate object tracking performance on several datasets, obtaining a high F-score value (0.86 for the most complex sequence used. Moreover, it outperforms implementations of a complete ALI algorithm and a simplified version of the ALI algorithm—named “accumulative computation”—which was run about ten years ago, now reaching real-time processing times that were simply not achievable at that time for ALI.

Algorithm for determining two-periodic steady-states in AC machines directly in time domain

Directory of Open Access Journals (Sweden)

Sobczyk Tadeusz J.

2016-09-01

Full Text Available This paper describes an algorithm for finding steady states in AC machines for the cases of their two-periodic nature. The algorithm enables to specify the steady-state solution identified directly in time domain despite of the fact that two-periodic waveforms are not repeated in any finite time interval. The basis for such an algorithm is a discrete differential operator that specifies the temporary values of the derivative of the two-periodic function in the selected set of points on the basis of the values of that function in the same set of points. It allows to develop algebraic equations defining the steady state solution reached in a chosen point set for the nonlinear differential equations describing the AC machines when electrical and mechanical equations should be solved together. That set of those values allows determining the steady state solution at any time instant up to infinity. The algorithm described in this paper is competitive with respect to the one known in literature an approach based on the harmonic balance method operated in frequency domain.

Machine intelligence and signal processing

CERN Document Server

Vatsa, Mayank; Majumdar, Angshul; Kumar, Ajay

2016-01-01

This book comprises chapters on key problems in machine learning and signal processing arenas. The contents of the book are a result of a 2014 Workshop on Machine Intelligence and Signal Processing held at the Indraprastha Institute of Information Technology. Traditionally, signal processing and machine learning were considered to be separate areas of research. However in recent times the two communities are getting closer. In a very abstract fashion, signal processing is the study of operator design. The contributions of signal processing had been to device operators for restoration, compression, etc. Applied Mathematicians were more interested in operator analysis. Nowadays signal processing research is gravitating towards operator learning – instead of designing operators based on heuristics (for example wavelets), the trend is to learn these operators (for example dictionary learning). And thus, the gap between signal processing and machine learning is fast converging. The 2014 Workshop on Machine Intel...

Approximate multi-state reliability expressions using a new machine learning technique

International Nuclear Information System (INIS)

Rocco S, Claudio M.; Muselli, Marco

2005-01-01

The machine-learning-based methodology, previously proposed by the authors for approximating binary reliability expressions, is now extended to develop a new algorithm, based on the procedure of Hamming Clustering, which is capable to deal with multi-state systems and any success criterion. The proposed technique is presented in details and verified on literature cases: experiment results show that the new algorithm yields excellent predictions

Program and abstracts

Energy Technology Data Exchange (ETDEWEB)

1975-01-01

Abstracts of the papers given at the conference are presented. The abstracts are arranged under sessions entitled:Theoretical Physics; Nuclear Physics; Solid State Physics; Spectroscopy; Physics Education; SANCGASS; Astronomy; Plasma Physics; Physics in Industry; Applied and General Physics.

Program and abstracts

International Nuclear Information System (INIS)

1975-01-01

Abstracts of the papers given at the conference are presented. The abstracts are arranged under sessions entitled:Theoretical Physics; Nuclear Physics; Solid State Physics; Spectroscopy; Physics Education; SANCGASS; Astronomy; Plasma Physics; Physics in Industry; Applied and General Physics

Dependency in State Transitions of Wind Turbines

DEFF Research Database (Denmark)

Herp, Jürgen; Ramezani, Mohammad Hossein; S. Nadimi, Esmaeil

2017-01-01

© 2017 IEEE. Turbine states and predicting the transition into failure states ahead of time is important in operation and maintenance of wind turbines. This study presents a method to monitor state transitions of a wind turbine based on the online inference on residuals. In a Bayesian framework...... be abstracted from generated data. Two models are presented: 1) assuming independence and 2) assuming dependence between states. In order to select the right models, machine learning is utilized to update hyperparameters on the conditional probabilities. Comparing fixed to learned hyperparameters points out...... the impact machine learning concepts have on the predictive performance of the presented models. In conclusion, a study on model residuals is performed to highlight the contribution to wind turbine monitoring. The presented algorithm can consistently detect the state transition under various configurations...

Quantum cloning machines and the applications

Energy Technology Data Exchange (ETDEWEB)

Fan, Heng, E-mail: hfan@iphy.ac.cn [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Collaborative Innovation Center of Quantum Matter, Beijing 100190 (China); Wang, Yi-Nan; Jing, Li [School of Physics, Peking University, Beijing 100871 (China); Yue, Jie-Dong [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu [School of Physics, Peking University, Beijing 100871 (China)

2014-11-20

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.

Quantum cloning machines and the applications

International Nuclear Information System (INIS)

Fan, Heng; Wang, Yi-Nan; Jing, Li; Yue, Jie-Dong; Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu

2014-01-01

No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results

A generic finite state machine framework for the ACNET control system

International Nuclear Information System (INIS)

Carmichael, L.; Warner, A.

2009-01-01

A significant level of automation and flexibility has been added to the ACNET control system through the development of a Java-based Finite State Machine (FSM) infrastructure. These FSMs are integrated into ACNET and allow users to easily build, test and execute scripts that have full access to ACNET's functionality. In this paper, a description will be given of the FSM design and its ties to the Java-based Data Acquisition Engine (DAE) framework. Each FSM is part of a client-server model with FSM display clients using Remote Method Invocation (RMI) to communicate with DAE servers heavily coupled to ACNET. A web-based monitoring system that allows users to utilize browsers to observe persistent FSMs will also be discussed. Finally, some key implementations such as the crash recovery FSM developed for the Electron Cooling machine protection system will be presented.

An object-oriented extension for debugging the virtual machine

Energy Technology Data Exchange (ETDEWEB)

Pizzi, Jr, Robert G. [Univ. of California, Davis, CA (United States)

1994-12-01

A computer is nothing more then a virtual machine programmed by source code to perform a task. The program`s source code expresses abstract constructs which are compiled into some lower level target language. When a virtual machine breaks, it can be very difficult to debug because typical debuggers provide only low-level target implementation information to the software engineer. We believe that the debugging task can be simplified by introducing aspects of the abstract design and data into the source code. We introduce OODIE, an object-oriented extension to programming languages that allows programmers to specify a virtual environment by describing the meaning of the design and data of a virtual machine. This specification is translated into symbolic information such that an augmented debugger can present engineers with a programmable debugging environment specifically tailored for the virtual machine that is to be debugged.

Program and abstracts

International Nuclear Information System (INIS)

1976-01-01

Abstracts of the papers given at the conference are presented. The abstracts are arranged under sessions entitled: Theoretical Physics; Nuclear Physics; Solid State Physics; Spectroscopy; Plasma Physics; Solar-Terrestrial Physics; Astrophysics and Astronomy; Radioastronomy; General Physics; Applied Physics; Industrial Physics

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

Directory of Open Access Journals (Sweden)

Malik N Ahmed

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

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

Science.gov (United States)

Bartoszek, Gregory; Cervone, Daniel

2017-11-01

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

Methods to extract information on the atomic and molecular states from scientific abstracts

International Nuclear Information System (INIS)

Sasaki, Akira; Ueshima, Yutaka; Yamagiwa, Mitsuru; Murata, Masaki; Kanamaru, Toshiyuki; Shirado, Tamotsu; Isahara, Hitoshi

2005-01-01

We propose a new application of information technology to recognize and extract expressions of atomic and molecular states from electrical forms of scientific abstracts. Present results will help scientists to understand atomic states as well as the physics discussed in the articles. Combining with the internet search engines, it will make one possible to collect not only atomic and molecular data but broader scientific information over a wide range of research fields. (author)

Real Time Robot Soccer Game Event Detection Using Finite State Machines with Multiple Fuzzy Logic Probability Evaluators

Directory of Open Access Journals (Sweden)

Elmer P. Dadios

2009-01-01

Full Text Available This paper presents a new algorithm for real time event detection using Finite State Machines with multiple Fuzzy Logic Probability Evaluators (FLPEs. A machine referee for a robot soccer game is developed and is used as the platform to test the proposed algorithm. A novel technique to detect collisions and other events in microrobot soccer game under inaccurate and insufficient information is presented. The robots' collision is used to determine goalkeeper charging and goal score events which are crucial for the machine referee's decisions. The Main State Machine (MSM handles the schedule of event activation. The FLPE calculates the probabilities of the true occurrence of the events. Final decisions about the occurrences of events are evaluated and compared through threshold crisp probability values. The outputs of FLPEs can be combined to calculate the probability of an event composed of subevents. Using multiple fuzzy logic system, the FLPE utilizes minimal number of rules and can be tuned individually. Experimental results show the accuracy and robustness of the proposed algorithm.

On Coding the States of Sequential Machines with the Use of Partition Pairs

DEFF Research Database (Denmark)

Zahle, Torben U.

1966-01-01

This article introduces a new technique of making state assignment for sequential machines. The technique is in line with the approach used by Hartmanis [l], Stearns and Hartmanis [3], and Curtis [4]. It parallels the work of Dolotta and McCluskey [7], although it was developed independently...

Abstracting Concepts and Methods.

Science.gov (United States)

Borko, Harold; Bernier, Charles L.

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

Completeness of Lyapunov Abstraction

DEFF Research Database (Denmark)

Wisniewski, Rafal; Sloth, Christoffer

2013-01-01

the vector field, which allows the generation of a complete abstraction. To compute the functions that define the subdivision of the state space in an algorithm, we formulate a sum of squares optimization problem. This optimization problem finds the best subdivisioning functions, with respect to the ability......This paper addresses the generation of complete abstractions of polynomial dynamical systems by timed automata. For the proposed abstraction, the state space is divided into cells by sublevel sets of functions. We identify a relation between these functions and their directional derivatives along...

Efficient abstraction selection in reinforcement learning

NARCIS (Netherlands)

Seijen, H. van; Whiteson, S.; Kester, L.

2013-01-01

This paper introduces a novel approach for abstraction selection in reinforcement learning problems modelled as factored Markov decision processes (MDPs), for which a state is described via a set of state components. In abstraction selection, an agent must choose an abstraction from a set of

WORK PRECARIOUSNESS: ERGONOMIC RISKS TO OPERATORS OF MACHINES ADAPTED FOR FOREST HARVESTING

Directory of Open Access Journals (Sweden)

Stanley Schettino

Full Text Available ABSTRACT This study aimed to assess different types of machines adapted for mechanized forest harvesting activities in order to quantify the degree of compliance with ergonomic principles applicable to forest machines, as well as the ergonomic risks to which workers are exposed. The following machines were evaluated: a feller buncher adapted into a wheel loader; a mini skidder coupled to an agricultural tractor; and a forest loader adapted to an agricultural tractor; operating in the states of Paraná and Minas Gerais. Biomechanical working conditions were assessed by applying a checklist for simplified assessment of the workplace biomechanical conditions. The forced postures assessment was performed using the REBA - "Rapid Entire Body Assessment" method. In turn, ergonomic classification was through guidelines contained in the ergonomic classification manual "Ergonomic Guidelines for Forest Machines". Moreover, the environmental factors noise, temperature and vibration to which the operators of these machines were exposed were assessed. The results showed all assessed machines had ergonomic standards below those indicated in all assessed aspects, mainly related to access and dimensions of the workplace, need to adopt forced postures during working hours, and exposure to environmental factors assessed above tolerance limits. It is concluded that machines adapted for use in forest harvesting processes have shown significant gaps in relation to ergonomic aspects, presenting high and imminent risk of development of occupational diseases in their operators.

Automatic Test Pattern Generator for Fuzzing Based on Finite State Machine

Directory of Open Access Journals (Sweden)

Ming-Hung Wang

2017-01-01

Full Text Available With the rapid development of the Internet, several emerging technologies are adopted to construct fancy, interactive, and user-friendly websites. Among these technologies, HTML5 is a popular one and is widely used in establishing modern sites. However, the security issues in the new web technologies are also raised and are worthy of investigation. For vulnerability investigation, many previous studies used fuzzing and focused on generation-based approaches to produce test cases for fuzzing; however, these methods require a significant amount of knowledge and mental efforts to develop test patterns for generating test cases. To decrease the entry barrier of conducting fuzzing, in this study, we propose a test pattern generation algorithm based on the concept of finite state machines. We apply graph analysis techniques to extract paths from finite state machines and use these paths to construct test patterns automatically. According to the proposal, fuzzing can be completed through inputting a regular expression corresponding to the test target. To evaluate the performance of our proposal, we conduct an experiment in identifying vulnerabilities of the input attributes in HTML5. According to the results, our approach is not only efficient but also effective for identifying weak validators in HTML5.

Compositional mining of multiple object API protocols through state abstraction.

Science.gov (United States)

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

2013-01-01

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

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...

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

Rotating electrical machines

CERN Document Server

Le Doeuff, René

2013-01-01

In this book a general matrix-based approach to modeling electrical machines is promulgated. The model uses instantaneous quantities for key variables and enables the user to easily take into account associations between rotating machines and static converters (such as in variable speed drives).   General equations of electromechanical energy conversion are established early in the treatment of the topic and then applied to synchronous, induction and DC machines. The primary characteristics of these machines are established for steady state behavior as well as for variable speed scenarios. I

Types: A data abstraction package in FORTRAN

International Nuclear Information System (INIS)

Youssef, S.

1990-01-01

TYPES is a collection of Fortran programs which allow the creation and manipulation of abstract ''data objects'' without the need for a preprocessor. Each data object is assigned a ''type'' as it is created which implies participation in a set of characteristic operations. Available types include scalars, logicals, ordered sets, stacks, queues, sequences, trees, arrays, character strings, block text, histograms, virtual and allocatable memories. A data object may contain integers, reals, or other data objects in any combination. In addition to the type specific operations, a set of universal utilities allows for copying input/output to disk, naming, editing, displaying, user input, interactive creation, tests for equality of contents or structure, machine to machine translation or source code creation for and data object. TYPES is available on VAX/VMS, SUN 3, SPARC, DEC/Ultrix, Silicon Graphics 4D and Cray/Unicos machines. The capabilities of the package are discussed together with characteristic applications and experience in writing the GVerify package

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

Science.gov (United States)

Gelbwaser-Klimovsky, D; Kurizki, G

2014-08-01

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

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

Self-organized critical pinball machine

DEFF Research Database (Denmark)

Flyvbjerg, H.

2004-01-01

The nature of self-organized criticality (SOC) is pin-pointed with a simple mechanical model: a pinball machine. Its phase space is fully parameterized by two integer variables, one describing the state of an on-going game, the other describing the state of the machine. This is the simplest...

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

Science.gov (United States)

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

2008-07-01

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

Diagnostics of the Technical State of Bearings of Mining Machines Base Assemblies

Science.gov (United States)

Gerike, Boris L.; Mokrushev, Andrey A.

2017-10-01

The article reviews the methods of technical diagnostics of equipment used during maintenance of mining machines in accordance with their actual technical state, and considers the basics of vibration parameters measuring. The classification of existing methods for diagnosing the technical condition of rolling bearings is given. The advantages and disadvantages of these methods are considered. The main defects of rolling bearings arising during manufacturing, transportation, storage, and operation are considered.

Humanizing machines: Anthropomorphization of slot machines increases gambling.

Science.gov (United States)

Riva, Paolo; Sacchi, Simona; Brambilla, Marco

2015-12-01

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

Modal abstractions of concurrent behavior

DEFF Research Database (Denmark)

Nielson, Flemming; Nanz, Sebastian; Nielson, Hanne Riis

2011-01-01

We present an effective algorithm for the automatic construction of finite modal transition systems as abstractions of potentially infinite concurrent processes. Modal transition systems are recognized as valuable abstractions for model checking because they allow for the validation as well...... as refutation of safety and liveness properties. However, the algorithmic construction of finite abstractions from potentially infinite concurrent processes is a missing link that prevents their more widespread usage for model checking of concurrent systems. Our algorithm is a worklist algorithm using concepts...... from abstract interpretation and operating upon mappings from sets to intervals in order to express simultaneous over- and underapprox-imations of the multisets of process actions available in a particular state. We obtain a finite abstraction that is 3-valued in both states and transitions...

Machining dynamics fundamentals, applications and practices

CERN Document Server

Cheng, Kai

2008-01-01

Machining dynamics are vital to the performance of machine tools and machining processes in manufacturing. This book discusses the state-of-the-art applications, practices and research in machining dynamics. It presents basic theory, analysis and control methodology. It is useful for manufacturing engineers, supervisors, engineers and designers.

Unified universal quantum cloning machine and fidelities

Energy Technology Data Exchange (ETDEWEB)

Wang Yinan; Shi Handuo; Xiong Zhaoxi; Jing Li; Mu Liangzhu [School of Physics, Peking University, Beijing 100871 (China); Ren Xijun [School of Physics and Electronics, Henan University, Kaifeng 4750011 (China); Fan Heng [Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China)

2011-09-15

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

Analysis of the steady-state operation of vacuum systems for fusion machines

International Nuclear Information System (INIS)

Roose, T.R.; Hoffman, M.A.; Carlson, G.A.

1975-01-01

A computer code named GASBAL was written to calculate the steady-state vacuum system performance of multi-chamber mirror machines as well as rather complex conventional multichamber vacuum systems. Application of the code, with some modifications, to the quasi-steady tokamak operating period should also be possible. Basically, GASBAL analyzes free molecular gas flow in a system consisting of a central chamber (the plasma chamber) connected by conductances to an arbitrary number of one- or two-chamber peripheral tanks. Each of the peripheral tanks may have vacuum pumping capability (pumping speed), sources of cold gas, and sources of energetic atoms. The central chamber may have actual vacuum pumping capability, as well as a plasma capable of ionizing injected atoms and impinging gas molecules and ''pumping'' them to a peripheral chamber. The GASBAL code was used in the preliminary design of a large mirror machine experiment--LLL's MX

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

Energy Technology Data Exchange (ETDEWEB)

None, None

1997-04-01

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

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

A Modal-Logic Based Graph Abstraction

NARCIS (Netherlands)

Bauer, J.; Boneva, I.B.; Kurban, M.E.; Rensink, Arend; Ehrig, H; Heckel, R.; Rozenberg, G.; Taentzer, G.

2008-01-01

Infinite or very large state spaces often prohibit the successful verification of graph transformation systems. Abstract graph transformation is an approach that tackles this problem by abstracting graphs to abstract graphs of bounded size and by lifting application of productions to abstract

Equivalence of restricted Boltzmann machines and tensor network states

Science.gov (United States)

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

2018-02-01

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

Enter the machine

Science.gov (United States)

Palittapongarnpim, Pantita; Sanders, Barry C.

2018-05-01

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

Asymmetric quantum cloning machines

International Nuclear Information System (INIS)

Cerf, N.J.

1998-01-01

A family of asymmetric cloning machines for quantum bits and N-dimensional quantum states is introduced. These machines produce two approximate copies of a single quantum state that emerge from two distinct channels. In particular, an asymmetric Pauli cloning machine is defined that makes two imperfect copies of a quantum bit, while the overall input-to-output operation for each copy is a Pauli channel. A no-cloning inequality is derived, characterizing the impossibility of copying imposed by quantum mechanics. If p and p ' are the probabilities of the depolarizing channels associated with the two outputs, the domain in (√p,√p ' )-space located inside a particular ellipse representing close-to-perfect cloning is forbidden. This ellipse tends to a circle when copying an N-dimensional state with N→∞, which has a simple semi-classical interpretation. The symmetric Pauli cloning machines are then used to provide an upper bound on the quantum capacity of the Pauli channel of probabilities p x , p y and p z . The capacity is proven to be vanishing if (√p x , √p y , √p z ) lies outside an ellipsoid whose pole coincides with the depolarizing channel that underlies the universal cloning machine. Finally, the tradeoff between the quality of the two copies is shown to result from a complementarity akin to Heisenberg uncertainty principle. (author)

Analysis of complex networks using aggressive abstraction.

Energy Technology Data Exchange (ETDEWEB)

Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

2008-10-01

This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

Constraint-Based Abstract Semantics for Temporal Logic

DEFF Research Database (Denmark)

Banda, Gourinath; Gallagher, John Patrick

2010-01-01

Abstract interpretation provides a practical approach to verifying properties of infinite-state systems. We apply the framework of abstract interpretation to derive an abstract semantic function for the modal mu-calculus, which is the basis for abstract model checking. The abstract semantic funct...

Abstraction Layer For Development And Deployment Of Cloud Services

Directory of Open Access Journals (Sweden)

Binh Minh Nguyen

2012-01-01

Full Text Available In this paper, we will present an abstraction layer for cloud computing, which intends to simplify the manipulation with virtual machines in clouds for easy and controlled development and deployment of cloud services. It also ensures interoperability between different cloud infrastructures and allows developers to create cloud appliances easily via inheritance mechanisms.

Learning Activity Packets for Milling Machines. Unit I--Introduction to Milling Machines.

Science.gov (United States)

Oklahoma State Board of Vocational and Technical Education, Stillwater. Curriculum and Instructional Materials Center.

This learning activity packet (LAP) outlines the study activities and performance tasks covered in a related curriculum guide on milling machines. The course of study in this LAP is intended to help students learn to identify parts and attachments of vertical and horizontal milling machines, identify work-holding devices, state safety rules, and…

Using support vector machines in the multivariate state estimation technique

International Nuclear Information System (INIS)

Zavaljevski, N.; Gross, K.C.

