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Sample records for state machine framework

  1. 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...

  2. 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.

  3. 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.

  4. 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.

  5. 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.

  6. Machinability of IPS Empress 2 framework ceramic.

    Science.gov (United States)

    Schmidt, C; Weigl, P

    2000-01-01

    Using ceramic materials for an automatic production of ceramic dentures by CAD/CAM is a challenge, because many technological, medical, and optical demands must be considered. The IPS Empress 2 framework ceramic meets most of them. This study shows the possibilities for machining this ceramic with economical parameters. The long life-time requirement for ceramic dentures requires a ductile machined surface to avoid the well-known subsurface damages of brittle materials caused by machining. Slow and rapid damage propagation begins at break outs and cracks, and limits life-time significantly. Therefore, ductile machined surfaces are an important demand for machine dental ceramics. The machining tests were performed with various parameters such as tool grain size and feed speed. Denture ceramics were machined by jig grinding on a 5-axis CNC milling machine (Maho HGF 500) with a high-speed spindle up to 120,000 rpm. The results of the wear test indicate low tool wear. With one tool, you can machine eight occlusal surfaces including roughing and finishing. One occlusal surface takes about 60 min machining time. Recommended parameters for roughing are middle diamond grain size (D107), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 1000 mm/min, depth of cut a(e) = 0.06 mm, width of contact a(p) = 0.8 mm, and for finishing ultra fine diamond grain size (D46), cutting speed v(c) = 4.7 m/s, feed speed v(ft) = 100 mm/min, depth of cut a(e) = 0.02 mm, width of contact a(p) = 0.8 mm. The results of the machining tests give a reference for using IPS Empress(R) 2 framework ceramic in CAD/CAM systems. Copyright 2000 John Wiley & Sons, Inc.

  7. 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....

  8. 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.

  9. Research on machine learning framework based on random forest algorithm

    Science.gov (United States)

    Ren, Qiong; Cheng, Hui; Han, Hai

    2017-03-01

    With the continuous development of machine learning, industry and academia have released a lot of machine learning frameworks based on distributed computing platform, and have been widely used. However, the existing framework of machine learning is limited by the limitations of machine learning algorithm itself, such as the choice of parameters and the interference of noises, the high using threshold and so on. This paper introduces the research background of machine learning framework, and combined with the commonly used random forest algorithm in machine learning classification algorithm, puts forward the research objectives and content, proposes an improved adaptive random forest algorithm (referred to as ARF), and on the basis of ARF, designs and implements the machine learning framework.

  10. State Energy Resilience Framework

    Energy Technology Data Exchange (ETDEWEB)

    Phillips, J. [Argonne National Lab. (ANL), Argonne, IL (United States); Finster, M. [Argonne National Lab. (ANL), Argonne, IL (United States); Pillon, J. [Argonne National Lab. (ANL), Argonne, IL (United States); Petit, F. [Argonne National Lab. (ANL), Argonne, IL (United States); Trail, J. [Argonne National Lab. (ANL), Argonne, IL (United States)

    2016-12-01

    The energy sector infrastructure’s high degree of interconnectedness with other critical infrastructure systems can lead to cascading and escalating failures that can strongly affect both economic and social activities.The operational goal is to maintain energy availability for customers and consumers. For this body of work, a State Energy Resilience Framework in five steps is proposed.

  11. 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....

  12. 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.

  13. 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....

  14. Security Frameworks for Machine-to-Machine Devices and Networks

    Science.gov (United States)

    Demblewski, Michael

    Attacks against mobile systems have escalated over the past decade. There have been increases of fraud, platform attacks, and malware. The Internet of Things (IoT) offers a new attack vector for Cybercriminals. M2M contributes to the growing number of devices that use wireless systems for Internet connection. As new applications and platforms are created, old vulnerabilities are transferred to next-generation systems. There is a research gap that exists between the current approaches for security framework development and the understanding of how these new technologies are different and how they are similar. This gap exists because system designers, security architects, and users are not fully aware of security risks and how next-generation devices can jeopardize safety and personal privacy. Current techniques, for developing security requirements, do not adequately consider the use of new technologies, and this weakens countermeasure implementations. These techniques rely on security frameworks for requirements development. These frameworks lack a method for identifying next generation security concerns and processes for comparing, contrasting and evaluating non-human device security protections. This research presents a solution for this problem by offering a novel security framework that is focused on the study of the "functions and capabilities" of M2M devices and improves the systems development life cycle for the overall IoT ecosystem.

  15. 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.

  16. 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...

  17. 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.

  18. A Machine Learning Framework for Plan Payment Risk Adjustment.

    Science.gov (United States)

    Rose, Sherri

    2016-12-01

    To introduce cross-validation and a nonparametric machine learning framework for plan payment risk adjustment and then assess whether they have the potential to improve risk adjustment. 2011-2012 Truven MarketScan database. We compare the performance of multiple statistical approaches within a broad machine learning framework for estimation of risk adjustment formulas. Total annual expenditure was predicted using age, sex, geography, inpatient diagnoses, and hierarchical condition category variables. The methods included regression, penalized regression, decision trees, neural networks, and an ensemble super learner, all in concert with screening algorithms that reduce the set of variables considered. The performance of these methods was compared based on cross-validated R 2 . Our results indicate that a simplified risk adjustment formula selected via this nonparametric framework maintains much of the efficiency of a traditional larger formula. The ensemble approach also outperformed classical regression and all other algorithms studied. The implementation of cross-validated machine learning techniques provides novel insight into risk adjustment estimation, possibly allowing for a simplified formula, thereby reducing incentives for increased coding intensity as well as the ability of insurers to "game" the system with aggressive diagnostic upcoding. © Health Research and Educational Trust.

  19. Representative Vector Machines: A Unified Framework for Classical Classifiers.

    Science.gov (United States)

    Gui, Jie; Liu, Tongliang; Tao, Dacheng; Sun, Zhenan; Tan, Tieniu

    2016-08-01

    Classifier design is a fundamental problem in pattern recognition. A variety of pattern classification methods such as the nearest neighbor (NN) classifier, support vector machine (SVM), and sparse representation-based classification (SRC) have been proposed in the literature. These typical and widely used classifiers were originally developed from different theory or application motivations and they are conventionally treated as independent and specific solutions for pattern classification. This paper proposes a novel pattern classification framework, namely, representative vector machines (or RVMs for short). The basic idea of RVMs is to assign the class label of a test example according to its nearest representative vector. The contributions of RVMs are twofold. On one hand, the proposed RVMs establish a unified framework of classical classifiers because NN, SVM, and SRC can be interpreted as the special cases of RVMs with different definitions of representative vectors. Thus, the underlying relationship among a number of classical classifiers is revealed for better understanding of pattern classification. On the other hand, novel and advanced classifiers are inspired in the framework of RVMs. For example, a robust pattern classification method called discriminant vector machine (DVM) is motivated from RVMs. Given a test example, DVM first finds its k -NNs and then performs classification based on the robust M-estimator and manifold regularization. Extensive experimental evaluations on a variety of visual recognition tasks such as face recognition (Yale and face recognition grand challenge databases), object categorization (Caltech-101 dataset), and action recognition (Action Similarity LAbeliNg) demonstrate the advantages of DVM over other classifiers.

  20. Yield curve and Recession Forecasting in a Machine Learning Framework

    OpenAIRE

    Theophilos Papadimitriou; Periklis Gogas; Maria Matthaiou; Efthymia Chrysanthidou

    2014-01-01

    In this paper, we investigate the forecasting ability of the yield curve in terms of the U.S. real GDP cycle. More specifically, within a Machine Learning (ML) framework, we use data from a variety of short (treasury bills) and long term interest rates (bonds) for the period from 1976:Q3 to 2011:Q4 in conjunction with the real GDP for the same period, to create a model that can successfully forecast output fluctuations (inflation and output gaps) around its long-run trend. We focus our attent...

  1. 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.

  2. A machine learning-based framework to identify type 2 diabetes through electronic health records.

    Science.gov (United States)

    Zheng, Tao; Xie, Wei; Xu, Liling; He, Xiaoying; Zhang, Ya; You, Mingrong; Yang, Gong; Chen, You

    2017-01-01

    To discover diverse genotype-phenotype associations affiliated with Type 2 Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide association study (PheWAS), more cases (T2DM subjects) and controls (subjects without T2DM) are required to be identified (e.g., via Electronic Health Records (EHR)). However, existing expert based identification algorithms often suffer in a low recall rate and could miss a large number of valuable samples under conservative filtering standards. The goal of this work is to develop a semi-automated framework based on machine learning as a pilot study to liberalize filtering criteria to improve recall rate with a keeping of low false positive rate. We propose a data informed framework for identifying subjects with and without T2DM from EHR via feature engineering and machine learning. We evaluate and contrast the identification performance of widely-used machine learning models within our framework, including k-Nearest-Neighbors, Naïve Bayes, Decision Tree, Random Forest, Support Vector Machine and Logistic Regression. Our framework was conducted on 300 patient samples (161 cases, 60 controls and 79 unconfirmed subjects), randomly selected from 23,281 diabetes related cohort retrieved from a regional distributed EHR repository ranging from 2012 to 2014. We apply top-performing machine learning algorithms on the engineered features. We benchmark and contrast the accuracy, precision, AUC, sensitivity and specificity of classification models against the state-of-the-art expert algorithm for identification of T2DM subjects. Our results indicate that the framework achieved high identification performances (∼0.98 in average AUC), which are much higher than the state-of-the-art algorithm (0.71 in AUC). Expert algorithm-based identification of T2DM subjects from EHR is often hampered by the high missing rates due to their conservative selection criteria. Our framework leverages machine learning and feature

  3. A Machine LearningFramework to Forecast Wave Conditions

    Science.gov (United States)

    Zhang, Y.; James, S. C.; O'Donncha, F.

    2017-12-01

    Recently, significant effort has been undertaken to quantify and extract wave energy because it is renewable, environmental friendly, abundant, and often close to population centers. However, a major challenge is the ability to accurately and quickly predict energy production, especially across a 48-hour cycle. Accurate forecasting of wave conditions is a challenging undertaking that typically involves solving the spectral action-balance equation on a discretized grid with high spatial resolution. The nature of the computations typically demands high-performance computing infrastructure. Using a case-study site at Monterey Bay, California, a machine learning framework was trained to replicate numerically simulated wave conditions at a fraction of the typical computational cost. Specifically, the physics-based Simulating WAves Nearshore (SWAN) model, driven by measured wave conditions, nowcast ocean currents, and wind data, was used to generate training data for machine learning algorithms. The model was run between April 1st, 2013 and May 31st, 2017 generating forecasts at three-hour intervals yielding 11,078 distinct model outputs. SWAN-generated fields of 3,104 wave heights and a characteristic period could be replicated through simple matrix multiplications using the mapping matrices from machine learning algorithms. In fact, wave-height RMSEs from the machine learning algorithms (9 cm) were less than those for the SWAN model-verification exercise where those simulations were compared to buoy wave data within the model domain (>40 cm). The validated machine learning approach, which acts as an accurate surrogate for the SWAN model, can now be used to perform real-time forecasts of wave conditions for the next 48 hours using available forecasted boundary wave conditions, ocean currents, and winds. This solution has obvious applications to wave-energy generation as accurate wave conditions can be forecasted with over a three-order-of-magnitude reduction in

  4. An explainable deep machine vision framework for plant stress phenotyping.

    Science.gov (United States)

    Ghosal, Sambuddha; Blystone, David; Singh, Asheesh K; Ganapathysubramanian, Baskar; Singh, Arti; Sarkar, Soumik

    2018-05-01

    Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework's ability to identify and classify a diverse set of foliar stresses in soybean [ Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress), classification (low, medium, or high stress), and quantification (stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar stresses would have significant implications in scientific research, plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of stress by farmers and researchers. Copyright © 2018 the Author(s). Published by PNAS.

  5. 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 ...

  6. Incremental Support Vector Machine Framework for Visual Sensor Networks

    Directory of Open Access Journals (Sweden)

    Yuichi Motai

    2007-01-01

    Full Text Available Motivated by the emerging requirements of surveillance networks, we present in this paper an incremental multiclassification support vector machine (SVM technique as a new framework for action classification based on real-time multivideo collected by homogeneous sites. The technique is based on an adaptation of least square SVM (LS-SVM formulation but extends beyond the static image-based learning of current SVM methodologies. In applying the technique, an initial supervised offline learning phase is followed by a visual behavior data acquisition and an online learning phase during which the cluster head performs an ensemble of model aggregations based on the sensor nodes inputs. The cluster head then selectively switches on designated sensor nodes for future incremental learning. Combining sensor data offers an improvement over single camera sensing especially when the latter has an occluded view of the target object. The optimization involved alleviates the burdens of power consumption and communication bandwidth requirements. The resulting misclassification error rate, the iterative error reduction rate of the proposed incremental learning, and the decision fusion technique prove its validity when applied to visual sensor networks. Furthermore, the enabled online learning allows an adaptive domain knowledge insertion and offers the advantage of reducing both the model training time and the information storage requirements of the overall system which makes it even more attractive for distributed sensor networks communication.

  7. A Conceptual Framework over Contextual Analysis of Concept Learning within Human-Machine Interplays

    DEFF Research Database (Denmark)

    Badie, Farshad

    2016-01-01

    This research provides a contextual description concerning existential and structural analysis of ‘Relations’ between human beings and machines. Subsequently, it will focus on conceptual and epistemological analysis of (i) my own semantics-based framework [for human meaning construction] and of (ii......) a well-structured machine concept learning framework. Accordingly, I will, semantically and epistemologically, focus on linking those two frameworks for logical analysis of concept learning in the context of human-machine interrelationships. It will be demonstrated that the proposed framework provides...

  8. An explainable deep machine vision framework for plant stress phenotyping

    Science.gov (United States)

    Blystone, David; Ganapathysubramanian, Baskar; Singh, Arti; Sarkar, Soumik

    2018-01-01

    Current approaches for accurate identification, classification, and quantification of biotic and abiotic stresses in crop research and production are predominantly visual and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intrarater cognitive variability. This translates to erroneous decisions and a significant waste of resources. Here, we demonstrate a machine learning framework’s ability to identify and classify a diverse set of foliar stresses in soybean [Glycine max (L.) Merr.] with remarkable accuracy. We also present an explanation mechanism, using the top-K high-resolution feature maps that isolate the visual symptoms used to make predictions. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress), classification (low, medium, or high stress), and quantification (stress severity) in a single framework without detailed symptom annotation by experts. We reliably identified and classified several biotic (bacterial and fungal diseases) and abiotic (chemical injury and nutrient deficiency) stresses by learning from over 25,000 images. The learned model is robust to input image perturbations, demonstrating viability for high-throughput deployment. We also noticed that the learned model appears to be agnostic to species, seemingly demonstrating an ability of transfer learning. The availability of an explainable model that can consistently, rapidly, and accurately identify and quantify foliar stresses would have significant implications in scientific research, plant breeding, and crop production. The trained model could be deployed in mobile platforms (e.g., unmanned air vehicles and automated ground scouts) for rapid, large-scale scouting or as a mobile application for real-time detection of stress by farmers and researchers. PMID:29666265

  9. An Evolutionary Machine Learning Framework for Big Data Sequence Mining

    Science.gov (United States)

    Kamath, Uday Krishna

    2014-01-01

    Sequence classification is an important problem in many real-world applications. Unlike other machine learning data, there are no "explicit" features or signals in sequence data that can help traditional machine learning algorithms learn and predict from the data. Sequence data exhibits inter-relationships in the elements that are…

  10. Framework for man-machine interface design evaluation system considering cognitive factor

    International Nuclear Information System (INIS)

    Itoh, Toru; Sasaki, Kazunori; Yoshikawa, Hidekazu; Takahashi, Makoto; Furuta, Tomihiko.

    1994-01-01

    It is necessary to improve human reliability in order to gain a higher reliability of the total plant system taking an account of development of plant automation and improvement of machine reliability. Therefore, the role of the man-machine system will come to be important. Accordingly, the evaluation of the man-machine system design information is desired in order to solve the mismatch problem between plant information presented by the man-machine system and information required by the operator comprehensively. This paper discusses required functions and software framework for the man-machine interface design evaluation system. The man-machine interface design evaluation system has features to extract the potential matters which are inherent on the design information of man-machine system by simulating the operator behavior, the plant system and the man-machine system, considering the operator's cognitive performance and time dependency. (author)

  11. 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.

  12. 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.

  13. 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.

  14. A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

    OpenAIRE

    Hamed Hassanzadeh; MohammadReza Keyvanpour

    2011-01-01

    The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as ...

  15. 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.

  16. A framework for detection of malicious software in Android handheld systems using machine learning techniques

    OpenAIRE

    Torregrosa García, Blas

    2015-01-01

    The present study aims at designing and developing new approaches to detect malicious applications in Android-based devices. More precisely, MaLDroide (Machine Learning-based Detector for Android malware), a framework for detection of Android malware based on machine learning techniques, is introduced here. It is devised to identify malicious applications. Este trabajo tiene como objetivo el diseño y el desarrollo de nuevas formas de detección de aplicaciones maliciosas en los dispositivos...

  17. 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.

  18. 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: ...

  19. A Symbiotic Framework for coupling Machine Learning and Geosciences in Prediction and Predictability

    Science.gov (United States)

    Ravela, S.

    2017-12-01

    In this presentation we review the two directions of a symbiotic relationship between machine learning and the geosciences in relation to prediction and predictability. In the first direction, we develop ensemble, information theoretic and manifold learning framework to adaptively improve state and parameter estimates in nonlinear high-dimensional non-Gaussian problems, showing in particular that tractable variational approaches can be produced. We demonstrate these applications in the context of autonomous mapping of environmental coherent structures and other idealized problems. In the reverse direction, we show that data assimilation, particularly probabilistic approaches for filtering and smoothing offer a novel and useful way to train neural networks, and serve as a better basis than gradient based approaches when we must quantify uncertainty in association with nonlinear, chaotic processes. In many inference problems in geosciences we seek to build reduced models to characterize local sensitivies, adjoints or other mechanisms that propagate innovations and errors. Here, the particular use of neural approaches for such propagation trained using ensemble data assimilation provides a novel framework. Through these two examples of inference problems in the earth sciences, we show that not only is learning useful to broaden existing methodology, but in reverse, geophysical methodology can be used to influence paradigms in learning.

  20. Mapping Burned Areas in Tropical Forests Using a Novel Machine Learning Framework

    OpenAIRE

    Varun Mithal; Guruprasad Nayak; Ankush Khandelwal; Vipin Kumar; Ramakrishna Nemani; Nikunj C. Oza

    2018-01-01

    This paper presents an application of a novel machine-learning framework on MODIS (moderate-resolution imaging spectroradiometer) data to map burned areas over tropical forests of South America and South-east Asia. The RAPT (RAre Class Prediction in the absence of True labels) framework is able to build data adaptive classification models using noisy training labels. It is particularly suitable when expert annotated training samples are difficult to obtain as in the case of wild fires in the ...

  1. 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.

  2. 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.

  3. Automated Analysis of ARM Binaries using the Low-Level Virtual Machine Compiler Framework

    Science.gov (United States)

    2011-03-01

    Maintenance ABACAS offers a level of flexibility in software development that would be very useful later in the software engineering life cycle. New... Blackjacking : security threats to blackberry devices, PDAs and cell phones in the enterprise. Indianapolis, Indiana, U.S.A.: Wiley Publishing, 2007...AUTOMATED ANALYSIS OF ARM BINARIES USING THE LOW- LEVEL VIRTUAL MACHINE COMPILER FRAMEWORK THESIS Jeffrey B. Scott

  4. 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

  5. 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.

  6. 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...

  7. Design of reinforcement welding machine within steel framework for marine engineering

    Science.gov (United States)

    Wang, Gang; Wu, Jin

    2017-04-01

    In this project, a design scheme that reinforcement welding machine is added within the steel framework is proposed according to the double-side welding technology for box-beam structure in marine engineering. Then the design and development of circuit and transmission mechanism for new welding equipment are completed as well with one sample machine being made. Moreover, the trial running is finished finally. Main technical parameters of the equipment are: the working stroke: ≥1500mm, the welding speed: 8˜15cm/min and the welding sheet thickness: ≥20mm.

  8. Development of Type 2 Diabetes Mellitus Phenotyping Framework Using Expert Knowledge and Machine Learning Approach.

    Science.gov (United States)

    Kagawa, Rina; Kawazoe, Yoshimasa; Ida, Yusuke; Shinohara, Emiko; Tanaka, Katsuya; Imai, Takeshi; Ohe, Kazuhiko

    2017-07-01

    Phenotyping is an automated technique that can be used to distinguish patients based on electronic health records. To improve the quality of medical care and advance type 2 diabetes mellitus (T2DM) research, the demand for T2DM phenotyping has been increasing. Some existing phenotyping algorithms are not sufficiently accurate for screening or identifying clinical research subjects. We propose a practical phenotyping framework using both expert knowledge and a machine learning approach to develop 2 phenotyping algorithms: one is for screening; the other is for identifying research subjects. We employ expert knowledge as rules to exclude obvious control patients and machine learning to increase accuracy for complicated patients. We developed phenotyping algorithms on the basis of our framework and performed binary classification to determine whether a patient has T2DM. To facilitate development of practical phenotyping algorithms, this study introduces new evaluation metrics: area under the precision-sensitivity curve (AUPS) with a high sensitivity and AUPS with a high positive predictive value. The proposed phenotyping algorithms based on our framework show higher performance than baseline algorithms. Our proposed framework can be used to develop 2 types of phenotyping algorithms depending on the tuning approach: one for screening, the other for identifying research subjects. We develop a novel phenotyping framework that can be easily implemented on the basis of proper evaluation metrics, which are in accordance with users' objectives. The phenotyping algorithms based on our framework are useful for extraction of T2DM patients in retrospective studies.

  9. Controlling misses and false alarms in a machine learning framework for predicting uniformity of printed pages

    Science.gov (United States)

    Nguyen, Minh Q.; Allebach, Jan P.

    2015-01-01

    In our previous work1 , we presented a block-based technique to analyze printed page uniformity both visually and metrically. The features learned from the models were then employed in a Support Vector Machine (SVM) framework to classify the pages into one of the two categories of acceptable and unacceptable quality. In this paper, we introduce a set of tools for machine learning in the assessment of printed page uniformity. This work is primarily targeted to the printing industry, specifically the ubiquitous laser, electrophotographic printer. We use features that are well-correlated with the rankings of expert observers to develop a novel machine learning framework that allows one to achieve the minimum "false alarm" rate, subject to a chosen "miss" rate. Surprisingly, most of the research that has been conducted on machine learning does not consider this framework. During the process of developing a new product, test engineers will print hundreds of test pages, which can be scanned and then analyzed by an autonomous algorithm. Among these pages, most may be of acceptable quality. The objective is to find the ones that are not. These will provide critically important information to systems designers, regarding issues that need to be addressed in improving the printer design. A "miss" is defined to be a page that is not of acceptable quality to an expert observer that the prediction algorithm declares to be a "pass". Misses are a serious problem, since they represent problems that will not be seen by the systems designers. On the other hand, "false alarms" correspond to pages that an expert observer would declare to be of acceptable quality, but which are flagged by the prediction algorithm as "fails". In a typical printer testing and development scenario, such pages would be examined by an expert, and found to be of acceptable quality after all. "False alarm" pages result in extra pages to be examined by expert observers, which increases labor cost. But "false

  10. 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.

  11. 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.

  12. Rancang Bangun Aplikasi Edutainment untuk Anak SD dengan Teknik Gamifikasi Berbasis Octalysis dan Machinations Framework

    Directory of Open Access Journals (Sweden)

    Imam Kuswardayan

    2017-01-01

    Full Text Available Teknologi berbasis edukasi terasa kurang begitu nampak aplikasinya di ranah pendidikan. Banyak faktor penarik, di antaranya lemahnya sisi User experience. Gamifikasi menawarkan perancangan aplikasi yang menyematkan elemen game sehingga lebih memiliki daya tarik terhadap konten aplikasi karena konsep game yang telah dikenal menyenangkan dan mudah dipahami. Konsep gamifikasi dirancang dengan Octalysis Framework yang menganalisis dari delapan sisi psikologi game. Perancangan gamifikasi kemudian divisualisasikan secara interaktif melalui Machinations Framework. Selanjutnya, diimplementasikan pada platform mobile menjadi aplikasi Edutainment. Didapatkan dari pengujian usabilitas sepuluh penguji bahwa gamifikasi yang diujikan memiliki dampak membuat aplikasi lebih menarik, edukatif, tidak membosankan, dan bisa meningkatkan ketertarikan anak dalam belajar.

  13. 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.

  14. 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.

  15. The fundamental structural framework of Goias state

    International Nuclear Information System (INIS)

    Hasui, Y.; Haralyi, N.L.E.

    1986-01-01

    The fundamental structural framework of the State of Goias is done by the Araguacema, Porangatu, Brasilia and Parana crustal blocks, linked through obduction zones at late Archean time. This first-order structure deduced from gravimetric and magnetic data is consistent with the distribution of granite-greenstone terrains high-grade terrains and associated supracrustals. This crustal geometry was modified by vertical shear zones and polycyclic faults, mostly of NW to WNW and NE to ENE trends, to which total displacements up to 200 km are related. Some isotope dating of the rocks are also presented. (author)

  16. Toward Confirming a Framework for Securing the Virtual Machine Image in Cloud Computing

    Directory of Open Access Journals (Sweden)

    Raid Khalid Hussein

    2017-04-01

    Full Text Available The concept of cloud computing has arisen thanks to academic work in the fields of utility computing, distributed computing, virtualisation, and web services. By using cloud computing, which can be accessed from anywhere, newly-launched businesses can minimise their start-up costs. Among the most important notions when it comes to the construction of cloud computing is virtualisation. While this concept brings its own security risks, these risks are not necessarily related to the cloud. The main disadvantage of using cloud computing is linked to safety and security. This is because anybody which chooses to employ cloud computing will use someone else’s hard disk and CPU in order to sort and store data. In cloud environments, a great deal of importance is placed on guaranteeing that the virtual machine image is safe and secure. Indeed, a previous study has put forth a framework with which to protect the virtual machine image in cloud computing. As such, the present study is primarily concerned with confirming this theoretical framework so as to ultimately secure the virtual machine image in cloud computing. This will be achieved by carrying out interviews with experts in the field of cloud security.

  17. Effective Cost Mechanism for Cloudlet Retransmission and Prioritized VM Scheduling Mechanism over Broker Virtual Machine Communication Framework

    OpenAIRE

    Raj, Gaurav; Setia, Sonika

    2012-01-01

    In current scenario cloud computing is most widely increasing platform for task execution. Lot of research is going on to cut down the cost and execution time. In this paper, we propose an efficient algorithm to have an effective and fast execution of task assigned by the user. We proposed an effective communication framework between broker and virtual machine for assigning the task and fetching the results in optimum time and cost using Broker Virtual Machine Communication Framework (BVCF). ...

  18. Rapid and Accurate Machine Learning Recognition of High Performing Metal Organic Frameworks for CO2 Capture.

    Science.gov (United States)

    Fernandez, Michael; Boyd, Peter G; Daff, Thomas D; Aghaji, Mohammad Zein; Woo, Tom K

    2014-09-04

    In this work, we have developed quantitative structure-property relationship (QSPR) models using advanced machine learning algorithms that can rapidly and accurately recognize high-performing metal organic framework (MOF) materials for CO2 capture. More specifically, QSPR classifiers have been developed that can, in a fraction of a section, identify candidate MOFs with enhanced CO2 adsorption capacity (>1 mmol/g at 0.15 bar and >4 mmol/g at 1 bar). The models were tested on a large set of 292 050 MOFs that were not part of the training set. The QSPR classifier could recover 945 of the top 1000 MOFs in the test set while flagging only 10% of the whole library for compute intensive screening. Thus, using the machine learning classifiers as part of a high-throughput screening protocol would result in an order of magnitude reduction in compute time and allow intractably large structure libraries and search spaces to be screened.

  19. A Bayesian least squares support vector machines based framework for fault diagnosis and failure prognosis

    Science.gov (United States)

    Khawaja, Taimoor Saleem

    A high-belief low-overhead Prognostics and Health Management (PHM) system is desired for online real-time monitoring of complex non-linear systems operating in a complex (possibly non-Gaussian) noise environment. This thesis presents a Bayesian Least Squares Support Vector Machine (LS-SVM) based framework for fault diagnosis and failure prognosis in nonlinear non-Gaussian systems. The methodology assumes the availability of real-time process measurements, definition of a set of fault indicators and the existence of empirical knowledge (or historical data) to characterize both nominal and abnormal operating conditions. An efficient yet powerful Least Squares Support Vector Machine (LS-SVM) algorithm, set within a Bayesian Inference framework, not only allows for the development of real-time algorithms for diagnosis and prognosis but also provides a solid theoretical framework to address key concepts related to classification for diagnosis and regression modeling for prognosis. SVM machines are founded on the principle of Structural Risk Minimization (SRM) which tends to find a good trade-off between low empirical risk and small capacity. The key features in SVM are the use of non-linear kernels, the absence of local minima, the sparseness of the solution and the capacity control obtained by optimizing the margin. The Bayesian Inference framework linked with LS-SVMs allows a probabilistic interpretation of the results for diagnosis and prognosis. Additional levels of inference provide the much coveted features of adaptability and tunability of the modeling parameters. The two main modules considered in this research are fault diagnosis and failure prognosis. With the goal of designing an efficient and reliable fault diagnosis scheme, a novel Anomaly Detector is suggested based on the LS-SVM machines. The proposed scheme uses only baseline data to construct a 1-class LS-SVM machine which, when presented with online data is able to distinguish between normal behavior

  20. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Directory of Open Access Journals (Sweden)

    Ahmad Karim

    Full Text Available Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS, disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  1. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications.

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks' back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps' detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies.

  2. SMARTbot: A Behavioral Analysis Framework Augmented with Machine Learning to Identify Mobile Botnet Applications

    Science.gov (United States)

    Karim, Ahmad; Salleh, Rosli; Khan, Muhammad Khurram

    2016-01-01

    Botnet phenomenon in smartphones is evolving with the proliferation in mobile phone technologies after leaving imperative impact on personal computers. It refers to the network of computers, laptops, mobile devices or tablets which is remotely controlled by the cybercriminals to initiate various distributed coordinated attacks including spam emails, ad-click fraud, Bitcoin mining, Distributed Denial of Service (DDoS), disseminating other malwares and much more. Likewise traditional PC based botnet, Mobile botnets have the same operational impact except the target audience is particular to smartphone users. Therefore, it is import to uncover this security issue prior to its widespread adaptation. We propose SMARTbot, a novel dynamic analysis framework augmented with machine learning techniques to automatically detect botnet binaries from malicious corpus. SMARTbot is a component based off-device behavioral analysis framework which can generate mobile botnet learning model by inducing Artificial Neural Networks’ back-propagation method. Moreover, this framework can detect mobile botnet binaries with remarkable accuracy even in case of obfuscated program code. The results conclude that, a classifier model based on simple logistic regression outperform other machine learning classifier for botnet apps’ detection, i.e 99.49% accuracy is achieved. Further, from manual inspection of botnet dataset we have extracted interesting trends in those applications. As an outcome of this research, a mobile botnet dataset is devised which will become the benchmark for future studies. PMID:26978523

  3. Towards Measuring the Abstractness of State Machines based on Mutation Testing

    Directory of Open Access Journals (Sweden)

    Thomas Baar

    2017-01-01

    Full Text Available Abstract. The notation of state machines is widely adopted as a formalism to describe the behaviour of systems. Usually, multiple state machine models can be developed for the very same software system. Some of these models might turn out to be equivalent, but, in many cases, different state machines describing the same system also differ in their level of abstraction. In this paper, we present an approach to actually measure the abstractness level of state machines w.r.t. a given implemented software system. A state machine is considered to be less abstract when it is conceptionally closer to the implemented system. In our approach, this distance between state machine and implementation is measured by applying coverage criteria known from software mutation testing. Abstractness of state machines can be considered as a new metric. As for other metrics as well, a known value for the abstractness of a given state machine allows to assess its quality in terms of a simple number. In model-based software development projects, the abstract metric can help to prevent model degradation since it can actually measure the semantic distance from the behavioural specification of a system in form of a state machine to the current implementation of the system. In contrast to other metrics for state machines, the abstractness cannot be statically computed based on the state machine’s structure, but requires to execute both state machine and corresponding system implementation. The article is published in the author’s wording. 

  4. 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.

  5. 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...

  6. 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.

  7. 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

  8. 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.

  9. Orchid: a novel management, annotation and machine learning framework for analyzing cancer mutations.

    Science.gov (United States)

    Cario, Clinton L; Witte, John S

    2018-03-15

    As whole-genome tumor sequence and biological annotation datasets grow in size, number and content, there is an increasing basic science and clinical need for efficient and accurate data management and analysis software. With the emergence of increasingly sophisticated data stores, execution environments and machine learning algorithms, there is also a need for the integration of functionality across frameworks. We present orchid, a python based software package for the management, annotation and machine learning of cancer mutations. Building on technologies of parallel workflow execution, in-memory database storage and machine learning analytics, orchid efficiently handles millions of mutations and hundreds of features in an easy-to-use manner. We describe the implementation of orchid and demonstrate its ability to distinguish tissue of origin in 12 tumor types based on 339 features using a random forest classifier. Orchid and our annotated tumor mutation database are freely available at https://github.com/wittelab/orchid. Software is implemented in python 2.7, and makes use of MySQL or MemSQL databases. Groovy 2.4.5 is optionally required for parallel workflow execution. JWitte@ucsf.edu. Supplementary data are available at Bioinformatics online.

  10. Figure of merit for macrouniformity based on image quality ruler evaluation and machine learning framework

    Science.gov (United States)

    Wang, Weibao; Overall, Gary; Riggs, Travis; Silveston-Keith, Rebecca; Whitney, Julie; Chiu, George; Allebach, Jan P.

    2013-01-01

    Assessment of macro-uniformity is a capability that is important for the development and manufacture of printer products. Our goal is to develop a metric that will predict macro-uniformity, as judged by human subjects, by scanning and analyzing printed pages. We consider two different machine learning frameworks for the metric: linear regression and the support vector machine. We have implemented the image quality ruler, based on the recommendations of the INCITS W1.1 macro-uniformity team. Using 12 subjects at Purdue University and 20 subjects at Lexmark, evenly balanced with respect to gender, we conducted subjective evaluations with a set of 35 uniform b/w prints from seven different printers with five levels of tint coverage. Our results suggest that the image quality ruler method provides a reliable means to assess macro-uniformity. We then defined and implemented separate features to measure graininess, mottle, large area variation, jitter, and large-scale non-uniformity. The algorithms that we used are largely based on ISO image quality standards. Finally, we used these features computed for a set of test pages and the subjects' image quality ruler assessments of these pages to train the two different predictors - one based on linear regression and the other based on the support vector machine (SVM). Using five-fold cross-validation, we confirmed the efficacy of our predictor.

  11. A Modular Framework for Transforming Structured Data into HTML with Machine-Readable Annotations

    Science.gov (United States)

    Patton, E. W.; West, P.; Rozell, E.; Zheng, J.

    2010-12-01

    There is a plethora of web-based Content Management Systems (CMS) available for maintaining projects and data, i.a. However, each system varies in its capabilities and often content is stored separately and accessed via non-uniform web interfaces. Moving from one CMS to another (e.g., MediaWiki to Drupal) can be cumbersome, especially if a large quantity of data must be adapted to the new system. To standardize the creation, display, management, and sharing of project information, we have assembled a framework that uses existing web technologies to transform data provided by any service that supports the SPARQL Protocol and RDF Query Language (SPARQL) queries into HTML fragments, allowing it to be embedded in any existing website. The framework utilizes a two-tier XML Stylesheet Transformation (XSLT) that uses existing ontologies (e.g., Friend-of-a-Friend, Dublin Core) to interpret query results and render them as HTML documents. These ontologies can be used in conjunction with custom ontologies suited to individual needs (e.g., domain-specific ontologies for describing data records). Furthermore, this transformation process encodes machine-readable annotations, namely, the Resource Description Framework in attributes (RDFa), into the resulting HTML, so that capable parsers and search engines can extract the relationships between entities (e.g, people, organizations, datasets). To facilitate editing of content, the framework provides a web-based form system, mapping each query to a dynamically generated form that can be used to modify and create entities, while keeping the native data store up-to-date. This open framework makes it easy to duplicate data across many different sites, allowing researchers to distribute their data in many different online forums. In this presentation we will outline the structure of queries and the stylesheets used to transform them, followed by a brief walkthrough that follows the data from storage to human- and machine-accessible web

  12. Craniux: a LabVIEW-based modular software framework for brain-machine interface research.

    Science.gov (United States)

    Degenhart, Alan D; Kelly, John W; Ashmore, Robin C; Collinger, Jennifer L; Tyler-Kabara, Elizabeth C; Weber, Douglas J; Wang, Wei

    2011-01-01

    This paper presents "Craniux," an open-access, open-source software framework for brain-machine interface (BMI) research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG) signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  13. Craniux: A LabVIEW-Based Modular Software Framework for Brain-Machine Interface Research

    Directory of Open Access Journals (Sweden)

    Alan D. Degenhart

    2011-01-01

    Full Text Available This paper presents “Craniux,” an open-access, open-source software framework for brain-machine interface (BMI research. Developed in LabVIEW, a high-level graphical programming environment, Craniux offers both out-of-the-box functionality and a modular BMI software framework that is easily extendable. Specifically, it allows researchers to take advantage of multiple features inherent to the LabVIEW environment for on-the-fly data visualization, parallel processing, multithreading, and data saving. This paper introduces the basic features and system architecture of Craniux and describes the validation of the system under real-time BMI operation using simulated and real electrocorticographic (ECoG signals. Our results indicate that Craniux is able to operate consistently in real time, enabling a seamless work flow to achieve brain control of cursor movement. The Craniux software framework is made available to the scientific research community to provide a LabVIEW-based BMI software platform for future BMI research and development.

  14. Statistical and Machine-Learning Classifier Framework to Improve Pulse Shape Discrimination System Design

    Energy Technology Data Exchange (ETDEWEB)

    Wurtz, R. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States); Kaplan, A. [Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)

    2015-10-28

    Pulse shape discrimination (PSD) is a variety of statistical classifier. Fully-­realized statistical classifiers rely on a comprehensive set of tools for designing, building, and implementing. PSD advances rely on improvements to the implemented algorithm. PSD advances can be improved by using conventional statistical classifier or machine learning methods. This paper provides the reader with a glossary of classifier-­building elements and their functions in a fully-­designed and operational classifier framework that can be used to discover opportunities for improving PSD classifier projects. This paper recommends reporting the PSD classifier’s receiver operating characteristic (ROC) curve and its behavior at a gamma rejection rate (GRR) relevant for realistic applications.

  15. 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

  16. Hybrid RGSA and Support Vector Machine Framework for Three-Dimensional Magnetic Resonance Brain Tumor Classification

    Directory of Open Access Journals (Sweden)

    R. Rajesh Sharma

    2015-01-01

    algorithm (RGSA. Support vector machines, over backpropagation network, and k-nearest neighbor are used to evaluate the goodness of classifier approach. The preliminary evaluation of the system is performed using 320 real-time brain MRI images. The system is trained and tested by using a leave-one-case-out method. The performance of the classifier is tested using the receiver operating characteristic curve of 0.986 (±002. The experimental results demonstrate the systematic and efficient feature extraction and feature selection algorithm to the performance of state-of-the-art feature classification methods.

  17. 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.

  18. 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

  19. Machine health prognostics using the Bayesian-inference-based probabilistic indication and high-order particle filtering framework

    Science.gov (United States)

    Yu, Jianbo

    2015-12-01

    Prognostics is much efficient to achieve zero-downtime performance, maximum productivity and proactive maintenance of machines. Prognostics intends to assess and predict the time evolution of machine health degradation so that machine failures can be predicted and prevented. A novel prognostics system is developed based on the data-model-fusion scheme using the Bayesian inference-based self-organizing map (SOM) and an integration of logistic regression (LR) and high-order particle filtering (HOPF). In this prognostics system, a baseline SOM is constructed to model the data distribution space of healthy machine under an assumption that predictable fault patterns are not available. Bayesian inference-based probability (BIP) derived from the baseline SOM is developed as a quantification indication of machine health degradation. BIP is capable of offering failure probability for the monitored machine, which has intuitionist explanation related to health degradation state. Based on those historic BIPs, the constructed LR and its modeling noise constitute a high-order Markov process (HOMP) to describe machine health propagation. HOPF is used to solve the HOMP estimation to predict the evolution of the machine health in the form of a probability density function (PDF). An on-line model update scheme is developed to adapt the Markov process changes to machine health dynamics quickly. The experimental results on a bearing test-bed illustrate the potential applications of the proposed system as an effective and simple tool for machine health prognostics.

  20. 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.

  1. Modeling Plan-Related Clinical Complications Using Machine Learning Tools in a Multiplan IMRT Framework

    International Nuclear Information System (INIS)

    Zhang, Hao H.; D'Souza, Warren D.; Shi Leyuan; Meyer, Robert R.

    2009-01-01

    Purpose: To predict organ-at-risk (OAR) complications as a function of dose-volume (DV) constraint settings without explicit plan computation in a multiplan intensity-modulated radiotherapy (IMRT) framework. Methods and Materials: Several plans were generated by varying the DV constraints (input features) on the OARs (multiplan framework), and the DV levels achieved by the OARs in the plans (plan properties) were modeled as a function of the imposed DV constraint settings. OAR complications were then predicted for each of the plans by using the imposed DV constraints alone (features) or in combination with modeled DV levels (plan properties) as input to machine learning (ML) algorithms. These ML approaches were used to model two OAR complications after head-and-neck and prostate IMRT: xerostomia, and Grade 2 rectal bleeding. Two-fold cross-validation was used for model verification and mean errors are reported. Results: Errors for modeling the achieved DV values as a function of constraint settings were 0-6%. In the head-and-neck case, the mean absolute prediction error of the saliva flow rate normalized to the pretreatment saliva flow rate was 0.42% with a 95% confidence interval of (0.41-0.43%). In the prostate case, an average prediction accuracy of 97.04% with a 95% confidence interval of (96.67-97.41%) was achieved for Grade 2 rectal bleeding complications. Conclusions: ML can be used for predicting OAR complications during treatment planning allowing for alternative DV constraint settings to be assessed within the planning framework.

  2. 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

  3. A machine learning framework involving EEG-based functional connectivity to diagnose major depressive disorder (MDD).

    Science.gov (United States)

    Mumtaz, Wajid; Ali, Syed Saad Azhar; Yasin, Mohd Azhar Mohd; Malik, Aamir Saeed

    2018-02-01

    Major depressive disorder (MDD), a debilitating mental illness, could cause functional disabilities and could become a social problem. An accurate and early diagnosis for depression could become challenging. This paper proposed a machine learning framework involving EEG-derived synchronization likelihood (SL) features as input data for automatic diagnosis of MDD. It was hypothesized that EEG-based SL features could discriminate MDD patients and healthy controls with an acceptable accuracy better than measures such as interhemispheric coherence and mutual information. In this work, classification models such as support vector machine (SVM), logistic regression (LR) and Naïve Bayesian (NB) were employed to model relationship between the EEG features and the study groups (MDD patient and healthy controls) and ultimately achieved discrimination of study participants. The results indicated that the classification rates were better than chance. More specifically, the study resulted into SVM classification accuracy = 98%, sensitivity = 99.9%, specificity = 95% and f-measure = 0.97; LR classification accuracy = 91.7%, sensitivity = 86.66%, specificity = 96.6% and f-measure = 0.90; NB classification accuracy = 93.6%, sensitivity = 100%, specificity = 87.9% and f-measure = 0.95. In conclusion, SL could be a promising method for diagnosing depression. The findings could be generalized to develop a robust CAD-based tool that may help for clinical purposes.

  4. 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

  5. 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...

  6. A Machine-Learning and Filtering Based Data Assimilation Framework for Geologic Carbon Sequestration Monitoring Optimization

    Science.gov (United States)

    Chen, B.; Harp, D. R.; Lin, Y.; Keating, E. H.; Pawar, R.

    2017-12-01

    Monitoring is a crucial aspect of geologic carbon sequestration (GCS) risk management. It has gained importance as a means to ensure CO2 is safely and permanently stored underground throughout the lifecycle of a GCS project. Three issues are often involved in a monitoring project: (i) where is the optimal location to place the monitoring well(s), (ii) what type of data (pressure, rate and/or CO2 concentration) should be measured, and (iii) What is the optimal frequency to collect the data. In order to address these important issues, a filtering-based data assimilation procedure is developed to perform the monitoring optimization. The optimal monitoring strategy is selected based on the uncertainty reduction of the objective of interest (e.g., cumulative CO2 leak) for all potential monitoring strategies. To reduce the computational cost of the filtering-based data assimilation process, two machine-learning algorithms: Support Vector Regression (SVR) and Multivariate Adaptive Regression Splines (MARS) are used to develop the computationally efficient reduced-order-models (ROMs) from full numerical simulations of CO2 and brine flow. The proposed framework for GCS monitoring optimization is demonstrated with two examples: a simple 3D synthetic case and a real field case named Rock Spring Uplift carbon storage site in Southwestern Wyoming.

  7. A Conjoint Analysis Framework for Evaluating User Preferences in Machine Translation.

    Science.gov (United States)

    Kirchhoff, Katrin; Capurro, Daniel; Turner, Anne M

    2014-03-01

    Despite much research on machine translation (MT) evaluation, there is surprisingly little work that directly measures users' intuitive or emotional preferences regarding different types of MT errors. However, the elicitation and modeling of user preferences is an important prerequisite for research on user adaptation and customization of MT engines. In this paper we explore the use of conjoint analysis as a formal quantitative framework to assess users' relative preferences for different types of translation errors. We apply our approach to the analysis of MT output from translating public health documents from English into Spanish. Our results indicate that word order errors are clearly the most dispreferred error type, followed by word sense, morphological, and function word errors. The conjoint analysis-based model is able to predict user preferences more accurately than a baseline model that chooses the translation with the fewest errors overall. Additionally we analyze the effect of using a crowd-sourced respondent population versus a sample of domain experts and observe that main preference effects are remarkably stable across the two samples.

  8. Multisource Data Fusion Framework for Land Use/Land Cover Classification Using Machine Vision

    Directory of Open Access Journals (Sweden)

    Salman Qadri

    2017-01-01

    Full Text Available Data fusion is a powerful tool for the merging of multiple sources of information to produce a better output as compared to individual source. This study describes the data fusion of five land use/cover types, that is, bare land, fertile cultivated land, desert rangeland, green pasture, and Sutlej basin river land derived from remote sensing. A novel framework for multispectral and texture feature based data fusion is designed to identify the land use/land cover data types correctly. Multispectral data is obtained using a multispectral radiometer, while digital camera is used for image dataset. It has been observed that each image contained 229 texture features, while 30 optimized texture features data for each image has been obtained by joining together three features selection techniques, that is, Fisher, Probability of Error plus Average Correlation, and Mutual Information. This 30-optimized-texture-feature dataset is merged with five-spectral-feature dataset to build the fused dataset. A comparison is performed among texture, multispectral, and fused dataset using machine vision classifiers. It has been observed that fused dataset outperformed individually both datasets. The overall accuracy acquired using multilayer perceptron for texture data, multispectral data, and fused data was 96.67%, 97.60%, and 99.60%, respectively.

  9. A State Cyber Hub Operations Framework

    Science.gov (United States)

    2016-06-01

    current and future decision support. Finally, the Hub operations team must track and report key performance indictors ( KPIs ) (established by the state...Region); then mutually agreed-upon core KPIs should be identified and reported at this time. The goal of the Act Element is to implement the decision...Force Headquarters Page 58 JIE Joint Information Environment JMC JIE Management Construct KPI Key Performance Indicator LEA Law Enforcement

  10. Employability and Related Context Prediction Framework for University Graduands: A Machine Learning Approach

    Directory of Open Access Journals (Sweden)

    Manushi P. Wijayapala

    2016-12-01

    Full Text Available In Sri Lanka (SL, graduands’ employability remains a national issue due to the increasing number of graduates produced by higher education institutions each year. Thus, predicting the employability of university graduands can mitigate this issue since graduands can identify what qualifications or skills they need to strengthen up in order to find a job of their desired field with a good salary, before they complete the degree. The main objective of the study is to discover the plausibility of applying machine learning approach efficiently and effectively towards predicting the employability and related context of university graduands in Sri Lanka by proposing an architectural framework which consists of four modules; employment status prediction, job salary prediction, job field prediction and job relevance prediction of graduands while also comparing performance of classification algorithms under each prediction module. Series of machine learning algorithms such as C4.5, Naïve Bayes and AODE have been experimented on the Graduand Employment Census - 2014 data. A pre-processing step is proposed to overcome challenges embedded in graduand employability data and a feature selection process is proposed in order to reduce computational complexity. Additionally, parameter tuning is also done to get the most optimized parameters. More importantly, this study utilizes several types of Sampling (Oversampling, Undersampling and Ensemble (Bagging, Boosting, RF techniques as well as a newly proposed hybrid approach to overcome the limitations caused by the class imbalance phenomena. For the validation purposes, a wide range of evaluation measures was used to analyze the effectiveness of applying classification algorithms and class imbalance mitigation techniques on the dataset. The experimented results indicated that RandomForest has recorded the highest classification performance for 3 modules, achieving the selected best predictive models under hybrid

  11. 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 ...

  12. 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.

  13. 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.

  14. Image Classification, Deep Learning and Convolutional Neural Networks : A Comparative Study of Machine Learning Frameworks

    OpenAIRE

    Airola, Rasmus; Hager, Kristoffer

    2017-01-01

    The use of machine learning and specifically neural networks is a growing trend in software development, and has grown immensely in the last couple of years in the light of an increasing need to handle big data and large information flows. Machine learning has a broad area of application, such as human-computer interaction, predicting stock prices, real-time translation, and self driving vehicles. Large companies such as Microsoft and Google have already implemented machine learning in some o...

  15. Regional geologic framework off northeastern United States

    International Nuclear Information System (INIS)

    Schlee, J.; Behrendt, J.C.; Grow, J.A.; Robb, J.M.; Mattick, R.E.; Taylor, P.T.; Lawson, B.J.

    1976-01-01

    Six multichannel seismic-reflection profiles taken across the Atlantic continental margin off the northeastern United States show an excess of 14 km of presumed Mesozoic and younger sedimentary rocks in the Baltimore Canyon trough and 8 km in the Georges Bank basin. Beneath the continental rise, the sedimentary prism thickness exceeds 7 km south of New Jersey and Maryland, and it is 4.5 km thick south of Georges Bank Stratigraphically, the continental slope--outer edge of the continental shelf is a transition zone of high-velocity sedimentary rock, probably carbonate, that covers deeply subsidized basement. The spatial separation of magnetic and gravity anomalies on line 2 (New Jersey) suggests that in the Baltimore Canyon region the magnetic-slope anomaly is due to edge effects and that the previously reported free-air and isostatic gravity anomalies over the outer shelf may be due in part to a lateral increase in sediment density (velocity) near the shelf edge. The East Coast magnetic anomaly and the free-air gravity high both coincide over the outer shelf edge on line 1 (Georges Bank) but are offset by 20 km from the ridge on the reflection profile

  16. Regional geologic framework off northeastern United States

    Science.gov (United States)

    Schlee, J.; Behrendt, John C.; Grow, J.A.; Robb, James M.; Mattick, R.; Taylor, P.T.; Lawson, B.J.