1999-01-01

One approach to validate nuclear power plant (NPP) signals makes use of pattern recognition techniques. This approach often assumes that there is a set of signal prototypes that are continuously compared with the actual sensor signals. These signal prototypes are often computed based on empirical models with little or no knowledge about physical processes. A common problem of all data-based models is their limited ability to make predictions on the basis of available training data. Another problem is related to suboptimal training algorithms. Both of these potential shortcomings with conventional approaches to signal validation and sensor operability validation are successfully resolved by adopting a recently proposed learning paradigm called the support vector machine (SVM). The work presented here is a novel application of SVM for data-based modeling of system state variables in an NPP, integrated with a nonlinear, nonparametric technique called the multivariate state estimation technique (MSET), an algorithm developed at Argonne National Laboratory for a wide range of nuclear plant applications

Tracking an open quantum system using a finite state machine: Stability analysis

International Nuclear Information System (INIS)

Karasik, R. I.; Wiseman, H. M.

2011-01-01

A finite-dimensional Markovian open quantum system will undergo quantum jumps between pure states, if we can monitor the bath to which it is coupled with sufficient precision. In general these jumps, plus the between-jump evolution, create a trajectory which passes through infinitely many different pure states, even for ergodic systems. However, as shown recently by us [Phys. Rev. Lett. 106, 020406 (2011)], it is possible to construct adaptive monitorings which restrict the system to jumping between a finite number of states. That is, it is possible to track the system using a finite state machine as the apparatus. In this paper we consider the question of the stability of these monitoring schemes. Restricting to cyclic jumps for a qubit, we give a strong analytical argument that these schemes are always stable and supporting analytical and numerical evidence for the example of resonance fluorescence. This example also enables us to explore a range of behaviors in the evolution of individual trajectories, for several different monitoring schemes.

Python for probability, statistics, and machine learning

CERN Document Server

Unpingco, José

2016-01-01

This book covers the key ideas that link probability, statistics, and machine learning illustrated using Python modules in these areas. The entire text, including all the figures and numerical results, is reproducible using the Python codes and their associated Jupyter/IPython notebooks, which are provided as supplementary downloads. The author develops key intuitions in machine learning by working meaningful examples using multiple analytical methods and Python codes, thereby connecting theoretical concepts to concrete implementations. Modern Python modules like Pandas, Sympy, and Scikit-learn are applied to simulate and visualize important machine learning concepts like the bias/variance trade-off, cross-validation, and regularization. Many abstract mathematical ideas, such as convergence in probability theory, are developed and illustrated with numerical examples. This book is suitable for anyone with an undergraduate-level exposure to probability, statistics, or machine learning and with rudimentary knowl...

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

Science.gov (United States)

Lin, Jin-Zhong

2018-05-01

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

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

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.

5. International conference on materials science and condensed matter physics and symposium 'Electrical methods of materials treatment'. Abstracts

International Nuclear Information System (INIS)

2010-09-01

This book includes abstracts of the communications presented at the 5th International Conference on Materials Science and Condensed-Matter Physics and at the Symposium dedicated to the 100th anniversary of academician Boris Lazarenko, the prominent scientist and inventor, the first director of the Institute of Applied Physics of the Academy of Sciences of Moldova. The abstracts presented in the book cover a vast range of subjects, such as: advanced materials and fabrication processes; methods of crystal growth, post-growth technological processes, doping and implantation, fabrication of solid state structures; defect engineering, engineering of molecular assembly; methods of nanostructures and nano materials fabrication and characterization; quantum wells and superlattices; nano composite, nanowires and nano dots; fullerenes and nano tubes, molecular materials, meso- and nano electronics; methods of material and structure characterization; structure and mechanical characterization; optical, electrical, magnetic and superconductor properties, transport processes, nonlinear phenomena, size and interface effects; advances in condensed matter theory; theory of low dimensional systems; modelling of materials and structure properties; development of theoretical methods of solid-state characterization; phase transition; advanced quantum physics for nano systems; device modelling and simulation, device structures and elements; micro- and optoelectronics; photonics; microsensors and micro electro-mechanical systems; microsystems; degradation and reliability, solid-state device design; theory and advanced technologies of electro-physico-chemical and combined methods of materials machining and treatment, including modification of surfaces; theory and advanced technologies of using electric fields, currents and discharges so as to intensify heat mass-transfer, to raise the efficiency of treatment of materials, of biological preparations and foodstuff; modern equipment for

Squeezed States and Uncertainty Relations. Abstracts

International Nuclear Information System (INIS)

Masahito, Hayashi; Reynaud, S.; Jaekel, M.Th.; Fiuraaek, J.; Garcia-Patron, R.; Cerf, N.J.; Hage, B.; Chelkowski, S.; Franzen, A.; Lastzka, N.; Vahlbruch, N.; Danzmann, K.; Schnabel, R.; Hassan, S.S.; Joshi, A.; Jakob, M.; Bergou, J.A.; Kozlovskii, A.V.; Prakash, H.; Kumar, R.

2005-01-01

The purpose of the conference was to bring together people working in the field of quantum optics, with special emphasis on non-classical light sources and related areas, quantum computing, statistical mechanics and mathematical physics. As a novelty, this edition will include the topics of quantum imaging, quantum phase noise and number theory in quantum mechanics. This document gives the program of the conference and gathers the abstracts

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

Energy Technology Data Exchange (ETDEWEB)

Qi, Junjian; Sun, Kai; Wang, Jianhui; Liu, Hui

2018-03-01

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

Estimation of the Dynamic States of Synchronous Machines Using an Extended Particle Filter

Energy Technology Data Exchange (ETDEWEB)

Zhou, Ning; Meng, Da; Lu, Shuai

2013-11-11

In this paper, an extended particle filter (PF) is proposed to estimate the dynamic states of a synchronous machine using phasor measurement unit (PMU) data. A PF propagates the mean and covariance of states via Monte Carlo simulation, is easy to implement, and can be directly applied to a non-linear system with non-Gaussian noise. The extended PF modifies a basic PF to improve robustness. Using Monte Carlo simulations with practical noise and model uncertainty considerations, the extended PF’s performance is evaluated and compared with the basic PF and an extended Kalman filter (EKF). The extended PF results showed high accuracy and robustness against measurement and model noise.

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

Science.gov (United States)

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

2018-05-01

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

Research on impacts of mechanical vibrations on the production machine to its rate of change of technical state

Directory of Open Access Journals (Sweden)

Štefánia Salokyová

2016-06-01

Full Text Available The article observes the amount of vibration on the bearing house of a turning lathe selected in advance through the change of the revolutions per minute and the thickness of the removed material in frontal type of lathe processing. Increase in mechanical vibration values depending on the value of nominal thickness of splinter was observed during changing technological parameters of the drilling process as a consequence of rotation speed of the motor. The vibration acceleration amplitude course changes depending on the frequencies are evaluated together for 400, 800 and 1200 motor r/min. A piezoelectric sensor of the type 4507B-004 from the Brüel & Kjaer Company was used for monitoring the frequency analysis of the vibration, which was attached to the bearing house of the lathe TOS SV 18RB. The vibration signal measured during the processing and during the time period is transformed through the means of a quick Fourier transformation to the frequency spectrum in the range of 3.0–10.0 kHz. Measured values of vibration acceleration amplitude were processed and evaluated by the SignalExpress software. Graphical abstract Unwanted vibration in machine tools like lathe is one of the main problems as it affects the quality of the machined parts and tool life and creates noise during machining operation. Bearings are of paramount importance to almost all forms of rotating machinery and are the most common among machine elements. The article describes in more detail the issue of vibrations created when machining the material by lathe turning. It also includes execution, experiment evaluation in this field, and comparison of measured vibrations’ acceleration amplitude values according to the standards.

Human Machine Learning Symbiosis

Science.gov (United States)

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

2017-01-01

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

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

Science.gov (United States)

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

2014-01-01

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

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

Directory of Open Access Journals (Sweden)

Daniel R Taber

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

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

Science.gov (United States)

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

2014-01-01

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

An Elgamal Encryption Scheme of Fibonacci Q-Matrix and Finite State Machine

Directory of Open Access Journals (Sweden)

B. Ravi Kumar

2015-12-01

Full Text Available Cryptography is the science of writing messages in unknown form using mathematical models. In Cryptography, several ciphers were introduced for the encryption schemes. Recent research focusing on designing various mathematical models in such a way that tracing the inverse of the designed mathematical models is infeasible for the eve droppers. In the present work, the ELGamal encryption scheme is executed using the generator of a cyclic group formed by the points on choosing elliptic curve, finite state machines and key matrices obtained from the Fibonacci sequences.

Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

KAUST Repository

Warriach, Ehsan Ullah; Claudel, Christian G.

2013-01-01

This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN

Identifying student stuck states in programmingassignments using machine learning

OpenAIRE

Lindell, Johan

2014-01-01

Intelligent tutors are becoming more popular with the increased use of computersand hand held devices in the education sphere. An area of research isinvestigating how machine learning can be used to improve the precision andfeedback of the tutor. This thesis compares machine learning clustering algorithmswith various distance functions in an attempt to cluster together codesnapshots of students solving a programming task. It investigates whethera general non-problem specific implementation of...

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...

Optimization of pocket machining strategy in HSM

OpenAIRE

Msaddek, El Bechir; Bouaziz, Zoubeir; Dessein, Gilles; Baili, Maher

2012-01-01

International audience; Our two major concerns, which should be taken into consideration as soon as we start the selecting the machining parameters, are the minimization of the machining time and the maintaining of the high-speed machining machine in good state. The manufacturing strategy is one of the parameters which practically influences the time of the different geometrical forms manufacturing, as well as the machine itself. In this article, we propose an optimization methodology of the ...

Abstraction of continuous dynamical systems utilizing lyapunov functions

DEFF Research Database (Denmark)

Sloth, Christoffer; Wisniewski, Rafael

2010-01-01

This paper considers the development of a method for abstracting continuous dynamical systems by timed automata. The method is based on partitioning the state space of dynamical systems with invariant sets, which form cells representing locations of the timed automata. To enable verification...... of the dynamical system based on the abstraction, conditions for obtaining sound, complete, and refinable abstractions are set up. It is proposed to partition the state space utilizing sub-level sets of Lyapunov functions, since they are positive invariant sets. The existence of sound abstractions for Morse......-Smale systems and complete and refinable abstractions for linear systems are shown....

Engineering Abstractions in Model Checking and Testing

DEFF Research Database (Denmark)

Achenbach, Michael; Ostermann, Klaus

2009-01-01

Abstractions are used in model checking to tackle problems like state space explosion or modeling of IO. The application of these abstractions in real software development processes, however, lacks engineering support. This is one reason why model checking is not widely used in practice yet...... and testing is still state of the art in falsification. We show how user-defined abstractions can be integrated into a Java PathFinder setting with tools like AspectJ or Javassist and discuss implications of remaining weaknesses of these tools. We believe that a principled engineering approach to designing...... and implementing abstractions will improve the applicability of model checking in practice....

Abstracting massive data for lightweight intrusion detection in computer networks

KAUST Repository

Wang, Wei

2016-10-15

Anomaly intrusion detection in big data environments calls for lightweight models that are able to achieve real-time performance during detection. Abstracting audit data provides a solution to improve the efficiency of data processing in intrusion detection. Data abstraction refers to abstract or extract the most relevant information from the massive dataset. In this work, we propose three strategies of data abstraction, namely, exemplar extraction, attribute selection and attribute abstraction. We first propose an effective method called exemplar extraction to extract representative subsets from the original massive data prior to building the detection models. Two clustering algorithms, Affinity Propagation (AP) and traditional . k-means, are employed to find the exemplars from the audit data. . k-Nearest Neighbor (k-NN), Principal Component Analysis (PCA) and one-class Support Vector Machine (SVM) are used for the detection. We then employ another two strategies, attribute selection and attribute extraction, to abstract audit data for anomaly intrusion detection. Two http streams collected from a real computing environment as well as the KDD\\'99 benchmark data set are used to validate these three strategies of data abstraction. The comprehensive experimental results show that while all the three strategies improve the detection efficiency, the AP-based exemplar extraction achieves the best performance of data abstraction.

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

OpenAIRE

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

2014-01-01

Background: Sodas are widely sold in vending machines and other school venues in the United States, particularly in high school. Research suggests that policy changes have reduced soda access, but the impact of reduced access on consumption is unclear. This study was designed to identify student, environmental, or policy characteristics that modify the associations between school vending machines and student dietary behaviors. Methods: Data on school vending machine access and student diet we...

Development of a skateboarding trick classifier using accelerometry and machine learning

Directory of Open Access Journals (Sweden)

Nicholas Kluge Corrêa

Full Text Available Abstract Introduction Skateboarding is one of the most popular cultures in Brazil, with more than 8.5 million skateboarders. Nowadays, the discipline of street skating has gained recognition among other more classical sports and awaits its debut at the Tokyo 2020 Summer Olympic Games. This study aimed to explore the state-of-the-art for inertial measurement unit (IMU use in skateboarding trick detection, and to develop new classification methods using supervised machine learning and artificial neural networks (ANN. Methods State-of-the-art knowledge regarding motion detection in skateboarding was used to generate 543 artificial acceleration signals through signal modeling, corresponding to 181 flat ground tricks divided into five classes (NOLLIE, NSHOV, FLIP, SHOV, OLLIE. The classifier consisted of a multilayer feed-forward neural network created with three layers and a supervised learning algorithm (backpropagation. Results The use of ANNs trained specifically for each measured axis of acceleration resulted in error percentages inferior to 0.05%, with a computational efficiency that makes real-time application possible. Conclusion Machine learning can be a useful technique for classifying skateboarding flat ground tricks, assuming that the classifiers are properly constructed and trained, and the acceleration signals are preprocessed correctly.

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

Science.gov (United States)

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

2017-06-01

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

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

Science.gov (United States)

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

2016-08-01

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

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

Science.gov (United States)

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

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

Abstracts of the Seminar on Modern State of Water Resources of Tajikistan - Problems and Perspectives of Rational Utilization

International Nuclear Information System (INIS)

2003-01-01

This publication contains the abstracts of papers presented at the Seminar on Modern State of Water Resources of Tajikistan - Problems and Perspectives of Rational Utilization, held in Dushanbe in 2003

High speed operation of permanent magnet machines

Science.gov (United States)

El-Refaie, Ayman M.

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

A Comparative Analysis of Machine Learning Techniques for Credit Scoring

OpenAIRE

Nwulu, Nnamdi; Oroja, Shola; İlkan, Mustafa

2012-01-01

Abstract Credit Scoring has become an oft researched topic in light of the increasing volatility of the global economy and the recent world financial crisis. Amidst the many methods used for credit scoring, machine learning techniques are becoming increasingly popular due to their efficient and accurate nature and relative simplicity. Furthermore machine learning techniques minimize the risk of human bias and error and maximize speed as they are able to perform computation...

Quantum machine learning for quantum anomaly detection

Science.gov (United States)

Liu, Nana; Rebentrost, Patrick

2018-04-01

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

Transport safety research abstracts. No. 1

International Nuclear Information System (INIS)

1991-07-01

The Transport Safety Research Abstracts is a collection of reports from Member States of the International Atomic Energy Agency, and other international organizations on research in progress or just completed in the area of safe transport of radioactive material. The main aim of TSRA is to draw attention to work that is about to be published, thus enabling interested parties to obtain further information through direct correspondence with the investigators. Information contained in this issue covers work being undertaken in 6 Member States and contracted by 1 international organization; it is hoped with succeeding issues that TSRA will be able to widen this base. TSRA is modelled after other IAEA publications describing work in progress in other programme areas, namely Health Physics Research Abstracts (No. 14 was published in 1989), Waste Management Research Abstracts (No. 20 was published in 1990), and Nuclear Safety Research Abstracts (No. 2 was published in 1990)

A Brief Introduction to Chinese Biological Abstracts

Institute of Scientific and Technical Information of China (English)

无

2005-01-01

Chinese Biological Abstracts (CBA), a state-level indexing and abstracting journal published monthly, is jointly sponsored by the Library of the Chinese Academy of Sciences, the Shanghai Institutes for Biological Sciences as well as the Biological Information Network of the Chinese Academy of Sciences, published and distributed by the Shanghai Institutes for Biological Sciences, and approved by the State Scientific and Technological Commission.

Generating feasible transition paths for testing from an extended finite state machine (EFSM) with the counter problem

OpenAIRE

Kalaji, AS; Hierons, RM; Swift, S

2009-01-01

The extended finite state machine (EFSM) is a powerful approach for modeling state-based systems. However, testing from EFSMs is complicated by the existence of infeasible paths. One important problem is the existence of a transition with a guard that references a counter variable whose value depends on previous transitions. The presence of such transitions in paths often leads to infeasible paths. This paper proposes a novel approach to bypass the counter problem. The proposed approach is ev...

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

Science.gov (United States)

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

2012-01-01

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

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

Science.gov (United States)

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

2010-07-01

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

output enhancement in the transfer-field machine using rotor circuit

African Journals Online (AJOL)

Dr Obe

1984-09-01

Sep 1, 1984 ... Electrical Engineering Department University of Nigeria, Nsukka. (Manuscript received March 1981). ABSTRACT. The output of a plain transfer-field machine would be much less than that of a .... by non-magnetic barriers: The.

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.)

TensorFlow: A system for large-scale machine learning

OpenAIRE

Abadi, Martín; Barham, Paul; Chen, Jianmin; Chen, Zhifeng; Davis, Andy; Dean, Jeffrey; Devin, Matthieu; Ghemawat, Sanjay; Irving, Geoffrey; Isard, Michael; Kudlur, Manjunath; Levenberg, Josh; Monga, Rajat; Moore, Sherry; Murray, Derek G.

2016-01-01

TensorFlow is a machine learning system that operates at large scale and in heterogeneous environments. TensorFlow uses dataflow graphs to represent computation, shared state, and the operations that mutate that state. It maps the nodes of a dataflow graph across many machines in a cluster, and within a machine across multiple computational devices, including multicore CPUs, general-purpose GPUs, and custom designed ASICs known as Tensor Processing Units (TPUs). This architecture gives flexib...

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...

Formal refinement of extended state machines

Directory of Open Access Journals (Sweden)

Thomas Fayolle

2016-06-01

Full Text Available In a traditional formal development process, e.g. using the B method, the informal user requirements are (manually translated into a global abstract formal specification. This translation is especially difficult to achieve. The Event-B method was developed to incrementally and formally construct such a specification using stepwise refinement. Each increment takes into account new properties and system aspects. In this paper, we propose to couple a graphical notation called Algebraic State-Transition Diagrams (ASTD with an Event-B specification in order to provide a better understanding of the software behaviour. The dynamic behaviour is captured by the ASTD, which is based on automata and process algebra operators, while the data model is described by means of an Event-B specification. We propose a methodology to incrementally refine such specification couplings, taking into account new refinement relations and consistency conditions between the control specification and the data specification. We compare the specifications obtained using each approach for readability and proof complexity. The advantages and drawbacks of the traditional approach and of our methodology are discussed. The whole process is illustrated by a railway CBTC-like case study. Our approach is supported by tools for translating ASTD's into B and Event-B into B.

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.

Photochemical hydrogen abstractions as radiationless transitions

International Nuclear Information System (INIS)

Burrows, H.D.; Formosinho, S.J.

1977-01-01

The tunnel-effect theory of radiationless transitions is applied to the quenching of the uranyl ion excited state by aliphatic compounds. The most important mechanism kinetically is suggested to involve chemical quenching via hydrogen abstraction, and rates for these reactions are analysed theoretically. Good agreement between theory and experiment is observed for a number of alcohols and ethers, and the reactions are suggested to possess considerable charge-transfer character. With t-butanol it is suggested that abstraction occurs preferentially from the hydroxylic hydrogen. Theoretical analysis of the rates of hydrogen abstraction from carboxylic acids suggests that the reaction geometry in this case may be different from the reaction with alcohols or ethers. The possibility that excited uranyl ion can abstract a hydrogen atom from water is examined, and theoretical evidence is presented to suggest that this is the main route for deactivation of uranyl ion lowest excited state in water at room temperature. (author)

7. IAEA Technical Meeting on Steady State Operation of Magnetic Fusion Devices - Booklet of abstracts

International Nuclear Information System (INIS)

2015-01-01

This meeting has provided an appropriate forum to discuss current issues covering a wide range of technical topics related to the steady state operation issues and also to encourage forecast of the ITER performances. The technical meeting includes invited and contributed papers. The topics that have been dealt with are: 1) Superconducting devices (ITER, KSTAR, Tore-Supra, HT-7U, EAST, LHD, Wendelstein-7-X,...); 2) Long-pulse operation and advanced tokamak physics; 3) steady state fusion technologies; 4) Long pulse heating and current drive; 5) Particle control and power exhaust, and 6) ITER-related research and development issues. This document gathers the abstracts

The Machine Scoring of Writing

Science.gov (United States)

McCurry, Doug

2010-01-01

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

Is searching full text more effective than searching abstracts?

Directory of Open Access Journals (Sweden)

Lin Jimmy

2009-02-01

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

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

Directory of Open Access Journals (Sweden)

Ying Zhang

2018-01-01

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

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

Science.gov (United States)

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-08

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

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

Science.gov (United States)

Zhang, Ying; Wang, Jun; Hao, Guan

2018-01-01

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

Abstraction/Representation Theory for heterotic physical computing.

Science.gov (United States)

Horsman, D C

2015-07-28

We give a rigorous framework for the interaction of physical computing devices with abstract computation. Device and program are mediated by the non-logical representation relation; we give the conditions under which representation and device theory give rise to commuting diagrams between logical and physical domains, and the conditions for computation to occur. We give the interface of this new framework with currently existing formal methods, showing in particular its close relationship to refinement theory, and the implications for questions of meaning and reference in theoretical computer science. The case of hybrid computing is considered in detail, addressing in particular the example of an Internet-mediated social machine, and the abstraction/representation framework used to provide a formal distinction between heterotic and hybrid computing. This forms the basis for future use of the framework in formal treatments of non-standard physical computers. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Completeness of Lyapunov Abstraction

Directory of Open Access Journals (Sweden)

Rafael Wisniewski

2013-08-01

Full Text Available In this work, we continue our study on discrete abstractions of dynamical systems. To this end, we use a family of partitioning functions to generate an abstraction. The intersection of sub-level sets of the partitioning functions defines cells, which are regarded as discrete objects. The union of cells makes up the state space of the dynamical systems. Our construction gives rise to a combinatorial object - a timed automaton. We examine sound and complete abstractions. An abstraction is said to be sound when the flow of the time automata covers the flow lines of the dynamical systems. If the dynamics of the dynamical system and the time automaton are equivalent, the abstraction is complete. The commonly accepted paradigm for partitioning functions is that they ought to be transversal to the studied vector field. We show that there is no complete partitioning with transversal functions, even for particular dynamical systems whose critical sets are isolated critical points. Therefore, we allow the directional derivative along the vector field to be non-positive in this work. This considerably complicates the abstraction technique. For understanding dynamical systems, it is vital to study stable and unstable manifolds and their intersections. These objects appear naturally in this work. Indeed, we show that for an abstraction to be complete, the set of critical points of an abstraction function shall contain either the stable or unstable manifold of the dynamical system.

Clustering Categories in Support Vector Machines

DEFF Research Database (Denmark)

Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero

2017-01-01

The support vector machine (SVM) is a state-of-the-art method in supervised classification. In this paper the Cluster Support Vector Machine (CLSVM) methodology is proposed with the aim to increase the sparsity of the SVM classifier in the presence of categorical features, leading to a gain in in...

22 CFR 121.10 - Forgings, castings and machined bodies.

Science.gov (United States)

2010-04-01

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

Abstract Interpretation of PIC programs through Logic Programming

DEFF Research Database (Denmark)

Henriksen, Kim Steen; Gallagher, John Patrick

2006-01-01

, are applied to the logic based model of the machine. A small PIC microcontroller is used as a case study. An emulator for this microcontroller is written in Prolog, and standard programming transformations and analysis techniques are used to specialise this emulator with respect to a given PIC program....... The specialised emulator can now be further analysed to gain insight into the given program for the PIC microcontroller. The method describes a general framework for applying abstractions, illustrated here by linear constraints and convex hull analysis, to logic programs. Using these techniques on the specialised...

Effect of different parameters on machining of SiC/SiC composites via pico-second laser

Energy Technology Data Exchange (ETDEWEB)

Li, Weinan; Zhang, Ruoheng [State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 10068 (China); Liu, Yongsheng, E-mail: yongshengliu@nwpu.edu.cn [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China); Wang, Chunhui; Wang, Jing [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China); Yang, Xiaojun [State Key Laboratory of Transient Optics and Photonics, Xi’an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi’an, Shaanxi 10068 (China); Cheng, Laifei [Science and technology on Thermostructure Composite Materials Laboratory, Northwestern Polytechnical University, Xi’an, Shaanxi 710072 (China)

2016-02-28

Graphical abstract: - Highlights: • The highlights of the manuscript include the following two aspects. • First, we found that the different machining modes (helical line scanning and single ring line scanning) and processing power of machining have remarkable effect on the surface morphology of the machined area, such as the shape, depth and the formation of different surface structures. • Secondly, we investigated that the debris consisted of C, Si and O was observed on the machined surface. • Some of the Si–C bonds of the SiC matrix and fibers would be transformed into Si–O bonds after machined, depending on the processing power. - Abstract: Pico-second laser plays an important role in modern machining technology, especially in machining high hardness materials. In this article, pico-second laser was utilized for irradiation on SiC/SiC composites, and effects of different processing parameters including the machining modes and laser power were discussed in detail. The results indicated that the machining modes and laser power had great effect on machining of SiC/SiC composites. Different types of surface morphology and structure were observed under helical line scanning and single ring line scanning, and the analysis of their formulation was discussed in detail. It was believed that the machining modes would be responsible to the different shapes of machining results at the same parameters. The processing power shall also influence the surface morphology and quality of machining results. In micro-hole drilling process, large amount of debris and fragments were observed within the micro-holes, and XPS analysis showed that there existed Si–O bonds and Si–C bonds, indicating that the oxidation during processing was incomplete. Other surface morphology, such as pores and pits were discussed as well.

Is searching full text more effective than searching abstracts?

Science.gov (United States)

Lin, Jimmy

2009-02-03

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

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

Science.gov (United States)

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

2018-01-01

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

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...

Challenges for coexistence of machine to machine and human to human applications in mobile network

DEFF Research Database (Denmark)

Sanyal, R.; Cianca, E.; Prasad, Ramjee

2012-01-01

A key factor for the evolution of the mobile networks towards 4G is to bring to fruition high bandwidth per mobile node. Eventually, due to the advent of a new class of applications, namely, Machine-to-Machine, we foresee new challenges where bandwidth per user is no more the primal driver...... be evolved to address various nuances of the mobile devices used by man and machines. The bigger question is as follows. Is the state-of-the-art mobile network designed optimally to cater both the Human-to-Human and Machine-to-Machine applications? This paper presents the primary challenges....... As an immediate impact of the high penetration of M2M devices, we envisage a surge in the signaling messages for mobility and location management. The cell size will shrivel due to high tele-density resulting in even more signaling messages related to handoff and location updates. The mobile network should...

Poster abstract: A machine learning approach for vehicle classification using passive infrared and ultrasonic sensors

KAUST Repository

Warriach, Ehsan Ullah

2013-01-01

This article describes the implementation of four different machine learning techniques for vehicle classification in a dual ultrasonic/passive infrared traffic flow sensors. Using k-NN, Naive Bayes, SVM and KNN-SVM algorithms, we show that KNN-SVM significantly outperforms other algorithms in terms of classification accuracy. We also show that some of these algorithms could run in real time on the prototype system. Copyright © 2013 ACM.

Riemann-Theta Boltzmann Machine arXiv

CERN Document Server

Krefl, Daniel; Haghighat, Babak; Kahlen, Jens

A general Boltzmann machine with continuous visible and discrete integer valued hidden states is introduced. Under mild assumptions about the connection matrices, the probability density function of the visible units can be solved for analytically, yielding a novel parametric density function involving a ratio of Riemann-Theta functions. The conditional expectation of a hidden state for given visible states can also be calculated analytically, yielding a derivative of the logarithmic Riemann-Theta function. The conditional expectation can be used as activation function in a feedforward neural network, thereby increasing the modelling capacity of the network. Both the Boltzmann machine and the derived feedforward neural network can be successfully trained via standard gradient- and non-gradient-based optimization techniques.

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

Energy Technology Data Exchange (ETDEWEB)

Jiang, Huaiguang; Zhang, Yingchen

2016-11-14

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

Finite Element Method in Machining Processes

CERN Document Server

Markopoulos, Angelos P

2013-01-01

Finite Element Method in Machining Processes provides a concise study on the way the Finite Element Method (FEM) is used in the case of manufacturing processes, primarily in machining. The basics of this kind of modeling are detailed to create a reference that will provide guidelines for those who start to study this method now, but also for scientists already involved in FEM and want to expand their research. A discussion on FEM, formulations and techniques currently in use is followed up by machining case studies. Orthogonal cutting, oblique cutting, 3D simulations for turning and milling, grinding, and state-of-the-art topics such as high speed machining and micromachining are explained with relevant examples. This is all supported by a literature review and a reference list for further study. As FEM is a key method for researchers in the manufacturing and especially in the machining sector, Finite Element Method in Machining Processes is a key reference for students studying manufacturing processes but al...

Superconducting three element synchronous ac machine

International Nuclear Information System (INIS)

Boyer, L.; Chabrerie, J.P.; Mailfert, A.; Renard, M.

1975-01-01

There is a growing interest in ac superconducting machines. Of several new concepts proposed for these machines in the last years one of the most promising seems to be the ''three elements'' concept which allows the cancellation of the torque acting on the superconducting field winding, thus overcoming some of the major contraints. This concept leads to a device of induction-type generator. A synchronous, three element superconducting ac machine is described, in which a room temperature, dc fed rotating winding is inserted between the superconducting field winding and the ac armature. The steady-state machine theory is developed, the flux linkages are established, and the torque expressions are derived. The condition for zero torque on the field winding, as well as the resulting electrical equations of the machine, are given. The theoretical behavior of the machine is studied, using phasor diagrams and assuming for the superconducting field winding either a constant current or a constant flux condition

Measurements for stresses in machine components

CERN Document Server

Yakovlev, V F

1964-01-01

Measurements for Stresses in Machine Components focuses on the state of stress and strain of components and members, which determines the service life and strength of machines and structures. This book is divided into four chapters. Chapter I describes the physical basis of several methods of measuring strains, which includes strain gauges, photoelasticity, X-ray diffraction, brittle coatings, and dividing grids. The basic concepts of the electric strain gauge method for measuring stresses inside machine components are covered in Chapter II. Chapter III elaborates on the results of experim

A Dynamic Continuation-Passing Style for Dynamic Delimited Continuations

DEFF Research Database (Denmark)

Biernacki, Dariusz; Danvy, Olivier; Millikin, Kevin Scott

2005-01-01

We present a new abstract machine that accounts for dynamic delimited continuations. We prove the correctness of this new abstract machine with respect to a pre-existing, definitional abstract machine. Unlike this definitional abstract machine, the new abstract machine is in defunctionalized form......, which makes it possible to state the corresponding higher-order evaluator. This evaluator is in continuation+state passing style and threads a trail of delimited continuations and a meta-continuation. Since this style accounts for dynamic delimited continuations, we refer to it as `dynamic continuation......-passing style.' We show that the new machine operates more efficiently than the definitional one and that the notion of computation induced by the corresponding evaluator takes the form of a monad. We also present new examples and a new simulation of dynamic delimited continuations in terms of static ones....

Acceleration of saddle-point searches with machine learning

Energy Technology Data Exchange (ETDEWEB)

Peterson, Andrew A., E-mail: andrew-peterson@brown.edu [School of Engineering, Brown University, Providence, Rhode Island 02912 (United States)

2016-08-21

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

Acceleration of saddle-point searches with machine learning

International Nuclear Information System (INIS)

Peterson, Andrew A.

2016-01-01

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

Acceleration of saddle-point searches with machine learning.

Science.gov (United States)

Peterson, Andrew A

2016-08-21

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

Abstract Interpretation as a Programming Language

DEFF Research Database (Denmark)

Rosendahl, Mads

2013-01-01

examine different programming styles and ways to represent states. Abstract interpretation is primarily a technique for derivation and specification of program analysis. As with denotational semantics we may also view abstract interpretations as programs and examine the implementation. The main focus...... in this paper is to show that results from higher-order strictness analysis may be used more generally as fixpoint operators for higher-order functions over lattices and thus provide a technique for immediate implementation of a large class of abstract interpretations. Furthermore, it may be seen...

National Energy Software Center: compilation of program abstracts

Energy Technology Data Exchange (ETDEWEB)

Brown, J.M.; Butler, M.K.; De Bruler, M.M.

1979-05-01

This is the third complete revision of program abstracts undertaken by the Center. Programs of the IBM 7040, 7090, and CDC 3600 vintage have been removed. Historical data and information on abstract format, program package contents, and subject classification are given. The following subject areas are included in the library: cross section and resonance integral calculations; spectrum calculations, generation of group constants, lattice and cell problems; static design studies; depletion, fuel management, cost analysis, and power plant economics; space-independent kinetics; space--time kinetics, coupled neutronics--hydrodynamics--thermodynamics and excursion simulations; radiological safety, hazard and accident analysis; heat transfer and fluid flow; deformation and stress distribution computations, structural analysis and engineering design studies; gamma heating and shield design; reactor systems analysis; data preparation; data management; subsidiary calculations; experimental data processing; general mathematical and computing system routines; materials; environmental and earth sciences; electronics, engineering equipment, and energy systems studies; chemistry; particle accelerators and high-voltage machines; physics; magnetic fusion research; data. (RWR)

National Energy Software Center: compilation of program abstracts

International Nuclear Information System (INIS)

Brown, J.M.; Butler, M.K.; De Bruler, M.M.

1979-05-01

This is the third complete revision of program abstracts undertaken by the Center. Programs of the IBM 7040, 7090, and CDC 3600 vintage have been removed. Historical data and information on abstract format, program package contents, and subject classification are given. The following subject areas are included in the library: cross section and resonance integral calculations; spectrum calculations, generation of group constants, lattice and cell problems; static design studies; depletion, fuel management, cost analysis, and power plant economics; space-independent kinetics; space--time kinetics, coupled neutronics--hydrodynamics--thermodynamics and excursion simulations; radiological safety, hazard and accident analysis; heat transfer and fluid flow; deformation and stress distribution computations, structural analysis and engineering design studies; gamma heating and shield design; reactor systems analysis; data preparation; data management; subsidiary calculations; experimental data processing; general mathematical and computing system routines; materials; environmental and earth sciences; electronics, engineering equipment, and energy systems studies; chemistry; particle accelerators and high-voltage machines; physics; magnetic fusion research; data

Impredicative concurrent abstract predicates

DEFF Research Database (Denmark)

Svendsen, Kasper; Birkedal, Lars

2014-01-01

We present impredicative concurrent abstract predicates { iCAP { a program logic for modular reasoning about concurrent, higher- order, reentrant, imperative code. Building on earlier work, iCAP uses protocols to reason about shared mutable state. A key novel feature of iCAP is the ability to dene...

Research on intelligent machine self-perception method based on LSTM

Science.gov (United States)

Wang, Qiang; Cheng, Tao

2018-05-01

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

Thermal models of pulse electrochemical machining

International Nuclear Information System (INIS)

Kozak, J.

2004-01-01

Pulse electrochemical machining (PECM) provides an economical and effective method for machining high strength, heat-resistant materials into complex shapes such as turbine blades, die, molds and micro cavities. Pulse Electrochemical Machining involves the application of a voltage pulse at high current density in the anodic dissolution process. Small interelectrode gap, low electrolyte flow rate, gap state recovery during the pulse off-times lead to improved machining accuracy and surface finish when compared with ECM using continuous current. This paper presents a mathematical model for PECM and employs this model in a computer simulation of the PECM process for determination of the thermal limitation and energy consumption in PECM. The experimental results and discussion of the characteristics PECM are presented. (authors)

Machine learning concepts in coherent optical communication systems

DEFF Research Database (Denmark)

Zibar, Darko; Schäffer, Christian G.

2014-01-01

Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA.......Powerful statistical signal processing methods, used by the machine learning community, are addressed and linked to current problems in coherent optical communication. Bayesian filtering methods are presented and applied for nonlinear dynamic state tracking. © 2014 OSA....

Sentence retrieval for abstracts of randomized controlled trials

Directory of Open Access Journals (Sweden)

Chung Grace Y

2009-02-01

Full Text Available Abstract Background The practice of evidence-based medicine (EBM requires clinicians to integrate their expertise with the latest scientific research. But this is becoming increasingly difficult with the growing numbers of published articles. There is a clear need for better tools to improve clinician's ability to search the primary literature. Randomized clinical trials (RCTs are the most reliable source of evidence documenting the efficacy of treatment options. This paper describes the retrieval of key sentences from abstracts of RCTs as a step towards helping users find relevant facts about the experimental design of clinical studies. Method Using Conditional Random Fields (CRFs, a popular and successful method for natural language processing problems, sentences referring to Intervention, Participants and Outcome Measures are automatically categorized. This is done by extending a previous approach for labeling sentences in an abstract for general categories associated with scientific argumentation or rhetorical roles: Aim, Method, Results and Conclusion. Methods are tested on several corpora of RCT abstracts. First structured abstracts with headings specifically indicating Intervention, Participant and Outcome Measures are used. Also a manually annotated corpus of structured and unstructured abstracts is prepared for testing a classifier that identifies sentences belonging to each category. Results Using CRFs, sentences can be labeled for the four rhetorical roles with F-scores from 0.93–0.98. This outperforms the use of Support Vector Machines. Furthermore, sentences can be automatically labeled for Intervention, Participant and Outcome Measures, in unstructured and structured abstracts where the section headings do not specifically indicate these three topics. F-scores of up to 0.83 and 0.84 are obtained for Intervention and Outcome Measure sentences. Conclusion Results indicate that some of the methodological elements of RCTs are

comparative study of moore and mealy machine models adaptation

African Journals Online (AJOL)

user

automata model was developed for ABS manufacturing process using Moore and Mealy Finite State Machines. Simulation ... The simulation results showed that the Mealy Machine is faster than the Moore ..... random numbers from MATLAB.

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

Science.gov (United States)

Lin, Y.; Zhang, W. J.

2005-02-01

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

Probabilistic machine learning and artificial intelligence.

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.

People’s Republic of China Scientific Abstracts No. 164.

Science.gov (United States)

1977-02-23

of these articles. CONTENTS PAGE K’ O-HSUEH SHIH-YEN /SCIENTIFIC EXPERIMENT/ No 8, Äug 76 1 CHIH-WU HSUEH-PAO /ÄCTA BOTANICA SINICA/ Vol 18, No...AUTHOR: None ORG: Digital Control Teaching Group, Shanghai Sparetime Industrial University TITLE: "On the Principles of Digital Machine Tool Control...8217 College" SOURCE: Peking CHIH-WU HSUEH-PAO [AGTA BOTANICA SINICA] Vol 18 No 3, Sep 76 pp 198-201 EXCERPT OF ENGLISH ABSTRACT: The May-7 Agricultural

Object-Oriented Support for Adaptive Methods on Paranel Machines

Directory of Open Access Journals (Sweden)

Sandeep Bhatt

1993-01-01

Full Text Available This article reports on experiments from our ongoing project whose goal is to develop a C++ library which supports adaptive and irregular data structures on distributed memory supercomputers. We demonstrate the use of our abstractions in implementing "tree codes" for large-scale N-body simulations. These algorithms require dynamically evolving treelike data structures, as well as load-balancing, both of which are widely believed to make the application difficult and cumbersome to program for distributed-memory machines. The ease of writing the application code on top of our C++ library abstractions (which themselves are application independent, and the low overhead of the resulting C++ code (over hand-crafted C code supports our belief that object-oriented approaches are eminently suited to programming distributed-memory machines in a manner that (to the applications programmer is architecture-independent. Our contribution in parallel programming methodology is to identify and encapsulate general classes of communication and load-balancing strategies useful across applications and MIMD architectures. This article reports experimental results from simulations of half a million particles using multiple methods.

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...

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

Directory of Open Access Journals (Sweden)

Héctor Herrero

2017-05-01

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

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.)

Use of system code to estimate equilibrium tritium inventory in fusion DT machines, such as ARIES-AT and components testing facilities

International Nuclear Information System (INIS)

Wong, C.P.C.; Merrill, B.

2014-01-01

Highlights: • With the use of a system code, tritium burn-up fraction (f burn ) can be determined. • Initial tritium inventory for steady state DT machines can be estimated. • f burn of ARIES-AT, CFETR and FNSF-AT are in the range of 1–2.8%. • Respective total tritium inventories of are 7.6 kg, 6.1 kg, and 5.2 kg. - Abstract: ITER is under construction and will begin operation in 2020. This is the first 500 MW fusion class DT device, and since it is not going to breed tritium, it will consume most of the limited supply of tritium resources in the world. Yet, in parallel, DT fusion nuclear component testing machines will be needed to provide technical data for the design of DEMO. It becomes necessary to estimate the tritium burn-up fraction and corresponding initial tritium inventory and the doubling time of these machines for the planning of future supply and utilization of tritium. With the use of a system code, tritium burn-up fraction and initial tritium inventory for steady state DT machines can be estimated. Estimated tritium burn-up fractions of FNSF-AT, CFETR-R and ARIES-AT are in the range of 1–2.8%. Corresponding total equilibrium tritium inventories of the plasma flow and tritium processing system, and with the DCLL blanket option are 7.6 kg, 6.1 kg, and 5.2 kg for ARIES-AT, CFETR-R and FNSF-AT, respectively

6. International conference on materials science and condensed matter physics. Abstracts

International Nuclear Information System (INIS)

2012-09-01

This book includes abstracts of the communications presented at the 6th International Conference on Materials Science and Condensed Matter Physics. The aim of this event is two-fold. First, it provides a nice opportunity for discussions and the dissemination of the latest results on selected topics in materials science, condensed-matter physics, and electrical methods of materials treatment. On the other hand, this is an occasion for sketching a broad perspective of scientific research and technological developments for the participants through oral and poster presentations. The abstracts presented in the book cover certain issues of modern theoretical and experimental physics and advanced technology, such as crystal growth, doping and implantation, fabrication of solid state structures; defect engineering, methods of fabrication and characterization of nanostructures including nanocomposites, nanowires and nano dots; fullerenes and nano tubes; quantum wells and superlattices; molecular-based materials, meso- and nano electronics; methods of structural and mechanical characterization; optical, transport, magnetic and superconductor properties, non-linear phenomena, size and interface effects; condensed matter theory; modelling of materials and structural properties including low dimensional systems; advanced materials and fabrication processes, device modelling and simulation of structures and elements; optoelectronics and photonics; microsensors and micro electro-mechanical systems; degradation and reliability, advanced technologies of electro-physico-chemical methods and equipment for materials machining, including modification of surfaces; electrophysical technologies of intensification of heat- and mass-transfer; treatment of biological preparations and foodstuff.