    1976-01-01

    Six multichannel seismic-reflection profiles taken across the Atlantic continental margin Previous HitoffTop the northeastern United States show an excess of 14 km of presumed Mesozoic and younger sedimentary rocks in the Baltimore Canyon trough and 8 km in the Georges Bank basin. Beneath the continental rise, the sedimentary prism thickness exceeds 7 km south of New Jersey and Maryland, and it is 4.5 km thick south of Georges Bank. Stratigraphically, the continental slope--outer edge of the continental shelf is a transition zone of high-velocity sedimentary rock, probably carbonate, that covers deeply subsided basement. Acoustically, the sedimentary sequence beneath the shelf is divided into three units which are correlated speculatively with the Cenozoic, the Cretaceous, and the Jurassic-Triassic sections. These units thicken offshore, and some have increased seismic velocities farther offshore. The uppermost unit thickens from a fraction of a kilometer to slightly more than a kilometer in a seaward direction, and velocity values range from 1.7 to 2.2 km/sec. The middle unit thickens from a fraction of a kilometer to as much as 5 km (northern Baltimore Canyon trough), and seismic velocity ranges from 2.2 to 5.4 km/sec. The lowest unit thickens to a maximum of 9 km (northern Baltimore Canyon), and velocities span the 3.9 to 5.9-km/sec interval. The spatial separation of magnetic and gravity anomalies on line 2 (New Jersey) suggests that in the Baltimore Canyon region the magnetic-slope anomaly is due to edge effects and that the previously reported free-air and isostatic gravity anomalies over the outer shelf may be due in part to a lateral increase in sediment density (velocity) near the shelf edge. The East Coast magnetic anomaly and the free-air gravity high both coincide over the outer shelf edge on line 1 (Georges Bank) but are offset by 20 km from the ridge on the reflection profile. Because the magnetic-slope-anomaly wavelength is nearly 50 km across, a

  17. 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...

  18. CernVM Co-Pilot: a Framework for Orchestrating Virtual Machines Running Applications of LHC Experiments on the Cloud

    International Nuclear Information System (INIS)

    Harutyunyan, A; Sánchez, C Aguado; Blomer, J; Buncic, P

    2011-01-01

    CernVM Co-Pilot is a framework for the delivery and execution of the workload on remote computing resources. It consists of components which are developed to ease the integration of geographically distributed resources (such as commercial or academic computing clouds, or the machines of users participating in volunteer computing projects) into existing computing grid infrastructures. The Co-Pilot framework can also be used to build an ad-hoc computing infrastructure on top of distributed resources. In this paper we present the architecture of the Co-Pilot framework, describe how it is used to execute the jobs of the ALICE and ATLAS experiments, as well as to run the Monte-Carlo simulation application of CERN Theoretical Physics Group.

  19. 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....

  20. 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.

  1. 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.

  2. Web of Objects Based Ambient Assisted Living Framework for Emergency Psychiatric State Prediction

    Science.gov (United States)

    Alam, Md Golam Rabiul; Abedin, Sarder Fakhrul; Al Ameen, Moshaddique; Hong, Choong Seon

    2016-01-01

    Ambient assisted living can facilitate optimum health and wellness by aiding physical, mental and social well-being. In this paper, patients’ psychiatric symptoms are collected through lightweight biosensors and web-based psychiatric screening scales in a smart home environment and then analyzed through machine learning algorithms to provide ambient intelligence in a psychiatric emergency. The psychiatric states are modeled through a Hidden Markov Model (HMM), and the model parameters are estimated using a Viterbi path counting and scalable Stochastic Variational Inference (SVI)-based training algorithm. The most likely psychiatric state sequence of the corresponding observation sequence is determined, and an emergency psychiatric state is predicted through the proposed algorithm. Moreover, to enable personalized psychiatric emergency care, a service a web of objects-based framework is proposed for a smart-home environment. In this framework, the biosensor observations and the psychiatric rating scales are objectified and virtualized in the web space. Then, the web of objects of sensor observations and psychiatric rating scores are used to assess the dweller’s mental health status and to predict an emergency psychiatric state. The proposed psychiatric state prediction algorithm reported 83.03 percent prediction accuracy in an empirical performance study. PMID:27608023

  3. Support Vector Hazards Machine: A Counting Process Framework for Learning Risk Scores for Censored Outcomes.

    Science.gov (United States)

    Wang, Yuanjia; Chen, Tianle; Zeng, Donglin

    2016-01-01

    Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines. Introducing counting processes to represent time-to-event data leads to a connection between support vector machines in supervised learning and hazards regression in standard survival analysis. To account for different at risk populations at observed event times, a time-varying offset is used in estimating risk scores. The resulting optimization is a convex quadratic programming problem that can easily incorporate non-linearity using kernel trick. We demonstrate an interesting link from the profiled empirical risk function of SVHM to the Cox partial likelihood. We then formally show that SVHM is optimal in discriminating covariate-specific hazard function from population average hazard function, and establish the consistency and learning rate of the predicted risk using the estimated risk scores. Simulation studies show improved prediction accuracy of the event times using SVHM compared to existing machine learning methods and standard conventional approaches. Finally, we analyze two real world biomedical study data where we use clinical markers and neuroimaging biomarkers to predict age-at-onset of a disease, and demonstrate superiority of SVHM in distinguishing high risk versus low risk subjects.

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

    International Nuclear Information System (INIS)

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

    1992-03-01

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

  5. 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.

  6. Framework for State-Level Renewable Energy Market Potential Studies

    Energy Technology Data Exchange (ETDEWEB)

    Kreycik, C.; Vimmerstedt, L.; Doris, E.

    2010-01-01

    State-level policymakers are relying on estimates of the market potential for renewable energy resources as they set goals and develop policies to accelerate the development of these resources. Therefore, accuracy of such estimates should be understood and possibly improved to appropriately support these decisions. This document provides a framework and next steps for state officials who require estimates of renewable energy market potential. The report gives insight into how to conduct a market potential study, including what supporting data are needed and what types of assumptions need to be made. The report distinguishes between goal-oriented studies and other types of studies, and explains the benefits of each.

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

    Directory of Open Access Journals (Sweden)

    Qing Ye

    2015-01-01

    Full Text Available This research proposes a novel framework of final drive simultaneous failure diagnosis containing feature extraction, training paired diagnostic models, generating decision threshold, and recognizing simultaneous failure modes. In feature extraction module, adopt wavelet package transform and fuzzy entropy to reduce noise interference and extract representative features of failure mode. Use single failure sample to construct probability classifiers based on paired sparse Bayesian extreme learning machine which is trained only by single failure modes and have high generalization and sparsity of sparse Bayesian learning approach. To generate optimal decision threshold which can convert probability output obtained from classifiers into final simultaneous failure modes, this research proposes using samples containing both single and simultaneous failure modes and Grid search method which is superior to traditional techniques in global optimization. Compared with other frequently used diagnostic approaches based on support vector machine and probability neural networks, experiment results based on F1-measure value verify that the diagnostic accuracy and efficiency of the proposed framework which are crucial for simultaneous failure diagnosis are superior to the existing approach.

  8. A framework for evaluating the performance of automated teller machine in banking industries: A queuing model-cum-TOPSIS approach

    Directory of Open Access Journals (Sweden)

    Christopher Osita Anyaeche

    2018-04-01

    Full Text Available The improvement in the provision of banking services to customers enhances bank’s performance (profitability and productivity and the amounts of dividend declared to shareholders as well as bank’s competitiveness. One means of fast tracking the service time for bank customers is through the use of self-servicing machines, such as automated teller machine (ATM. Total service cost, expected waiting time in queue, ATM utilization and percentage of customer loss are some of the performance indices that are used to evaluate the service rendered by a bank’s ATM. This study proposes a framework for evaluating the performance of ATM by integrating queuing model and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS methodology. Applicability of the framework was tested using practical data obtained from four banks in Nigeria. It was observed that the average ATM usage in the study area was less than 50%. The TOPSIS results identified Bank A as the best ranked bank. In addition, the results obtained revealed that banks with two ATM were ranked higher than banks with more than two ATM

  9. 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. 

  10. 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.

  11. Institutional framework of state property pledge in nizhniy Novgorod Rregion

    Directory of Open Access Journals (Sweden)

    Tat'yana Nikolaevna Danilova

    2012-09-01

    Full Text Available Collateral legal relations are an important factor of the investment process development. They reduce opportunistic loan risks and increase partners’ confidence within the transaction. The mortgage fund of Nizhniy Novgorod region is nowadays involved in the implementation of investment and innovation projects up to 70%. Although theinstitutionalenvironment of regional collateral relation has drastically improved in the last 11 years, the current status is far away from the ideal. Imperfect legal framework of collateral legal relations with the public property leads not only to information asymmetry, but also to a reduced states’ incomefrom thesetransactions. The paper analyzes the current legislative pledge of state property, describes the main steps of its modernization, identifies the positive aspects and trends, as well as deficiencies, provides and gives a proof of necessary legal framework changes aimed at collateral financial relations efficiency improvement in the region

  12. First UN member state conference on the climate framework convention

    International Nuclear Information System (INIS)

    Lamprecht, F.

    1995-01-01

    The ''Framework Convention of the United Nations Concerning Climate Changes'' (Climate Framework Convention - KRK), which was passed at the 1992 World Environment Conference (UNCEO) in Rio de Janeiro and took effect on 21 March 1994, has instituted the UN Conference of Contracting Parties (VSK) as the organ presiding over this issue (Article 7 KRK). This annual conference has the task to implement the KRK and its associated legal instruments and pass the resolutions required to this end. Its premiere took place in Berlin (28 March to 7 April 1995). Delegates from 117 signatory and 53 observer states struggled before an audience of 2000 news reportes to find a solution to the pending tasks that might be tolerable for all participants. The present article gives a brief outline of these tasks and the results achieved in Berlin. The picture is rounded off by information on the different positions defended, lines of conflict and the course of the conference. (orig.) [de

  13. 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

  14. 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.

  15. 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...

  16. 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.

  17. A general framework for unambiguous detection of quantum states

    International Nuclear Information System (INIS)

    Eldar, Y.

    2004-01-01

    Full Text:The problem of detecting information stored in the state of a quantum system is a fundamental problem in quantum information theory. Several approaches have emerged to distinguishing between a collection of non-orthogonal quantum states. We consider the problem of unambiguous detection where we seek a measurement that with a certain probability returns an inconclusive result, but such that if the measurement returns an answer, then the answer is correct with probability 1. We begin by considering unambiguous discrimination between a set of linearly independent pure quantum states. We show that the design of the optimal measurement that minimizes the probability of an inconclusive result can be formulated as a semidefinite programming problem. Based on this formulation, we develop a set of necessary and sufficient conditions for an optimal quantum measurement. We show that the optimal measurement can be computed very efficiently in polynomial time by exploiting the many well-known algorithms for solving semidefinite programs, which are guaranteed to converge to the global optimum. Using the general conditions for optimality, we derive necessary and sufficient conditions so that the measurement that results in an equal probability of an inconclusive result for each one of the quantum states is optimal. We refer to this measurement as the equal-probability measurement (EPM). We then show that for any state set, the prior probabilities of the states can be chosen such that the EPM is optimal. Finally, we consider state sets with strong symmetry properties and equal prior probabilities for which the EPM is optimal. We next develop a general framework for unambiguous state discrimination between a collection of mixed quantum states, which can be applied to any number of states with arbitrary prior probabilities. In particular, we derive a set of necessary and sufficient conditions for an optimal measurement that minimizes the probability of an inconclusive

  18. A hybrid stock trading framework integrating technical analysis with machine learning techniques

    Directory of Open Access Journals (Sweden)

    Rajashree Dash

    2016-03-01

    Full Text Available In this paper, a novel decision support system using a computational efficient functional link artificial neural network (CEFLANN and a set of rules is proposed to generate the trading decisions more effectively. Here the problem of stock trading decision prediction is articulated as a classification problem with three class values representing the buy, hold and sell signals. The CEFLANN network used in the decision support system produces a set of continuous trading signals within the range 0–1 by analyzing the nonlinear relationship exists between few popular technical indicators. Further the output trading signals are used to track the trend and to produce the trading decision based on that trend using some trading rules. The novelty of the approach is to engender the profitable stock trading decision points through integration of the learning ability of CEFLANN neural network with the technical analysis rules. For assessing the potential use of the proposed method, the model performance is also compared with some other machine learning techniques such as Support Vector Machine (SVM, Naive Bayesian model, K nearest neighbor model (KNN and Decision Tree (DT model.

  19. 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.

  20. 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.

  1. Experimental Validation of Mathematical Framework for Fast Switching Valves used in Digital Hydraulic Machines

    DEFF Research Database (Denmark)

    Nørgård, Christian; Roemer, Daniel Beck; Bech, Michael Møller

    2015-01-01

    of 10 kW during switching (mean of approximately 250 W) and a pressure loss below 0.5 bar at 600 l/min. The main goal of this article is validate parts of the mathematical framework based on a series of experiments. Furthermore, this article aims to document the experience gained from the experimental...

  2. Development of a finite state machine for the automates operation of the LLRF control at FLASH

    Energy Technology Data Exchange (ETDEWEB)

    Brandt, A.

    2007-07-15

    The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical

  3. Development of a finite state machine for the automated operation of the LLRF control at FLASH

    International Nuclear Information System (INIS)

    Brandt, A.

    2007-07-01

    The entry of digital signal processors in modern control systems not only allows for extended diagnostics compared to analog systems but also for sophisticated and tricky extensions of the control algorithms. With modern DSP- and FPGA-technology, the processing speed of digital systems is no longer inferior to analog systems in many applications. A higher degree of digitalization leads to an increased complexity of the systems and hence to higher requirements on their operators. The focus of research and development in the field of high frequency control has changed in the last few years and moved towards the direction of software development and complexity management. In the presented thesis, a frame for an automation concept of modern high frequency control systems is developed. The developed automation is based on the concept of finite state machines (FSM), which is established in industry for years. A flexible framework was developed, in which procedures communicate using standardized interfaces and can be exchanged easily. With that, the developer of high frequency control components as well as the operator on shift shall be empowered to improve and adapt the automation to changed conditions without special programming skills required. Along the automation concept a number of algorithms addressing various problems were developed which satisfy the needs of modern high frequency control systems. Among the developed and successfully tested algorithms are the calibration of incident and reflected wave of resonators without antennas, the fast adaptive compensation of repetitive errors, the robust estimation of the phase advance in the control loop and the latency adjustment for the rejection of instabilities caused by passband modes. During the development of the resonator theory, high value was set on the usability of the equation in algorithms for high frequency control. The usage of the common nomenclature of control theory emphasizes the underlying mathematical

  4. 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.

  5. 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...

  6. 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...

  7. Handling machine breakdown for dynamic scheduling by a colony of cognitive agents in a holonic manufacturing framework

    Directory of Open Access Journals (Sweden)

    T. K. Jana

    2015-09-01

    Full Text Available There is an ever increasing need of providing quick, yet improved solution to dynamic scheduling by better responsiveness following simple coordination mechanism to better adapt to the changing environments. In this endeavor, a cognitive agent based approach is proposed to deal with machine failure. A Multi Agent based Holonic Adaptive Scheduling (MAHoAS architecture is developed to frame the schedule by explicit communication between the product holons and the resource holons in association with the integrated process planning and scheduling (IPPS holon under normal situation. In the event of breakdown of a resource, the cooperation is sought by implicit communication. Inspired by the cognitive behavior of human being, a cognitive decision making scheme is proposed that reallocates the incomplete task to another resource in the most optimized manner and tries to expedite the processing in view of machine failure. A metamorphic algorithm is developed and implemented in Oracle 9i to identify the best candidate resource for task re-allocation. Integrated approach to process planning and scheduling realized under Multi Agent System (MAS framework facilitates dynamic scheduling with improved performance under such situations. The responsiveness of the resources having cognitive capabilities helps to overcome the adverse consequences of resource failure in a better way.

  8. An Improved Bacterial-Foraging Optimization-Based Machine Learning Framework for Predicting the Severity of Somatization Disorder

    Directory of Open Access Journals (Sweden)

    Xinen Lv

    2018-02-01

    Full Text Available It is of great clinical significance to establish an accurate intelligent model to diagnose the somatization disorder of community correctional personnel. In this study, a novel machine learning framework is proposed to predict the severity of somatization disorder in community correction personnel. The core of this framework is to adopt the improved bacterial foraging optimization (IBFO to optimize two key parameters (penalty coefficient and the kernel width of a kernel extreme learning machine (KELM and build an IBFO-based KELM (IBFO-KELM for the diagnosis of somatization disorder patients. The main innovation point of the IBFO-KELM model is the introduction of opposition-based learning strategies in traditional bacteria foraging optimization, which increases the diversity of bacterial species, keeps a uniform distribution of individuals of initial population, and improves the convergence rate of the BFO optimization process as well as the probability of escaping from the local optimal solution. In order to verify the effectiveness of the method proposed in this study, a 10-fold cross-validation method based on data from a symptom self-assessment scale (SCL-90 is used to make comparison among IBFO-KELM, BFO-KELM (model based on the original bacterial foraging optimization model, GA-KELM (model based on genetic algorithm, PSO-KELM (model based on particle swarm optimization algorithm and Grid-KELM (model based on grid search method. The experimental results show that the proposed IBFO-KELM prediction model has better performance than other methods in terms of classification accuracy, Matthews correlation coefficient (MCC, sensitivity and specificity. It can distinguish very well between severe somatization disorder and mild somatization and assist the psychological doctor with clinical diagnosis.

  9. 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)

  10. The Deflector Selector: A machine learning framework for prioritizing hazardous object deflection technology development

    Science.gov (United States)

    Nesvold, E. R.; Greenberg, A.; Erasmus, N.; van Heerden, E.; Galache, J. L.; Dahlstrom, E.; Marchis, F.

    2018-05-01

    Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

  11. The Deflector Selector: A Machine Learning Framework for Prioritizing Hazardous Object Deflection Technology Development

    Science.gov (United States)

    Nesvold, Erika; Greenberg, Adam; Erasmus, Nicolas; Van Heerden, Elmarie; Galache, J. L.; Dahlstrom, Eric; Marchis, Franck

    2018-01-01

    Several technologies have been proposed for deflecting a hazardous Solar System object on a trajectory that would otherwise impact the Earth. The effectiveness of each technology depends on several characteristics of the given object, including its orbit and size. The distribution of these parameters in the likely population of Earth-impacting objects can thus determine which of the technologies are most likely to be useful in preventing a collision with the Earth. None of the proposed deflection technologies has been developed and fully tested in space. Developing every proposed technology is currently prohibitively expensive, so determining now which technologies are most likely to be effective would allow us to prioritize a subset of proposed deflection technologies for funding and development. We will present a new model, the Deflector Selector, that takes as its input the characteristics of a hazardous object or population of such objects and predicts which technology would be able to perform a successful deflection. The model consists of a machine-learning algorithm trained on data produced by N-body integrations simulating the deflections. We will describe the model and present the results of tests of the effectiveness of nuclear explosives, kinetic impactors, and gravity tractors on three simulated populations of hazardous objects.

  12. 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.

  13. 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.

  14. 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%.

  15. A hybrid human and machine resource curation pipeline for the Neuroscience Information Framework.

    Science.gov (United States)

    Bandrowski, A E; Cachat, J; Li, Y; Müller, H M; Sternberg, P W; Ciccarese, P; Clark, T; Marenco, L; Wang, R; Astakhov, V; Grethe, J S; Martone, M E

    2012-01-01

    The breadth of information resources available to researchers on the Internet continues to expand, particularly in light of recently implemented data-sharing policies required by funding agencies. However, the nature of dense, multifaceted neuroscience data and the design of contemporary search engine systems makes efficient, reliable and relevant discovery of such information a significant challenge. This challenge is specifically pertinent for online databases, whose dynamic content is 'hidden' from search engines. The Neuroscience Information Framework (NIF; http://www.neuinfo.org) was funded by the NIH Blueprint for Neuroscience Research to address the problem of finding and utilizing neuroscience-relevant resources such as software tools, data sets, experimental animals and antibodies across the Internet. From the outset, NIF sought to provide an accounting of available resources, whereas developing technical solutions to finding, accessing and utilizing them. The curators therefore, are tasked with identifying and registering resources, examining data, writing configuration files to index and display data and keeping the contents current. In the initial phases of the project, all aspects of the registration and curation processes were manual. However, as the number of resources grew, manual curation became impractical. This report describes our experiences and successes with developing automated resource discovery and semiautomated type characterization with text-mining scripts that facilitate curation team efforts to discover, integrate and display new content. We also describe the DISCO framework, a suite of automated web services that significantly reduce manual curation efforts to periodically check for resource updates. Lastly, we discuss DOMEO, a semi-automated annotation tool that improves the discovery and curation of resources that are not necessarily website-based (i.e. reagents, software tools). Although the ultimate goal of automation was to

  16. The National Legal Framework of the United States

    International Nuclear Information System (INIS)

    Crosland, Martha S.

    2017-01-01

    Ms Crosland presented the United States legal framework regarding public participation. Under the Administrative Procedure Act, the primary way of conducting public participation is through 'notice and comment rulemaking'. A proposed rule is published in the Federal Register and is open to comment by the general public; the final publication of the rule includes the answers to the comments received. The various agencies in the United States make use of several digital tools to expand effective public participation and manage the process. The Atomic Energy Act established an adjudicatory process including 'trial-type' hearings, providing participation opportunities to any individual or group whose interests may be affected by a Nuclear Regulatory Commission licensing action. The National Environmental Policy Act requires several levels of review for all actions with potentially significant environmental impacts. An environmental assessment (EA) is conducted, to determine whether there is no significant impact or if a more detailed environmental impact statement (EIS) is needed. The EA requires notification of the host state and/or tribe, and the agency in charge has discretion as to the level of public involvement. The EIS requires public notification, a period for public comments on the draft EIS, and at least one public hearing. Ms Crosland presented stakeholder involvement initiatives carried out beyond the legal requirements, such as Citizen Advisory Boards at certain Department of Energy nuclear sites or the National Transportation Stakeholders Forum

  17. 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.

  18. 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.

  19. 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

  20. 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...

  1. 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.

  2. 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.

  3. 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.

  4. Integrated radiomic framework for breast cancer and tumor biology using advanced machine learning and multiparametric MRI.

    Science.gov (United States)

    Parekh, Vishwa S; Jacobs, Michael A

    2017-01-01

    Radiomics deals with the high throughput extraction of quantitative textural information from radiological images that not visually perceivable by radiologists. However, the biological correlation between radiomic features and different tissues of interest has not been established. To that end, we present the radiomic feature mapping framework to generate radiomic MRI texture image representations called the radiomic feature maps (RFM) and correlate the RFMs with quantitative texture values, breast tissue biology using quantitative MRI and classify benign from malignant tumors. We tested our radiomic feature mapping framework on a retrospective cohort of 124 patients (26 benign and 98 malignant) who underwent multiparametric breast MR imaging at 3 T. The MRI parameters used were T1-weighted imaging, T2-weighted imaging, dynamic contrast enhanced MRI (DCE-MRI) and diffusion weighted imaging (DWI). The RFMs were computed by convolving MRI images with statistical filters based on first order statistics and gray level co-occurrence matrix features. Malignant lesions demonstrated significantly higher entropy on both post contrast DCE-MRI (Benign-DCE entropy: 5.72 ± 0.12, Malignant-DCE entropy: 6.29 ± 0.06, p  = 0.0002) and apparent diffusion coefficient (ADC) maps as compared to benign lesions (Benign-ADC entropy: 5.65 ± 0.15, Malignant ADC entropy: 6.20 ± 0.07, p  = 0.002). There was no significant difference between glandular tissue entropy values in the two groups. Furthermore, the RFMs from DCE-MRI and DWI demonstrated significantly different RFM curves for benign and malignant lesions indicating their correlation to tumor vascular and cellular heterogeneity respectively. There were significant differences in the quantitative MRI metrics of ADC and perfusion. The multiview IsoSVM model classified benign and malignant breast tumors with sensitivity and specificity of 93 and 85%, respectively, with an AUC of 0.91.

  5. An Online Causal Inference Framework for Modeling and Designing Systems Involving User Preferences: A State-Space Approach

    Directory of Open Access Journals (Sweden)

    Ibrahim Delibalta

    2017-01-01

    Full Text Available We provide a causal inference framework to model the effects of machine learning algorithms on user preferences. We then use this mathematical model to prove that the overall system can be tuned to alter those preferences in a desired manner. A user can be an online shopper or a social media user, exposed to digital interventions produced by machine learning algorithms. A user preference can be anything from inclination towards a product to a political party affiliation. Our framework uses a state-space model to represent user preferences as latent system parameters which can only be observed indirectly via online user actions such as a purchase activity or social media status updates, shares, blogs, or tweets. Based on these observations, machine learning algorithms produce digital interventions such as targeted advertisements or tweets. We model the effects of these interventions through a causal feedback loop, which alters the corresponding preferences of the user. We then introduce algorithms in order to estimate and later tune the user preferences to a particular desired form. We demonstrate the effectiveness of our algorithms through experiments in different scenarios.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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

  11. 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.

  12. An Improved Abstract State Machine Based Choreography Specification and Execution Algorithm for Semantic Web Services

    Directory of Open Access Journals (Sweden)

    Shahin Mehdipour Ataee

    2018-01-01

    Full Text Available We identify significant weaknesses in the original Abstract State Machine (ASM based choreography algorithm of Web Service Modeling Ontology (WSMO, which make it impractical for use in semantic web service choreography engines. We present an improved algorithm which rectifies the weaknesses of the original algorithm, as well as a practical, fully functional choreography engine implementation in Flora-2 based on the improved algorithm. Our improvements to the choreography algorithm include (i the linking of the initial state of the ASM to the precondition of the goal, (ii the introduction of the concept of a final state in the execution of the ASM and its linking to the postcondition of the goal, and (iii modification to the execution of the ASM so that it stops when the final state condition is satisfied by the current configuration of the machine. Our choreography engine takes as input semantic web service specifications written in the Flora-2 dialect of F-logic. Furthermore, we prove the equivalence of ASMs (evolving algebras and evolving ontologies in the sense that one can simulate the other, a first in literature. Finally, we present a visual editor which facilitates the design and deployment of our F-logic based web service and goal specifications.