The ASDEX upgrade digital video processing system for real-time machine protection

Energy Technology Data Exchange (ETDEWEB)

Drube, Reinhard, E-mail: reinhard.drube@ipp.mpg.de [Max-Planck-Institut für Plasmaphysik, EURATOM Association, Boltzmannstr. 2, 85748 Garching (Germany); Neu, Gregor [Max-Planck-Institut für Plasmaphysik, EURATOM Association, Boltzmannstr. 2, 85748 Garching (Germany); Cole, Richard H.; Lüddecke, Klaus [Unlimited Computer Systems GmbH, Seeshaupterstr. 15, 82393 Iffeldorf (Germany); Lunt, Tilmann; Herrmann, Albrecht [Max-Planck-Institut für Plasmaphysik, EURATOM Association, Boltzmannstr. 2, 85748 Garching (Germany)

2013-11-15

Highlights: • We present the Real-Time Video diagnostic system of ASDEX Upgrade. • We show the implemented image processing algorithms for machine protection. • The way to achieve a robust operating multi-threading Real-Time system is described. -- Abstract: This paper describes the design, implementation, and operation of the Video Real-Time (VRT) diagnostic system of the ASDEX Upgrade plasma experiment and its integration with the ASDEX Upgrade Discharge Control System (DCS). Hot spots produced by heating systems erroneously or accidentally hitting the vessel walls, or from objects in the vessel reaching into the plasma outer border, show up as bright areas in the videos during and after the reaction. A system to prevent damage to the machine by allowing for intervention in a running discharge of the experiment was proposed and implemented. The VRT was implemented on a multi-core real-time Linux system. Up to 16 analog video channels (color and b/w) are acquired and multiple regions of interest (ROI) are processed on each video frame. Detected critical states can be used to initiate appropriate reactions – e.g. gracefully terminate the discharge. The system has been in routine operation since 2007.

Compositional Abstraction of PEPA Models for Transient Analysis

DEFF Research Database (Denmark)

Smith, Michael James Andrew

2010-01-01

- or interval - Markov chains allow us to aggregate states in such a way as to safely bound transient probabilities of the original Markov chain. Whilst we can apply this technique directly to a PEPA model, it requires us to obtain the CTMC of the model, whose state space may be too large to construct......Stochastic process algebras such as PEPA allow complex stochastic models to be described in a compositional way, but this leads to state space explosion problems. To combat this, there has been a great deal of work in developing techniques for abstracting Markov chains. In particular, abstract...

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...

Numerical identifiability of the parameters of induction machines

Energy Technology Data Exchange (ETDEWEB)

Corcoles, F.; Pedra, J.; Salichs, M. [Dep. d' Eng. Electrica ETSEIB. UPC, Barcelona (Spain)

2000-08-01

This paper analyses the numerical identifiability of the electrical parameters of induction machines. Relations between parameters and the impossibility to estimate all of them - when only external measures are used: voltage, current, speed and torque - are shown. Formulations of the single and double-cage induction machine, with and without core losses in both models, are developed. The proposed solution is the formulation of machine equations by using the minimum number of parameters (which are identifiable parameters). As an application example, the parameters of a double-cage induction machine are identified using steady-state measurements corresponding to different angular speeds. (orig.)

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).

Abstracts of Research, July 1973 through June 1974.

Science.gov (United States)

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

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

a design to digitalize hydraulic cylinder control of a machine tool

African Journals Online (AJOL)

Dr Obe

1995-09-01

Sep 1, 1995 ... Department of Mechanical Engineering. FEDERAL UNIVERSITY OF TECHNOLOGY, OWERRI,. P.M.B. 1526, OWERRI. ABSTRACT. Conventionally hydraulic piston - cylinder servos are actuated using analogue controls for machine tool axis drives. In this paper a design of the axis control system of an NC ...

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

Science.gov (United States)

Gallos, Ioannis; Siettos, Constantinos

2017-11-01

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

Finite State Machine Analysis of Remote Sensor Data

International Nuclear Information System (INIS)

Barbson, John M.

1999-01-01

The use of unattended monitoring systems for monitoring the status of high value assets and processes has proven to be less costly and less intrusive than the on-site inspections which they are intended to replace. However, these systems present a classic information overload problem to anyone trying to analyze the resulting sensor data. These data are typically so voluminous and contain information at such a low level that the significance of any single reading (e.g., a door open event) is not obvious. Sophisticated, automated techniques are needed to extract expected patterns in the data and isolate and characterize the remaining patterns that are due to undeclared activities. This paper describes a data analysis engine that runs a state machine model of each facility and its sensor suite. It analyzes the raw sensor data, converting and combining the inputs from many sensors into operator domain level information. It compares the resulting activities against a set of activities declared by an inspector or operator, and then presents the differences in a form comprehensible to an inspector. Although the current analysis engine was written with international nuclear material safeguards, nonproliferation, and transparency in mind, since there is no information about any particular facility in the software, there is no reason why it cannot be applied anywhere it is important to verify processes are occurring as expected, to detect intrusion into a secured area, or to detect the diversion of valuable assets

Online State Space Model Parameter Estimation in Synchronous Machines

Directory of Open Access Journals (Sweden)

Z. Gallehdari

2014-06-01

The suggested approach is evaluated for a sample synchronous machine model. Estimated parameters are tested for different inputs at different operating conditions. The effect of noise is also considered in this study. Simulation results show that the proposed approach provides good accuracy for parameter estimation.

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu

2011-01-01

International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...

Scikit-learn: Machine Learning in Python

OpenAIRE

Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu

2012-01-01

Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....

[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 Tools for Electrical Machines Modelling: Teaching and ...

African Journals Online (AJOL)

Simulation tools are used both for research and teaching to allow a good comprehension of the systems under study before practical implementations. This paper illustrates the way MATLAB is used to model non-linearites in synchronous machine. The machine is modeled in rotor reference frame with currents as state ...

Random non-proportional fatigue tests with planar tri-axial fatigue testing machine

OpenAIRE

Inoue, T.; Nagao, R.; Takeda, N.

2016-01-01

Complex stresses, which occur on the mechanical surfaces of transport machinery in service, bring a drastic degradation in fatigue life. However, it is hard to reproduce such complex stress states for evaluating the fatigue life with conventional multiaxial fatigue machines. We have developed a fatigue testing machine that enables reproduction of such complex stresses. The testing machine can reproduce arbitrary in-plane stress states by applying three independent loads to the test specimen u...

What does the brain tell us about abstract art?

Directory of Open Access Journals (Sweden)

Vered eAviv

2014-02-01

Full Text Available In this essay I focus on the question of why we are attracted to abstract art (or, perhaps more accurately, non-representational art. After elaborating on the processing of visual art in general and abstract art in particular, I discuss recent data from neuroscience and behavioral studies related to abstract art. I conclude with several speculations concerning our apparent appeal to this particular type of art. In particular, I claim that abstract art frees our brain from the dominance of reality, enabling it to flow within its inner states, create new emotional and cognitive associations, and activate brain-states that are otherwise harder to access. This process is apparently rewarding as it enables the exploration of yet undiscovered inner territories of the viewer’s brain.

Anisotropic wetting characteristics versus roughness on machined surfaces of hydrophilic and hydrophobic materials

International Nuclear Information System (INIS)

Liang, Yande; Shu, Liming; Natsu, Wataru; He, Fuben

2015-01-01

Graphical abstract: - Highlights: • The aim is to investigate the influence of roughness on anisotropic wetting on machined surfaces. • The relationship between roughness and anisotropic wetting is modeled by thermodynamical analysis. • The effect of roughness on anisotropic wetting on hydrophilic materials is stronger than that on hydrophobic materials. • The energy barrier existing in the direction perpendicular to the lay is one of the main reasons for the anisotropic wetting. • The contact angle in the parallel direction is larger than that in the perpendicular direction. - Abstract: Anisotropic wetting of machined surfaces is widely applied in industries which can be greatly affected by roughness and solid's chemical properties. However, there has not been much work on it. A free-energy thermodynamic model is presented by analyzing geometry morphology of machined surfaces (2-D model surfaces), which demonstrates the influence of roughness on anisotropic wetting. It can be concluded that the energy barrier is one of the main reasons for the anisotropic wetting existing in the direction perpendicular to the lay. In addition, experiments in investigating anisotropic wetting, which was characterized by the static contact angle and droplet's distortion, were performed on machined surfaces with different roughness on hydrophilic and hydrophobic materials. The droplet's anisotropy found on machined surfaces increased with mean slope of roughness profile Kr. It indicates that roughness on anisotropic wetting on hydrophilic materials has a stronger effect than that on hydrophobic materials. Furthermore, the contact angles predicted by the model are basically consistent with the experimentally ones

2002 Conference Programme and Book of Abstracts

International Nuclear Information System (INIS)

2002-01-01

The 25th Annual (Silver Jubilee) Conference 2002 Conference Programme and Book of Abstracts gives a brief on the Nigerian Institute of Physics, the Sheda Science and Technology Complex. It carries the Conference programme and carries the abstracts of all the papers presented. The abstracts cover a wide range of subjects including topics in atmospheric physics, education, policy and planning, geophysics, instrumentation, mathematical sciences, theoretical physics, nuclear and health physics, solid state, electronic and health physics. We are grateful to the Nigerian Institute of Physics for this volume

Machine learning for healthcare technologies

CERN Document Server

Clifton, David A

2016-01-01

This book brings together chapters on the state-of-the-art in machine learning (ML) as it applies to the development of patient-centred technologies, with a special emphasis on 'big data' and mobile data.

Towards Abstract Interpretation of Epistemic Logic

DEFF Research Database (Denmark)

Ajspur, Mai; Gallagher, John Patrick

applicable to infinite models. The abstract model-checker allows model-checking with infinite-state models. When applied to the problem of whether M |= φ, it terminates and returns the set of states in M at which φ might hold. If the set is empty, then M definitely does not satisfy φ, while if the set is non...

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

Science.gov (United States)

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

2015-11-01

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

Predicting genome-wide redundancy using machine learning

Directory of Open Access Journals (Sweden)

Shasha Dennis E

2010-11-01

Full Text Available Abstract Background Gene duplication can lead to genetic redundancy, which masks the function of mutated genes in genetic analyses. Methods to increase sensitivity in identifying genetic redundancy can improve the efficiency of reverse genetics and lend insights into the evolutionary outcomes of gene duplication. Machine learning techniques are well suited to classifying gene family members into redundant and non-redundant gene pairs in model species where sufficient genetic and genomic data is available, such as Arabidopsis thaliana, the test case used here. Results Machine learning techniques that combine multiple attributes led to a dramatic improvement in predicting genetic redundancy over single trait classifiers alone, such as BLAST E-values or expression correlation. In withholding analysis, one of the methods used here, Support Vector Machines, was two-fold more precise than single attribute classifiers, reaching a level where the majority of redundant calls were correctly labeled. Using this higher confidence in identifying redundancy, machine learning predicts that about half of all genes in Arabidopsis showed the signature of predicted redundancy with at least one but typically less than three other family members. Interestingly, a large proportion of predicted redundant gene pairs were relatively old duplications (e.g., Ks > 1, suggesting that redundancy is stable over long evolutionary periods. Conclusions Machine learning predicts that most genes will have a functionally redundant paralog but will exhibit redundancy with relatively few genes within a family. The predictions and gene pair attributes for Arabidopsis provide a new resource for research in genetics and genome evolution. These techniques can now be applied to other organisms.

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

Science.gov (United States)

Tominaga, Isao; Sato, Masayuki

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

Machine learning for epigenetics and future medical applications

OpenAIRE

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

2017-01-01

ABSTRACT Understanding epigenetic processes holds immense promise for medical applications. Advances in Machine Learning (ML) are critical to realize this promise. Previous studies used epigenetic data sets associated with the germline transmission of epigenetic transgenerational inheritance of disease and novel ML approaches to predict genome-wide locations of critical epimutations. A combination of Active Learning (ACL) and Imbalanced Class Learning (ICL) was used to address past problems w...

Design and Construction of Wireless Control System for Drilling Machine

Directory of Open Access Journals (Sweden)

Nang Su Moan Hsam

2015-06-01

Full Text Available Abstract Drilling machine is used for boring holes in various materials and used in woodworking metalworking construction and do-it-yourself projects. When the machine operate for a long time the temperature increases and so we need to control the temperature of the machine and some lubrication system need to apply to reduce the temperature. Due to the improvement of technology the system can be controlled with wireless network. This control system use Window Communication Foundation WCF which is the latest service oriented technology to control all drilling machines in industries simultaneously. All drilling machines are start working when they received command from server. After the machine is running for a long time the temperature is gradually increased. This system used LM35 temperature sensor to measure the temperature. When the temperature is over the safely level that is programmed in host server the controller at the server will command to control the speed of motor and applying some lubrication system at the tip and edges of drill. The command from the server is received by the client and sends to PIC. In this control system PIC microcontroller is used as an interface between the client computer and the machine. The speed of motor is controlled with PWM and water pump system is used for lubrication. This control system is designed and simulated with 12V DC motor LM35 sensor LCD displayand relay which is to open the water container to spray water between drill and work piece. The host server choosing to control the drilling machine that are overheat by selecting the clients IP address that is connected with that machine.

Responding to the will of the machine: Leadership in the age of artificial intelligence

OpenAIRE

NAQVI, Al

2017-01-01

Abstract. The advent of artificial intelligence in the modern economy will revolutionize the workplace of tomorrow. It will alsocreate never-seen-before challenges for leadership. The current leadership theory is extensive but it does not address on how to lead in a workplace composed of intelligent machines. However, it can be observed that leadership theory tends to develop in tandem with the developments in technology - metaphorically termed as will of the machine in this article. Specific...

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 ...

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

International Nuclear Information System (INIS)

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

2016-01-01

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

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

Energy Technology Data Exchange (ETDEWEB)

Klymenko, M. V. [Department of Chemistry, University of Liège, B4000 Liège (Belgium); Klein, M. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Levine, R. D. [The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel); Crump Institute for Molecular Imaging and Department of Molecular and Medical Pharmacology, David Geffen School of Medicine and Department of Chemistry and Biochemistry, University of California, Los Angeles, California 90095 (United States); Remacle, F., E-mail: fremacle@ulg.ac.be [Department of Chemistry, University of Liège, B4000 Liège (Belgium); The Fritz Haber Center for Molecular Dynamics and the Institute of Chemistry, The Hebrew University of Jerusalem, Jerusalem 91904 (Israel)

2016-07-14

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

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

Science.gov (United States)

Du, Liang; Yang, Yi; Harley, Ronald Gordon; Habetler, Thomas G.; He, Dawei

2016-08-09

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

Automation of a universal machine

International Nuclear Information System (INIS)

Rodriguez S, J.

1997-01-01

The development of the hardware and software of a control system for a servo-hydraulic machine is presented. The universal machine is an Instron, model 1331, used to make mechanical tests. The software includes the acquisition of data from the measurements, processing and graphic presentation of the results in the assay of the 'tension' type. The control is based on a PPI (Programmable Peripheral Interface) 8255, in which the different states of the machine are set. The control functions of the machine are: a) Start of an assay, b) Pause in the assay, c) End of the assay, d) Choice of the control mode of the machine, that they could be in load, stroke or strain modes. For the data acquisition, a commercial card, National Products, model DAS-16, plugged in a slot of a Pc was used. Three transducers provide the analog signals, a cell of load, a LVDT and a extensometer. All the data are digitalized and handled in order to get the results in the appropriate working units. A stress-strain graph is obtained in the screen of the Pc for a tension test for a specific material. The points of maximum stress, rupture stress and the yield stress of the material under test are shown. (Author)

Use of system code to estimate equilibrium tritium inventory in fusion DT machines, such as ARIES-AT and components testing facilities

Energy Technology Data Exchange (ETDEWEB)

Wong, C.P.C., E-mail: wongc@fusion.gat.com [General Atomics, San Diego, CA (United States); Merrill, B. [Idaho National Laboratory, Idaho Falls, ID (United States)

2014-10-15

Highlights: • With the use of a system code, tritium burn-up fraction (f{sub burn}) can be determined. • Initial tritium inventory for steady state DT machines can be estimated. • f{sub burn} of ARIES-AT, CFETR and FNSF-AT are in the range of 1–2.8%. • Respective total tritium inventories of are 7.6 kg, 6.1 kg, and 5.2 kg. - Abstract: ITER is under construction and will begin operation in 2020. This is the first 500 MW{sub fusion} class DT device, and since it is not going to breed tritium, it will consume most of the limited supply of tritium resources in the world. Yet, in parallel, DT fusion nuclear component testing machines will be needed to provide technical data for the design of DEMO. It becomes necessary to estimate the tritium burn-up fraction and corresponding initial tritium inventory and the doubling time of these machines for the planning of future supply and utilization of tritium. With the use of a system code, tritium burn-up fraction and initial tritium inventory for steady state DT machines can be estimated. Estimated tritium burn-up fractions of FNSF-AT, CFETR-R and ARIES-AT are in the range of 1–2.8%. Corresponding total equilibrium tritium inventories of the plasma flow and tritium processing system, and with the DCLL blanket option are 7.6 kg, 6.1 kg, and 5.2 kg for ARIES-AT, CFETR-R and FNSF-AT, respectively.

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

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)

Complete Abstractions of Dynamical Systems by Timed Automata

DEFF Research Database (Denmark)

Sloth, Christoffer; Wisniewski, Rafael

2013-01-01

This paper addresses the generation of complete abstractions of polynomial dynamical systems by timed automata. For the proposed abstraction, the state space is divided into cells by sublevel sets of functions. We identify a relation between these functions and their directional derivatives along...... to approximate the dynamical system, in a subset of admissible subdivisioning functions....

Method of control of machining accuracy of low-rigidity elastic-deformable shafts

Directory of Open Access Journals (Sweden)

Antoni Świć

Full Text Available The paper presents an analysis of the possibility of increasing the accuracy and stability of machining of low-rigidity shafts while ensuring high efficiency and economy of their machining. An effective way of improving the accuracy of machining of shafts is increasing their rigidity as a result of oriented change of the elastic-deformable state through the application of a tensile force which, combined with the machining force, forms longitudinal-lateral strains. The paper also presents mathematical models describing the changes of the elastic-deformable state resulting from the application of the tensile force. It presents the results of experimental studies on the deformation of elastic low-rigidity shafts, performed on a special test stand developed on the basis of a lathe. An estimation was made of the effectiveness of the method of control of the elastic-deformable state with the use, as the regulating effects, the tensile force and eccentricity. It was demonstrated that controlling the two parameters: tensile force and eccentricity, one can improve the accuracy of machining, and thus achieve a theoretically assumed level of accuracy.

Running and machine studies in 1990

International Nuclear Information System (INIS)

1991-03-01

This annual report described the GANIL performance and machine studies. During the year 1990, the machine has been operated for 36 weeks divided into periods of 5, 6 or 7 weeks; consequently the number of beam setting up has been reduced. From 5682 hours of scheduled beam 3239 hours have been delivered on target. Very heavy ions (Pb, U) are now accelerated owing to the OAE modification. Many experiments have been completed with the new medium energy beam facility. The machine studies were devoted to the development ot the following items: production of 157 Gd 19+ ions, acceleration of 238 U 59+ at 24 MeV/u, SSC1 orbit precession, charge state distribution and energy spread after stripping [fr

Reviewing the current state of machine learning for artificial intelligence with regards to the use of contextual information

OpenAIRE

Kinch, Martin W.; Melis, Wim J.C.; Keates, Simeon

2017-01-01

This paper will consider the current state of Machine Learning for Artificial Intelligence, more specifically for applications, such as: Speech Recognition, Game Playing and Image Processing. The artificial world tends to make limited use of context in comparison to what currently happens in human life, while it would benefit from improvements in this area. Additionally, the process of transferring knowledge between application domains is another important area where artificial system can imp...

A review of supervised machine learning applied to ageing research.

Science.gov (United States)

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

2017-04-01

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

Health physics research abstracts no. 11

International Nuclear Information System (INIS)

1984-07-01

The present issue No. 11 of Health Physics Research Abstracts is the continuation of a series of Bulletins published by the Agency since 1967. They collect reports from Member States on Health Physics research in progress or just completed. The main aim in issuing such reports is to draw attention to work that is about to be published and to enable interested scientists to obtain further information through direct correspondence with the investigators. The attention of users of this publication is drawn to the fact that abstracts of published documents on Health Physics are published eventually in INIS Atomindex, which is one of the output products of the Agency's International Nuclear Information System. The present issue contains 235 reports received up to December 1983 from the following Member States. In parentheses the country's ISO code and number of reports are given

TF.Learn: TensorFlow's High-level Module for Distributed Machine Learning

OpenAIRE

Tang, Yuan

2016-01-01

TF.Learn is a high-level Python module for distributed machine learning inside TensorFlow. It provides an easy-to-use Scikit-learn style interface to simplify the process of creating, configuring, training, evaluating, and experimenting a machine learning model. TF.Learn integrates a wide range of state-of-art machine learning algorithms built on top of TensorFlow's low level APIs for small to large-scale supervised and unsupervised problems. This module focuses on bringing machine learning t...

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.

Gelcasting compositions having improved drying characteristics and machinability

Science.gov (United States)

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

2001-01-01

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

CMP 2009: conference of moldavian physicists. Abstracts

International Nuclear Information System (INIS)

2009-11-01

This book includes 151 abstracts on various aspects of: materials processing and characterization, crystal growth methods, solid-state and crystal technology, development of condensed matter theory and modeling of materials properties, solid-state device physics, nano science and nano technology, heterostructures, superlattices, quantum wells and wires, advanced quantum physics for nano systems, etc.

CMP 2012: conference of moldavian physicists. Abstracts

International Nuclear Information System (INIS)

2012-10-01

This book includes abstracts on various aspects of: materials processing and characterization, crystal growth methods, solid-state and crystal technology, development of condensed matter theory and modeling of materials properties, solid-state device physics, nano science and nano technology, heterostructures, superlattices, quantum wells and wires, advanced quantum physics for nano systems, etc.

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.

Machine Translation Tools - Tools of The Translator's Trade

DEFF Research Database (Denmark)

Kastberg, Peter

2012-01-01

In this article three of the more common types of translation tools are presented, discussed and critically evaluated. The types of translation tools dealt with in this article are: Fully Automated Machine Translation (or FAMT), Human Aided Machine Translation (or HAMT) and Machine Aided Human...... Translation (or MAHT). The strengths and weaknesses of the different types of tools are discussed and evaluated by means of a number of examples. The article aims at two things: at presenting a sort of state of the art of what is commonly referred to as “machine translation” as well as at providing the reader...... with a sound basis for considering what translation tool (if any) is the most appropriate in order to meet his or her specific translation needs....

Advanced Light Source. Compendium of User Abstracts and Technical Reports 1997

International Nuclear Information System (INIS)

Cross, J.; Devereaux, M.K.; Dixon, D.J.; Greiner, A.

1998-01-01

The Advanced Light Source (ALS), a national user facility located at Ernest Orlando Lawrence Berkeley National Laboratory of the University of California is available to researchers from academia, industry, and government laboratories. Operation of the ALS is funded by the Department of Energy's Office of Basic Energy Sciences. This Compendium contains abstracts written by users summarizing research completed or in progress during 1997, ALS technical reports describing ongoing efforts related to improvement in machine operations and research and development projects, and information on ALS beamlines planned through 1998

Advanced Light Source Compendium of User Abstracts andTechnical Reports 1997

Energy Technology Data Exchange (ETDEWEB)

Cross, J.; Devereaux, M.K.; Dixon, D.J.; Greiner, A.; editors

1998-07-01

The Advanced Light Source (ALS), a national user facility located at Ernest Orlando Lawrence Berkeley National Laboratory of the University of California is available to researchers from academia, industry, and government laboratories. Operation of the ALS is funded by the Department of Energy's Office of Basic Energy Sciences. This Compendium contains abstracts written by users summarizing research completed or in progress during 1997, ALS technical reports describing ongoing efforts related to improvement in machine operations and research and development projects, and information on ALS beamlines planned through 1998.

Novel cloning machine with supplementary information

International Nuclear Information System (INIS)

Qiu Daowen

2006-01-01

Probabilistic cloning was first proposed by Duan and Guo. Then Pati established a novel cloning machine (NCM) for copying superposition of multiple clones simultaneously. In this paper, we deal with the novel cloning machine with supplementary information (NCMSI). For the case of cloning two states, we demonstrate that the optimal efficiency of the NCMSI in which the original party and the supplementary party can perform quantum communication equals that achieved by a two-step cloning protocol wherein classical communication is only allowed between the original and the supplementary parties. From this equivalence, it follows that NCMSI may increase the success probabilities for copying. Also, an upper bound on the unambiguous discrimination of two nonorthogonal pure product states is derived. Our investigation generalizes and completes the results in the literature

NPP life management (abstracts)

International Nuclear Information System (INIS)

Litvinskij, L.L.; Barbashev, S.V.

2002-01-01

Abstracts of the papers presented at the International conference of the Ukrainian Nuclear Society 'NPP Life Management'. The following problems are considered: modernization of the NPP; NPP life management; waste and spent nuclear fuel management; decommissioning issues; control systems (including radiation and ecological control systems); information and control systems; legal and regulatory framework. State nuclear regulatory control; PR in nuclear power; training of personnel; economics of nuclear power engineering

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

Science.gov (United States)

Johnson, Steven D.

1995-01-01

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

Performance analysis of a composite dual-winding reluctance machine

International Nuclear Information System (INIS)

Anih, Linus U.; Obe, Emeka S.

2009-01-01

The electromagnetic energy conversion process of a composite dual-winding asynchronous reluctance machine is presented. The mechanism of torque production is explained using the magnetic fields distributions. The dynamic model developed in dq-rotor reference frame from first principles depicts the machine operation and response to sudden load change. The device is self-starting in the absence of rotor conductors and its starting current is lower than that of a conventional induction machine. Although the machine possesses salient pole rotors, it is clearly shown that its performance is that of an induction motor operating at half the synchronous speed. Hence the device produces synchronous torque while operating asynchronously. Simple tests were conducted on a prototype demonstration machine and the results obtained are seen to be in tune with the theory and the steady-state calculations.

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

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

Magnet management in electric machines

Science.gov (United States)

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

2017-03-21

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

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.

AIF-ω: Set-Based Protocol Abstraction with Countable Families

DEFF Research Database (Denmark)

Mödersheim, Sebastian Alexander; Bruni, Alessandro

2016-01-01

this limitation by abstracting state into countable families of sets. We can then formalize a problem with unbounded agents, where each agent maintains its own set of keys. Still, our method does not loose the benefits of the abstraction approach, in particular, it translates a verification problem to a set...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2015-01-01

Techniques from the machine learning community are reviewed and employed for laser characterization, signal detection in the presence of nonlinear phase noise, and nonlinearity mitigation. Bayesian filtering and expectation maximization are employed within nonlinear state-space framework...

Processes of energy recovery / energy valorization at low temperature levels. State of the art. Extended abstract

International Nuclear Information System (INIS)

Manificat, A.; Megret, O.

2012-09-01

This study aims to realize a state of art of the processes of energy recovery at low level of temperature and their valorizations. The information provided will target particularly the thermal systems of waste and biomass treatment. After reminding the adequate context of development with these solutions and define the scope of the current work, the study begins with the definition of different concepts such as low-grade heat (fatal energy) and exergy, and also the presentation of the fiscal environment as well as the economic and regulatory situation, with information about the TGAP, prices of energy and energy efficiency. The second chapter focuses on the different sources of energy at low temperature level that can be recoverable in order to assess their potentials and their characteristics. The Determination of the temperature range of these energy sources will be put in relation with the needs and demands of users from different industrial sectors. The third part of the study is a review of various technologies for energy recovery and valorization at low temperature. It is useful to distinguish different types of heat exchangers interesting to implement. Moreover, innovative processes allow us to consider new perspectives other than a direct use of heat recovered. For example, we can take into account systems for producing electricity (ORC cycle, hot air engines, thermoelectric conversion), or cold generation (sorption refrigeration machine, Thermo-ejector refrigeration machine) or techniques for energy storage with PCM (Phase Change Material). The last chapter deals to the achievement of four study cases written in the form of sheet and aimed at assess the applicability of the processes previously considered, concerning the field of waste. (authors)

4th International Conference on Man–Machine Interactions

CERN Document Server

Brachman, Agnieszka; Kozielski, Stanisław; Czachórski, Tadeusz

2016-01-01

This book provides an overview of the current state of research on development and application of methods, algorithms, tools and systems associated with the studies on man-machine interaction. Modern machines and computer systems are designed not only to process information, but also to work in dynamic environment, supporting or even replacing human activities in areas such as business, industry, medicine or military. The interdisciplinary field of research on man-machine interactions focuses on broad range of aspects related to the ways in which human make or use computational artifacts, systems and infrastructure.   This monograph is the fourth edition in the series and presents new concepts concerning analysis, design and evaluation of man-machine systems. The selection of high-quality, original papers covers a wide scope of research topics focused on the main problems and challenges encountered within rapidly evolving new forms of human-machine relationships. The presented material is structured into fol...

Machine learning techniques in optical communication

DEFF Research Database (Denmark)

Zibar, Darko; Piels, Molly; Jones, Rasmus Thomas

2016-01-01

Machine learning techniques relevant for nonlinearity mitigation, carrier recovery, and nanoscale device characterization are reviewed and employed. Markov Chain Monte Carlo in combination with Bayesian filtering is employed within the nonlinear state-space framework and demonstrated for parameter...

Introducing Stable Radicals into Molecular Machines.

Science.gov (United States)

Wang, Yuping; Frasconi, Marco; Stoddart, J Fraser

2017-09-27

Ever since their discovery, stable organic radicals have received considerable attention from chemists because of their unique optical, electronic, and magnetic properties. Currently, one of the most appealing challenges for the chemical community is to develop sophisticated artificial molecular machines that can do work by consuming external energy, after the manner of motor proteins. In this context, radical-pairing interactions are important in addressing the challenge: they not only provide supramolecular assistance in the synthesis of molecular machines but also open the door to developing multifunctional systems relying on the various properties of the radical species. In this Outlook, by taking the radical cationic state of 1,1'-dialkyl-4,4'-bipyridinium (BIPY •+ ) as an example, we highlight our research on the art and science of introducing radical-pairing interactions into functional systems, from prototypical molecular switches to complex molecular machines, followed by a discussion of the (i) limitations of the current systems and (ii) future research directions for designing BIPY •+ -based molecular machines with useful functions.

Automated reasoning in man-machine control systems

International Nuclear Information System (INIS)

Stratton, R.C.; Lusk, E.L.

1983-01-01

This paper describes a project being undertaken at Argonne National Laboratory to demonstrate the usefulness of automated reasoning techniques in the implementation of a man-machine control system being designed at the EBR-II nuclear power plant. It is shown how automated reasoning influences the choice of optimal roles for both man and machine in the system control process, both for normal and off-normal operation. In addition, the requirements imposed by such a system for a rigorously formal specification of operating states, subsystem states, and transition procedures have a useful impact on the analysis phase. The definitions and rules are discussed for a prototype system which is physically simple yet illustrates some of the complexities inherent in real systems

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

Science.gov (United States)

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

2017-07-01

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

A nonlinear support vector machine model with hard penalty function based on glowworm swarm optimization for forecasting daily global solar radiation

International Nuclear Information System (INIS)

Jiang, He; Dong, Yao

2016-01-01

Highlights: • Eclat data mining algorithm is used to determine the possible predictors. • Support vector machine is converted into a ridge regularization problem. • Hard penalty selects the number of radial basis functions to simply the structure. • Glowworm swarm optimization is utilized to determine the optimal parameters. - Abstract: For a portion of the power which is generated by grid connected photovoltaic installations, an effective solar irradiation forecasting approach must be crucial to ensure the quality and the security of power grid. This paper develops and investigates a novel model to forecast 30 daily global solar radiation at four given locations of the United States. Eclat data mining algorithm is first presented to discover association rules between solar radiation and several meteorological factors laying a theoretical foundation for these correlative factors as input vectors. An effective and innovative intelligent optimization model based on nonlinear support vector machine and hard penalty function is proposed to forecast solar radiation by converting support vector machine into a regularization problem with ridge penalty, adding a hard penalty function to select the number of radial basis functions, and using glowworm swarm optimization algorithm to determine the optimal parameters of the model. In order to illustrate our validity of the proposed method, the datasets at four sites of the United States are split to into training data and test data, separately. The experiment results reveal that the proposed model delivers the best forecasting performances comparing with other competitors.

Testing and Modeling of Machine Properties in Resistance Welding

DEFF Research Database (Denmark)

Wu, Pei

The objective of this work has been to test and model the machine properties including the mechanical properties and the electrical properties in resistance welding. The results are used to simulate the welding process more accurately. The state of the art in testing and modeling machine properties...... as real projection welding tests, is easy to realize in industry, since tests may be performed in situ. In part II, an approach of characterizing the electrical properties of AC resistance welding machines is presented, involving testing and mathematical modelling of the weld current, the firing angle...... in resistance welding has been described based on a comprehensive literature study. The present thesis has been subdivided into two parts: Part I: Mechanical properties of resistance welding machines. Part II: Electrical properties of resistance welding machines. In part I, the electrode force in the squeeze...

AC machine control : robust and sensorless control by parameter independency

Energy Technology Data Exchange (ETDEWEB)

Samuelsen, Dag Andreas Hals

2009-06-15

In this thesis it is first presented how robust control can be used to give AC motor drive systems competitive dynamic performance under parameter variations. These variations are common to all AC machines, and are a result of temperature change in the machine, and imperfect machine models. This robust control is, however, dependent on sensor operation in the sense that the rotor position is needed in the control loop. Elimination of this control loop has been for many years, and still is, a main research area of AC machines control systems. An integrated PWM modulator and sampler unit has been developed and tested. The sampler unit is able to give current and voltage measurements with a reduced noise component. It is further used to give the true derivative of currents and voltages in the machine and the power converter, as an average over a PWM period, and as separate values for all states of the power converter. In this way, it can give measurements of the currents as well as the derivative of the currents, at the start and at the end of a single power inverter state. This gave a large degree of freedom in parameter and state identification during uninterrupted operation of the induction machine. The special measurement scheme of the system achieved three main goals: By avoiding the time frame where the transistors commutate and the noise in the measurement of the current is large, filtering of the current measurement is no longer needed. The true derivative of the current in the machine is can be measured with far less noise components. This was extended to give any separate derivative in all three switching states of the power converter. Using the computational resources of the FPGA, more advanced information was supplied to the control system, in order to facilitate sensor less operation, with low computational demands on the DSP. As shown in the papers, this extra information was first used to estimate some of the states of the machine, in some or all of the

A two-level real-time vision machine combining coarse and fine grained parallelism

DEFF Research Database (Denmark)

Jensen, Lars Baunegaard With; Kjær-Nielsen, Anders; Pauwels, Karl

2010-01-01

In this paper, we describe a real-time vision machine having a stereo camera as input generating visual information on two different levels of abstraction. The system provides visual low-level and mid-level information in terms of dense stereo and optical flow, egomotion, indicating areas...... a factor 90 and a reduction of latency of a factor 26 compared to processing on a single CPU--core. Since the vision machine provides generic visual information it can be used in many contexts. Currently it is used in a driver assistance context as well as in two robotic applications....

Protection of Mission-Critical Applications from Untrusted Execution Environment: Resource Efficient Replication and Migration of Virtual Machines

Science.gov (United States)

2015-09-28

in the same LAN ; this setup resembles the typical setup in a virtualized datacenter where protected and backup hosts are connected by an internal LAN ... Virtual Machines 5a. CONTRACT NUMBER 5b. GRANT NUMBER FA9550-10-1-0393 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Kang G. Shin 5d. PROJECT...Distribution A - Approved for Public Release 13. SUPPLEMENTARY NOTES None 14. ABSTRACT Continuous replication and live migration of Virtual Machines (VMs

Chatter suppression methods of a robot machine for ITER vacuum vessel assembly and maintenance

International Nuclear Information System (INIS)

Wu, Huapeng; Wang, Yongbo; Li, Ming; Al-Saedi, Mazin; Handroos, Heikki

2014-01-01

Highlights: •A redundant 10-DOF serial-parallel hybrid robot for ITER assembly and maintains is presented. •A dynamic model of the robot is developed. •A feedback and feedforward controller is presented to suppress machining vibration of the robot. -- Abstract: In the process of assembly and maintenance of ITER vacuum vessel (ITER VV), various machining tasks including threading, milling, welding-defects cutting and flexible hose boring are required to be performed from inside of ITER VV by on-site machining tools. Robot machine is a promising option for these tasks, but great chatter (machine vibration) would happen in the machining process. The chatter vibration will deteriorate the robot accuracy and surface quality, and even cause some damages on the end-effector tools and the robot structure itself. This paper introduces two vibration control methods, one is passive and another is active vibration control. For the passive vibration control, a parallel mechanism is presented to increase the stiffness of robot machine; for the active vibration control, a hybrid control method combining feedforward controller and nonlinear feedback controller is introduced for chatter suppression. A dynamic model and its chatter vibration phenomena of a hybrid robot is demonstrated. Simulation results are given based on the proposed hybrid robot machine which is developed for the ITER VV assembly and maintenance

Chatter suppression methods of a robot machine for ITER vacuum vessel assembly and maintenance

Energy Technology Data Exchange (ETDEWEB)

Wu, Huapeng; Wang, Yongbo, E-mail: yongbo.wang@lut.fi; Li, Ming; Al-Saedi, Mazin; Handroos, Heikki

2014-10-15

Highlights: •A redundant 10-DOF serial-parallel hybrid robot for ITER assembly and maintains is presented. •A dynamic model of the robot is developed. •A feedback and feedforward controller is presented to suppress machining vibration of the robot. -- Abstract: In the process of assembly and maintenance of ITER vacuum vessel (ITER VV), various machining tasks including threading, milling, welding-defects cutting and flexible hose boring are required to be performed from inside of ITER VV by on-site machining tools. Robot machine is a promising option for these tasks, but great chatter (machine vibration) would happen in the machining process. The chatter vibration will deteriorate the robot accuracy and surface quality, and even cause some damages on the end-effector tools and the robot structure itself. This paper introduces two vibration control methods, one is passive and another is active vibration control. For the passive vibration control, a parallel mechanism is presented to increase the stiffness of robot machine; for the active vibration control, a hybrid control method combining feedforward controller and nonlinear feedback controller is introduced for chatter suppression. A dynamic model and its chatter vibration phenomena of a hybrid robot is demonstrated. Simulation results are given based on the proposed hybrid robot machine which is developed for the ITER VV assembly and maintenance.

Employing Ti nano-powder dielectric to enhance surface characteristics in electrical discharge machining of AISI D2 steel

Energy Technology Data Exchange (ETDEWEB)

Marashi, Houriyeh, E-mail: houriyeh@marashi.co [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia); Sarhan, Ahmed A.D., E-mail: ah_sarhan@yahoo.com [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia); Department of Mechanical Engineering, Faculty of Engineering, Assiut University, Assiut 71516 (Egypt); Hamdi, Mohd [Department of Mechanical Engineering, Faculty of Engineering, University of Malaya, 50603 Kuala Lumpur (Malaysia); Center of Advanced Manufacturing and Materials Processing (AMMP), 50603 Kuala Lumpur (Malaysia)

2015-12-01

Graphical abstract: - Highlights: • We proposed adding Ti nano-powder to dielectric in EDM. • Average and peak-valley surface roughness was improved by 35 and 40%, respectively. • Improvement of up to 69% in material removal rate was obtained. • Enhanced surface morphology and formation of shallower craters were observed. - Abstract: Manufacturing components with superior surface characteristics is challenging when electrical discharge machining (EDM) is employed for mass production. The aim of this research is to enhance the characteristics of AISI D2 steel surface machined with EDM through adding Ti nano-powder to dielectric under various machining parameters, including discharge duration (T{sub on}) and peak current (I). Surface roughness profilometer, FESEM and AFM analysis were utilized to reveal the machined surface characteristics in terms of surface roughness, surface morphology and surface micro-defects. Moreover, EDX analysis was performed in order to evaluate the atomic deposition of Ti nano-powder on the surface. The concentration of Ti nano-powder in dielectric was also examined using ESEM and EDX. According to the results, the addition of Ti nano-powder to dielectric notably enhanced the surface morphology and surface roughness at all machining parameters except T{sub on} = 340 μs. Of these parameters, maximum enhancement was observed at T{sub on} = 210 μs, where the material removal rate and average surface roughness improved by ∼69 and ∼35% for peak current of 6 and 12 A, respectively. Elemental analysis signified negligible Ti deposition on the machined surface while the atomic concentration of Ti was increased around the crack areas.

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.

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.

COSY Control Status. First results with rapid prototyped man-machine interface for accelerator control

Energy Technology Data Exchange (ETDEWEB)

Hacker, U [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Haberbosch, C [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Henn, K [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany); Weinert, A [Forschungszentrum Juelich, Postfach 1913, 52425 Juelich (Germany)

1994-12-15

The experience gained with the COSY Control System after a six month commissioning period followed by a six month production period will be presented. The COSY Control System runs approximately 300 VME and VXI target systems using a total of about 1000 CPUs, the systems are driven by the diskless operating environment RT/OS, hosted by eight workcells. Application software is implemented using Object-Orientated programming paradigms. All accelerator components become interface functions as instances of an abstract device model class. Methods defined here present an abstract picture of the accelerator giving immediate access to device states and parameters. Operator interaction is defined by building views and controllers for the model. Higher level functions, such as defining an acceleration cycle, are easily developed and modified with the accelerator connected on-line to the model. In the first year of COSY operation the object based approach for a control system, together with a rapid prototyped man-machine interface has brought to light the potential of new functions such as on-line, real time programming on a running system yielding high programming performance. The advantages of this approach have not been, until now, fully appreciated. ((orig.))

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

Science.gov (United States)

1975-01-01

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

Residual stresses generated in F-522 steel by different machining processes

International Nuclear Information System (INIS)

Gracia-Navas, V.; Ferreres, I.; Maranon, J. A.; Garcia-Rosales, C.; Gil-Sevillano, J.

2005-01-01

Machining operations induce plastic deformation and heat generation in the near surface area of the machined part, giving rise to residual stresses. Depending on their magnitude and sign, these stresses can be detrimental or beneficial to the service life of the part. The final stress state depends on the machining process applied, as well as on the machining parameters. Therefore, the establishment of adequate machining guidelines requires the measurement of the residual stresses generated both at the surface and inside the material. in this work, the residual stresses generated in F-522 steel by two hard turning (conventional and laser assisted) and two grinding (production and finishing) processes were measured by X-ray diffraction. Additionally, depth profiles of the volume fraction of retained austenite, microstructure and nano hardness were obtained in order to correlate those results with the residual stress state obtained for each machining process. It has been observed that turning generates tensile stresses in the surface while grinding causes compressive stresses. Below the surface grinding generates weak tensile or nearly null stresses whereas turning generates strong compressive stresses. These results show that the optimum mechanising process (disregarding economical considerations) implies the combination of turning plus elimination of a small thickness by final grinding. (Author) 19 refs

Reliability analysis in intelligent machines

Science.gov (United States)

Mcinroy, John E.; Saridis, George N.