  13. A logical correspondence between natural semantics and abstract machines

    DEFF Research Database (Denmark)

    Simmons, Robert J.; Zerny, Ian

    2013-01-01

    We present a logical correspondence between natural semantics and abstract machines. This correspondence enables the mechanical and fully-correct construction of an abstract machine from a natural semantics. Our logical correspondence mirrors the Reynolds functional correspondence, but we...... manipulate semantic specifications encoded in a logical framework instead of manipulating functional programs. Natural semantics and abstract machines are instances of substructural operational semantics. As a byproduct, using a substructural logical framework, we bring concurrent and stateful models...

  14. Floodplain Mapping for the Continental United States Using Machine Learning Techniques and Watershed Characteristics

    Science.gov (United States)

    Jafarzadegan, K.; Merwade, V.; Saksena, S.

    2017-12-01

    Using conventional hydrodynamic methods for floodplain mapping in large-scale and data-scarce regions is problematic due to the high cost of these methods, lack of reliable data and uncertainty propagation. In this study a new framework is proposed to generate 100-year floodplains for any gauged or ungauged watershed across the United States (U.S.). This framework uses Flood Insurance Rate Maps (FIRMs), topographic, climatic and land use data which are freely available for entire U.S. for floodplain mapping. The framework consists of three components, including a Random Forest classifier for watershed classification, a Probabilistic Threshold Binary Classifier (PTBC) for generating the floodplains, and a lookup table for linking the Random Forest classifier to the PTBC. The effectiveness and reliability of the proposed framework is tested on 145 watersheds from various geographical locations in the U.S. The validation results show that around 80 percent of total watersheds are predicted well, 14 percent have acceptable fit and less than five percent are predicted poorly compared to FIRMs. Another advantage of this framework is its ability in generating floodplains for all small rivers and tributaries. Due to the high accuracy and efficiency of this framework, it can be used as a preliminary decision making tool to generate 100-year floodplain maps for data-scarce regions and all tributaries where hydrodynamic methods are difficult to use.

  15. 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.

  16. Framework of Information Science in Japan − Introduction: Comparison with United States

    OpenAIRE

    加藤, 淳一; KATO, Junichi

    2008-01-01

    This report concisely explains the history of information science in the United States. The purpose of this report is to reconfirm the field framework of information science. The framework of information science of Japan is different from the information science that Machlup and Mansfield define, because it is a framework similar to informatics for Japan.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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.

  4. A Rest Time-Based Prognostic Framework for State of Health Estimation of Lithium-Ion Batteries with Regeneration Phenomena

    Directory of Open Access Journals (Sweden)

    Taichun Qin

    2016-11-01

    Full Text Available State of health (SOH prognostics is significant for safe and reliable usage of lithium-ion batteries. To accurately predict regeneration phenomena and improve long-term prediction performance of battery SOH, this paper proposes a rest time-based prognostic framework (RTPF in which the beginning time interval of two adjacent cycles is adopted to reflect the rest time. In this framework, SOH values of regeneration cycles, the number of cycles in regeneration regions and global degradation trends are extracted from raw SOH time series and predicted respectively, and then the three sets of prediction results are integrated to calculate the final overall SOH prediction values. Regeneration phenomena can be found by support vector machine and hyperplane shift (SVM-HS model by detecting long beginning time intervals. Gaussian process (GP model is utilized to predict the global degradation trend, and nonlinear models are utilized to predict the regeneration amplitude and the cycle number of each regeneration region. The proposed framework is validated through experimental data from the degradation tests of lithium-ion batteries. The results demonstrate that both the global degradation trend and the regeneration phenomena of the testing batteries can be well predicted. Moreover, compared with the published methods, more accurate SOH prediction results can be obtained under this framework.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. New Applications of Learning Machines

    DEFF Research Database (Denmark)

    Larsen, Jan

    * Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection......* Machine learning framework for sound search * Genre classification * Music separation * MIMO channel estimation and symbol detection...

  11. A framework to protect mobile agents by using reference states

    OpenAIRE

    Hohl, Fritz

    2000-01-01

    To protect mobile agents from attacks by their execution environments, or hosts, one class of protection mechanisms uses 'reference states' to detect modification attacks. Reference states are agent states that have been produced by non-attacking, or reference hosts. This paper examines this class of mechanisms and present the bandwidth of the achieved protection. First, a new general definition of attacks against mobile agents is presented. As this general definition does not lead to a pract...

  12. Machine listening intelligence

    Science.gov (United States)

    Cella, C. E.

    2017-05-01

    This manifesto paper will introduce machine listening intelligence, an integrated research framework for acoustic and musical signals modelling, based on signal processing, deep learning and computational musicology.

  13. Framework for Naval Cooperation between Vietnam and the United States

    Science.gov (United States)

    2017-06-09

    the Vietnam-United States relationship has taken giant steps forward in virtually every aspect, especially solidified by a Comprehensive Partnership...United States relationship has taken giant steps forward in virtually every aspect, especially solidified by a Comprehensive Partnership Agreement signed...Economic Zone FTA Free Trade Agreement GDP Gross Domestic Product IMET International Military Education and Training MIA Missing in Action

  14. 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.

  15. The new forest carbon accounting framework for the United States

    Science.gov (United States)

    Domke, G. M.; Woodall, C. W.; Coulston, J.; Wear, D. N.; Healey, S. P.; Walters, B. F.

    2015-12-01

    The forest carbon accounting system used in recent National Greenhouse Gas Inventories (NGHGI) was developed more than a decade ago when the USDA Forest Service, Forest Inventory and Analysis annual inventory system was in its infancy and contemporary questions regarding the terrestrial sink (e.g., attribution) did not exist. The time has come to develop a new framework that can quickly address new questions, enables forest carbon analytics, and uses all the inventory information (e.g., disturbances and land use change) while having the flexibility to engage a wider breadth of stakeholders and partner agencies. The Forest Carbon Accounting Framework (FCAF) is comprised of a forest dynamics module and a land use dynamics module. Together these modules produce data-driven estimates of carbon stocks and stock changes in forest ecosystems that are sensitive to carbon sequestration, forest aging, and disturbance effects as well as carbon stock transfers associated with afforestation and deforestation. The new accounting system was used in the 2016 NGHGI report and research is currently underway to incorporate emerging non-live tree carbon pool data, remotely sensed information, and auxiliary data (e.g., climate information) into the FCAF.

  16. 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.

  17. 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.

  18. Effects of the Strategic Prevention Framework State Incentives Grant (SPF SIG) on state prevention infrastructure in 26 states.

    Science.gov (United States)

    Orwin, Robert G; Stein-Seroussi, Alan; Edwards, Jessica M; Landy, Ann L; Flewelling, Robert L

    2014-06-01

    The Strategic Prevention Framework State Incentive Grant (SPF SIG) program is a national public health initiative sponsored by the U.S. Substance Abuse and Mental Health Services Administration's Center for Substance Abuse Prevention to prevent substance abuse and its consequences. State grantees used a data-driven planning model to allocate resources to 450 communities, which in turn launched over 2,200 intervention strategies to target prevention priorities in their respective populations. An additional goal was to build prevention capacity and infrastructure at the state and community levels. This paper addresses whether the state infrastructure goal was achieved, and what contextual and implementation factors were associated with success. The findings are consistent with claims that, overall, the SPF SIG program met its goal of increasing prevention capacity and infrastructure across multiple infrastructure domains, though the mediating effects of implementation were evident only in the evaluation/monitoring domain. The results also show that an initiative like the SPF SIG, which could easily have been compartmentalized within the states, has the potential to permeate more broadly throughout state prevention systems.

  19. PCA-based polling strategy in machine learning framework for coronary artery disease risk assessment in intravascular ultrasound: A link between carotid and coronary grayscale plaque morphology.

    Science.gov (United States)

    Araki, Tadashi; Ikeda, Nobutaka; Shukla, Devarshi; Jain, Pankaj K; Londhe, Narendra D; Shrivastava, Vimal K; Banchhor, Sumit K; Saba, Luca; Nicolaides, Andrew; Shafique, Shoaib; Laird, John R; Suri, Jasjit S

    2016-05-01

    Percutaneous coronary interventional procedures need advance planning prior to stenting or an endarterectomy. Cardiologists use intravascular ultrasound (IVUS) for screening, risk assessment and stratification of coronary artery disease (CAD). We hypothesize that plaque components are vulnerable to rupture due to plaque progression. Currently, there are no standard grayscale IVUS tools for risk assessment of plaque rupture. This paper presents a novel strategy for risk stratification based on plaque morphology embedded with principal component analysis (PCA) for plaque feature dimensionality reduction and dominant feature selection technique. The risk assessment utilizes 56 grayscale coronary features in a machine learning framework while linking information from carotid and coronary plaque burdens due to their common genetic makeup. This system consists of a machine learning paradigm which uses a support vector machine (SVM) combined with PCA for optimal and dominant coronary artery morphological feature extraction. Carotid artery proven intima-media thickness (cIMT) biomarker is adapted as a gold standard during the training phase of the machine learning system. For the performance evaluation, K-fold cross validation protocol is adapted with 20 trials per fold. For choosing the dominant features out of the 56 grayscale features, a polling strategy of PCA is adapted where the original value of the features is unaltered. Different protocols are designed for establishing the stability and reliability criteria of the coronary risk assessment system (cRAS). Using the PCA-based machine learning paradigm and cross-validation protocol, a classification accuracy of 98.43% (AUC 0.98) with K=10 folds using an SVM radial basis function (RBF) kernel was achieved. A reliability index of 97.32% and machine learning stability criteria of 5% were met for the cRAS. This is the first Computer aided design (CADx) system of its kind that is able to demonstrate the ability of coronary

  20. Improving adolescent health policy: incorporating a framework for assessing state-level policies.

    Science.gov (United States)

    Brindis, Claire D; Moore, Kristin

    2014-01-01

    Many US policies that affect health are made at the state, not the federal, level. Identifying state-level policies and data to analyze how different policies affect outcomes may help policy makers ascertain the usefulness of their public policies and funding decisions in improving the health of adolescent populations. A framework for describing and assessing the role of federal and state policies on adolescent health and well-being is proposed; an example of how the framework might be applied to the issue of teen childbearing is included. Such a framework can also help inform analyses of whether and how state and federal policies contribute to the variation across states in meeting adolescent health needs. A database on state policies, contextual variables, and health outcomes data can further enable researchers and policy makers to examine how these factors are associated with behaviors they aim to impact.

  1. The autonomy of religions from the state: the normative framework

    OpenAIRE

    Carp, Radu

    2010-01-01

    The principle of the autonomy of religious cults from the state is found in many of the Constitutions of European states and it has also been asserted by ECHR. In the case of Romania, this principle was noted for the first time by the 1869 Organic Statute of the Romanian Greek Orthodox Church of Hungary and Transylvania. This was not the case after 1918 when the term autonomy cannot be found in the 1923 Constitution, the 1928 Law on the general regime of religions or in the 1925 Statute of th...

  2. 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.

  3. 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...

  4. Simple and efficient machine learning frameworks for identifying protein-protein interaction relevant articles and experimental methods used to study the interactions.

    Science.gov (United States)

    Agarwal, Shashank; Liu, Feifan; Yu, Hong

    2011-10-03

    Protein-protein interaction (PPI) is an important biomedical phenomenon. Automatically detecting PPI-relevant articles and identifying methods that are used to study PPI are important text mining tasks. In this study, we have explored domain independent features to develop two open source machine learning frameworks. One performs binary classification to determine whether the given article is PPI relevant or not, named "Simple Classifier", and the other one maps the PPI relevant articles with corresponding interaction method nodes in a standardized PSI-MI (Proteomics Standards Initiative-Molecular Interactions) ontology, named "OntoNorm". We evaluated our system in the context of BioCreative challenge competition using the standardized data set. Our systems are amongst the top systems reported by the organizers, attaining 60.8% F1-score for identifying relevant documents, and 52.3% F1-score for mapping articles to interaction method ontology. Our results show that domain-independent machine learning frameworks can perform competitively well at the tasks of detecting PPI relevant articles and identifying the methods that were used to study the interaction in such articles. Simple Classifier is available at http://sourceforge.net/p/simpleclassify/home/ and OntoNorm at http://sourceforge.net/p/ontonorm/home/.

  5. Mathematical model of the crystallizing blank`s thermal state at the horizontal continuous casting machine

    Directory of Open Access Journals (Sweden)

    Kryukov Igor Yu.

    2017-01-01

    Full Text Available Present article is devoted to the development of the mathematical model, which describes thermal state and crystallization process of the rectangular cross-section blank while continious process of extraction from a horysontal continious casting machine (HCCM.The developed model took cue for the heat-transfer properties of non-iron metal teeming; its temperature on entry to the casting mold; cooling conditions of blank in the carbon molds in the presence of a copper water cooler. Besides, has been considered the asymmetry of heat interchange from blank`s head and drag at mold, coming out from fluid contraction and features of the horizontal casting mold. The developed mathematical model allows to determine alterations in crystallizing blank of the following factors with respect to time: temperature pattern of crystallizing blank under different technical working regimes of HCCM; boundaries of solid two-phase field and liquid two-phase filed; blank`s thickness variation under shrinkage of the ingot`s material

  6. 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.

  7. Legislative framework for sediment management in the United States

    Directory of Open Access Journals (Sweden)

    P. A. Garcia-Chevesich

    2018-04-01

    Full Text Available Sediment erosion is a serious issue, with approximately 75 billion tons of soil is eroded annually around the world (Pimentel and Kounang, 1998. Although erosion is a natural process, it can accelerate due to human activity and land use changes. Increasing soil erosion beyond its natural threshold can result in significant environmental degradation and decreased economic productivity. Implementing sediment management laws and practices is critical to significantly decrease soil erosion and preserve environmental resources. In the United States, there is a comprehensive system of laws and regulations at national, state, county, and city level that govern erosion and sediment control. The laws and voluntary incentives outlined in our paper have significantly reduced the negative impacts of sediment carried in urban and storm-generated runoff, have reduced chemical and biological pollutants in sediment transported in aquatic ecosystems, and have improved the air quality in several cities with air pollution problems. Having a multi-faceted approach to monitoring erosion and improving soil management is important for a healthy, productive environment and economy.

  8. Quasiequilibrium states of black hole-neutron star binaries in the moving-puncture framework

    International Nuclear Information System (INIS)

    Kyutoku, Koutarou; Shibata, Masaru; Taniguchi, Keisuke

    2009-01-01

    General relativistic quasiequilibrium states of black hole-neutron star binaries are computed in the moving-puncture framework. We propose three conditions for determining the quasiequilibrium states and compare the numerical results with those obtained in the excision framework. We find that the results obtained in the moving-puncture framework agree with those in the excision framework and with those in the third post-Newtonian approximation for the cases that (i) the mass ratio of the binary is close to unity irrespective of the orbital separation, and (ii) the orbital separation is large enough (m 0 Ω 0 and Ω are the total mass and the orbital angular velocity, respectively) irrespective of the mass ratio. For m 0 Ω > or approx. 0.03, both of the results in the moving-puncture and excision frameworks deviate, more or less, from those in the third post-Newtonian approximation. Thus the numerical results do not provide a quasicircular state, rather they seem to have a non-negligible eccentricity of order 0.01-0.1. We show by numerical simulation that a method in the moving-puncture framework can provide approximately quasicircular states in which the eccentricity is by a factor of ∼2 smaller than those in quasiequilibrium given by other approaches.

  9. 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

  10. Analysis of Management Practices in Lagos State Tertiary Institutions through Total Quality Management Structural Framework

    Science.gov (United States)

    AbdulAzeez, Abbas Tunde

    2016-01-01

    This research investigated total quality management practices and quality teacher education in public tertiary institutions in Lagos State. The study was therefore designed to analyse management practices in Lagos state tertiary institutions through total quality management structural framework. The selected public tertiary institutions in Lagos…

  11. 77 FR 71009 - Framework for Pharmacy Compounding: State and Federal Roles

    Science.gov (United States)

    2012-11-28

    ...] Framework for Pharmacy Compounding: State and Federal Roles AGENCY: Food and Drug Administration, HHS... Federal Roles.'' At this public meeting, FDA and State representatives will share their perspectives. Date... would require compliance with Federal standards. In addition, there are open questions about whether...

  12. Accommodating state shifts within the conceptual framework of the wetland continuum

    Science.gov (United States)

    Mushet, David M.; McKenna, Owen; LaBaugh, James W.; Euliss, Ned H.; Rosenberry, Donald O.

    2018-01-01

    The Wetland Continuum is a conceptual framework that facilitates the interpretation of biological studies of wetland ecosystems. Recently summarized evidence documenting how a multi-decadal wet period has influenced aspects of wetland, lake and stream systems in the southern prairie-pothole region of North America has revealed the potential for wetlands to shift among alternate states. We propose that incorporation of state shifts into the Wetland Continuum, as originally proposed or as modified by Hayashi et al., is a relatively simple matter if one allows for shifts of wetlands along the horizontal, groundwater axis of the framework under conditions of extreme and sustained wet or dry conditions. We suggest that the ease by which state shifts can be accommodated within both the original and modified frameworks of the Wetland Continuum is a testament to the robustness of the concept when it is related to the alternative-stable-state concept.

  13. 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.

  14. SEE Action Guide for States: Evaluation, Measurement, and Verification Frameworks$-$Guidance for Energy Efficiency Portfolios Funded by Utility Customers

    Energy Technology Data Exchange (ETDEWEB)

    Li, Michael [Dept. of Energy (DOE), Washington DC (United States); Dietsch, Niko [US Environmental Protection Agency (EPA), Cincinnati, OH (United States)

    2018-01-01

    This guide describes frameworks for evaluation, measurement, and verification (EM&V) of utility customer–funded energy efficiency programs. The authors reviewed multiple frameworks across the United States and gathered input from experts to prepare this guide. This guide provides the reader with both the contents of an EM&V framework, along with the processes used to develop and update these frameworks.

  15. 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.

  16. Cultural framework, anger expression, and health status in Russian immigrant women in the United States.

    Science.gov (United States)

    Bagdasarov, Zhanna; Edmondson, Christine B

    2013-01-01

    We investigated the role of anger expression and cultural framework in predicting Russian immigrant women's physical and psychological health status. One hundred Russian immigrant women between the ages of 30 and 65 completed questionnaires assessing anger expression, cultural framework, and health status. All research questions were addressed using hierarchical regression procedures. The results are discussed in terms of implications for understanding immigration experiences of Russian women who migrate from countries that are more collectivistic and less individualistic than the United States.

  17. Discrimination of schizophrenia auditory hallucinators by machine learning of resting-state functional MRI.

    Science.gov (United States)

    Chyzhyk, Darya; Graña, Manuel; Öngür, Döst; Shinn, Ann K

    2015-05-01

    Auditory hallucinations (AH) are a symptom that is most often associated with schizophrenia, but patients with other neuropsychiatric conditions, and even a small percentage of healthy individuals, may also experience AH. Elucidating the neural mechanisms underlying AH in schizophrenia may offer insight into the pathophysiology associated with AH more broadly across multiple neuropsychiatric disease conditions. In this paper, we address the problem of classifying schizophrenia patients with and without a history of AH, and healthy control (HC) subjects. To this end, we performed feature extraction from resting state functional magnetic resonance imaging (rsfMRI) data and applied machine learning classifiers, testing two kinds of neuroimaging features: (a) functional connectivity (FC) measures computed by lattice auto-associative memories (LAAM), and (b) local activity (LA) measures, including regional homogeneity (ReHo) and fractional amplitude of low frequency fluctuations (fALFF). We show that it is possible to perform classification within each pair of subject groups with high accuracy. Discrimination between patients with and without lifetime AH was highest, while discrimination between schizophrenia patients and HC participants was worst, suggesting that classification according to the symptom dimension of AH may be more valid than discrimination on the basis of traditional diagnostic categories. FC measures seeded in right Heschl's gyrus (RHG) consistently showed stronger discriminative power than those seeded in left Heschl's gyrus (LHG), a finding that appears to support AH models focusing on right hemisphere abnormalities. The cortical brain localizations derived from the features with strong classification performance are consistent with proposed AH models, and include left inferior frontal gyrus (IFG), parahippocampal gyri, the cingulate cortex, as well as several temporal and prefrontal cortical brain regions. Overall, the observed findings suggest that

  18. A Practical Framework Toward Prediction of Breaking Force and Disintegration of Tablet Formulations Using Machine Learning Tools.

    Science.gov (United States)

    Akseli, Ilgaz; Xie, Jingjin; Schultz, Leon; Ladyzhynsky, Nadia; Bramante, Tommasina; He, Xiaorong; Deanne, Rich; Horspool, Keith R; Schwabe, Robert

    2017-01-01

    Enabling the paradigm of quality by design requires the ability to quantitatively correlate material properties and process variables to measureable product performance attributes. Conventional, quality-by-test methods for determining tablet breaking force and disintegration time usually involve destructive tests, which consume significant amount of time and labor and provide limited information. Recent advances in material characterization, statistical analysis, and machine learning have provided multiple tools that have the potential to develop nondestructive, fast, and accurate approaches in drug product development. In this work, a methodology to predict the breaking force and disintegration time of tablet formulations using nondestructive ultrasonics and machine learning tools was developed. The input variables to the model include intrinsic properties of formulation and extrinsic process variables influencing the tablet during manufacturing. The model has been applied to predict breaking force and disintegration time using small quantities of active pharmaceutical ingredient and prototype formulation designs. The novel approach presented is a step forward toward rational design of a robust drug product based on insight into the performance of common materials during formulation and process development. It may also help expedite drug product development timeline and reduce active pharmaceutical ingredient usage while improving efficiency of the overall process. Copyright © 2016 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.

  19. A Framework for Diagnosing the Out-of-Control Signals in Multivariate Process Using Optimized Support Vector Machines

    Directory of Open Access Journals (Sweden)

    Tai-fu Li

    2013-01-01

    Full Text Available Multivariate statistical process control is the continuation and development of unitary statistical process control. Most multivariate statistical quality control charts are usually used (in manufacturing and service industries to determine whether a process is performing as intended or if there are some unnatural causes of variation upon an overall statistics. Once the control chart detects out-of-control signals, one difficulty encountered with multivariate control charts is the interpretation of an out-of-control signal. That is, we have to determine whether one or more or a combination of variables is responsible for the abnormal signal. A novel approach for diagnosing the out-of-control signals in the multivariate process is described in this paper. The proposed methodology uses the optimized support vector machines (support vector machine classification based on genetic algorithm to recognize set of subclasses of multivariate abnormal patters, identify the responsible variable(s on the occurrence of abnormal pattern. Multiple sets of experiments are used to verify this model. The performance of the proposed approach demonstrates that this model can accurately classify the source(s of out-of-control signal and even outperforms the conventional multivariate control scheme.

  20. 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.

  1. 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...

  2. Ecoregions of the conterminous United States: evolution of a hierarchical spatial framework

    Science.gov (United States)

    Omernik, James M.; Griffith, Glenn E.

    2014-01-01

    A map of ecological regions of the conterminous United States, first published in 1987, has been greatly refined and expanded into a hierarchical spatial framework in response to user needs, particularly by state resource management agencies. In collaboration with scientists and resource managers from numerous agencies and institutions in the United States, Mexico, and Canada, the framework has been expanded to cover North America, and the original ecoregions (now termed Level III) have been refined, subdivided, and aggregated to identify coarser as well as more detailed spatial units. The most generalized units (Level I) define 10 ecoregions in the conterminous U.S., while the finest-scale units (Level IV) identify 967 ecoregions. In this paper, we explain the logic underpinning the approach, discuss the evolution of the regional mapping process, and provide examples of how the ecoregions were distinguished at each hierarchical level. The variety of applications of the ecoregion framework illustrates its utility in resource assessment and management.

  3. 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...

  4. An open source framework for tracking and state estimation ('Stone Soup')

    Science.gov (United States)

    Thomas, Paul A.; Barr, Jordi; Balaji, Bhashyam; White, Kruger

    2017-05-01

    The ability to detect and unambiguously follow all moving entities in a state-space is important in multiple domains both in defence (e.g. air surveillance, maritime situational awareness, ground moving target indication) and the civil sphere (e.g. astronomy, biology, epidemiology, dispersion modelling). However, tracking and state estimation researchers and practitioners have difficulties recreating state-of-the-art algorithms in order to benchmark their own work. Furthermore, system developers need to assess which algorithms meet operational requirements objectively and exhaustively rather than intuitively or driven by personal favourites. We have therefore commenced the development of a collaborative initiative to create an open source framework for production, demonstration and evaluation of Tracking and State Estimation algorithms. The initiative will develop a (MIT-licensed) software platform for researchers and practitioners to test, verify and benchmark a variety of multi-sensor and multi-object state estimation algorithms. The initiative is supported by four defence laboratories, who will contribute to the development effort for the framework. The tracking and state estimation community will derive significant benefits from this work, including: access to repositories of verified and validated tracking and state estimation algorithms, a framework for the evaluation of multiple algorithms, standardisation of interfaces and access to challenging data sets. Keywords: Tracking,

  5. Performance optimization of a CNC machine through exploration of the timed state space

    NARCIS (Netherlands)

    Mota, M.A. Mujica; Piera, Miquel Angel

    2010-01-01

    Flexible production units provide very efficient mechanisms to adapt the type and production rate according to fluctuations in demand. The optimal sequence of the different manufacturing tasks in each machine is a challenging problem that can deal with important productivity benefits.

  6. From corruption to state capture: A new analytical framework with empirical applications from Hungary

    OpenAIRE

    Fazekas, Mihaly; Tóth, István János

    2016-01-01

    State capture and corruption are widespread phenomena across the globe, but their empirical study still lacks sufficient analytical tools. This paper develops a new conceptual and analytical framework for gauging state capture based on micro-level contractual networks in public procurement. To this end, it establishes a novel measure of corruption risk in government contracting focusing on the behaviour of individual organisations. Then, it identifies clusters of high corruption risk organisa...