1990-01-01

Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.

Algorithmic Approach to Abstracting Linear Systems by Timed Automata

DEFF Research Database (Denmark)

Sloth, Christoffer; Wisniewski, Rafael

2011-01-01

This paper proposes an LMI-based algorithm for abstracting dynamical systems by timed automata, which enables automatic formal verification of linear systems. The proposed abstraction is based on partitioning the state space of the system using positive invariant sets, generated by Lyapunov...... functions. This partitioning ensures that the vector field of the dynamical system is transversal to all facets of the cells, which induces some desirable properties of the abstraction. The algorithm is based on identifying intersections of level sets of quadratic Lyapunov functions, and determining...

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Science.gov (United States)

Park, Saerom; Lee, Jaewook; Son, Youngdoo

2016-01-01

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

Single Machine Multi-product Capacitated Lotsizing with Sequence-dependent Setups

OpenAIRE

Almada-Lobo , Bernardo; Klabjan , Diego; Carravilla , Maria Antónia; Oliveira , Jose Fernando

2007-01-01

Abstract In production planning in the glass container industry, machine dependent setup times and costs are incurred for switchovers from one product to another. The resulting multi-item capacitated lot sizing problem has sequence-dependent setup times and costs. We present two novel linear mixed integer programming formulations for this problem, incorporating all the necessary features of setup carryovers. The compact formulation has polynomially many constraints, while, on the o...

Ultrashort pulse laser machining of metals and alloys

Science.gov (United States)

Perry, Michael D.; Stuart, Brent C.

2003-09-16

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

Some relations between quantum Turing machines and Turing machines

OpenAIRE

Sicard, Andrés; Vélez, Mario

1999-01-01

For quantum Turing machines we present three elements: Its components, its time evolution operator and its local transition function. The components are related with the components of deterministic Turing machines, the time evolution operator is related with the evolution of reversible Turing machines and the local transition function is related with the transition function of probabilistic and reversible Turing machines.

Ab initio calculation of transition state normal mode properties and rate constants for the H(T)+CH4(CD4) abstraction and exchange reactions

International Nuclear Information System (INIS)

Schatz, G.C.; Walch, S.P.; Wagner, A.F.

1980-01-01

We present ab initio (GVB--POL--CI) calculations for enough of the region about the abstraction and exchange saddle points for H(T)+CH 4 (CD 4 ) to perform a full normal mode analysis of the transition states. The resulting normal mode frequencies are compared to four other published surfaces: an ab initio UHF--SCF calculation by Carsky and Zahradnik, a semiempirical surface by Raff, and two semiempirical surfaces by Kurylo, Hollinden, and Timmons. Significant quantitative and qualitative differences exist between the POL--CI results and those of the other surfaces. Transition state theory rate constants and vibrationally adiabatic reaction threshold energies were computed for all surfaces and compared to available experimental values. For abstraction, the POL--CI rates are in good agreement with experimental rates and in better agreement than are the rates of any of the other surfaces. For exchange, uncertainties in the experimental values and in the importance of vibrationally nonadiabatic effects cloud the comparison of theory to experiment. Tentative conclusions are that the POL--CI barrier is too low by several kcal. Unless vibrationaly nonadiabatic effects are severe, the POL--CI surface is still in better agreement with experiment than are the other surfaces. The rates for a simple 3-atom transition state theory model (where CH 3 is treated as an atom) are compared to the rates for the full 6-atom model. The kinetic energy coupling of reaction coordinate modes to methyl group modes is identified as being of primary importance in determining the accuracy of the 3-atom model for this system. Substantial coupling in abstraction, but not exchange, causes the model to fail for abstraction but succeed for exchange

5. conference of the physicists of Moldova. Abstracts

International Nuclear Information System (INIS)

2014-10-01

This book includes abstracts on various aspects of: materials processing and characterization, crystal growth methods, solid-state and crystal technology, development of condensed matter theory and modeling of materials properties, solid-state device physics, nano science and nano technology, heterostructures, superlattices, quantum wells and wires, advanced quantum physics for nano systems, etc.

5. conference of the physicists of Moldova. Abstracts

Energy Technology Data Exchange (ETDEWEB)

NONE

2014-10-15

This book includes abstracts on various aspects of: materials processing and characterization, crystal growth methods, solid-state and crystal technology, development of condensed matter theory and modeling of materials properties, solid-state device physics, nano science and nano technology, heterostructures, superlattices, quantum wells and wires, advanced quantum physics for nano systems, etc.

Support vector machine in machine condition monitoring and fault diagnosis

Science.gov (United States)

Widodo, Achmad; Yang, Bo-Suk

2007-08-01

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

Machine Learning for Neuroimaging with Scikit-Learn

Directory of Open Access Journals (Sweden)

Alexandre eAbraham

2014-02-01

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

Machine learning for neuroimaging with scikit-learn.

Science.gov (United States)

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

2014-01-01

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

Investigation of High-Speed Cryogenic Machining Based on Finite Element Approach

Directory of Open Access Journals (Sweden)

Pooyan Vahidi Pashaki

Full Text Available Abstract The simulation of cryogenic machining process because of using a three-dimensional model and high process duration time in the finite element method, have been studied rarely. In this study, to overcome this limitation, a 2.5D finite element model using the commercial finite element software ABAQUS has been developed for the cryogenic machining process and by considering more realistic assumptions, the chip formation procedure investigated. In the proposed method, the liquid nitrogen has been used as a coolant. At the modeling of friction during the interaction of tools - chip, the Coulomb law has been used. In order to simulate the behavior of plasticity and failure criterion, Johnson-Cook model was used, and unlike previous investigations, thermal and mechanical properties of materials as a function of temperature were applied to the software. After examining accuracy of the model with present experimental data, the effect of parameters such as rake angle and the cutting speed as well as dry machining of aluminum alloy by the use of coupled dynamic temperature solution has been studied. Results indicated that at the cutting velocity of 10 m/s, cryogenic cooling has caused into decreasing 60 percent of tools temperature in comparison with the dry cooling. Furthermore, a chip which has been made by cryogenic machining were connected and without fracture in contrast to dry machining.

Temporal abstraction for the analysis of intensive care information

Science.gov (United States)

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

2007-11-01

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

Temporal abstraction for the analysis of intensive care information

International Nuclear Information System (INIS)

Hadad, Alejandro J; Evin, Diego A; Drozdowicz, Bartolome; Chiotti, Omar

2007-01-01

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

Human and machine perception communication, interaction, and integration

CERN Document Server

Cantoni, Virginio; Setti, Alessandra

2005-01-01

The theme of this book on human and machine perception is communication, interaction, and integration. For each basic topic there are invited lectures, corresponding to approaches in nature and machines, and a panel discussion. The lectures present the state of the art, outlining open questions and stressing synergies among the disciplines related to perception. The panel discussions are forums for open debate. The wide spectrum of topics allows comparison and synergy and can stimulate new approaches.

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

Science.gov (United States)

Liu, Xueyi; Gao, Chuanhou; Li, Ping

2012-09-01

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

Simulations of Quantum Turing Machines by Quantum Multi-Stack Machines

OpenAIRE

Qiu, Daowen

2005-01-01

As was well known, in classical computation, Turing machines, circuits, multi-stack machines, and multi-counter machines are equivalent, that is, they can simulate each other in polynomial time. In quantum computation, Yao [11] first proved that for any quantum Turing machines $M$, there exists quantum Boolean circuit $(n,t)$-simulating $M$, where $n$ denotes the length of input strings, and $t$ is the number of move steps before machine stopping. However, the simulations of quantum Turing ma...

Abstract Interpretation as a Programming Language

Directory of Open Access Journals (Sweden)

Mads Rosendahl

2013-09-01

Full Text Available In David Schmidt's PhD work he explored the use of denotational semantics as a programming language. It was part of an effort to not only treat formal semantics as specifications but also as interpreters and input to compiler generators. The semantics itself can be seen as a program and one may examine different programming styles and ways to represent states. Abstract interpretation is primarily a technique for derivation and specification of program analysis. As with denotational semantics we may also view abstract interpretations as programs and examine the implementation. The main focus in this paper is to show that results from higher-order strictness analysis may be used more generally as fixpoint operators for higher-order functions over lattices and thus provide a technique for immediate implementation of a large class of abstract interpretations. Furthermore, it may be seen as a programming paradigm and be used to write programs in a circular style.

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

Science.gov (United States)

Armour, Mim, Ed.

2014-01-01

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

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

Science.gov (United States)

Armour, Mim, Ed.

2015-01-01

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

Active learning machine learns to create new quantum experiments.

Science.gov (United States)

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

2018-02-06

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

Predicting Market Impact Costs Using Nonparametric Machine Learning Models.

Directory of Open Access Journals (Sweden)

Saerom Park

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

Quantum cloning of mixed states in symmetric subspaces

International Nuclear Information System (INIS)

Fan Heng

2003-01-01

Quantum-cloning machine for arbitrary mixed states in symmetric subspaces is proposed. This quantum-cloning machine can be used to copy part of the output state of another quantum-cloning machine and is useful in quantum computation and quantum information. The shrinking factor of this quantum cloning achieves the well-known upper bound. When the input is identical pure states, two different fidelities of this cloning machine are optimal

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

Science.gov (United States)

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

2017-06-01

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

New Abstractions for Mobile Connectivity and Resource Management

Science.gov (United States)

2016-05-01

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

Machine tool structures

CERN Document Server

Koenigsberger, F

1970-01-01

Machine Tool Structures, Volume 1 deals with fundamental theories and calculation methods for machine tool structures. Experimental investigations into stiffness are discussed, along with the application of the results to the design of machine tool structures. Topics covered range from static and dynamic stiffness to chatter in metal cutting, stability in machine tools, and deformations of machine tool structures. This volume is divided into three sections and opens with a discussion on stiffness specifications and the effect of stiffness on the behavior of the machine under forced vibration c

An assessment of Internet Abuse in Nigeria Abstract

African Journals Online (AJOL)

Anambra State Nigeria, Email : drhcinyiama@gmail.com Phone: 08034701121. Abstract ... Is the addiction paradigm appropriate for Internet use? Is ... proposed a global network of computers in. 1962 ... biggest reference library, social media.

Application of Machine Learning to Rotorcraft Health Monitoring

Science.gov (United States)

Cody, Tyler; Dempsey, Paula J.

2017-01-01

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

Grounding abstractness: Abstract concepts and the activation of the mouth

Directory of Open Access Journals (Sweden)

Anna M Borghi

2016-10-01

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

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.)

Exploiting the systematic review protocol for classification of medical abstracts.

Science.gov (United States)

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

2011-01-01

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

Electricity of machine tool

International Nuclear Information System (INIS)

Gijeon media editorial department

1977-10-01

This book is divided into three parts. The first part deals with electricity machine, which can taints from generator to motor, motor a power source of machine tool, electricity machine for machine tool such as switch in main circuit, automatic machine, a knife switch and pushing button, snap switch, protection device, timer, solenoid, and rectifier. The second part handles wiring diagram. This concludes basic electricity circuit of machine tool, electricity wiring diagram in your machine like milling machine, planer and grinding machine. The third part introduces fault diagnosis of machine, which gives the practical solution according to fault diagnosis and the diagnostic method with voltage and resistance measurement by tester.

76 FR 46853 - International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and...

Science.gov (United States)

2011-08-03

... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-73,218; TA-W-73,218A] International Business Machines Corporation, ITD Business Unit, Division 7, E-mail and Collaboration Group, Including Workers Off-Site From Various States in the United States Reporting to Armonk, NY; International Business Machines Corporation, Web Strategy...

Present status and prospects for vending machines; Jido hanbaiki no genjo to tenbo

Energy Technology Data Exchange (ETDEWEB)

Hirano, Y. [Fuji Denki Reiki Co. Ltd., Tokyo (Japan); Ota, T.; Iwamoto, S. [Fuji Electric Co. Ltd., Tokyo (Japan)

1999-08-10

The number of automatic vending and service machines installed in Japan at the end of 1998 is about 5.5 million. The number of units per head exceeds that of USA, a pioneer in vending machines. They are now playing an indispensable role in daily living. Recent needs of vending machines have increased in social requests or problems, such as (1) strengthening of crime prevention, (2) consideration of global ecology and (3) prevention of minors from using alcoholic drinks vending machines. This paper describes the current state of marketing and future prospects for vending machines. (author)

Feedback optimal control of dynamic stochastic two-machine flowshop with a finite buffer

Directory of Open Access Journals (Sweden)

Thang Diep

2010-06-01

Full Text Available This paper examines the optimization of production involving a tandem two-machine system producing a single part type, with each machine being subject to random breakdowns and repairs. An analytical model is formulated with a view to solving an optimal stochastic production problem of the system with machines having up-downtime non-exponential distributions. The model developed is obtained by using a dynamic programming approach and a semi-Markov process. The control problem aims to find the production rates needed by the machines to meet the demand rate, through a minimization of the inventory/shortage cost. Using the Bellman principle, the optimality conditions obtained satisfy the Hamilton-Jacobi-Bellman equation, which depends on time and system states, and ultimately, leads to a feedback control. Consequently, the new model enables us to improve the coefficient of variation (CVup/down to be less than one while it is equal to one in Markov model. Heuristics methods are used to involve the problem because of the difficulty of the analytical model using several states, and to show what control law should be used in each system state (i.e., including Kanban, feedback and CONWIP control. Numerical methods are used to solve the optimality conditions and to show how a machine should produce.

A control approach for plasma density in tokamak machines

Energy Technology Data Exchange (ETDEWEB)

Boncagni, Luca, E-mail: luca.boncagni@enea.it [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Pucci, Daniele; Piesco, F.; Zarfati, Emanuele [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy); Mazzitelli, G. [EURATOM – ENEA Fusion Association, Frascati Research Center, Division of Fusion Physics, Rome, Frascati (Italy); Monaco, S. [Dipartimento di Ingegneria Informatica, Automatica e Gestionale ' ' Antonio Ruberti' ' , Sapienza Università di Roma (Italy)

2013-10-15

Highlights: •We show a control approach for line plasma density in tokamak. •We show a control approach for pressure in a tokamak chamber. •We show experimental results using one valve. -- Abstract: In tokamak machines, chamber pre-fill is crucial to attain plasma breakdown, while plasma density control is instrumental for several tasks such as machine protection and achievement of desired plasma performances. This paper sets the principles of a new control strategy for attaining both chamber pre-fill and plasma density regulation. Assuming that the actuation mean is a piezoelectric valve driven by a varying voltage, the proposed control laws ensure convergence to reference values of chamber pressure during pre-fill, and of plasma density during plasma discharge. Experimental results at FTU are presented to discuss weaknesses and strengths of the proposed control strategy. The whole system has been implemented by using the MARTe framework [1].

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

Science.gov (United States)

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

2017-09-01

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

Compositional abstractions for long-run properties of stochastic systems

DEFF Research Database (Denmark)

Smith, Michael James Andrew

2011-01-01

its underlying Markov chain. However, this also leads to state space explosion problems as the number of components in the model increases, which severely limits the size of models we can analyse. Because of this, we look for abstraction techniques that allow us to analyse a smaller model that safely...... be reduced in size. Importantly, we do this compositionally, so that we bound each component of the model separately, and compose these to obtain a bound for the entire model. We present an algorithm for this, based on extending the algorithm by Fourneau et al. to deal with partially-ordered state spaces....... Finally, we present some results from our implementation, which forms part of the PEPA plug-in for Eclipse. We compare the precision and state space reduction with results obtained by computing long-run averages on a CTMDP-based abstraction....

Control processes and machine protection on ASDEX Upgrade

International Nuclear Information System (INIS)

Raupp, G.; Treutterer, W.; Mertens, V.; Neu, G.; Sips, A.; Zasche, D.; Zehetbauer, Th.

2007-01-01

Safe operation of ASDEX Upgrade is guaranteed by a conventional hierarchy of simple and robust hard-wired systems for personnel and machine protection featuring standardized switch-off procedures. Machine protection and handling of off-normal events is further enhanced and peak and lifetime stress minimized through the plasma control system. Based on a real-time process model supporting safety critical applications with data quality tagging, process self-monitoring, watchdog monitoring and alarm propagation, processes detect complex and critical failures and reliably perform case-sensitive counter measures. Intelligent real-time failure handling is done with hardware or software redundancy and performance degradation, or modification of reference values to continue or terminate discharges with reduced machine stress. Examples implemented so far on ASDEX Upgrade are given, such as recovery from measurement failures, switch-over of redundant actuators, handling of actuator limitations, detection of plasma instabilities, plasma state dependent soft landing, or handling of failed switch-off procedures through breakers disconnecting the machine from grid

Code-expanded radio access protocol for machine-to-machine communications

DEFF Research Database (Denmark)

Thomsen, Henning; Kiilerich Pratas, Nuno; Stefanovic, Cedomir

2013-01-01

The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated b...... subframes and orthogonal preambles, the amount of available contention resources is drastically increased, enabling the massive support of Machine-Type Communication users that is beyond the reach of current systems.......The random access methods used for support of machine-to-machine, also referred to as Machine-Type Communications, in current cellular standards are derivatives of traditional framed slotted ALOHA and therefore do not support high user loads efficiently. We propose an approach that is motivated...... by the random access method employed in LTE, which significantly increases the amount of contention resources without increasing the system resources, such as contention subframes and preambles. This is accomplished by a logical, rather than physical, extension of the access method in which the available system...

Probabilistic cloning of equidistant states

International Nuclear Information System (INIS)

Jimenez, O.; Roa, Luis; Delgado, A.

2010-01-01

We study the probabilistic cloning of equidistant states. These states are such that the inner product between them is a complex constant or its conjugate. Thereby, it is possible to study their cloning in a simple way. In particular, we are interested in the behavior of the cloning probability as a function of the phase of the overlap among the involved states. We show that for certain families of equidistant states Duan and Guo's cloning machine leads to cloning probabilities lower than the optimal unambiguous discrimination probability of equidistant states. We propose an alternative cloning machine whose cloning probability is higher than or equal to the optimal unambiguous discrimination probability for any family of equidistant states. Both machines achieve the same probability for equidistant states whose inner product is a positive real number.

Linear parallel processing machines I

Energy Technology Data Exchange (ETDEWEB)

Von Kunze, M

1984-01-01

As is well-known, non-context-free grammars for generating formal languages happen to be of a certain intrinsic computational power that presents serious difficulties to efficient parsing algorithms as well as for the development of an algebraic theory of contextsensitive languages. In this paper a framework is given for the investigation of the computational power of formal grammars, in order to start a thorough analysis of grammars consisting of derivation rules of the form aB ..-->.. A/sub 1/ ... A /sub n/ b/sub 1/...b /sub m/ . These grammars may be thought of as automata by means of parallel processing, if one considers the variables as operators acting on the terminals while reading them right-to-left. This kind of automata and their 2-dimensional programming language prove to be useful by allowing a concise linear-time algorithm for integer multiplication. Linear parallel processing machines (LP-machines) which are, in their general form, equivalent to Turing machines, include finite automata and pushdown automata (with states encoded) as special cases. Bounded LP-machines yield deterministic accepting automata for nondeterministic contextfree languages, and they define an interesting class of contextsensitive languages. A characterization of this class in terms of generating grammars is established by using derivation trees with crossings as a helpful tool. From the algebraic point of view, deterministic LP-machines are effectively represented semigroups with distinguished subsets. Concerning the dualism between generating and accepting devices of formal languages within the algebraic setting, the concept of accepting automata turns out to reduce essentially to embeddability in an effectively represented extension monoid, even in the classical cases.

Machinability and scratch wear resistance of carbon-coated WC inserts

Energy Technology Data Exchange (ETDEWEB)

Pazhanivel, B., E-mail: palcecri@yahoo.co.in; Kumar, T. Prem; Sozhan, G.

2015-03-15

Highlights: • Cemented WC inserts were coated with carbon by CVD. • The deposits were either loosely held MWCNTs or adherent carbides. • Co-efficient of friction (ramp load; 1–13 N); 0.2 and 0.1 μ, respectively, for the uncoated and carbide-coated inserts. • The carbide-coated insert exhibited better machinability and surface finish than a commercial TiCN-coated insert. - Abstract: In this work, cemented tungsten carbide (WC) inserts were coated with nanocarbons/carbides by chemical vapor deposition (CVD) and their machinability and scratch wear resistance were investigated. The hardness and surface conditions of the WC substrate were studied before and after coating. The CVD-generated nanocarbons on the insert surfaces were examined by SEM, FE-SEM and TEM. The electron microscopic images revealed that the carbons generated were multi-walled carbon nanotubes (MWCNTs) or carbides depending on the experimental conditions. In both the cases, the cutting edges of the inserts had dense deposits. Scratch wear test with the coated inserts showed that the co-efficient of friction was 0.1 μ as against 0.2 μ for the uncoated inserts under a ramp load of 1–13 N. The machinability characteristics of commercially available TiCN-coated inserts and the carbon-coated WC inserts were compared by using a CNC machine and a Rapid I vision inspection system. It was found that the carbide-coated inserts exhibited machinability with better surface finish comparable to that of the TiCN-coated inserts while the MWCNT-coated inserts showed inferior adhesion properties.

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

Energy Technology Data Exchange (ETDEWEB)

Timberlake, W.E.