  7. Framework for control system development

    International Nuclear Information System (INIS)

    Cork, C.; Nishimura, Hiroshi

    1992-01-01

    Control systems being developed for the present generation of accelerators will need to adapt to changing machine and operating state conditions. Such systems must also be capable of evolving over the life of the accelerator operation. In this paper we present a framework for the development of adaptive control systems

  8. Framework for control system development

    International Nuclear Information System (INIS)

    Cork, C.; Nishimura, Hiroshi.

    1991-11-01

    Control systems being developed for the present generation of accelerators will need to adapt to changing machine and operating state conditions. Such systems must also be capable of evolving over the life of the accelerator operation. In this paper we present a framework for the development of adaptive control systems

  9. A supply chain analysis framework for assessing state-level forest biomass utilization policies in the United States

    International Nuclear Information System (INIS)

    Becker, Dennis R.; Moseley, Cassandra; Lee, Christine

    2011-01-01

    The number of state policies aimed at fostering biomass utilization has proliferated in recent years in the United States. Several states aim to increase the use of forest and agriculture biomass through renewable energy production. Several more indirectly encourage utilization by targeting aspects of the supply chain from trees standing in the forest to goods sold. This research classifies 370 state policies from across the United States that provides incentives for forest biomass utilization. We compare those policies by types of incentives relative to the supply chain and geographic clustering. We then develop a framework for policy evaluation building on the supply chain steps, which can be used to assess intended and unintended consequences of policy interactions. These findings may inform policy development and identify synergies at different steps in the supply chain to enhance forest biomass utilization.

  10. Application of machine-learning methods to solid-state chemistry: ferromagnetism in transition metal alloys

    International Nuclear Information System (INIS)

    Landrum, G.A.Gregory A.; Genin, Hugh

    2003-01-01

    Machine-learning methods are a collection of techniques for building predictive models from experimental data. The algorithms are problem-independent: the chemistry and physics of the problem being studied are contained in the descriptors used to represent the known data. The application of a variety of machine-learning methods to the prediction of ferromagnetism in ordered and disordered transition metal alloys is presented. Applying a decision tree algorithm to build a predictive model for ordered phases results in a model that is 100% accurate. The same algorithm achieves 99% accuracy when trained on a data set containing both ordered and disordered phases. Details of the descriptor sets for both applications are also presented

  11. Integrating resource efficiency and EU State aid. An evaluation of resource efficiency considerations in the current EU State aid framework

    Energy Technology Data Exchange (ETDEWEB)

    Bennink, D.; Faber, J.; Smit, M. [CE Delft, Delft (Netherlands); Goba, V. [SIA Estonian, Latvian and Lithuanian Environment ELLE, Tallinn (Estonia); Miller, K.; Williams, E. [AEA Technology plc, London (United Kingdom)

    2012-10-15

    This study, for the European Commission, analyses the issues that need to be addressed in the revision of the EU State aid framework to ensure that they do not hinder environmental, resource efficiency and sustainable development goals. In some cases, State aid can be considered an environmentally harmful subsidy (EHS). The study analyses (1) the extent to which the Environmental Aid Guidelines (EAG) need to be changed to take into account recent European environmental policy developments; (2) existing and potential resource efficiency considerations in a) the Regional Aid Guidelines; b) the Research, Development and Innovation (RDI) Guidelines and c) the Agriculture and Forestry Guidelines; assesses cases and schemes using these guidelines to identify whether resource efficiency considerations are taken into account. The study also considers the social, environmental and economic impacts of these cases and schemes. It develops recommendations for the review of the EAG and a number of horizontal guidelines. One of the conclusions of the analysis is that the way in which multiple objectives and impacts are balanced, when deciding to approve state aid, is unclear. Also, EU member states are not required to provide information on certain types of (estimated) impacts. To guarantee that multiple objectives and impacts are sufficiently balanced, it is recommended that the State aid framework prescribes that applicants identify social, economic and environmental objectives and impacts and describe how these are taken into account in the procedure of balancing multiple (conflicting) objectives. Objectives and impacts should be quantified as much as possible, for example by making use of the method of external cost calculation laid down in 'the Handbook on estimation of external costs in the transport Sector'. The results of the study are used by the European Commission as an input for evaluating and improving the EU State aid framework.

  12. Integrating resource efficiency and EU State aid. An evaluation of resource efficiency considerations in the current EU State aid framework

    Energy Technology Data Exchange (ETDEWEB)

    Bennink, D.; Faber, J.; Smit, M. [CE Delft, Delft (Netherlands); Goba, V. [SIA Estonian, Latvian and Lithuanian Environment ELLE, Tallinn (Estonia); Miller, K.; Williams, E. [AEA Technology plc, London (United Kingdom)

    2012-10-15

    This study, for the European Commission, analyses the issues that need to be addressed in the revision of the EU State aid framework to ensure that they do not hinder environmental, resource efficiency and sustainable development goals. In some cases, State aid can be considered an environmentally harmful subsidy (EHS). The study analyses (1) the extent to which the Environmental Aid Guidelines (EAG) need to be changed to take into account recent European environmental policy developments; (2) existing and potential resource efficiency considerations in a) the Regional Aid Guidelines; b) the Research, Development and Innovation (RDI) Guidelines and c) the Agriculture and Forestry Guidelines; assesses cases and schemes using these guidelines to identify whether resource efficiency considerations are taken into account. The study also considers the social, environmental and economic impacts of these cases and schemes. It develops recommendations for the review of the EAG and a number of horizontal guidelines. One of the conclusions of the analysis is that the way in which multiple objectives and impacts are balanced, when deciding to approve state aid, is unclear. Also, EU member states are not required to provide information on certain types of (estimated) impacts. To guarantee that multiple objectives and impacts are sufficiently balanced, it is recommended that the State aid framework prescribes that applicants identify social, economic and environmental objectives and impacts and describe how these are taken into account in the procedure of balancing multiple (conflicting) objectives. Objectives and impacts should be quantified as much as possible, for example by making use of the method of external cost calculation laid down in 'the Handbook on estimation of external costs in the transport Sector'. The results of the study are used by the European Commission as an input for evaluating and improving the EU State aid framework.

  13. Developing a competency framework for U.S. state food and feed testing laboratory personnel.

    Science.gov (United States)

    Kaml, Craig; Weiss, Christopher C; Dezendorf, Paul; Ishida, Maria; Rice, Daniel H; Klein, Ron; Salfinger, Yvonne

    2014-01-01

    A competency-based training curriculum framework for U.S. state food and feed testing laboratories personnel is being developed by the International Food Protection Training Institute (IFPTI) and three partners. The framework will help laboratories catalog existing training courses/modules, identify training gaps, inform training curricula, and create career-spanning professional development learning paths, ensuring consistent performance expectations and increasing confidence in shared test results. Ultimately, the framework will aid laboratories in meeting the requirements of ISO/IEC 17025 (2005) international accreditation and the U.S. Food Safety Modernization Act (U.S. Public Law 111-353). In collaboration with the Association of Food and Drug Officials, the Association of Public Health Laboratories, and the Association of American Feed Control Officials, IFPTI is carrying out the project in two phases. In 2013, an expert panel of seven subject matter experts developed competency and curriculum frameworks for five professional levels (entry, mid-level, expert, supervisor/manager, and senior administration) across four competency domains (technical, communication, programmatic, and leadership) including approximately 80 competencies. In 2014 the expert panel will elicit feedback from peers and finalize the framework.

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

    Science.gov (United States)

    2009-11-01

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

  15. A stochastic global identification framework for aerospace structures operating under varying flight states

    Science.gov (United States)

    Kopsaftopoulos, Fotis; Nardari, Raphael; Li, Yu-Hung; Chang, Fu-Kuo

    2018-01-01

    In this work, a novel data-based stochastic "global" identification framework is introduced for aerospace structures operating under varying flight states and uncertainty. In this context, the term "global" refers to the identification of a model that is capable of representing the structure under any admissible flight state based on data recorded from a sample of these states. The proposed framework is based on stochastic time-series models for representing the structural dynamics and aeroelastic response under multiple flight states, with each state characterized by several variables, such as the airspeed, angle of attack, altitude and temperature, forming a flight state vector. The method's cornerstone lies in the new class of Vector-dependent Functionally Pooled (VFP) models which allow the explicit analytical inclusion of the flight state vector into the model parameters and, hence, system dynamics. This is achieved via the use of functional data pooling techniques for optimally treating - as a single entity - the data records corresponding to the various flight states. In this proof-of-concept study the flight state vector is defined by two variables, namely the airspeed and angle of attack of the vehicle. The experimental evaluation and assessment is based on a prototype bio-inspired self-sensing composite wing that is subjected to a series of wind tunnel experiments under multiple flight states. Distributed micro-sensors in the form of stretchable sensor networks are embedded in the composite layup of the wing in order to provide the sensing capabilities. Experimental data collected from piezoelectric sensors are employed for the identification of a stochastic global VFP model via appropriate parameter estimation and model structure selection methods. The estimated VFP model parameters constitute two-dimensional functions of the flight state vector defined by the airspeed and angle of attack. The identified model is able to successfully represent the wing

  16. State of the art in nuclear telerobotics: focus on the man/machine connection

    Science.gov (United States)

    Greaves, Amna E.

    1995-12-01

    The interface between the human controller and remotely operated device is a crux of telerobotic investigation today. This human-to-machine connection is the means by which we communicate our commands to the device, as well as the medium for decision-critical feedback to the operator. The amount of information transferred through the user interface is growing. This can be seen as a direct result of our need to support added complexities, as well as a rapidly expanding domain of applications. A user interface, or UI, is therefore subject to increasing demands to present information in a meaningful manner to the user. Virtual reality, and multi degree-of-freedom input devices lend us the ability to augment the man/machine interface, and handle burgeoning amounts of data in a more intuitive and anthropomorphically correct manner. Along with the aid of 3-D input and output devices, there are several visual tools that can be employed as part of a graphical UI that enhance and accelerate our comprehension of the data being presented. Thus an advanced UI that features these improvements would reduce the amount of fatigue on the teleoperator, increase his level of safety, facilitate learning, augment his control, and potentially reduce task time. This paper investigates the cutting edge concepts and enhancements that lead to the next generation of telerobotic interface systems.

  17. 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.

  18. 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.

  19. Plant state identification using fuzzy logic in the framework of computerized accident management support (CAMS)

    International Nuclear Information System (INIS)

    Van Dyck, Claude

    1997-05-01

    CAMS (computerized accident management support) is a system that will provide assistance in case of accident in a nuclear power plant. In order to support the user in evaluating the plant state, it contains a state identification module. The state identification module provides high-level, qualitative information about the status of critical safety functions, about the availability of safety systems and about the occurrence of initiating events. This information is sent to the man-machine interface and to other CAMS modules. The state identification module is developed using a specific tool: GPS (Goal Processing System) which is based on the Goal Tree - Success Tree formalism. GPS is a tool designed to manage ''process related'' knowledge and aimed at process supervision via real-time acquisition of process variables. Fuzzy logic has been introduced in GPS in order to have smoother transitions between different states of critical safety functions and systems changes and to have a truth value associated to each piece of information provided to the user. The whole system has been tested, integrated with the rest of CAMS, on several accident scenarios. The test results are satisfactory. A brief comparison is made between the present work and previous related work at the HRP. (author)

  20. 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.

  1. Ensemble support vector machine classification of dementia using structural MRI and mini-mental state examination.

    Science.gov (United States)

    Sørensen, Lauge; Nielsen, Mads

    2018-05-15

    The International Challenge for Automated Prediction of MCI from MRI data offered independent, standardized comparison of machine learning algorithms for multi-class classification of normal control (NC), mild cognitive impairment (MCI), converting MCI (cMCI), and Alzheimer's disease (AD) using brain imaging and general cognition. We proposed to use an ensemble of support vector machines (SVMs) that combined bagging without replacement and feature selection. SVM is the most commonly used algorithm in multivariate classification of dementia, and it was therefore valuable to evaluate the potential benefit of ensembling this type of classifier. The ensemble SVM, using either a linear or a radial basis function (RBF) kernel, achieved multi-class classification accuracies of 55.6% and 55.0% in the challenge test set (60 NC, 60 MCI, 60 cMCI, 60 AD), resulting in a third place in the challenge. Similar feature subset sizes were obtained for both kernels, and the most frequently selected MRI features were the volumes of the two hippocampal subregions left presubiculum and right subiculum. Post-challenge analysis revealed that enforcing a minimum number of selected features and increasing the number of ensemble classifiers improved classification accuracy up to 59.1%. The ensemble SVM outperformed single SVM classifications consistently in the challenge test set. Ensemble methods using bagging and feature selection can improve the performance of the commonly applied SVM classifier in dementia classification. This resulted in competitive classification accuracies in the International Challenge for Automated Prediction of MCI from MRI data. Copyright © 2018 Elsevier B.V. All rights reserved.

  2. TU-H-CAMPUS-JeP2-03: Machine-Learning-Based Delineation Framework of GTV Regions of Solid and Ground Glass Opacity Lung Tumors at Datasets of Planning CT and PET/CT Images

    Energy Technology Data Exchange (ETDEWEB)

    Ikushima, K; Arimura, H; Jin, Z; Yabuuchi, H; Sasaki, T; Honda, H; Sasaki, M [Kyushu University, Fukuoka, Fukuoka (Japan); Kuwazuru, J [Saiseikai Fukuoka General Hospital, Fukuoka, Fukuoka (Japan); Shioyama, Y [Saga Heavy Ion Medical Accelerator in Tosu, Tosu, Saga (Japan)

    2016-06-15

    Purpose: In radiation treatment planning, delineation of gross tumor volume (GTV) is very important, because the GTVs affect the accuracies of radiation therapy procedure. To assist radiation oncologists in the delineation of GTV regions while treatment planning for lung cancer, we have proposed a machine-learning-based delineation framework of GTV regions of solid and ground glass opacity (GGO) lung tumors following by optimum contour selection (OCS) method. Methods: Our basic idea was to feed voxel-based image features around GTV contours determined by radiation oncologists into a machine learning classifier in the training step, after which the classifier produced the degree of GTV for each voxel in the testing step. Ten data sets of planning CT and PET/CT images were selected for this study. The support vector machine (SVM), which learned voxel-based features which include voxel value and magnitudes of image gradient vector that obtained from each voxel in the planning CT and PET/CT images, extracted initial GTV regions. The final GTV regions were determined using the OCS method that was able to select a global optimum object contour based on multiple active delineations with a level set method around the GTV. To evaluate the results of proposed framework for ten cases (solid:6, GGO:4), we used the three-dimensional Dice similarity coefficient (DSC), which denoted the degree of region similarity between the GTVs delineated by radiation oncologists and the proposed framework. Results: The proposed method achieved an average three-dimensional DSC of 0.81 for ten lung cancer patients, while a standardized uptake value-based method segmented GTV regions with the DSC of 0.43. The average DSCs for solid and GGO were 0.84 and 0.76, respectively, obtained by the proposed framework. Conclusion: The proposed framework with the support vector machine may be useful for assisting radiation oncologists in delineating solid and GGO lung tumors.

  3. 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.

  4. System state estimation and optimal energy control framework for multicell lithium-ion battery system

    International Nuclear Information System (INIS)

    Wei, Jingwen; Dong, Guangzhong; Chen, Zonghai; Kang, Yu

    2017-01-01

    Highlights: • Employed a dual-scale EKF based estimator for in-pack cells’ SOC values. • Proposed a two-stage hybrid state-feedback and output-feedback equalization algorithm. • A switchable balance current mode is designed in the equalization topology. • Verified the performance of proposed method under two conditions. - Abstract: Cell variations caused by the inevitable inconsistency during manufacture and use of battery cells have significant impacts on battery capacity, security and durability for battery energy storage systems. Thus, the battery equalization systems are essentially required to reduce variations of in-pack cells and increase battery pack capability. In order to protect all in-pack cells from damaging, estimate battery state and reduce variations, a system state estimation and energy optimal control framework for multicell lithium-ion battery system is proposed. The state-of-charge (SOC) values of all in-pack cells are firstly estimated using a dual-scale extended Kalman filtering (EKF) to improve estimation accuracy and reduce computation simultaneously. These estimated SOC values provide specific details of battery system, which cannot only be used to protect cells from over-charging/over-discharging, but also be employed to design state-feedback controller for battery equalization system. A two-stage hybrid state-feedback and output-feedback equalization algorithm is proposed. The state-feedback controller is firstly employed for coarse-grained adjustment to reduce equalization time cost with large current. However, due to the inevitable SOC estimation errors, the output-feedback controller is then used for fine-grained adjustment with trickle current. Experimental results show that the proposed framework can provide an effectively estimation and energy control for multicell battery systems. Finally, the implementation of the proposed method is further discussed for the real applications.

  5. The Nature Index: A General Framework for Synthesizing Knowledge on the State of Biodiversity

    Science.gov (United States)

    Certain, Grégoire; Skarpaas, Olav; Bjerke, Jarle-Werner; Framstad, Erik; Lindholm, Markus; Nilsen, Jan-Erik; Norderhaug, Ann; Oug, Eivind; Pedersen, Hans-Christian; Schartau, Ann-Kristin; van der Meeren, Gro I.; Aslaksen, Iulie; Engen, Steinar; Garnåsjordet, Per-Arild; Kvaløy, Pål; Lillegård, Magnar; Yoccoz, Nigel G.; Nybø, Signe

    2011-01-01

    The magnitude and urgency of the biodiversity crisis is widely recognized within scientific and political organizations. However, a lack of integrated measures for biodiversity has greatly constrained the national and international response to the biodiversity crisis. Thus, integrated biodiversity indexes will greatly facilitate information transfer from science toward other areas of human society. The Nature Index framework samples scientific information on biodiversity from a variety of sources, synthesizes this information, and then transmits it in a simplified form to environmental managers, policymakers, and the public. The Nature Index optimizes information use by incorporating expert judgment, monitoring-based estimates, and model-based estimates. The index relies on a network of scientific experts, each of whom is responsible for one or more biodiversity indicators. The resulting set of indicators is supposed to represent the best available knowledge on the state of biodiversity and ecosystems in any given area. The value of each indicator is scaled relative to a reference state, i.e., a predicted value assessed by each expert for a hypothetical undisturbed or sustainably managed ecosystem. Scaled indicator values can be aggregated or disaggregated over different axes representing spatiotemporal dimensions or thematic groups. A range of scaling models can be applied to allow for different ways of interpreting the reference states, e.g., optimal situations or minimum sustainable levels. Statistical testing for differences in space or time can be implemented using Monte-Carlo simulations. This study presents the Nature Index framework and details its implementation in Norway. The results suggest that the framework is a functional, efficient, and pragmatic approach for gathering and synthesizing scientific knowledge on the state of biodiversity in any marine or terrestrial ecosystem and has general applicability worldwide. PMID:21526118

  6. 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...

  7. A unified theoretical framework for mapping models for the multi-state Hamiltonian.

    Science.gov (United States)

    Liu, Jian

    2016-11-28

    We propose a new unified theoretical framework to construct equivalent representations of the multi-state Hamiltonian operator and present several approaches for the mapping onto the Cartesian phase space. After mapping an F-dimensional Hamiltonian onto an F+1 dimensional space, creation and annihilation operators are defined such that the F+1 dimensional space is complete for any combined excitation. Commutation and anti-commutation relations are then naturally derived, which show that the underlying degrees of freedom are neither bosons nor fermions. This sets the scene for developing equivalent expressions of the Hamiltonian operator in quantum mechanics and their classical/semiclassical counterparts. Six mapping models are presented as examples. The framework also offers a novel way to derive such as the well-known Meyer-Miller model.

  8. 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.)

  9. 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.)

  10. Fall prevention intervention technologies: A conceptual framework and survey of the state of the art.

    Science.gov (United States)

    Hamm, Julian; Money, Arthur G; Atwal, Anita; Paraskevopoulos, Ioannis

    2016-02-01

    In recent years, an ever increasing range of technology-based applications have been developed with the goal of assisting in the delivery of more effective and efficient fall prevention interventions. Whilst there have been a number of studies that have surveyed technologies for a particular sub-domain of fall prevention, there is no existing research which surveys the full spectrum of falls prevention interventions and characterises the range of technologies that have augmented this landscape. This study presents a conceptual framework and survey of the state of the art of technology-based fall prevention systems which is derived from a systematic template analysis of studies presented in contemporary research literature. The framework proposes four broad categories of fall prevention intervention system: Pre-fall prevention; Post-fall prevention; Fall injury prevention; Cross-fall prevention. Other categories include, Application type, Technology deployment platform, Information sources, Deployment environment, User interface type, and Collaborative function. After presenting the conceptual framework, a detailed survey of the state of the art is presented as a function of the proposed framework. A number of research challenges emerge as a result of surveying the research literature, which include a need for: new systems that focus on overcoming extrinsic falls risk factors; systems that support the environmental risk assessment process; systems that enable patients and practitioners to develop more collaborative relationships and engage in shared decision making during falls risk assessment and prevention activities. In response to these challenges, recommendations and future research directions are proposed to overcome each respective challenge. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

  11. Health state evaluation of an item: A general framework and graphical representation

    International Nuclear Information System (INIS)

    Jiang, R.; Jardine, A.K.S.

    2008-01-01

    This paper presents a general theoretical framework to evaluate the health state of an item based on condition monitoring information. The item's health state is defined in terms of its relative health level and overall health level. The former is evaluated based on the relative magnitude of the composite covariate and the latter is evaluated using a fractile life of the residual life distribution at the decision instant. In addition, a method is developed to graphically represent the degradation model, failure threshold model, and the observation history of the composite covariate. As a result, the health state of the monitored item can be intuitively presented and the evaluated result can be subsequently used in a condition-based maintenance optimization decision model, which is amenable to computer modeling. A numerical example is included to illustrate the proposed approach and its appropriateness

  12. Developing a software for tracking the memory states of the machines in the LHCb Filter Farm

    CERN Document Server

    Jain, Harshit

    2017-01-01

    The LHCb Event Filter Farm consists of more than 1500 server nodes with a total amount of roughly 65 TB operating memory .The memory is crucial for the success of the LHCb experiment, since the proton-proton collisions are temporarily stored on these memory modules. Unfortunately, the aging nodes of the server farm occasionally suffer losses of their memory modules. The lower the available memory, the lower performance we can get out of it. Inducing the users or administrators to pay attention to this matter is inefficient. One needs to upgrade it to an acceptable way. The aim of this project was to develop a software to monitor a set of test machines. The software stores the data of the memory sticks in advance in a database which will be used for future reference. Then it checks the memory sticks at a future time instant to find any failures. In the case of any such losses the software looks up in the database to find out which memory sticks have lost and displays all information of those sticks in a log fi...

  13. Machine Protection and High Energy Density States in Matter for High Energy Hadron Accelerators

    CERN Document Server

    Blanco Sancho, Juan; Schmidt, R

    The Large Hadron Collider (LHC) is the largest accelerator in the world. It is designed to collide two proton beams with unprecedented particle energy of 7TeV. The energy stored in each beam is 362MJ, sufficient to melt 500kg of copper. An accidental release of even a small fraction of the beam energy can result in severe damage to the equipment. Machine protection systems are essential to safely operate the accelerator and handle all possible accidents. This thesis deals with the study of different failure scenarios and its possible consequences. It addresses failure scenarios ranging from low intensity losses on high-Z materials and superconductors to high intensity losses on carbon and copper collimators. Low beam losses are sufficient to quench the superconducting magnets and the stabilized superconducting cables (bus-bars) that connects the main magnets. If this occurs and the energy from the bus-bar is not extracted fast enough it can lead to a situation similar to the accident in 2008 at LHC during pow...

  14. State of the art and future challenges for Machine Protection Systems

    CERN Document Server

    Wenninger, J

    2014-01-01

    Current frontier accelerators explore regimes of increasing power and stored energy, with beam energies spanning more than three orders of magnitude from the GeV to theTeV scale. In many cases the high beam power has to cohabit with superconducting equipment in the form of magnets or RF cavities requiring careful control of losses and of halos to mitigate quenches. Despite their large diversity in physics goals and operation modes, all facilities depend on their Machine Protection Systems (MPS) for safe and efficient running. This presentation will aim to give an overview of current MPS and on how the MPS act on or control the beams. Lessons from the LHC and other accelerators show that ever tighter monitoring of accelerator equipment and of beam parameters is required in the future. Such new monitoring systems must not only be very accurate but also be extremely reliable to minimize false alarms. Novel MPS ideas and concepts for linear colliders, high intensity hadron accelerators and to other high power acc...

  15. A state-space Bayesian framework for estimating biogeochemical transformations using time-lapse geophysical data

    Energy Technology Data Exchange (ETDEWEB)

    Chen, J.; Hubbard, S.; Williams, K.; Pride, S.; Li, L.; Steefel, C.; Slater, L.

    2009-04-15

    We develop a state-space Bayesian framework to combine time-lapse geophysical data with other types of information for quantitative estimation of biogeochemical parameters during bioremediation. We consider characteristics of end-products of biogeochemical transformations as state vectors, which evolve under constraints of local environments through evolution equations, and consider time-lapse geophysical data as available observations, which could be linked to the state vectors through petrophysical models. We estimate the state vectors and their associated unknown parameters over time using Markov chain Monte Carlo sampling methods. To demonstrate the use of the state-space approach, we apply it to complex resistivity data collected during laboratory column biostimulation experiments that were poised to precipitate iron and zinc sulfides during sulfate reduction. We develop a petrophysical model based on sphere-shaped cells to link the sulfide precipitate properties to the time-lapse geophysical attributes and estimate volume fraction of the sulfide precipitates, fraction of the dispersed, sulfide-encrusted cells, mean radius of the aggregated clusters, and permeability over the course of the experiments. Results of the case study suggest that the developed state-space approach permits the use of geophysical datasets for providing quantitative estimates of end-product characteristics and hydrological feedbacks associated with biogeochemical transformations. Although tested here on laboratory column experiment datasets, the developed framework provides the foundation needed for quantitative field-scale estimation of biogeochemical parameters over space and time using direct, but often sparse wellbore data with indirect, but more spatially extensive geophysical datasets.

  16. 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.

  17. 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.

  18. Nonexistence of a universal quantum machine to examine the precision of unknown quantum states

    International Nuclear Information System (INIS)

    Pang, Shengshi; Wu, Shengjun; Chen, Zeng-Bing

    2011-01-01

    In this work, we reveal a type of impossibility discovered in our recent research which forbids comparing the closeness of multiple unknown quantum states with any nontrivial threshold in a perfect or unambiguous way. This impossibility is distinct from the existing impossibilities in that it is a ''collective'' impossibility on multiple quantum states; most other ''no-go'' theorems are concerned with only one single state each time, i.e., it is an impossibility on a nonlocal quantum operation. This impossibility may provide new insight into the nature of quantum mechanics, and it implies more limitations on quantum information tasks than the existing no-go theorems.

  19. A robust state-space kinetics-guided framework for dynamic PET image reconstruction

    International Nuclear Information System (INIS)

    Tong, S; Alessio, A M; Kinahan, P E; Liu, H; Shi, P

    2011-01-01

    Dynamic PET image reconstruction is a challenging issue due to the low SNR and the large quantity of spatio-temporal data. We propose a robust state-space image reconstruction (SSIR) framework for activity reconstruction in dynamic PET. Unlike statistically-based frame-by-frame methods, tracer kinetic modeling is incorporated to provide physiological guidance for the reconstruction, harnessing the temporal information of the dynamic data. Dynamic reconstruction is formulated in a state-space representation, where a compartmental model describes the kinetic processes in a continuous-time system equation, and the imaging data are expressed in a discrete measurement equation. Tracer activity concentrations are treated as the state variables, and are estimated from the dynamic data. Sampled-data H ∞ filtering is adopted for robust estimation. H ∞ filtering makes no assumptions on the system and measurement statistics, and guarantees bounded estimation error for finite-energy disturbances, leading to robust performance for dynamic data with low SNR and/or errors. This alternative reconstruction approach could help us to deal with unpredictable situations in imaging (e.g. data corruption from failed detector blocks) or inaccurate noise models. Experiments on synthetic phantom and patient PET data are performed to demonstrate feasibility of the SSIR framework, and to explore its potential advantages over frame-by-frame statistical reconstruction approaches.

  20. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Science.gov (United States)

    Song, Jiangning; Li, Fuyi; Takemoto, Kazuhiro; Haffari, Gholamreza; Akutsu, Tatsuya; Chou, Kuo-Chen; Webb, Geoffrey I

    2018-04-14

    Determining the catalytic residues in an enzyme is critical to our understanding the relationship between protein sequence, structure, function, and enhancing our ability to design novel enzymes and their inhibitors. Although many enzymes have been sequenced, and their primary and tertiary structures determined, experimental methods for enzyme functional characterization lag behind. Because experimental methods used for identifying catalytic residues are resource- and labor-intensive, computational approaches have considerable value and are highly desirable for their ability to complement experimental studies in identifying catalytic residues and helping to bridge the sequence-structure-function gap. In this study, we describe a new computational method called PREvaIL for predicting enzyme catalytic residues. This method was developed by leveraging a comprehensive set of informative features extracted from multiple levels, including sequence, structure, and residue-contact network, in a random forest machine-learning framework. Extensive benchmarking experiments on eight different datasets based on 10-fold cross-validation and independent tests, as well as side-by-side performance comparisons with seven modern sequence- and structure-based methods, showed that PREvaIL achieved competitive predictive performance, with an area under the receiver operating characteristic curve and area under the precision-recall curve ranging from 0.896 to 0.973 and from 0.294 to 0.523, respectively. We demonstrated that this method was able to capture useful signals arising from different levels, leveraging such differential but useful types of features and allowing us to significantly improve the performance of catalytic residue prediction. We believe that this new method can be utilized as a valuable tool for both understanding the complex sequence-structure-function relationships of proteins and facilitating the characterization of novel enzymes lacking functional annotations

  1. A framework model for water-sharing among co-basin states of a river basin

    Science.gov (United States)

    Garg, N. K.; Azad, Shambhu

    2018-05-01

    A new framework model is presented in this study for sharing of water in a river basin using certain governing variables, in an effort to enhance the objectivity for a reasonable and equitable allocation of water among co-basin states. The governing variables were normalised to reduce the governing variables of different co-basin states of a river basin on same scale. In the absence of objective methods for evaluating the weights to be assigned to co-basin states for water allocation, a framework was conceptualised and formulated to determine the normalised weighting factors of different co-basin states as a function of the governing variables. The water allocation to any co-basin state had been assumed to be proportional to its struggle for equity, which in turn was assumed to be a function of the normalised discontent, satisfaction, and weighting factors of each co-basin state. System dynamics was used effectively to represent and solve the proposed model formulation. The proposed model was successfully applied to the Vamsadhara river basin located in the South-Eastern part of India, and a sensitivity analysis of the proposed model parameters was carried out to prove its robustness in terms of the proposed model convergence and validity over the broad spectrum values of the proposed model parameters. The solution converged quickly to a final allocation of 1444 million cubic metre (MCM) in the case of the Odisha co-basin state, and to 1067 MCM for the Andhra Pradesh co-basin state. The sensitivity analysis showed that the proposed model's allocation varied from 1584 MCM to 1336 MCM for Odisha state and from 927 to 1175 MCM for Andhra, depending upon the importance weights given to the governing variables for the calculation of the weighting factors. Thus, the proposed model was found to be very flexible to explore various policy options to arrive at a decision in a water sharing problem. It can therefore be effectively applied to any trans-boundary problem where

  2. Soft Sensing of Key State Variables in Fermentation Process Based on Relevance Vector Machine with Hybrid Kernel Function

    Directory of Open Access Journals (Sweden)

    Xianglin ZHU

    2014-06-01

    Full Text Available To resolve the online detection difficulty of some important state variables in fermentation process with traditional instruments, a soft sensing modeling method based on relevance vector machine (RVM with a hybrid kernel function is presented. Based on the characteristic analysis of two commonly-used kernel functions, that is, local Gaussian kernel function and global polynomial kernel function, a hybrid kernel function combing merits of Gaussian kernel function and polynomial kernel function is constructed. To design optimal parameters of this kernel function, the particle swarm optimization (PSO algorithm is applied. The proposed modeling method is used to predict the value of cell concentration in the Lysine fermentation process. Simulation results show that the presented hybrid-kernel RVM model has a better accuracy and performance than the single kernel RVM model.

  3. From Physiological data to Emotional States: Conducting a User Study and Comparing Machine Learning Classifiers

    Directory of Open Access Journals (Sweden)

    Ali Mehmood KHAN

    2016-06-01

    Full Text Available Recognizing emotional states is becoming a major part of a user's context for wearable computing applications. The system should be able to acquire a user's emotional states by using physiological sensors. We want to develop a personal emotional states recognition system that is practical, reliable, and can be used for health-care related applications. We propose to use the eHealth platform 1 which is a ready-made, light weight, small and easy to use device for recognizing a few emotional states like ‘Sad’, ‘Dislike’, ‘Joy’, ‘Stress’, ‘Normal’, ‘No-Idea’, ‘Positive’ and ‘Negative’ using decision tree (J48 and k-Nearest Neighbors (IBK classifiers. In this paper, we present an approach to build a system that exhibits this property and provides evidence based on data for 8 different emotional states collected from 24 different subjects. Our results indicate that the system has an accuracy rate of approximately 98 %. In our work, we used four physiological sensors i.e. ‘Blood Volume Pulse’ (BVP, ‘Electromyogram’ (EMG, ‘Galvanic Skin Response’ (GSR, and ‘Skin Temperature’ in order to recognize emotional states (i.e. Stress, Joy/Happy, Sad, Normal/Neutral, Dislike, No-idea, Positive and Negative.

  4. Agreed framework of 21 October 1994 between the United States of America and the Democratic People's Republic of Korea

    International Nuclear Information System (INIS)

    1994-01-01

    The attached text of the Agreed Framework between the United States of America and the Democratic People's Republic of Korea, signed in Geneva on 21 October 1994, is being circulated to all Member States of the Agency at the request of the Resident Representative of the United States of America

  5. Synthesis of state observer and nonlinear output feedback controller design of AC machines

    International Nuclear Information System (INIS)

    Al-Tahir, Ali Abdul Razzaq

    2016-01-01

    The research work developed in this thesis has been mainly devoted to the observation and sensor-less control problems of electrical systems. Three major contributions have been carried out using the high - gain concept and output feedback adaptive nonlinear control for online UPS. In this thesis, we dealt with synthesis of sampled high - gain observers for nonlinear systems application to PMSMs and DFIGs. We particularly focus on two constraints: sampling effect and tracking unmeasured mechanical and magnetic state variables. The first contribution consists in a high gain observer design that performs a relatively accurate estimation of both mechanical and magnetic state variable using the available measurements on stator currents and voltages of PMSM. We propose a global exponential observer having state predictor for a class of nonlinear globally Lipschitz system. In second contribution, we proposed a novel non - standard HGO design for non-injective feedback relation application to variable speed DFIG based WPGS. Meanwhile, a reduced system model is analyzed, provided by observability test to check is it possible synthesis state observer for sensor-less control. In last contribution, an adaptive observer for states and parameters estimation are designed for a class of state - affine systems application to output feedback adaptive nonlinear control of three-phase AC/DC boost power converter for online UPS systems. Basically, the problem focused on cascade nonlinear adaptive controller that is developed making use Lyapunov theory. The parameters uncertainties are processed by the practical control laws under back-stepping design techniques with capacity of adaptation. (author)

  6. 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.

  7. Implications of the dependence of neuronal activity on neural network states for the design of brain-machine interfaces

    Directory of Open Access Journals (Sweden)

    Stefano ePanzeri

    2016-04-01

    Full Text Available Brain-machine interfaces (BMIs can improve the quality of life of patients with sensory and motor disabilities by both decoding motor intentions expressed by neural activity, and by encoding artificially sensed information into patterns of neural activity elicited by causal interventions on the neural tissue. Yet, current BMIs can exchange relatively small amounts of information with the brain. This problem has proved difficult to overcome by simply increasing the number of recording or stimulating electrodes, because trial-to-trial variability of neural activity partly arises from intrinsic factors (collectively known as the network state that include ongoing spontaneous activity and neuromodulation, and so is shared among neurons. Here we review recent progress in characterizing the state dependence of neural responses, and in particular of how neural responses depend on endogenous slow fluctuations of network excitability. We then elaborate on how this knowledge may be used to increase the amount of information that BMIs exchange with brains. Knowledge of network state can be used to fine-tune the stimulation pattern that should reliably elicit a target neural response used to encode information in the brain, and to discount part of the trial-by-trial variability of neural responses, so that they can be decoded more accurately.

  8. A Markovian state-space framework for integrating flexibility into space system design decisions

    Science.gov (United States)

    Lafleur, Jarret M.

    The past decades have seen the state of the art in aerospace system design progress from a scope of simple optimization to one including robustness, with the objective of permitting a single system to perform well even in off-nominal future environments. Integrating flexibility, or the capability to easily modify a system after it has been fielded in response to changing environments, into system design represents a further step forward. One challenge in accomplishing this rests in that the decision-maker must consider not only the present system design decision, but also sequential future design and operation decisions. Despite extensive interest in the topic, the state of the art in designing flexibility into aerospace systems, and particularly space systems, tends to be limited to analyses that are qualitative, deterministic, single-objective, and/or limited to consider a single future time period. To address these gaps, this thesis develops a stochastic, multi-objective, and multi-period framework for integrating flexibility into space system design decisions. Central to the framework are five steps. First, system configuration options are identified and costs of switching from one configuration to another are compiled into a cost transition matrix. Second, probabilities that demand on the system will transition from one mission to another are compiled into a mission demand Markov chain. Third, one performance matrix for each design objective is populated to describe how well the identified system configurations perform in each of the identified mission demand environments. The fourth step employs multi-period decision analysis techniques, including Markov decision processes from the field of operations research, to find efficient paths and policies a decision-maker may follow. The final step examines the implications of these paths and policies for the primary goal of informing initial system selection. Overall, this thesis unifies state-centric concepts of

  9. A framework with nonlinear system model and nonparametric noise for gas turbine degradation state estimation

    International Nuclear Information System (INIS)

    Hanachi, Houman; Liu, Jie; Banerjee, Avisekh; Chen, Ying

    2015-01-01

    Modern health management approaches for gas turbine engines (GTEs) aim to precisely estimate the health state of the GTE components to optimize maintenance decisions with respect to both economy and safety. In this research, we propose an advanced framework to identify the most likely degradation state of the turbine section in a GTE for prognostics and health management (PHM) applications. A novel nonlinear thermodynamic model is used to predict the performance parameters of the GTE given the measurements. The ratio between real efficiency of the GTE and simulated efficiency in the newly installed condition is defined as the health indicator and provided at each sequence. The symptom of nonrecoverable degradations in the turbine section, i.e. loss of turbine efficiency, is assumed to be the internal degradation state. A regularized auxiliary particle filter (RAPF) is developed to sequentially estimate the internal degradation state in nonuniform time sequences upon receiving sets of new measurements. The effectiveness of the technique is examined using the operating data over an entire time-between-overhaul cycle of a simple-cycle industrial GTE. The results clearly show the trend of degradation in the turbine section and the occasional fluctuations, which are well supported by the service history of the GTE. The research also suggests the efficacy of the proposed technique to monitor the health state of the turbine section of a GTE by implementing model-based PHM without the need for additional instrumentation. (paper)

  10. Deconstructing Civil Society Actors and Functions: On the Limitations of International Frameworks for Fragile States

    Directory of Open Access Journals (Sweden)

    Simone Datzberger

    2018-02-01

    Full Text Available Over the past three decades, there has been a steady increase of funds by the international community to support civil society organizations (CSOs in fragile states. Surprisingly, this growing attention has not strengthened local civil society landscapes in a way that it would lead to processes of social transformation. On the contrary, civic freedom and space is shrinking around the globe. In analyzing prominent international aid-effectiveness frameworks and donor strategies towards civil society, this paper will put forward one central argument. The way in which civil society actors and functions are currently appropriated threatens deep-rooted social transformation thereby impeding processes of structural and political change—necessary for the transition from conflict to sustainable peace. In delineating, how actors and functional approaches informed peacebuilding and development policy and practice, their strengths and limitations will be examined. Doing so, we draw on different case studies and examples from the literature. We find that existing frameworks for fragile states operate on a presumed model of a public sphere and civil society that may or may not exist. Such an approach disregards an organic formation of a civil society landscape thereby impeding processes of structural, social, and political change in times of fragility.

  11. 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.

  12. Generation of multipartite entangled states for chains of atoms in the framework of cavity-QED

    Energy Technology Data Exchange (ETDEWEB)

    Gonta, Denis

    2010-07-07

    Cavity quantum electrodynamics is a research field that studies electromagnetic fields in confined spaces and the radiative properties of atoms in such fields. Experimentally, the simplest example of such system is a single atom interacting with modes of a high-finesse resonator. Theoretically, such system bears an excellent framework for quantum information processing in which atoms and light are interpreted as bits of quantum information and their mutual interaction provides a controllable entanglement mechanism. In this thesis, we present several practical schemes for generation of multipartite entangled states for chains of atoms which pass through one or more high-finesse resonators. In the first step, we propose two schemes for generation of one- and two-dimensional cluster states of arbitrary size. These schemes are based on the resonant interaction of a chain of Rydberg atoms with one or more microwave cavities. In the second step, we propose a scheme for generation of multipartite W states. This scheme is based on the off-resonant interaction of a chain of three-level atoms with an optical cavity and a laser beam. We describe in details all the individual steps which are required to realize the proposed schemes and, moreover, we discuss several techniques to reveal the non-classical correlations associated with generated small-sized entangled states. (orig.)

  13. Generation of multipartite entangled states for chains of atoms in the framework of cavity-QED

    International Nuclear Information System (INIS)

    Gonta, Denis

    2010-01-01

    Cavity quantum electrodynamics is a research field that studies electromagnetic fields in confined spaces and the radiative properties of atoms in such fields. Experimentally, the simplest example of such system is a single atom interacting with modes of a high-finesse resonator. Theoretically, such system bears an excellent framework for quantum information processing in which atoms and light are interpreted as bits of quantum information and their mutual interaction provides a controllable entanglement mechanism. In this thesis, we present several practical schemes for generation of multipartite entangled states for chains of atoms which pass through one or more high-finesse resonators. In the first step, we propose two schemes for generation of one- and two-dimensional cluster states of arbitrary size. These schemes are based on the resonant interaction of a chain of Rydberg atoms with one or more microwave cavities. In the second step, we propose a scheme for generation of multipartite W states. This scheme is based on the off-resonant interaction of a chain of three-level atoms with an optical cavity and a laser beam. We describe in details all the individual steps which are required to realize the proposed schemes and, moreover, we discuss several techniques to reveal the non-classical correlations associated with generated small-sized entangled states. (orig.)

  14. Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems

    Science.gov (United States)

    Hearn, Tristan A.

    2015-01-01

    This document is intended as an introduction to a set of common signal processing learning methods that may be used in the software portion of a functional crew state monitoring system. This includes overviews of both the theory of the methods involved, as well as examples of implementation. Practical considerations are discussed for implementing modular, flexible, and scalable processing and classification software for a multi-modal, multi-channel monitoring system. Example source code is also given for all of the discussed processing and classification methods.

  15. Machine Shop Grinding Machines.

    Science.gov (United States)

    Dunn, James

    This curriculum manual is one in a series of machine shop curriculum manuals intended for use in full-time secondary and postsecondary classes, as well as part-time adult classes. The curriculum can also be adapted to open-entry, open-exit programs. Its purpose is to equip students with basic knowledge and skills that will enable them to enter the…

  16. 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

  17. State-and-transition simulation models: a framework for forecasting landscape change

    Science.gov (United States)

    Daniel, Colin; Frid, Leonardo; Sleeter, Benjamin M.; Fortin, Marie-Josée

    2016-01-01

    SummaryA wide range of spatially explicit simulation models have been developed to forecast landscape dynamics, including models for projecting changes in both vegetation and land use. While these models have generally been developed as separate applications, each with a separate purpose and audience, they share many common features.We present a general framework, called a state-and-transition simulation model (STSM), which captures a number of these common features, accompanied by a software product, called ST-Sim, to build and run such models. The STSM method divides a landscape into a set of discrete spatial units and simulates the discrete state of each cell forward as a discrete-time-inhomogeneous stochastic process. The method differs from a spatially interacting Markov chain in several important ways, including the ability to add discrete counters such as age and time-since-transition as state variables, to specify one-step transition rates as either probabilities or target areas, and to represent multiple types of transitions between pairs of states.We demonstrate the STSM method using a model of land-use/land-cover (LULC) change for the state of Hawai'i, USA. Processes represented in this example include expansion/contraction of agricultural lands, urbanization, wildfire, shrub encroachment into grassland and harvest of tree plantations; the model also projects shifts in moisture zones due to climate change. Key model output includes projections of the future spatial and temporal distribution of LULC classes and moisture zones across the landscape over the next 50 years.State-and-transition simulation models can be applied to a wide range of landscapes, including questions of both land-use change and vegetation dynamics. Because the method is inherently stochastic, it is well suited for characterizing uncertainty in model projections. When combined with the ST-Sim software, STSMs offer a simple yet powerful means for developing a wide range of models of

  18. 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.

  19. 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.

  20. 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

  1. 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

  2. Implementation of a state-to-state analytical framework for the calculation of expansion tube flow properties

    Science.gov (United States)

    James, C. M.; Gildfind, D. E.; Lewis, S. W.; Morgan, R. G.; Zander, F.

    2018-03-01

    Expansion tubes are an important type of test facility for the study of planetary entry flow-fields, being the only type of impulse facility capable of simulating the aerothermodynamics of superorbital planetary entry conditions from 10 to 20 km/s. However, the complex flow processes involved in expansion tube operation make it difficult to fully characterise flow conditions, with two-dimensional full facility computational fluid dynamics simulations often requiring tens or hundreds of thousands of computational hours to complete. In an attempt to simplify this problem and provide a rapid flow condition prediction tool, this paper presents a validated and comprehensive analytical framework for the simulation of an expansion tube facility. It identifies central flow processes and models them from state to state through the facility using established compressible and isentropic flow relations, and equilibrium and frozen chemistry. How the model simulates each section of an expansion tube is discussed, as well as how the model can be used to simulate situations where flow conditions diverge from ideal theory. The model is then validated against experimental data from the X2 expansion tube at the University of Queensland.

  3. Evaluation of quality assurance of some diagnostic x-ray machines in Khartoum state

    International Nuclear Information System (INIS)

    Mohammed, Sirelkatim Khogali

    2013-04-01

    Availability and the use of x-ray equipment in both private and government hospitals are on the increase today in Khartoum state. Quality control of such equipment is of particular importance to prevent avoidable high doses, radiation leakages and to ensure dose optimization. The results of quality control in this study indicated that: all centers were within the k Vp reproducibility level (5%). At k Vp 50 and 60 there were 93% of centers within the limit and 7% were out. For 70, 81 and 90 k Vp all centers were within the limit. 73% of centers were within the level of HVL test, but 27% of them were out of the limit 80% of centers had a linear relationship between m As and dose, but three centers had no linear relationship. For time reproducibility 80% of centers were within the time reproducibility and 13% were out of limit. The beam on control and indicator were available and functional for all centers. The warning light was present in one center. But 93% of centers, but 20% of centers had no window lead glass. Lead aprons were available and functional in all centers. The gloves were available and functional in 33% of centers. But in 67% of centers they were not present. Gonads shields were present in 33% of centers, but not available for 67% of centers.(Author)

  4. Comparison between state & private bank sectors using the COBIT4.1 framework

    Directory of Open Access Journals (Sweden)

    mahdi Ghazanfari

    2011-03-01

    Full Text Available In current dynamic and often turbulent work environment, information technology becomes an effective competitive advantage that organizations largely rely on it. Due to this affiliation, the importance of integrity and accommodation between the information technology strategies and business strategies in organizations has been increased. This alliance is the initial aim of the governance of information technology. The purpose of this study is evaluating and comparing the maturity of information technology governance of organization that are active in financial services sector (state and private banks. In order to measure and compare the maturity of IT governance of Iranian banks in adaption of business strategies, IT governance and COBIT4.1 framework has been used. In this study, data have been collected from 17 large state and private banks. The results suggest that private banks, due to structure type and maturity of IT governance and organizational strategies, have higher degree of maturity (1.98 in applying the technology and compliance with IT compare to state banks (1.60

  5. AutoIHC-scoring: a machine learning framework for automated Allred scoring of molecular expression in ER- and PR-stained breast cancer tissue.

    Science.gov (United States)

    Tewary, S; Arun, I; Ahmed, R; Chatterjee, S; Chakraborty, C

    2017-11-01

    In prognostic evaluation of breast cancer Immunohistochemical (IHC) markers namely, oestrogen receptor (ER) and progesterone receptor (PR) are widely used. The expert pathologist investigates qualitatively the stained tissue slide under microscope to provide the Allred score; which is clinically used for therapeutic decision making. Such qualitative judgment is time-consuming, tedious and more often suffers from interobserver variability. As a result, it leads to imprecise IHC score for ER and PR. To overcome this, there is an urgent need of developing a reliable and efficient IHC quantifier for high throughput decision making. In view of this, our study aims at developing an automated IHC profiler for quantitative assessment of ER and PR molecular expression from stained tissue images. We propose here to use CMYK colour space for positively and negatively stained cell extraction for proportion score. Also colour features are used for quantitative assessment of intensity scoring among the positively stained cells. Five different machine learning models namely artificial neural network, Naïve Bayes, K-nearest neighbours, decision tree and random forest are considered for learning the colour features using average red, green and blue pixel values of positively stained cell patches. Fifty cases of ER- and PR-stained tissues have been evaluated for validation with the expert pathologist's score. All five models perform adequately where random forest shows the best correlation with the expert's score (Pearson's correlation coefficient = 0.9192). In the proposed approach the average variation of diaminobenzidine (DAB) to nuclear area from the expert's score is found to be 7.58%, as compared to 27.83% for state-of-the-art ImmunoRatio software. © 2017 The Authors Journal of Microscopy © 2017 Royal Microscopical Society.

  6. Verifying the agreed framework between the United States and North Korea

    International Nuclear Information System (INIS)

    May, M.M.

    2001-01-01

    Under the 1994 Agreed Framework (AF) between the United States and the Democratic People Republic of Korea (DPRK), the US and its allies will provide two nuclear-power reactors and other benefits to the DPRK in exchange for an agreement by the DPRK to declare how much nuclear-weapon material it has produced; to identify, freeze, and eventually dismantle specified facilities for producing this material; and to remain a party to the nuclear Non- Proliferation Treaty (NPT) and allow the implementation of its safeguards agreement. This study assesses the verifiability of these provisions. The study concludes verification can be accomplished, given cooperation and openness from the DPRK. Special effort will be needed from the IAEA, as well as support from the US and the Republic of Korea. (author)

  7. Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.

    Science.gov (United States)

    Pound, Michael P; Atkinson, Jonathan A; Townsend, Alexandra J; Wilson, Michael H; Griffiths, Marcus; Jackson, Aaron S; Bulat, Adrian; Tzimiropoulos, Georgios; Wells, Darren M; Murchie, Erik H; Pridmore, Tony P; French, Andrew P

    2017-10-01

    In plant phenotyping, it has become important to be able to measure many features on large image sets in order to aid genetic discovery. The size of the datasets, now often captured robotically, often precludes manual inspection, hence the motivation for finding a fully automated approach. Deep learning is an emerging field that promises unparalleled results on many data analysis problems. Building on artificial neural networks, deep approaches have many more hidden layers in the network, and hence have greater discriminative and predictive power. We demonstrate the use of such approaches as part of a plant phenotyping pipeline. We show the success offered by such techniques when applied to the challenging problem of image-based plant phenotyping and demonstrate state-of-the-art results (>97% accuracy) for root and shoot feature identification and localization. We use fully automated trait identification using deep learning to identify quantitative trait loci in root architecture datasets. The majority (12 out of 14) of manually identified quantitative trait loci were also discovered using our automated approach based on deep learning detection to locate plant features. We have shown deep learning-based phenotyping to have very good detection and localization accuracy in validation and testing image sets. We have shown that such features can be used to derive meaningful biological traits, which in turn can be used in quantitative trait loci discovery pipelines. This process can be completely automated. We predict a paradigm shift in image-based phenotyping bought about by such deep learning approaches, given sufficient training sets. © The Authors 2017. Published by Oxford University Press.