1993-04-01

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

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

Science.gov (United States)

Young, Sean Gregory

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

Optimization of operating schedule of machines in granite industry using evolutionary algorithms

International Nuclear Information System (INIS)

Loganthurai, P.; Rajasekaran, V.; Gnanambal, K.

2014-01-01

Highlights: • Operating time of machines in granite industries was studied. • Operating time has been optimized using evolutionary algorithms such as PSO, DE. • The maximum demand has been reduced. • Hence the electricity cost of the industry and feeder stress have been reduced. - Abstract: Electrical energy consumption cost plays an important role in the production cost of any industry. The electrical energy consumption cost is calculated as two part tariff, the first part is maximum demand cost and the second part is energy consumption cost or unit cost (kW h). The maximum demand cost can be reduced without affecting the production. This paper focuses on the reduction of maximum demand by proper operating schedule of major equipments. For this analysis, various granite industries are considered. The major equipments in granite industries are cutting machine, polishing machine and compressor. To reduce the maximum demand, the operating time of polishing machine is rescheduled by optimization techniques such as Differential Evolution (DE) and particle swarm optimization (PSO). The maximum demand costs are calculated before and after rescheduling. The results show that if the machines are optimally operated, the cost is reduced. Both DE and PSO algorithms reduce the maximum demand cost at the same rate for all the granite industries. However, the optimum scheduling obtained by DE reduces the feeder power flow than the PSO scheduling

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

Science.gov (United States)

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

2016-02-01

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

Probabilistic cloning and deleting of quantum states

International Nuclear Information System (INIS)

Feng Yuan; Zhang Shengyu; Ying Mingsheng

2002-01-01

We construct a probabilistic cloning and deleting machine which, taking several copies of an input quantum state, can output a linear superposition of multiple cloning and deleting states. Since the machine can perform cloning and deleting in a single unitary evolution, the probabilistic cloning and other cloning machines proposed in the previous literature can be thought of as special cases of our machine. A sufficient and necessary condition for successful cloning and deleting is presented, and it requires that the copies of an arbitrarily presumed number of the input states are linearly independent. This simply generalizes some results for cloning. We also derive an upper bound for the success probability of the cloning and deleting machine

Abstraction of Dynamical Systems by Timed Automata

DEFF Research Database (Denmark)

Wisniewski, Rafael; Sloth, Christoffer

2011-01-01

To enable formal verification of a dynamical system, given by a set of differential equations, it is abstracted by a finite state model. This allows for application of methods for model checking. Consequently, it opens the possibility of carrying out the verification of reachability and timing re...

Quantum Virtual Machine (QVM)

Energy Technology Data Exchange (ETDEWEB)

2016-11-18

There is a lack of state-of-the-art HPC simulation tools for simulating general quantum computing. Furthermore, there are no real software tools that integrate current quantum computers into existing classical HPC workflows. This product, the Quantum Virtual Machine (QVM), solves this problem by providing an extensible framework for pluggable virtual, or physical, quantum processing units (QPUs). It enables the execution of low level quantum assembly codes and returns the results of such executions.

The modelling of dynamic chemical state of paper machine unit operations; Dynaamisen kemiallisen tilan mallintaminen paperikoneen yksikkoeoperaatioissa - MPKT 04

Energy Technology Data Exchange (ETDEWEB)

Ylen, J.P.; Jutila, P. [Helsinki Univ. of Technology, Otaniemi (Finland)

1998-12-31

The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)

The modelling of dynamic chemical state of paper machine unit operations; Dynaamisen kemiallisen tilan mallintaminen paperikoneen yksikkoeoperaatioissa - MPKT 04

Energy Technology Data Exchange (ETDEWEB)

Ylen, J P; Jutila, P [Helsinki Univ. of Technology, Otaniemi (Finland)

1999-12-31

The chemical state of paper mass is considered to be a key factor to the smooth operation of the paper machine. There are simulators that have been developed either for dynamic energy and mass balances or for static chemical phenomena, but the combination of these is not a straight forward task. Control Engineering Laboratory of Helsinki University of Technology has studied the paper machine wet end phenomena with the emphasis on pH-modelling. VTT (Technical Research Centre of Finland) Process Physics has used thermodynamical modelling successfully in e.g. Bleaching processes. In this research the different approaches are combined in order to get reliable dynamical models and modelling procedures for various unit operations. A flexible pilot process will be constructed and different materials will be processed starting from simple inorganic substances (e.g. Calcium carbonate and distilled water) working towards more complex masses (thick pulp with process waters and various reagents). The pilot process is well instrumented with ion selective electrodes, total calcium analysator and all basic measurements. (orig.)

Waste management research abstracts no. 22. Information on radioactive waste programmes in progress

International Nuclear Information System (INIS)

1995-07-01

The research abstracts contained in this issue have been collected during recent months and cover the period between January 1992 - February 1994 (through July 1994 for abstracts from the United States). The abstracts reflect research currently in progress in the field of radioactive waste management: environmental impacts, site selection, decontamination and decommissioning, environmental restoration and legal aspects of radioactive waste management. Though the information contained in this publication covers a wide range of programmes in many countries, the WMRA should not be interpreted as providing a complete survey of on-going research and IAEA Member States. For the first time, the abstracts published in document are only in English language. In addition, the abstracts received for this issue have been assigned INIS subject category codes and thesaurus terms to facilitate searches and also to fully utilize established sets of technical categories and terms

Simple machines

CERN Document Server

Graybill, George

2007-01-01

Just how simple are simple machines? With our ready-to-use resource, they are simple to teach and easy to learn! Chocked full of information and activities, we begin with a look at force, motion and work, and examples of simple machines in daily life are given. With this background, we move on to different kinds of simple machines including: Levers, Inclined Planes, Wedges, Screws, Pulleys, and Wheels and Axles. An exploration of some compound machines follows, such as the can opener. Our resource is a real time-saver as all the reading passages, student activities are provided. Presented in s

Machine performance assessment and enhancement for a hexapod machine

Energy Technology Data Exchange (ETDEWEB)

Mou, J.I. [Arizona State Univ., Tempe, AZ (United States); King, C. [Sandia National Labs., Livermore, CA (United States). Integrated Manufacturing Systems Center

1998-03-19

The focus of this study is to develop a sensor fused process modeling and control methodology to model, assess, and then enhance the performance of a hexapod machine for precision product realization. Deterministic modeling technique was used to derive models for machine performance assessment and enhancement. Sensor fusion methodology was adopted to identify the parameters of the derived models. Empirical models and computational algorithms were also derived and implemented to model, assess, and then enhance the machine performance. The developed sensor fusion algorithms can be implemented on a PC-based open architecture controller to receive information from various sensors, assess the status of the process, determine the proper action, and deliver the command to actuators for task execution. This will enhance a hexapod machine`s capability to produce workpieces within the imposed dimensional tolerances.

Understanding and modelling man-machine interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1996-01-01

This paper gives an overview of the current state of the art in man-machine system interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to the design and analysis of man-machine interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans an their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (orig.)

Understanding and modelling Man-Machine Interaction

International Nuclear Information System (INIS)

Cacciabue, P.C.

1991-01-01

This paper gives an overview of the current state of the art in man machine systems interaction studies, focusing on the problems derived from highly automated working environments and the role of humans in the control loop. In particular, it is argued that there is a need for sound approaches to design and analysis of Man-Machine Interaction (MMI), which stem from the contribution of three expertises in interfacing domains, namely engineering, computer science and psychology: engineering for understanding and modelling plants and their material and energy conservation principles; psychology for understanding and modelling humans and their cognitive behaviours; computer science for converting models in sound simulations running in appropriate computer architectures. (author)

Machine-Learning Algorithms to Code Public Health Spending Accounts.

Science.gov (United States)

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

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

Kinetic Isotope Effect Determination Probes the Spin of the Transition State, Its Stereochemistry, and Its Ligand Sphere in Hydrogen Abstraction Reactions of Oxoiron(IV) Complexes.

Science.gov (United States)

Mandal, Debasish; Mallick, Dibyendu; Shaik, Sason

2018-01-16

This Account outlines interplay of theory and experiment in the quest to identify the reactive-spin-state in chemical reactions that possess a few spin-dependent routes. Metalloenzymes and synthetic models have forged in recent decades an area of increasing appeal, in which oxometal species bring about functionalization of hydrocarbons under mild conditions and via intriguing mechanisms that provide a glimpse of Nature's designs to harness these reactions. Prominent among these are oxoiron(IV) complexes, which are potent H-abstractors. One of the key properties of oxoirons is the presence of close-lying spin-states, which can mediate H-abstractions. As such, these complexes form a fascinating chapter of spin-state chemistry, in which chemical reactivity involves spin-state interchange, so-called two-state reactivity (TSR) and multistate reactivity (MSR). TSR and MSR pose mechanistic challenges. How can one determine the structure of the reactive transition state (TS) and its spin state for these mechanisms? Calculations can do it for us, but the challenge is to find experimental probes. There are, however, no clear kinetic signatures for the reactive-spin-state in such reactions. This is the paucity that our group has been trying to fill for sometime. Hence, it is timely to demonstrate how theory joins experiment in realizing this quest. This Account uses a set of the H-abstraction reactions of 24 synthetic oxoiron(IV) complexes and 11 hydrocarbons, together undergoing H-abstraction reactions with TSR/MSR options, which provide experimentally determined kinetic isotope effect (KIE exp ) data. For this set, we demonstrate that comparing KIE exp results with calculated tunneling-augmented KIE (KIE TC ) data leads to a clear identification of the reactive spin-state during H-abstraction reactions. In addition, generating KIE exp data for a reaction of interest, and comparing these to KIE TC values, provides the mechanistic chemist with a powerful capability to

A hierarchical SVG image abstraction layer for medical imaging

Science.gov (United States)

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

2010-03-01

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

Sustainable machining

CERN Document Server

2017-01-01

This book provides an overview on current sustainable machining. Its chapters cover the concept in economic, social and environmental dimensions. It provides the reader with proper ways to handle several pollutants produced during the machining process. The book is useful on both undergraduate and postgraduate levels and it is of interest to all those working with manufacturing and machining technology.

Waste management research abstracts No. 17

International Nuclear Information System (INIS)

1986-11-01

The research data sheets contained in this issue have been collected during the period ending August 1986, and reflect research currently in progress in the field of radioactive waste management. This publication covers a wide range of programmes in the IAEA Member States. Abstracts intended for inclusion in this publication were submitted in the English, French, Russian or Spanish language

Tensor Network Quantum Virtual Machine (TNQVM)

Energy Technology Data Exchange (ETDEWEB)

2016-11-18

There is a lack of state-of-the-art quantum computing simulation software that scales on heterogeneous systems like Titan. Tensor Network Quantum Virtual Machine (TNQVM) provides a quantum simulator that leverages a distributed network of GPUs to simulate quantum circuits in a manner that leverages recent results from tensor network theory.

Geometrical conditions for completely positive trace-preserving maps and their application to a quantum repeater and a state-dependent quantum cloning machine

International Nuclear Information System (INIS)

Carlini, A.; Sasaki, M.

2003-01-01

We address the problem of finding optimal CPTP (completely positive trace-preserving) maps between a set of binary pure states and another set of binary generic mixed state in a two-dimensional space. The necessary and sufficient conditions for the existence of such CPTP maps can be discussed within a simple geometrical picture. We exploit this analysis to show the existence of an optimal quantum repeater which is superior to the known repeating strategies for a set of coherent states sent through a lossy quantum channel. We also show that the geometrical formulation of the CPTP mapping conditions can be a simpler method to derive a state-dependent quantum (anti) cloning machine than the study so far based on the explicit solution of several constraints imposed by unitarity in an extended Hilbert space

Advanced Electrical Machines and Machine-Based Systems for Electric and Hybrid Vehicles

Directory of Open Access Journals (Sweden)

Ming Cheng

2015-09-01

Full Text Available The paper presents a number of advanced solutions on electric machines and machine-based systems for the powertrain of electric vehicles (EVs. Two types of systems are considered, namely the drive systems designated to the EV propulsion and the power split devices utilized in the popular series-parallel hybrid electric vehicle architecture. After reviewing the main requirements for the electric drive systems, the paper illustrates advanced electric machine topologies, including a stator permanent magnet (stator-PM motor, a hybrid-excitation motor, a flux memory motor and a redundant motor structure. Then, it illustrates advanced electric drive systems, such as the magnetic-geared in-wheel drive and the integrated starter generator (ISG. Finally, three machine-based implementations of the power split devices are expounded, built up around the dual-rotor PM machine, the dual-stator PM brushless machine and the magnetic-geared dual-rotor machine. As a conclusion, the development trends in the field of electric machines and machine-based systems for EVs are summarized.

Asynchronized synchronous machines

CERN Document Server

Botvinnik, M M

1964-01-01

Asynchronized Synchronous Machines focuses on the theoretical research on asynchronized synchronous (AS) machines, which are "hybrids” of synchronous and induction machines that can operate with slip. Topics covered in this book include the initial equations; vector diagram of an AS machine; regulation in cases of deviation from the law of full compensation; parameters of the excitation system; and schematic diagram of an excitation regulator. The possible applications of AS machines and its calculations in certain cases are also discussed. This publication is beneficial for students and indiv

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

Science.gov (United States)

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

2017-07-01

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

Machine Phase Fullerene Nanotechnology: 1996

Science.gov (United States)

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

1997-01-01

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

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.

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.

Machine Shop Lathes.

Science.gov (United States)

Dunn, James

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

Towards a Standard-based Domain-specific Platform to Solve Machine Learning-based Problems

Directory of Open Access Journals (Sweden)

Vicente García-Díaz

2015-12-01

Full Text Available Machine learning is one of the most important subfields of computer science and can be used to solve a variety of interesting artificial intelligence problems. There are different languages, framework and tools to define the data needed to solve machine learning-based problems. However, there is a great number of very diverse alternatives which makes it difficult the intercommunication, portability and re-usability of the definitions, designs or algorithms that any developer may create. In this paper, we take the first step towards a language and a development environment independent of the underlying technologies, allowing developers to design solutions to solve machine learning-based problems in a simple and fast way, automatically generating code for other technologies. That can be considered a transparent bridge among current technologies. We rely on Model-Driven Engineering approach, focusing on the creation of models to abstract the definition of artifacts from the underlying technologies.

Reliability of measuring abductor hallucis muscle parameters using two different diagnostic ultrasound machines

Directory of Open Access Journals (Sweden)

Cameron Alyse FM

2009-11-01

Full Text Available Abstract Background Diagnostic ultrasound provides a method of analysing soft tissue structures of the musculoskeletal system effectively and reliably. The aim of this study was to evaluate within and between session reliability of measuring muscle dorso-plantar thickness, medio-lateral length and cross-sectional area, of the abductor hallucis muscle using two different ultrasound machines, a higher end Philips HD11 Ultrasound machine and clinically orientated Chison 8300 Deluxe Digital Portable Ultrasound System. Methods The abductor hallucis muscle of both the left and right feet of thirty asymptomatic participants was imaged and then measured using both ultrasound machines. Interclass correlation coefficients (ICC with 95% confidence intervals (CI were used to calculate both within and between session intra-tester reliability. Standard error of the measurement (SEM calculations were undertaken to assess difference between the actual measured score across trials and the smallest real difference (SRD was calculated from the SEM to indicate the degree of change that would exceed the expected trial to trial variability. Results The ICCs, SEM and SRD for dorso-plantar thickness and medial-lateral length were shown to have excellent to high within and between-session reliability for both ultrasound machines. The between-session reliability indices for cross-sectional area were acceptable for both ultrasound machines. Conclusion The results of the current study suggest that regardless of the type ultrasound machine, intra-tester reliability for the measurement the abductor hallucis muscle parameters is very high.

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...

Modelling and Simulation of a Synchronous Machine with Power Electronic Systems

DEFF Research Database (Denmark)

Chen, Zhe; Blaabjerg, Frede

2005-01-01

is modelled in SIMULINK as well. The resulting model can more accurately represent non-idea situations such as non-symmetrical parameters of the electrical machines and unbalance conditions. The model may be used for both steady state and large-signal dynamic analysis. This is particularly useful......This paper reports the modeling and simulation of a synchronous machine with a power electronic interface in direct phase model. The implementation of a direct phase model of synchronous machines in MATLAB/SIMULINK is presented .The power electronic system associated with the synchronous machine...... in the systems where a detailed study is needed in order to assess the overall system stability. Simulation studies are performed under various operation conditions. It is shown that the developed model could be used for studies of various applications of synchronous machines such as in renewable and DG...

STUDY OF TRANSIENT AND STATIONARY OPERATION MODES OF SYNCHRONOUS SYSTEM CONSISTING IN TWO MACHINES

Directory of Open Access Journals (Sweden)

V. S. Safaryan

2017-01-01

Full Text Available The solution of the problem of reliable functioning of an electric power system (EPS in steady-state and transient regimes, prevention of EPS transition into asynchronous regime, maintenance and restoration of stability of post-emergency processes is based on formation and realization of mathematical models of an EPS processes. During the functioning of electric power system in asynchronous regime, besides the main frequencies, the currents and voltages include harmonic components, the frequencies of which are multiple of the difference of main frequencies. At the two-frequency asynchronous regime the electric power system is being made equivalent in a form of a two-machine system, functioning for a generalized load. In the article mathematical models of transient process of a two-machine system in natural form and in d–q coordinate system are presented. The mathematical model of two-machine system is considered in case of two windings of excitement at the rotors. Also, in the article varieties of mathematical models of EPS transient regimes (trivial, simple, complete are presented. Transient process of a synchronous two-machine system is described by the complete model. The quality of transient processes of a synchronous machine depends on the number of rotor excitation windings. When there are two excitation windings on the rotor (dual system of excitation, the mathematical model of electromagnetic transient processes of a synchronous machine is represented in a complex form, i.e. in coordinate system d, q, the current of rotor being represented by a generalized vector. In asynchronous operation of a synchronous two-machine system with two excitation windings on the rotor the current and voltage systems include only harmonics of two frequencies. The mathematical model of synchronous steady-state process of a two-machine system is also provided, and the steady-state regimes with different structures of initial information are considered.

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...

PIMA Point of Care CD4+ Cell Count Machines in Remote MNCH Settings: Lessons Learned from Seven Districts in Zimbabwe

Science.gov (United States)

Mtapuri-Zinyowera, Sekesai; Chiyaka, Edward T.; Mushayi, Wellington; Musuka, Godfrey; Naluyinda-Kitabire, Florence; Mushavi, Angella; Chikwasha, Vasco

2013-01-01

An evaluation was commissioned to generate evidence on the impact of PIMA point-of-care CD4+ count machines in maternal and new-born child health settings in Zimbabwe; document best practices, lessons learned, challenges, and recommendations related to scale up of this new technology. A mixed methodology approach that included 31 in-depth interviews with stakeholders involved in procurement, distribution, and use of the POC machines was employed. Additionally, data was also abstracted from 207 patient records from 35 sites with the PIMA POC CD4+ count machines and 10 other comparative sites without the machine. A clearer training strategy was found to be necessary. The average time taken to initiate clients on antiretroviral treatment (ART) was substantially less, 15 days (IQR-1-149) for sites with a PIMA POC machine as compared to 32.7 days (IQR-1-192) at sites with no PIMA POC machine. There was general satisfaction because of the presence of the PIMA POC CD4+ count machine at sites that also initiated ART. PMID:24847177

Author Details

African Journals Online (AJOL)

Constructing a Parser for a given Deterministic Syntax Graph: A Procedural Approach Abstract · Vol 5, No 3 (2009) - Articles Removing the Restrictions Imposed on Finite State Machines-Employing the Push Down Machine Abstract · Vol 6, No 4 (2010) - Articles Design, development and implementation of a simple program ...

Seminar on the use of research reactors in fundamental and applied sciences. Programme and abstracts

International Nuclear Information System (INIS)

1984-01-01

The document includes the programme and the abstracts of papers presented at the ''Seminar on the use of research reactors in fundamental and applied sciences'' organized by the Tajoura Nuclear Research Centre in cooperation with the IAEA at Tajoura, Tripoli (Libya) between 16-20 September 1984. The abstracts are grouped in seven sessions: reactor physics (five abstracts), research reactor programmes (three abstracts), solid state physics (two abstracts), nuclear physics (two abstracts), radiochemistry (eleven abstracts), activation analysis (five abstracts), diverse topics (six abstracts). Separate indexing was provided for each abstract

Microtask crowdsourcing for disease mention annotation in PubMed abstracts.

Science.gov (United States)

Good, Benjamin M; Nanis, Max; Wu, Chunlei; Su, Andrew I

2015-01-01

Identifying concepts and relationships in biomedical text enables knowledge to be applied in computational analyses. Many biological natural language processing (BioNLP) projects attempt to address this challenge, but the state of the art still leaves much room for improvement. Progress in BioNLP research depends on large, annotated corpora for evaluating information extraction systems and training machine learning models. Traditionally, such corpora are created by small numbers of expert annotators often working over extended periods of time. Recent studies have shown that workers on microtask crowdsourcing platforms such as Amazon's Mechanical Turk (AMT) can, in aggregate, generate high-quality annotations of biomedical text. Here, we investigated the use of the AMT in capturing disease mentions in PubMed abstracts. We used the NCBI Disease corpus as a gold standard for refining and benchmarking our crowdsourcing protocol. After several iterations, we arrived at a protocol that reproduced the annotations of the 593 documents in the 'training set' of this gold standard with an overall F measure of 0.872 (precision 0.862, recall 0.883). The output can also be tuned to optimize for precision (max = 0.984 when recall = 0.269) or recall (max = 0.980 when precision = 0.436). Each document was completed by 15 workers, and their annotations were merged based on a simple voting method. In total 145 workers combined to complete all 593 documents in the span of 9 days at a cost of $.066 per abstract per worker. The quality of the annotations, as judged with the F measure, increases with the number of workers assigned to each task; however minimal performance gains were observed beyond 8 workers per task. These results add further evidence that microtask crowdsourcing can be a valuable tool for generating well-annotated corpora in BioNLP. Data produced for this analysis are available at http://figshare.com/articles/Disease_Mention_Annotation_with_Mechanical_Turk/1126402.