  8. Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art.

    Science.gov (United States)

    Walia, Rasna R; Caragea, Cornelia; Lewis, Benjamin A; Towfic, Fadi; Terribilini, Michael; El-Manzalawy, Yasser; Dobbs, Drena; Honavar, Vasant

    2012-05-10

    RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naïve Bayes (NB) and Support Vector Machine (SVM)) using three different data representations in which features are encoded using either sequence- or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix (PSSM

  9. Strategic Prevention Framework State Incentive Grant Progress Report: Building a Sustainable Substance Abuse Prevention System, State of Hawai'i, 2006-2010

    Science.gov (United States)

    Yuan, S.; Lai, M.C.; Heusel, K.

    2011-01-01

    In 2006, the Hawai'i State Department of Health (DOH) received the Strategic Prevention Framework State Incentive Grant (SPF-SIG) from the Substance Abuse and Mental Health Services Administration (SAMHSA) to establish a comprehensive, coordinated, and sustainable substance abuse prevention infrastructure in Hawai'i. The SPF-SIG Project is funded…

  10. 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…

  11. 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

  12. Coupling brain-machine interfaces with cortical stimulation for brain-state dependent stimulation: enhancing motor cortex excitability for neurorehabilitation

    Directory of Open Access Journals (Sweden)

    Alireza eGharabaghi

    2014-03-01

    Full Text Available Motor recovery after stroke is an unsolved challenge despite intensive rehabilitation training programs. Brain stimulation techniques have been explored in addition to traditional rehabilitation training to increase the excitability of the stimulated motor cortex. This modulation of cortical excitability augments the response to afferent input during motor exercises, thereby enhancing skilled motor learning by long-term potentiation-like plasticity. Recent approaches examined brain stimulation applied concurrently with voluntary movements to induce more specific use-dependent neural plasticity during motor training for neurorehabilitation. Unfortunately, such approaches are not applicable for the many severely affected stroke patients lacking residual hand function. These patients require novel activity-dependent stimulation paradigms based on intrinsic brain activity. Here, we report on such brain state-dependent stimulation (BSDS combined with haptic feedback provided by a robotic hand orthosis. Transcranial magnetic stimulation of the motor cortex and haptic feedback to the hand were controlled by sensorimotor desynchronization during motor-imagery and applied within a brain-machine interface environment in one healthy subject and one patient with severe hand paresis in the chronic phase after stroke. BSDS significantly increased the excitability of the stimulated motor cortex in both healthy and post-stroke conditions, an effect not observed in non-BSDS protocols. This feasibility study suggests that closing the loop between intrinsic brain state, cortical stimulation and haptic feedback provides a novel neurorehabilitation strategy for stroke patients lacking residual hand function, a proposal that warrants further investigation in a larger cohort of stroke patients.

  13. An input-to-state stability approach to verify almost global stability of a synchronous-machine-infinite-bus system.

    Science.gov (United States)

    Schiffer, Johannes; Efimov, Denis; Ortega, Romeo; Barabanov, Nikita

    2017-08-13

    Conditions for almost global stability of an operating point of a realistic model of a synchronous generator with constant field current connected to an infinite bus are derived. The analysis is conducted by employing the recently proposed concept of input-to-state stability (ISS)-Leonov functions, which is an extension of the powerful cell structure principle developed by Leonov and Noldus to the ISS framework. Compared with the original ideas of Leonov and Noldus, the ISS-Leonov approach has the advantage of providing additional robustness guarantees. The efficiency of the derived sufficient conditions is illustrated via numerical experiments.This article is part of the themed issue 'Energy management: flexibility, risk and optimization'. © 2017 The Author(s).

  14. Catalytic dehydrogenation of alcohol over solid-state molybdenum sulfide clusters with an octahedral metal framework

    Energy Technology Data Exchange (ETDEWEB)

    Kamiguchi, Satoshi, E-mail: kamigu@riken.jp [Advanced Catalysis Research Group, RIKEN Center for Sustainable Resource Science, 2-1 Hirosawa, Wako City, Saitama 351-0198 (Japan); Organometallic Chemistry Laboratory, RIKEN, 2-1 Hirosawa, Wako City, Saitama 351-0198 (Japan); Okumura, Kazu [School of Advanced Engineering, Kogakuin University, Nakano-machi, Hachioji City, Tokyo 192-0015 (Japan); Nagashima, Sayoko; Chihara, Teiji [Graduate School of Science and Engineering, Saitama University, Shimo-Okubo, Sakura-ku, Saitama City, Saitama 338-8570 (Japan)

    2015-12-15

    Graphical abstract: - Highlights: • Solid-state molybdenum sulfide clusters catalyzed the dehydrogenation of alcohol. • The dehydrogenation proceeded without the addition of any oxidants. • The catalytic activity developed when the cluster was activated at 300–500 °C in H{sub 2}. • The Lewis-acidic molybdenum atom and basic sulfur ligand were catalytically active. • The clusters function as bifunctional acid–base catalysts. - Abstract: Solid-state molybdenum sulfide clusters with an octahedral metal framework, the superconducting Chevrel phases, are applied to catalysis. A copper salt of a nonstoichiometric sulfur-deficient cluster, Cu{sub x}Mo{sub 6}S{sub 8–δ} (x = 2.94 and δ ≈ 0.3), is stored in air for more than 90 days. When the oxygenated cluster is thermally activated in a hydrogen stream above 300 °C, catalytic activity for the dehydrogenation of primary alcohols to aldehydes and secondary alcohols to ketones develops. The addition of pyridine or benzoic acid decreases the dehydrogenation activity, indicating that both a Lewis-acidic coordinatively unsaturated molybdenum atom and a basic sulfur ligand synergistically act as the catalytic active sites.

  15. The Interaction between Interoceptive and Action States within a Framework of Predictive Coding

    Science.gov (United States)

    Marshall, Amanda C.; Gentsch, Antje; Schütz-Bosbach, Simone

    2018-01-01

    The notion of predictive coding assumes that perception is an iterative process between prior knowledge and sensory feedback. To date, this perspective has been primarily applied to exteroceptive perception as well as action and its associated phenomenological experiences such as agency. More recently, this predictive, inferential framework has been theoretically extended to interoception. This idea postulates that subjective feeling states are generated by top–down inferences made about internal and external causes of interoceptive afferents. While the processing of motor signals for action control and the emergence of selfhood have been studied extensively, the contributions of interoceptive input and especially the potential interaction of motor and interoceptive signals remain largely unaddressed. Here, we argue for a specific functional relation between motor and interoceptive awareness. Specifically, we implicate interoceptive predictions in the generation of subjective motor-related feeling states. Furthermore, we propose a distinction between reflexive and pre-reflexive modes of agentic action control and suggest that interoceptive input may affect each differently. Finally, we advocate the necessity of continuous interoceptive input for conscious forms of agentic action control. We conclude by discussing further research contributions that would allow for a fuller understanding of the interaction between agency and interoceptive awareness. PMID:29515495

  16. Existing machine propulsion is transformed by state-of-the-art gearbox apparatus saves at least 50% energy

    Science.gov (United States)

    Abramov, V.

    2013-12-01

    This innovation on www.repowermachine.com is finalist at Clean-tech and Energy of 2012 Minnesota's TEKNE AWARDS. Vehicles are pushed by force of friction between their wheels and land, propellers and water or air according to Third Newton's law of physics of moving. Force of friction is dependent to vehicle weight as highest torque of wheel or propeller for vehicle moving from stop. Friction force DOES NOT dependent to motor power. Why existing SUV of 2,000 lb uses 550 hp motor when first vehicle has 0.75 hp motor (Carl Benz';s patent #37435, January 29, 1886 in Germany)? Gas or magnet field reaches needed torque of wheels too slowly because requires huge motor power for acceleration SUV from 0 to 100 mph for 5 second. The acceleration system by gas or magnet field uses additional energy for increasing motor shaft idle speed and reduces its highest torque of physical volume because necessary to increase motor power that equal/exceed motor power according to vehicle weight. Therefore, any transmission torque DOES NOT NEED and it is use as second brake. Ship, locomotives, helicopters, CNC machine tools, etc motor(s) directly turn wheels, propellers, spindles or ignore to use gear -transmission designs. How do you follow to Creator's physics law of LEVER for saving energy? Existing machine propulsion is transformed by one comprising least numbers of gears and maybe shafts from above state-of-the-art 1,000 gearbox apparatus designs. It is installed or replaced transmission in existing propulsion that is transformed to non-accelerated propulsion. It cuts about 80% mechanical energy that acceleration system wastes in motor heat form, cuts time of movement by reaching each speed for 1-2 seconds. It produces all needed speeds and uses only idle speed of cheapest motor with reduced power and cost that have replaced existing motor too. There is opportunity to eliminate vehicle/machine roads traffics in cities that creates additional unknown GHG emissions Revolutionary

  17. Extracting meaning from audio signals - a machine learning approach

    DEFF Research Database (Denmark)

    Larsen, Jan

    2007-01-01

    * Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression......* Machine learning framework for sound search * Genre classification * Music and audio separation * Wind noise suppression...

  18. 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.

  19. A framework for assessing global change risks to forest carbon stocks in the United States.

    Directory of Open Access Journals (Sweden)

    Christopher W Woodall

    Full Text Available Among terrestrial environments, forests are not only the largest long-term sink of atmospheric carbon (C, but are also susceptible to global change themselves, with potential consequences including alterations of C cycles and potential C emission. To inform global change risk assessment of forest C across large spatial/temporal scales, this study constructed and evaluated a basic risk framework which combined the magnitude of C stocks and their associated probability of stock change in the context of global change across the US. For the purposes of this analysis, forest C was divided into five pools, two live (aboveground and belowground biomass and three dead (dead wood, soil organic matter, and forest floor with a risk framework parameterized using the US's national greenhouse gas inventory and associated forest inventory data across current and projected future Köppen-Geiger climate zones (A1F1 scenario. Results suggest that an initial forest C risk matrix may be constructed to focus attention on short- and long-term risks to forest C stocks (as opposed to implementation in decision making using inventory-based estimates of total stocks and associated estimates of variability (i.e., coefficient of variation among climate zones. The empirical parameterization of such a risk matrix highlighted numerous knowledge gaps: 1 robust measures of the likelihood of forest C stock change under climate change scenarios, 2 projections of forest C stocks given unforeseen socioeconomic conditions (i.e., land-use change, and 3 appropriate social responses to global change events for which there is no contemporary climate/disturbance analog (e.g., severe droughts in the Lake States. Coupling these current technical/social limits of developing a risk matrix to the biological processes of forest ecosystems (i.e., disturbance events and interaction among diverse forest C pools, potential positive feedbacks, and forest resiliency/recovery suggests an operational

  20. Driver drowsiness detection using behavioral measures and machine learning techniques: A review of state-of-art techniques

    CSIR Research Space (South Africa)

    Ngxande, Mkhuseli

    2017-11-01

    Full Text Available This paper presents a literature review of driver drowsiness detection based on behavioral measures using machine learning techniques. Faces contain information that can be used to interpret levels of drowsiness. There are many facial features...

  1. Nuclear Legislation in OECD and NEA Countries. Regulatory and Institutional Framework for Nuclear Activities - United States

    International Nuclear Information System (INIS)

    2015-01-01

    This country profile provide comprehensive information on the regulatory and Institutional Framework governing nuclear activities as well as a detailed review of a full range of nuclear law topics, including: mining regime; radioactive substances; nuclear installations; trade in nuclear materials and equipment; radiation protection; radioactive waste management; non-proliferation and physical protection; transport; and nuclear third party liability. The profile is complemented by reproductions of the primary legislation regulating nuclear activities in the country. Content: I. General Regulatory Regime: 1. Introduction; 2. Mining regime; 3. Radioactive substances, nuclear fuel and equipment (Special nuclear material; Source material; By-product material; Agreement state programmes); 4. Nuclear installations (Initial licensing; Operation and inspection, including nuclear safety; Operating licence renewal; Decommissioning; Emergency response); 5. Radiological protection (Protection of workers; Protection of the public); 6. Radioactive waste management (High-level waste; Low-level waste; Disposal at sea; Uranium mill tailings; Formerly Utilized Sites Remedial Action Program - FUSRAP); 7. Non-proliferation and exports (Exports of source material, special nuclear material, production or utilisation facilities and sensitive nuclear technology; Exports of components; Exports of by-product material; Exports and imports of radiation sources; Conduct resulting in the termination of exports or economic assistance; Subsequent arrangements; Technology exports; Information and restricted data); 8. Nuclear security; 9. Transport; 10. Nuclear third party liability; II. Institutional Framework: 1. Regulatory and supervisory authorities (Nuclear Regulatory Commission - NRC; Department of Energy - DOE; Department of Labor - DOL; Department of Transportation - DOT; Environmental Protection Agency - EPA); 2. Public and semi-public agencies: A. Cabinet-level departments (Department of

  2. 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

  3. 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

  4. 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.

  5. Education in the workplace for the physician: clinical management states as an organizing framework.

    Science.gov (United States)

    Greenes, R A

    2000-01-01

    Medical educators are interested in approaches to making selected relevant knowledge available in the context of problem-based care. This is of value both during the process of care and as a means of organizing information for offline self-study. Four trends in health information technology are relevant to achieving the goal and can be expected to play a growing role in the future. First, health care enterprises are developing approaches for access to information resources related to the care of a patient, including clinical data and images but also communication tools, referral and other logistic tools, decision support, and educational materials. Second, information for patients and methods for patient-doctor interaction and decision making are becoming available. Third, computer-based methods for representation of practice guidelines are being developed to support applications that can incorporate their logic. Finally, considering patients as being in particular "clinical management states" (or CMSs) for specific problems, approaches are being developed to use guidelines as a kind of "predictive" framework to enable development of interfaces for problem-based clinical encounters. The guidelines for a CMS can be used to identify the kinds of resources specifically needed for clinical encounters of that type. As the above trends converge to produce problem-specific environments, professional specialty organizations and continuing medical education course designers will need to focus energies on organizing and updating medical knowledge to make it available in CMS-specific contexts.

  6. A framework to practical predictive maintenance modeling for multi-state systems

    International Nuclear Information System (INIS)

    Cher Ming Tan; Raghavan, Nagarajan

    2008-01-01

    A simple practical framework for predictive maintenance (PdM)-based scheduling of multi-state systems (MSS) is developed. The maintenance schedules are derived from a system-perspective using the failure times of the overall system as estimated from its performance degradation trends. The system analyzed in this work is a flow transmission water pipe system. The various factors influencing PdM-based scheduling are identified and their impact on the system reliability and performance are quantitatively studied. The estimated times to replacement of the MSS may also be derived from the developed model. The results of the model simulation demonstrate the significant impact of maintenance quality and the criteria for the call for maintenance (user demand) on the system reliability and mean performance characteristics. A slight improvement in maintenance quality is found to postpone the system replacement time by manifold. The consistency in the quality of maintenance work with minimal variance is also identified as a very important factor that enhances the system's future operational and downtime event predictability. The studies also reveal that in order to reduce the frequency of maintenance actions, it is necessary to lower the minimum user demand from the system if possible, ensuring at the same time that the system still performs its intended function effectively. The model proposed can be utilized to implement a PdM program in the industry with a few modifications to suit the individual industrial systems' needs

  7. Probing Framework-Restricted Metal Axial Ligation and Spin State Patterns in a Post-Synthetically Reduced Iron-Porphyrin-Based Metal–Organic Framework

    Energy Technology Data Exchange (ETDEWEB)

    Kucheryavy, Pavel; Lahanas, Nicole; Velasco, Ever; Sun, Cheng-Jun; Lockard, Jenny V.

    2016-04-07

    An iron porphyrin-based metal organic framework, PCN-222(Fe) is investigated upon post-synthetic reduction with piperidine. Fe K-edge X-ray absorption and Kβ mainline emission spectroscopy measurements reveal the local coor-dination geometry, oxidation and spin state changes experi-enced by the Fe sites upon reaction with this axially coordi-nating reducing agent. Analysis and fitting of these data con-firm the binding pattern predicted by a space filling model of the structurally constrained pore environments. These results are further support by UV-vis diffuse reflectance, IR and Raman spectroscopy data.

  8. Model Wind Turbine Design in a Project-Based Middle School Engineering Curriculum Built on State Frameworks

    Science.gov (United States)

    Cogger, Steven D.; Miley, Daniel H.

    2012-01-01

    This paper proposes that project-based active learning is a key part of engineering education at the middle school level. One project from a comprehensive middle school engineering curriculum developed by the authors is described to show how active learning and state frameworks can coexist. The theoretical basis for learning and assessment in a…

  9. 76 FR 23940 - Fisheries of the Northeastern United States; Atlantic Sea Scallop Fishery; Framework Adjustment 22

    Science.gov (United States)

    2011-04-29

    ... the LA fleet, Amendment 15 proposes a management uncertainty buffer based on the F associated with a... to implement Framework Adjustment 22 (Framework 22) to the Atlantic Sea Scallop Fishery Management Plan (FMP), which was developed and adopted by the New England Fishery Management Council (Council) and...

  10. 76 FR 47533 - Fisheries of the Northeastern United States; Monkfish; Framework Adjustment 7

    Science.gov (United States)

    2011-08-05

    ... for management uncertainty, and is intended to prevent overfishing from occurring in the event... implement measures in Framework Adjustment 7 (Framework 7) to the Monkfish Fishery Management Plan (Monkfish FMP). The New England Fishery Management Council and Mid- Atlantic Fishery Management Council...

  11. An indicator framework for assessing US state carbon emissions reduction efforts (with baseline trends from 1990 to 2001)

    International Nuclear Information System (INIS)

    Jiusto, Scott

    2008-01-01

    States are at the forefront of climate-related energy policy in the US, developing innovative policy and regional institutions for reducing carbon dioxide and other greenhouse gases. States matter because the larger ones use more energy and produce more carbon emissions than most nations and because their policies, though heterogeneous and until recently quite limited in scope, are shaping the context for national climate action. Despite this significance, little is known about trends in state carbon emissions or the effectiveness of state policies in reducing emissions. This paper describes a framework for analyzing and comparing state carbon emissions performance using sectoral indicators of emissions, energy consumption and carbon intensity linked to key policy domains. The paper also describes the range of state experience across indicators during the period 1990-2001, establishing a baseline of leading, lagging and average experience against which future state and regional change can be assessed. The conceptual framework and the empirical analysis of emission trends are intended to provide a better understanding of, and means for monitoring, state contributions toward achieving energy system sustainability

  12. 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.

  13. 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

  14. 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

  15. Dr Mauro Dell’Ambrogio, State Secretary for Education and Research of the Swiss Confederation visit the ATLAS Cavern and the LHC Machine with with Collaboration Spokesperson P. Jenni and Technical Coordinator M. Nessi.

    CERN Multimedia

    Maximilien Brice

    2008-01-01

    Dr Mauro Dell’Ambrogio, State Secretary for Education and Research of the Swiss Confederation visit the ATLAS Cavern and the LHC Machine with with Collaboration Spokesperson P. Jenni and Technical Coordinator M. Nessi.

  16. 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.

  17. The regulatory framework of accounting and accounting standard-setting bodies in the European Union member states

    Directory of Open Access Journals (Sweden)

    Ivana Mamić-Sačer

    2015-12-01

    Full Text Available One of the principal features of accounting in the 21st century is harmonisation and stanardisation. Regulation of the European Parliament and European Council No. 1606/2002 harmonizes financial reporting for certain companies in the EU. However, national accounting principles are of great importance for financial reporting. The main purpose of this research was to investigate the application of generally accepted accounting principles, the regulatory accounting framework and the standard-setting bodies of EU member states. The analysis of these accounting issues was conducted with respect to all 28 EU member states. The results indicate that EU member states regulate their principal accounting issues through separate accounting acts or implement those issues in companies acts. Some EU member states do not have national accounting standards, the national accounting principles being incorporated in companies acts and accounting acts. Nevertheless, national accounting standard-setting bodies are governmental organisations in almost half the member states.

  18. AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning

    Science.gov (United States)

    Ivezic, Zeljko; Connolly, Andrew J.; Vanderplas, Jacob

    2015-01-01

    We present AstroML, a Python module for machine learning and data mining built on numpy, scipy, scikit-learn, matplotlib, and astropy, and distributed under an open license. AstroML contains a growing library of statistical and machine learning routines for analyzing astronomical data in Python, loaders for several open astronomical datasets (such as SDSS and other recent major surveys), and a large suite of examples of analyzing and visualizing astronomical datasets. AstroML is especially suitable for introducing undergraduate students to numerical research projects and for graduate students to rapidly undertake cutting-edge research. The long-term goal of astroML is to provide a community repository for fast Python implementations of common tools and routines used for statistical data analysis in astronomy and astrophysics (see http://www.astroml.org).

  19. 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.

  20. Legal frameworks and key concepts regulating diversion and treatment of mentally disordered offenders in European Union member states.

    Science.gov (United States)

    Dressing, Harald; Salize, Hans Joachim; Gordon, Harvey

    2007-10-01

    There is only limited research on the various legal regulations governing assessment, placement and treatment of mentally ill offenders in European Union member states (EU-member states). To provide a structured description and cross-boundary comparison of legal frameworks regulating diversion and treatment of mentally disordered offenders in EU-member states before the extension in May 2004. A special focus is on the concept of criminal responsibility. Information on legislation and practice concerning the assessment, placement and treatment of mentally ill offenders was gathered by means of a detailed, structured questionnaire which was filled in by national experts. The legal regulations relevant for forensic psychiatry in EU-member states are outlined. Definitions of mental disorders given within these acts are introduced and compared with ICD-10 diagnoses. Finally the application of the concept of criminal responsibility by the law and in routine practice is presented. Legal frameworks for the processing and placement of mentally disordered offenders varied markedly across EU-member states. Since May 2004 the European Union has expanded to 25 member states and in January 2007 it will reach 27. With increasing mobility across Europe, the need for increasing trans-national co-operation is becoming apparent in which great variation in legal tradition pertains.

  1. Face machines

    Energy Technology Data Exchange (ETDEWEB)

    Hindle, D.

    1999-06-01

    The article surveys latest equipment available from the world`s manufacturers of a range of machines for tunnelling. These are grouped under headings: excavators; impact hammers; road headers; and shields and tunnel boring machines. Products of thirty manufacturers are referred to. Addresses and fax numbers of companies are supplied. 5 tabs., 13 photos.

  2. Electric machine

    Science.gov (United States)

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

    2012-07-17

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

  3. Machine Learning.

    Science.gov (United States)

    Kirrane, Diane E.

    1990-01-01

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

  4. Nonplanar machines

    International Nuclear Information System (INIS)

    Ritson, D.

    1989-05-01

    This talk examines methods available to minimize, but never entirely eliminate, degradation of machine performance caused by terrain following. Breaking of planar machine symmetry for engineering convenience and/or monetary savings must be balanced against small performance degradation, and can only be decided on a case-by-case basis. 5 refs

  5. Long-term labour productivity and GDP projections for the EU25 Member States : a production function framework

    OpenAIRE

    Carone, Giuseppe; Denis, Cécile; Mc Morrow, Kieran; Mourre, Gilles; Röger, Werner

    2006-01-01

    This paper presents the results of long run labour productivity and GDP growth rate projections (until 2050) for each of the 25 EU Member States and provides a detailed overview of the forecast methodology used. These projections were undertaken in order to provide an internationally comparable macroeconomic framework against which to assess the potential economic and fiscal effects of ageing populations. The projections presented in this paper, using a common production function methodology ...

  6. Long-term labour productivity and GDP projections for the EU25 Member States : a production function framework

    OpenAIRE

    Carone, Giuseppe; Denis, Cécile; Mc Morrow, Kieran; Mourre, Gilles; Röger, Werner

    2006-01-01

    This paper presents the results of long run labour productivity and GDP growth rate projections (until 2050) for each of the 25 EU Member States and provides a detailed overview of the forecast methodology used. These projections were undertaken in order to provide an internationally comparable macroeconomic framework against which to assess the potential economic and fiscal effects of ageing populations. The projections presented in this paper, using a common production function methodol...

  7. The potential role of stated preference methods in the Water Framework Directive to assess disproportionate costs

    NARCIS (Netherlands)

    Brouwer, R.

    2008-01-01

    This paper examines the issue of disproportionate costs of Water Framework Directive (WFD) implementation using public surveys as a means to inform policy and decision making. Public taxpayers are asked their opinion regarding the implementation of the WFD and its costs. Taxpayers are expected to

  8. Towards nuclear disarmament: State of affairs in the international legal framework

    International Nuclear Information System (INIS)

    Fanielle, Sylvain

    2016-01-01

    Since the dawn of the nuclear era, nuclear disarmament has been one of the highest priorities of the international community in ensuring global peace and security. Accordingly, numerous multilateral and bilateral political initiatives have been launched to fulfil this objective in a comprehensive manner. Many of these political efforts have resulted in the negotiation and adoption of legal instruments, which currently comprise the international legal framework on nuclear disarmament. Despite numerous achievements, this framework appears to be at a turning point. As a matter of fact, recent political and diplomatic tensions have reminded the international community that the far-reaching objective of global nuclear disarmament is under continuous pressure. In this context, is the international legal framework on nuclear disarmament effective? This article addresses both development and effectiveness of the international legal framework on nuclear disarmament. It first describes the position of nuclear disarmament within the United Nations (UN) machinery and the related political challenges. It then focuses on the Nuclear Non-Proliferation Treaty (NPT),1 with a particular focus on the interpretation and legal requirements associated with Article VI. Finally, it provides an overview of the Nuclear-Weapon-Free Zones (NWFZs) and their role in the international denuclearization dynamics. (author)

  9. Fingerprint states of odd mass 115I nuclei in the framework of particle rotor model

    International Nuclear Information System (INIS)

    Goswami, R.; Saha Sarkar, M.; Sen, S.