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

Science.gov (United States)

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

2013-01-01

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

Machining of Machine Elements Made of Polymer Composite Materials

Science.gov (United States)

Baurova, N. I.; Makarov, K. A.

2017-12-01

The machining of the machine elements that are made of polymer composite materials (PCMs) or are repaired using them is considered. Turning, milling, and drilling are shown to be most widely used among all methods of cutting PCMs. Cutting conditions for the machining of PCMs are presented. The factors that most strongly affect the roughness parameters and the accuracy of cutting PCMs are considered.

Fault detection and isolation in processes involving induction machines

Energy Technology Data Exchange (ETDEWEB)

Zell, K; Medvedev, A [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

1998-12-31

A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

Fault detection and isolation in processes involving induction machines

Energy Technology Data Exchange (ETDEWEB)

Zell, K.; Medvedev, A. [Control Engineering Group, Luleaa University of Technology, Luleaa (Sweden)

1997-12-31

A model-based technique for fault detection and isolation in electro-mechanical systems comprising induction machines is introduced. Two coupled state observers, one for the induction machine and another for the mechanical load, are used to detect and recognize fault-specific behaviors (fault signatures) from the real-time measurements of the rotor angular velocity and terminal voltages and currents. Practical applicability of the method is verified in full-scale experiments with a conveyor belt drive at SSAB, Luleaa Works. (orig.) 3 refs.

Machine learning and statistical methods for the prediction of maximal oxygen uptake: recent advances

Directory of Open Access Journals (Sweden)

Abut F

2015-08-01

Full Text Available Fatih Abut, Mehmet Fatih AkayDepartment of Computer Engineering, Çukurova University, Adana, TurkeyAbstract: Maximal oxygen uptake (VO2max indicates how many milliliters of oxygen the body can consume in a state of intense exercise per minute. VO2max plays an important role in both sport and medical sciences for different purposes, such as indicating the endurance capacity of athletes or serving as a metric in estimating the disease risk of a person. In general, the direct measurement of VO2max provides the most accurate assessment of aerobic power. However, despite a high level of accuracy, practical limitations associated with the direct measurement of VO2max, such as the requirement of expensive and sophisticated laboratory equipment or trained staff, have led to the development of various regression models for predicting VO2max. Consequently, a lot of studies have been conducted in the last years to predict VO2max of various target audiences, ranging from soccer athletes, nonexpert swimmers, cross-country skiers to healthy-fit adults, teenagers, and children. Numerous prediction models have been developed using different sets of predictor variables and a variety of machine learning and statistical methods, including support vector machine, multilayer perceptron, general regression neural network, and multiple linear regression. The purpose of this study is to give a detailed overview about the data-driven modeling studies for the prediction of VO2max conducted in recent years and to compare the performance of various VO2max prediction models reported in related literature in terms of two well-known metrics, namely, multiple correlation coefficient (R and standard error of estimate. The survey results reveal that with respect to regression methods used to develop prediction models, support vector machine, in general, shows better performance than other methods, whereas multiple linear regression exhibits the worst performance

Improving Machining Accuracy of CNC Machines with Innovative Design Methods

Science.gov (United States)

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

2018-03-01

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

Machinability of nickel based alloys using electrical discharge machining process

Science.gov (United States)

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

2018-04-01

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

The achievements of the Z-machine; Les exploits de la Z-machine

Energy Technology Data Exchange (ETDEWEB)

Larousserie, D

2008-03-15

The ZR-machine that represents the latest generation of Z-pinch machines has recently begun preliminary testing before its full commissioning in Albuquerque (Usa). During its test the machine has well operated with electrical currents whose intensities of 26 million Ampere are already 2 times as high as the intensity of the operating current of the previous Z-machine. In 2006 the Z-machine reached temperatures of 2 billions Kelvin while 100 million Kelvin would be sufficient to ignite thermonuclear fusion. In fact the concept of Z-pinch machines was imagined in the fifties but the technological breakthrough that has allowed this recent success and the reborn of Z-machine, was the replacement of gas by an array of metal wires through which the electrical current flows and vaporizes it creating an imploding plasma. It is not well understood why Z-pinch machines generate far more radiation than theoretically expected. (A.C.)

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.

Introduction to machine learning for brain imaging.

Science.gov (United States)

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

2011-05-15

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

A review of machine learning in obesity.

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.

Machine protection systems

CERN Document Server

Macpherson, A L

2010-01-01

A summary of the Machine Protection System of the LHC is given, with particular attention given to the outstanding issues to be addressed, rather than the successes of the machine protection system from the 2009 run. In particular, the issues of Safe Machine Parameter system, collimation and beam cleaning, the beam dump system and abort gap cleaning, injection and dump protection, and the overall machine protection program for the upcoming run are summarised.

Methods for solving reasoning problems in abstract argumentation – A survey

Science.gov (United States)

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

2015-01-01

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

Man machine interface and its implementation

International Nuclear Information System (INIS)

Hills, B.G.; Boettcher, D.B.; Reed, R.

1992-01-01

Sizewell B is the latest nuclear power station to be constructed in the United Kingdom: its Man-Machine Interfaces are therefore, by definition, the state-of-the-art. This paper discusses the principal Man-Machine Interfaces used in the operation of the station, and the systems that implement them. The Man-Machine Interface facilities discussed are: in the Main Control Room, which is used for normal operation and shutdown of the plant: in the Auxiliary Shutdown Room, which allows shutdown of the reactor if evacuation of the main Control Room is necessary: and in the Technical Support Centre, which is used for remote monitoring of the plant. The Man-Machine Interfaces that are described are parts of a station-wide group of interlinked computer systems called the Data Processing and Control System. This system collects data from the plant and displays it to the operators via discrete devices and on graphical computer displays. It also acquires control inputs from the operators via switches, which are then used to provide remote manual control, modulating control and sequence control. The computer system that handles the plant process data and alarm information displays uses a windowing interface with keyboard and trackerball navigation to allow easy retrieval and viewing of information. It is this system that is the main topic of this paper. (author)

Modelling Machine Tools using Structure Integrated Sensors for Fast Calibration

Directory of Open Access Journals (Sweden)

Benjamin Montavon

2018-02-01

Full Text Available Monitoring of the relative deviation between commanded and actual tool tip position, which limits the volumetric performance of the machine tool, enables the use of contemporary methods of compensation to reduce tolerance mismatch and the uncertainties of on-machine measurements. The development of a primarily optical sensor setup capable of being integrated into the machine structure without limiting its operating range is presented. The use of a frequency-modulating interferometer and photosensitive arrays in combination with a Gaussian laser beam allows for fast and automated online measurements of the axes’ motion errors and thermal conditions with comparable accuracy, lower cost, and smaller dimensions as compared to state-of-the-art optical measuring instruments for offline machine tool calibration. The development is tested through simulation of the sensor setup based on raytracing and Monte-Carlo techniques.

Preliminary Test of Upgraded Conventional Milling Machine into PC Based CNC Milling Machine

International Nuclear Information System (INIS)

Abdul Hafid

2008-01-01

CNC (Computerized Numerical Control) milling machine yields a challenge to make an innovation in the field of machining. With an action job is machining quality equivalent to CNC milling machine, the conventional milling machine ability was improved to be based on PC CNC milling machine. Mechanically and instrumentally change. As a control replacing was conducted by servo drive and proximity were used. Computer programme was constructed to give instruction into milling machine. The program structure of consists GUI model and ladder diagram. Program was put on programming systems called RTX software. The result of up-grade is computer programming and CNC instruction job. The result was beginning step and it will be continued in next time. With upgrading ability milling machine becomes user can be done safe and optimal from accident risk. By improving performance of milling machine, the user will be more working optimal and safely against accident risk. (author)

3D knitting using large circular knitting machines

Science.gov (United States)

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

2017-10-01

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

Study of on-machine error identification and compensation methods for micro machine tools

International Nuclear Information System (INIS)

Wang, Shih-Ming; Yu, Han-Jen; Lee, Chun-Yi; Chiu, Hung-Sheng

2016-01-01

Micro machining plays an important role in the manufacturing of miniature products which are made of various materials with complex 3D shapes and tight machining tolerance. To further improve the accuracy of a micro machining process without increasing the manufacturing cost of a micro machine tool, an effective machining error measurement method and a software-based compensation method are essential. To avoid introducing additional errors caused by the re-installment of the workpiece, the measurement and compensation method should be on-machine conducted. In addition, because the contour of a miniature workpiece machined with a micro machining process is very tiny, the measurement method should be non-contact. By integrating the image re-constructive method, camera pixel correction, coordinate transformation, the error identification algorithm, and trajectory auto-correction method, a vision-based error measurement and compensation method that can on-machine inspect the micro machining errors and automatically generate an error-corrected numerical control (NC) program for error compensation was developed in this study. With the use of the Canny edge detection algorithm and camera pixel calibration, the edges of the contour of a machined workpiece were identified and used to re-construct the actual contour of the work piece. The actual contour was then mapped to the theoretical contour to identify the actual cutting points and compute the machining errors. With the use of a moving matching window and calculation of the similarity between the actual and theoretical contour, the errors between the actual cutting points and theoretical cutting points were calculated and used to correct the NC program. With the use of the error-corrected NC program, the accuracy of a micro machining process can be effectively improved. To prove the feasibility and effectiveness of the proposed methods, micro-milling experiments on a micro machine tool were conducted, and the results

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

Science.gov (United States)

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

2004-09-01

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

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

Science.gov (United States)

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

2015-11-01

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

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

An Expectation-Maximization Method for Calibrating Synchronous Machine Models

Energy Technology Data Exchange (ETDEWEB)

Meng, Da; Zhou, Ning; Lu, Shuai; Lin, Guang

2013-07-21

The accuracy of a power system dynamic model is essential to its secure and efficient operation. Lower confidence in model accuracy usually leads to conservative operation and lowers asset usage. To improve model accuracy, this paper proposes an expectation-maximization (EM) method to calibrate the synchronous machine model using phasor measurement unit (PMU) data. First, an extended Kalman filter (EKF) is applied to estimate the dynamic states using measurement data. Then, the parameters are calculated based on the estimated states using maximum likelihood estimation (MLE) method. The EM method iterates over the preceding two steps to improve estimation accuracy. The proposed EM method’s performance is evaluated using a single-machine infinite bus system and compared with a method where both state and parameters are estimated using an EKF method. Sensitivity studies of the parameter calibration using EM method are also presented to show the robustness of the proposed method for different levels of measurement noise and initial parameter uncertainty.

Experience gained with the 3D machining of the W7-X HHF divertor target elements

International Nuclear Information System (INIS)

Junghanns, P.; Boscary, J.; Peacock, A.

2015-01-01

Highlights: • The Wendelstein 7-X surface of the actively cooled divertor is built up of 890 individually 3D machined target elements. • To date 300 target elements have been 3D machined with an accuracy of ±0.015 mm. • Copper discovered on the surface of few elements is no risk to operation. - Abstract: The high heat flux (HHF) divertor of W7-X consists of 100 target modules assembled from 890 actively water-cooled target elements protected with CFC tiles. The divertor surface will be built up of individually 3D machined target elements with 89 individual element types. To date 300 of the 890 target elements have been 3D machined with a very good accuracy. To achieve this successful result, a prototyping phase has been conducted to qualify the manufacturing route and to define the acceptance criteria with measures taken to minimize the risk of unacceptable damage during the manufacturing. After the 3D-machining, during the incoming inspection, copper infiltration from the interface between the CFC tiles and the CuCrZr heat sink to the plasma facing surface was detected in a small number of elements.

Non-conventional electrical machines

CERN Document Server

Rezzoug, Abderrezak

2013-01-01

The developments of electrical machines are due to the convergence of material progress, improved calculation tools, and new feeding sources. Among the many recent machines, the authors have chosen, in this first book, to relate the progress in slow speed machines, high speed machines, and superconducting machines. The first part of the book is dedicated to materials and an overview of magnetism, mechanic, and heat transfer.

Automatic microseismic event picking via unsupervised machine learning

Science.gov (United States)

Chen, Yangkang

2018-01-01

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

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

Science.gov (United States)

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

2017-10-18

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

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

Science.gov (United States)

Angu, Rittu; Mehta, R. K.

2018-04-01

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

How the machine ‘thinks’: Understanding opacity in machine learning algorithms

Directory of Open Access Journals (Sweden)

Jenna Burrell

2016-01-01

Full Text Available This article considers the issue of opacity as a problem for socially consequential mechanisms of classification and ranking, such as spam filters, credit card fraud detection, search engines, news trends, market segmentation and advertising, insurance or loan qualification, and credit scoring. These mechanisms of classification all frequently rely on computational algorithms, and in many cases on machine learning algorithms to do this work. In this article, I draw a distinction between three forms of opacity: (1 opacity as intentional corporate or state secrecy, (2 opacity as technical illiteracy, and (3 an opacity that arises from the characteristics of machine learning algorithms and the scale required to apply them usefully. The analysis in this article gets inside the algorithms themselves. I cite existing literatures in computer science, known industry practices (as they are publicly presented, and do some testing and manipulation of code as a form of lightweight code audit. I argue that recognizing the distinct forms of opacity that may be coming into play in a given application is a key to determining which of a variety of technical and non-technical solutions could help to prevent harm.

Software architecture for time-constrained machine vision applications

Science.gov (United States)

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

2013-01-01

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

Investigation of diffractive optical element femtosecond laser machining

Energy Technology Data Exchange (ETDEWEB)

Chabrol, Grégoire R., E-mail: g.chabrol@ecam-strasbourg.eu [ECAM Strasbourg-Europe, Espace Européen de l’entreprise, 2, rue de Madrid – 67300 SCHILTIGHEIM, CS. 20013, 67012 Strasbourg CEDEX (France); Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), UDS-CNRS, UMR 7357, 300 bld Sébastien Brant, CS 10413, 67412 Illkirch cedex (France); Ciceron, Adline [ECAM Strasbourg-Europe, Espace Européen de l’entreprise, 2, rue de Madrid – 67300 SCHILTIGHEIM, CS. 20013, 67012 Strasbourg CEDEX (France); Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), UDS-CNRS, UMR 7357, 300 bld Sébastien Brant, CS 10413, 67412 Illkirch cedex (France); Twardowski, Patrice; Pfeiffer, Pierre [Laboratoire des Sciences de l’Ingénieur, de l’Informatique et de l’Imagerie (ICube), UDS-CNRS, UMR 7357, 300 bld Sébastien Brant, CS 10413, 67412 Illkirch cedex (France); Télécom Physique Strasbourg – Pôle API – 300 Bd Sébastien Brant – CS 10413, Illkirch Graffenstaden F 67400 (France); and others

2016-06-30

Highlights: • A method for rapid manufacturing of optical diffractive element in BK7 is proposed. • A binary grating in BK7 was successfully machined by femtosecond laser pulses. • Process relying on nonlinear absorption in the dielectric due to photoionization. • The binary grating was analysed by SEM and interferometric microscopy. • Simulations by Fourier modal method supported the measured diffractive efficiency. - Abstract: This paper presents an explorative study on the machining of diffractive optical elements (DOEs) in transparent materials using a femtosecond laser source. A simple form of DOE, a binary phase grating with a period of 20.85 μm (σ = 0.5 μm), a groove depth and width of 0.7 μm (σ = 0.2 μm) and 8.8 μm (σ = 0.5 μm) respectively, was successfully machined in BK7. The topographic characteristics were measured by white light interferometry and scanning electron microscopy (SEM). The processing was carried out on high precision stages with an ultrafast fibre laser (350 fs) emitting a 343 nm pulse focused onto the sample with a stationary microscope objective. A diffracted efficiency of 27%, obtained with a spectro goniometer, was corroborated by the theoretical results obtained by the Fourier modal method (FMM), taking into account the measured topographic values. These encouraging results demonstrate that high-speed femtosecond laser manufacturing of DOE in bulk glasses can be achieved, opening the way to rapid prototyping of multi-layered-DOEs.

Electrical machines & drives

CERN Document Server

Hammond, P

1985-01-01

Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o

96 THE EFFECT OF AUTOMATED TELLER MACHINES ON BANKS ...

African Journals Online (AJOL)

reduces the number of human deployment by banks thereby reducing cost of operations. ... United States (PLUS and CIRRUS) drooped their long standing opposition to allowing ..... Automated teller machine network pricing – A review of the.

DNA-based machines.

Science.gov (United States)

Wang, Fuan; Willner, Bilha; Willner, Itamar

2014-01-01

The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.

Abstracts of 20. International Symposium Radiation Protection Physics

International Nuclear Information System (INIS)

1988-01-01

51 papers are presented as titles with abstracts which are processed individually for the INIS data base. They deal with general aspects of radiation protection physics, international activities in radiation protection, solid state dosimetry, models and calculation methods in radiation protection, and measuring techniques in radiation protection

IEEE conference record -- Abstracts

International Nuclear Information System (INIS)

Anon.

1994-01-01

This conference covers the following areas: computational plasma physics; vacuum electronic; basic phenomena in fully ionized plasmas; plasma, electron, and ion sources; environmental/energy issues in plasma science; space plasmas; plasma processing; ball lightning/spherical plasma configurations; plasma processing; fast wave devices; magnetic fusion; basic phenomena in partially ionized plasma; dense plasma focus; plasma diagnostics; basic phenomena in weakly ionized gases; fast opening switches; MHD; fast z-pinches and x-ray lasers; intense ion and electron beams; laser-produced plasmas; microwave plasma interactions; EM and ETH launchers; solid state plasmas and switches; intense beam microwaves; and plasmas for lighting. Separate abstracts were prepared for 416 papers in this conference

Machine translation

Energy Technology Data Exchange (ETDEWEB)

Nagao, M

1982-04-01

Each language has its own structure. In translating one language into another one, language attributes and grammatical interpretation must be defined in an unambiguous form. In order to parse a sentence, it is necessary to recognize its structure. A so-called context-free grammar can help in this respect for machine translation and machine-aided translation. Problems to be solved in studying machine translation are taken up in the paper, which discusses subjects for semantics and for syntactic analysis and translation software. 14 references.

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

Science.gov (United States)

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

2015-01-01

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

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

Science.gov (United States)

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

2013-01-01

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

Machine learning in cardiovascular medicine: are we there yet?

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.

Modeling Geomagnetic Variations using a Machine Learning Framework

Science.gov (United States)

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

2017-12-01

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

Horn clause verification with convex polyhedral abstraction and tree automata-based refinement

DEFF Research Database (Denmark)

Kafle, Bishoksan; Gallagher, John Patrick

2017-01-01

In this paper we apply tree-automata techniques to refinement of abstract interpretation in Horn clause verification. We go beyond previous work on refining trace abstractions; firstly we handle tree automata rather than string automata and thereby can capture traces in any Horn clause derivations...... underlying the Horn clauses. Experiments using linear constraint problems and the abstract domain of convex polyhedra show that the refinement technique is practical and that iteration of abstract interpretation with tree automata-based refinement solves many challenging Horn clause verification problems. We...... compare the results with other state-of-the-art Horn clause verification tools....

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

Chaotic Boltzmann machines

Science.gov (United States)

Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki

2013-01-01

The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425

Your Sewing Machine.

Science.gov (United States)

Peacock, Marion E.

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

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.

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

Energy Technology Data Exchange (ETDEWEB)

1993-08-05

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

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

International Nuclear Information System (INIS)

1993-01-01

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

TURING MACHINE AS UNIVERSAL ALGORITHM EXECUTOR AND ITS APPLICATION IN THE PROCESS OF HIGH-SCHOOL STUDENTS` ADVANCED STUDY OF ALGORITHMIZATION AND PROGRAMMING FUNDAMENTALS

Directory of Open Access Journals (Sweden)

Oleksandr B. Yashchyk

2016-05-01

Full Text Available The article discusses the importance of studying the notion of algorithm and its formal specification using Turing machines. In the article it was identified the basic hypothesis of the theory of algorithms for Turing as well as reviewed scientific research of modern scientists devoted to this issue and found the main principles of the Turing machine as an abstract mathematical model. The process of forming information competencies components, information culture and students` logical thinking development with the inclusion of the topic “Study and Application of Turing machine as Universal Algorithm Executor” in the course of Informatics was analyzed.

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

Science.gov (United States)

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

2016-08-01

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

Investigation of the Machining Stability of a Milling Machine with Hybrid Guideway Systems

Directory of Open Access Journals (Sweden)

Jui-Pin Hung

2016-03-01

Full Text Available This study was aimed to investigate the machining stability of a horizontal milling machine with hybrid guideway systems by finite element method. To this purpose, we first created finite element model of the milling machine with the introduction of the contact stiffness defined at the sliding and rolling interfaces, respectively. Also, the motorized built-in spindle model was created and implemented in the whole machine model. Results of finite element simulations reveal that linear guides with different preloads greatly affect the dynamic responses and machining stability of the horizontal milling machine. The critical cutting depth predicted at the vibration mode associated with the machine tool structure is about 10 mm and 25 mm in the X and Y direction, respectively, while the cutting depth predicted at the vibration mode associated with the spindle structure is about 6.0 mm. Also, the machining stability can be increased when the preload of linear roller guides of the feeding mechanism is changed from lower to higher amount.

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 * ...