    2008-01-01

    Extensive theoretical as well as experimental investigation of the nuclear structure of odd-mass iodine nuclei have revealed systematic presence of strongly coupled bands in all neutron deficient as well as neutron rich odd-mass iodine isotopes. The present work shows that the positive as well as the negative parity are fairly well reproduced in the framework of particle rotor model

  10. Web Tutorials on Systems Thinking Using the Driver-Pressure-State-Impact-Response (DPSIR) Framework

    Science.gov (United States)

    This set of tutorials provides an overview of incorporating systems thinking into decision-making, an introduction to the DPSIR framework as one approach that can assist in the decision analysis process, and an overview of DPSIR tools, including concept mapping and keyword lists,...

  11. Sample Grade Level Benchmarks, Grades 5-8, Based on the 1998 Arkansas State Mathematics Framework.

    Science.gov (United States)

    Arkansas State Dept. of Education, Little Rock.

    This document presents the application and use of mathematics learning proposed by the Arkansas curriculum frameworks for grades 5-8. The standards are presented in chart form and organized into five strands: (1) number sense, properties, and operations; (2) geometry and spatial sense; (3) measurement; (4) data analysis, statistics, and…

  12. 77 FR 20728 - Fisheries of the Northeastern United States; Atlantic Sea Scallop Fishery; Framework Adjustment 23

    Science.gov (United States)

    2012-04-06

    ... to the proposed rule from: A representative from Nordic Fisheries, a family-owned company that runs... Nordic Fisheries generally supports the proposed measures in Framework 23, but commented that the final... regulatory language describing the TDD requirement. However, FSF continue to note their opinion that the TDD...

  13. 75 FR 36559 - Fisheries of the Northeastern United States; Atlantic Sea Scallop Fishery; Framework Adjustment 21

    Science.gov (United States)

    2010-06-28

    ... of the fishing year (FY). Framework 21 specifies measures only for FY 2010. Amendment 15 to the FMP... trips in the Elephant Trunk Access Area (ETAA), and one trip in the Delmarva Access Area (Delmarva). A... through October 31 into other areas and times of year when sea turtle interactions are less likely. This...

  14. 75 FR 63721 - Fisheries of the Northeastern United States; Atlantic Sea Scallop Fishery; Framework Adjustment...

    Science.gov (United States)

    2010-10-18

    ... 2010 scallop fishing year (FY). Following publication, NMFS identified errors, omissions, and possible..., 2010, start of the fishing year (FY) and the implementation of Framework 21 FY 2010 management measures... comment specifically addressing the provision for Elephant Trunk Access Area (ETAA) trip overages for...

  15. An Analytical Framework for the Steady State Impact of Carbonate Compensation on Atmospheric CO2

    Science.gov (United States)

    Omta, Anne Willem; Ferrari, Raffaele; McGee, David

    2018-04-01

    The deep-ocean carbonate ion concentration impacts the fraction of the marine calcium carbonate production that is buried in sediments. This gives rise to the carbonate compensation feedback, which is thought to restore the deep-ocean carbonate ion concentration on multimillennial timescales. We formulate an analytical framework to investigate the impact of carbonate compensation under various changes in the carbon cycle relevant for anthropogenic change and glacial cycles. Using this framework, we show that carbonate compensation amplifies by 15-20% changes in atmospheric CO2 resulting from a redistribution of carbon between the atmosphere and ocean (e.g., due to changes in temperature, salinity, or nutrient utilization). A counterintuitive result emerges when the impact of organic matter burial in the ocean is examined. The organic matter burial first leads to a slight decrease in atmospheric CO2 and an increase in the deep-ocean carbonate ion concentration. Subsequently, enhanced calcium carbonate burial leads to outgassing of carbon from the ocean to the atmosphere, which is quantified by our framework. Results from simulations with a multibox model including the minor acids and bases important for the ocean-atmosphere exchange of carbon are consistent with our analytical predictions. We discuss the potential role of carbonate compensation in glacial-interglacial cycles as an example of how our theoretical framework may be applied.

  16. Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

    Energy Technology Data Exchange (ETDEWEB)

    Varpula, T [VTT Automation, Espoo (Finland). Measurement Technology

    1999-12-31

    A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

  17. Monitoring of the state of the paper machine circulation water with a wide-band impedance measurement; Paperikoneen kiertoveden tilan seuranta laajakaistaisella impedanssimittauksella - MPKT 02

    Energy Technology Data Exchange (ETDEWEB)

    Varpula, T. [VTT Automation, Espoo (Finland). Measurement Technology

    1998-12-31

    A new measurement method for monitoring the chemical state of the circulation water in the paper machine is proposed and studied. In the method, the electrical properties - conductivity and permittivity - of the water are measured in a wide frequency band: 20 Hz - 10 mhz. Large-molecule organic compounds in the water are expected cause characteristic changes in the dielectric properties of the water. Continuous monitoring of the permittivity in the wide frequency band thus reveals their presence. Various electronic measurement setups for the measurement are constructed and studied by using test samples. If the method turns out to be promising, a prototype device will be made. (orig.)

  18. Changing practice and policy to move to scale: a framework for age-friendly communities across the United States.

    Science.gov (United States)

    Ball, M Scott; Lawler, Kathryn

    2014-01-01

    A new body of work has emerged under the category of creating age-friendly communities. This article briefly reviews the current state of the work and discusses a potential framework for moving to scale. Based on an understanding that the majority of the local challenges to aging in community stem from state and national policies and practices, the article calls for a measure of "creative destruction" in local efforts. That is, dysfunctional state and national systems should be boldly marked for demolition. Local age-friendly community work must be conceived of and positioned to engage larger policy issues, identify problems, and be part of a process of reinventing larger federal, state, and local policies and practices.

  19. Wind Development in the United States: A Comprehensive Policy Framework for Effective Wind Development as Framed by PJM Stakeholders

    Science.gov (United States)

    Stewart, Courtney A.

    Wind energy has been lauded as a resource for the United States to lessen its dependency on foreign fuels, reduce carbon output, and potentially create millions of jobs. Accordingly, wind energy is in the forefront of many government officials' minds throughout the United States; however, there are several barriers to wind farm development. This research reviews the social and political barriers to wind farm development and examines the successful renewable energy policies that have been used throughout Europe and the United States. This research consists of interviews with various stakeholders in the PJM region who compare and contrast renewable energy policies in Europe from those in the United States. The resulting information from the interviews creates a comprehensive policy framework that policy makers at all levels of government can utilize and refer to when discussing and drafting wind energy legislation.

  20. 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 ...

  1. A practical framework for regulating for-profit recreational marijuana in US States: Lessons from Colorado and Washington.

    Science.gov (United States)

    Carnevale, John T; Kagan, Raanan; Murphy, Patrick J; Esrick, Josh

    2017-04-01

    Despite the federal prohibition against marijuana, state-level recreational use appears to be moving forward. Public opinion is shifting. Following well-publicized state-legalization in Washington and Colorado, states across the US have begun considering similar measures. Since the 2016 election, over 21% of Americans now live in places where recreational marijuana is state-legal, and over 63% of the country permits medical or recreational use at the state level. This paper does not consider whether states should legalize marijuana nor does it weigh all regulatory options available to states. Instead, it considers how states can create a practical framework to regulate recreational marijuana, particularly in a climate of federal uncertainty where marijuana remains illegal. We draw lessons from Colorado and Washington-assuming that other states will adopt similar models and employ commercial, for-profit systems. Considering both the variety of goals that states could adopt and how they interact, we offer recommendations in five areas: cultivation, production, and processing; sale, consumption, and possession; taxes and finance; public health and safety; and governance. We recommend that states implement a relatively restrictive regulatory approach, with a single market for recreational and medical marijuana, if appropriate. This should make marijuana laws easier to enforce, help reduce diversion, and satisfy federal guidance. Moreover, drawing from Colorado and Washington's experience, we suggest a flexible system with robust data collection and performance monitoring that supports a thorough evaluation. This should allow states to "learn as they go"-a must, given the uncertainty surrounding such policy shifts. Of course, a tightly regulated approach will have drawbacks-including a significant illegal market. But political experience teaches that states will be better off loosening a tight market than attempting to tighten a loose one. We also consider a potential

  2. 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.

  3. Application of driving force- Pressure- State- Impact- Response (DPSIR Framework for Analyzing the Human habitat in City of Tehran

    Directory of Open Access Journals (Sweden)

    Esmail Salehi

    2015-09-01

    Full Text Available Human habitat change is a complicated issue that many factors play different roles in its formation and distribution. Considering this complication, a more comprehensive and holistic approach is needed for a better understanding and management of those factors. The causal frameworks are among systemic and integrated methods for addressing the causes of environmental problems and the relationships that exist between the environmental systems for proposing proper solutions. The DPSIR model is a functional analysis framework to depict the cause-effect relationships that exist in creating environmental problems. Tehran is one of the major megacities in the Middle East that faces environmental consequences of over population and unplanned urban sprawl, and because of its location in arid region, its vulnerable to rise of environmental problem. In this research, by using the DPSIR framework, different aspects of habitat condition of Tehran are analyzed and later with the help of this conceptual framework, strategies for controlling urban environment. The results show that urbanization is the major driving force that is induced by overpopulation and the need for further urban sprawl, which cause pressure on natural resources. The state of housing and rapid land use changes have brought about unfavorable living conditions that result in unfavorable impacts on public health and safety, which are the results of ineffective policies and solutions.

  4. 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...

  5. Information Communication Technology, State building, and Globalization in the 21st Century: Regional Frameworks for Emerging State Assistance

    National Research Council Canada - National Science Library

    Reese, Justin Y

    2008-01-01

    .... Globalization has modified the essential role of the nation-state towards managing global flows of resource, capital, and populations rather than, as in the past, presiding over distinct national economies...

  6. Recommendations for institutional policy and network regulatory frameworks towards distributed generation in EU Member States

    International Nuclear Information System (INIS)

    Ten Donkelaar, M.; Van Oostvoorn, F.

    2005-01-01

    Recommendations regarding the development of regulatory frameworks and institutional policies towards an optimal integration of distributed generation (DG) into electricity networks are presented. These recommendations are based on findings from a benchmarking study conducted in the framework of the ENIRDG-net project. The aim of the benchmarking exercise was to identify examples of well-defined pro-DG policies, with clear targets and adequate implementation mechanisms. In this study an adequate pro-DG policy is defined on the basis of a level playing field, a situation where distributed and centralised generation receive equal incentives and have equal access to the liberalised markets for electricity. The benchmark study includes the results of a similar study conducted in the framework of the SUSTELNET project. When comparing the results a certain discrepancy can be noticed between the actual regulation and policy in a number of countries, the medium to long-term targets and the ideal situation described by the level playing field objective. To overcome this discrepancy, a number of recommendations have been drafted for future policy and regulation towards distributed generation

  7. 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 ...

  8. Machine Translation

    Indian Academy of Sciences (India)

    Research Mt System Example: The 'Janus' Translating Phone Project. The Janus ... based on laptops, and simultaneous translation of two speakers in a dialogue. For more ..... The current focus in MT research is on using machine learning.

  9. Engineering design framework for a shape memory alloy coil spring actuator using a static two-state model

    International Nuclear Information System (INIS)

    An, Sung-Min; Cho, Kyu-Jin; Ryu, Junghyun; Cho, Maenghyo

    2012-01-01

    A shape memory alloy (SMA) coil spring actuator is fabricated by annealing an SMA wire wound on a rod. Four design parameters are required for the winding: the wire diameter, the rod diameter, the pitch angle and the number of active coils. These parameters determine the force and stroke produced by the actuator. In this paper, we present an engineering design framework to select these parameters on the basis of the desired force and stoke. The behavior of the SMA coil spring actuator is described in detail to provide information about the inner workings of the actuator and to aid in selecting the design parameters. A new static two-state model, which represents a force–deflection relation of the actuator at the fully martensitic state (M 100% ) and fully austenitic state (A 100% ), is derived for use in the design. Two nonlinear effects are considered in the model: the nonlinear detwinning effect of the SMA and the nonlinear geometric effect of the coil spring for large deformations. The design process is organized into six steps and is presented with a flowchart and design equations. By following this systematic approach, an SMA coil spring actuator can be designed for various applications. Experimental results verified the static two-state model for the SMA coil spring actuator and a case study showed that an actuator designed using this framework met the design requirements. The proposed design framework was developed to assist application engineers such as robotics researchers in designing SMA coil spring actuators without the need for full thermomechanical models. (paper)

  10. Abstract quantum computing machines and quantum computational logics

    Science.gov (United States)

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

    2016-06-01

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

  11. 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.

  12. 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...

  13. 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)

  14. (Mutual Security Mutual Affluence) Negative Factors = Sustained Stability: A Framework for Establishing Stability Between Like States

    Science.gov (United States)

    2017-03-31

    160-163. 2 The Concept of Mutually Assured Destruction (MAD) dates back to the post-WWFI em and the Cold War where the United States and Soviet Unions...United States. Following its defeat in W\\VH, Japan was in shambles. The bombing campaigns left nine million Japanese homeless and three million more...the United States, the Charlie Hebdo attacks in Paris in 2015, and the bombings in Istanbul in 2016. Michael Bamier, “From Mutual Assistance to

  15. State recognition of the viscoelastic sandwich structure based on the adaptive redundant second generation wavelet packet transform, permutation entropy and the wavelet support vector machine

    International Nuclear Information System (INIS)

    Qu, Jinxiu; Zhang, Zhousuo; Guo, Ting; Luo, Xue; Sun, Chuang; Li, Bing; Wen, Jinpeng

    2014-01-01

    The viscoelastic sandwich structure is widely used in mechanical equipment, yet the structure always suffers from damage during long-term service. Therefore, state recognition of the viscoelastic sandwich structure is very necessary for monitoring structural health states and keeping the equipment running with high reliability. Through the analysis of vibration response signals, this paper presents a novel method for this task based on the adaptive redundant second generation wavelet packet transform (ARSGWPT), permutation entropy (PE) and the wavelet support vector machine (WSVM). In order to tackle the non-linearity existing in the structure vibration response, the PE is introduced to reveal the state changes of the structure. In the case of complex non-stationary vibration response signals, in order to obtain more effective information regarding the structural health states, the ARSGWPT, which can adaptively match the characteristics of a given signal, is proposed to process the vibration response signals, and then multiple PE features are extracted from the resultant wavelet packet coefficients. The WSVM, which can benefit from the conventional SVM as well as wavelet theory, is applied to classify the various structural states automatically. In this study, to achieve accurate and automated state recognition, the ARSGWPT, PE and WSVM are combined for signal processing, feature extraction and state classification, respectively. To demonstrate the effectiveness of the proposed method, a typical viscoelastic sandwich structure is designed, and the different degrees of preload on the structure are used to characterize the various looseness states. The test results show that the proposed method can reliably recognize the different looseness states of the viscoelastic sandwich structure, and the WSVM can achieve a better classification performance than the conventional SVM. Moreover, the superiority of the proposed ARSGWPT in processing the complex vibration response

  16. A Snapshot of State Regulatory Framework Development in Elementary and Secondary Online Education

    Science.gov (United States)

    Stedrak, Luke J.; Rose, Amanda L.

    2015-01-01

    With the advent and growth of elementary and secondary online education in the United States, teaching and learning has undergone radical change with unimagined alternatives to traditional brick-and-mortar classrooms, and online education is here to stay. Law and policy in some states has lagged behind the emergence of online K-12 education. The…

  17. A framework for modeling information propagation of biological systems at critical states.

    Science.gov (United States)

    Hu, Feng; Yang, Fang

    2016-03-01

    We explore the dynamics of information propagation at the critical state of a biologically inspired system by an individual-based computer model. "Quorum response", a type of social interaction which has been recognized taxonomically in animal groups, is applied as the sole interaction rule among individuals. In the model, we assume a truncated Gaussian distribution to depict the distribution of the individuals' vigilance level. Each individual can assume either a naïve state or an alarmed one and only switches from the former state to the latter one. If an individual has turned into an alarmed state, it stays in the state during the process of information propagation. Initially, each individual is set to be at the naïve state and information is tapped into the system by perturbing an individual at the boundaries (alerting it to the alarmed state). The system evolves as individuals turn into the alarmed state, according to the quorum response rules, consecutively. We find that by fine-tuning the parameters of the mean and the standard deviation of the Gaussian distribution, the system is poised at a critical state. We present the phase diagrams to exhibit that the parameter space is divided into a super-critical and a sub-critical zone, in which the dynamics of information propagation varies largely. We then investigate the effects of the individuals' mobility on the critical state, and allow a proportion of randomly chosen individuals to exchange their positions at each time step. We find that mobility breaks down criticality of the system. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  18. An accelerated framework for the classification of biological targets from solid-state micropore data.

    Science.gov (United States)

    Hanif, Madiha; Hafeez, Abdul; Suleman, Yusuf; Mustafa Rafique, M; Butt, Ali R; Iqbal, Samir M

    2016-10-01

    Micro- and nanoscale systems have provided means to detect biological targets, such as DNA, proteins, and human cells, at ultrahigh sensitivity. However, these devices suffer from noise in the raw data, which continues to be significant as newer and devices that are more sensitive produce an increasing amount of data that needs to be analyzed. An important dimension that is often discounted in these systems is the ability to quickly process the measured data for an instant feedback. Realizing and developing algorithms for the accurate detection and classification of biological targets in realtime is vital. Toward this end, we describe a supervised machine-learning approach that records single cell events (pulses), computes useful pulse features, and classifies the future patterns into their respective types, such as cancerous/non-cancerous cells based on the training data. The approach detects cells with an accuracy of 70% from the raw data followed by an accurate classification when larger training sets are employed. The parallel implementation of the algorithm on graphics processing unit (GPU) demonstrates a speedup of three to four folds as compared to a serial implementation on an Intel Core i7 processor. This incredibly efficient GPU system is an effort to streamline the analysis of pulse data in an academic setting. This paper presents for the first time ever, a non-commercial technique using a GPU system for realtime analysis, paired with biological cluster targeting analysis. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  19. Learning with Support Vector Machines

    CERN Document Server

    Campbell, Colin

    2010-01-01

    Support Vectors Machines have become a well established tool within machine learning. They work well in practice and have now been used across a wide range of applications from recognizing hand-written digits, to face identification, text categorisation, bioinformatics, and database marketing. In this book we give an introductory overview of this subject. We start with a simple Support Vector Machine for performing binary classification before considering multi-class classification and learning in the presence of noise. We show that this framework can be extended to many other scenarios such a

  20. Current state of renewable energies performances in the European Union: A new reference framework

    International Nuclear Information System (INIS)

    D’Adamo, Idiano; Rosa, Paolo

    2016-01-01

    Highlights: • A common mathematic model, based on historical values, defines future trends. • Luxembourg, Ireland and Netherlands do not reach their 2020 national targets. • The selected indexes significantly influences the ranking of European countries. • Sweden, Finland, Austria and Latvia have a dominant position. • Ten countries have a value greater than the EU 28 average for each index analysed. - Abstract: Initially pushed by the European Union (EU) through the Europe 2020 strategy, the development of renewable energies is a strategic action aiming to limit climate changes and cut greenhouse gas emissions. National subsidies favored the diffusion of this new kind of energy sources, even if there are interesting economic opportunities also in non-subsidized markets. Renewable energy (RE) is a sustainable choice, but its management requires a proper analysis, both from political and operational levels. The aim of this paper is the assessment of European renewable energy source (RES) trajectory towards 2020, starting from historical values and through common scientific methods. In addition, a new reference framework is proposed, in order to evaluate RESs performances in Europe. The framework is based on three indicators: (i) share of energy from RESs in gross final energy consumption, (ii) REs primary production per capita and (iii) gross final consumption of REs per capita. Results could have practical implications for the decision makers involved in the management of energy sources throughout Europe and could be used for the comparison on a global scale.

  1. Mindfulness and Cardiovascular Disease Risk: State of the Evidence, Plausible Mechanisms, and Theoretical Framework

    Science.gov (United States)

    Schuman-Olivier, Zev; Britton, Willoughby B.; Fresco, David M.; Desbordes, Gaelle; Brewer, Judson A.; Fulwiler, Carl

    2016-01-01

    The purpose of this review is to provide (1) a synopsis on relations of mindfulness with cardiovascular disease (CVD) and major CVD risk factors, and (2) an initial consensus-based overview of mechanisms and theoretical framework by which mindfulness might influence CVD. Initial evidence, often of limited methodological quality, suggests possible impacts of mindfulness on CVD risk factors including physical activity, smoking, diet, obesity, blood pressure, and diabetes regulation. Plausible mechanisms include (1) improved attention control (e.g., ability to hold attention on experiences related to CVD risk, such as smoking, diet, physical activity, and medication adherence), (2) emotion regulation (e.g., improved stress response, self-efficacy, and skills to manage craving for cigarettes, palatable foods, and sedentary activities), and (3) self-awareness (e.g., self-referential processing and awareness of physical sensations due to CVD risk factors). Understanding mechanisms and theoretical framework should improve etiologic knowledge, providing customized mindfulness intervention targets that could enable greater mindfulness intervention efficacy. PMID:26482755

  2. Coulomb excitation of rotational states in the 162Dy nucleus in the framework of the generalized semiclassical approximation

    International Nuclear Information System (INIS)

    Bolotin, Yu.L.; Gonchar, V.Yu.; Chekanov, N.A.

    1985-01-01

    Coulomb excitation of rotational states induced in heavyion collisions is treated in the framework of the generalized semiclassical approximation. The Hamiltonian of the system under consideration involves not only Coulomb forces (monopole, quadrupole, and hexadecapole) but as well a real nuclear potential in the form of the deformed Woods-Saxon potential. Strong dependence of the excitation probability on the interference between the Coulomb and nuclear interactions is shown. Calculations are carried out for the reaction 40 Ar+ 162 Dy at E=148.6 MeV. The calculated Coulomb excitation probabilities agree satisfactory with the corresponding experimental values

  3. 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.

  4. 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

  5. A software framework for analysing solid-state MAS NMR data

    International Nuclear Information System (INIS)

    Stevens, Tim J.; Fogh, Rasmus H.; Boucher, Wayne; Higman, Victoria A.; Eisenmenger, Frank; Bardiaux, Benjamin; Rossum, Barth-Jan van; Oschkinat, Hartmut; Laue, Ernest D.

    2011-01-01

    Solid-state magic-angle-spinning (MAS) NMR of proteins has undergone many rapid methodological developments in recent years, enabling detailed studies of protein structure, function and dynamics. Software development, however, has not kept pace with these advances and data analysis is mostly performed using tools developed for solution NMR which do not directly address solid-state specific issues. Here we present additions to the CcpNmr Analysis software package which enable easier identification of spinning side bands, straightforward analysis of double quantum spectra, automatic consideration of non-uniform labelling schemes, as well as extension of other existing features to the needs of solid-state MAS data. To underpin this, we have updated and extended the CCPN data model and experiment descriptions to include transfer types and nomenclature appropriate for solid-state NMR experiments, as well as a set of experiment prototypes covering the experiments commonly employed by solid-sate MAS protein NMR spectroscopists. This work not only improves solid-state MAS NMR data analysis but provides a platform for anyone who uses the CCPN data model for programming, data transfer, or data archival involving solid-state MAS NMR data.

  6. New Framework of Sustainable Indicators for Outdoor LED (Light Emitting Diodes Lighting and SSL (Solid State Lighting

    Directory of Open Access Journals (Sweden)

    Annika K. Jägerbrand

    2015-01-01

    Full Text Available Light emitting diodes (LEDs and SSL (solid state lighting are relatively new light sources, but are already widely applied for outdoor lighting. Despite this, there is little available information allowing planners and designers to evaluate and weigh different sustainability aspects of LED/SSL lighting when making decisions. Based on a literature review, this paper proposes a framework of sustainability indicators and/or measures that can be used for a general evaluation or to highlight certain objectives or aspects of special interest when choosing LED/SSL lighting. LED/SSL lighting is reviewed from a conventional sustainable development perspective, i.e., covering the three dimensions, including ecological, economic and social sustainability. The new framework of sustainable indicators allow prioritization when choosing LED/SSL products and can thereby help ensure that short-term decisions on LED/SSL lighting systems are in line with long-term sustainability goals established in society. The new framework can also be a beneficial tool for planners, decision-makers, developers and lighting designers, or for consumers wishing to use LED/SSL lighting in a sustainable manner. Moreover, since some aspects of LED/SSL lighting have not yet been thoroughly studied or developed, some possible future indicators are suggested.

  7. 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.…

  8. 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

  9. Bayesian phylogeography of influenza A/H3N2 for the 2014-15 season in the United States using three frameworks of ancestral state reconstruction.

    Directory of Open Access Journals (Sweden)

    Daniel Magee

    2017-02-01

    Full Text Available Ancestral state reconstructions in Bayesian phylogeography of virus pandemics have been improved by utilizing a Bayesian stochastic search variable selection (BSSVS framework. Recently, this framework has been extended to model the transition rate matrix between discrete states as a generalized linear model (GLM of genetic, geographic, demographic, and environmental predictors of interest to the virus and incorporating BSSVS to estimate the posterior inclusion probabilities of each predictor. Although the latter appears to enhance the biological validity of ancestral state reconstruction, there has yet to be a comparison of phylogenies created by the two methods. In this paper, we compare these two methods, while also using a primitive method without BSSVS, and highlight the differences in phylogenies created by each. We test six coalescent priors and six random sequence samples of H3N2 influenza during the 2014-15 flu season in the U.S. We show that the GLMs yield significantly greater root state posterior probabilities than the two alternative methods under five of the six priors, and significantly greater Kullback-Leibler divergence values than the two alternative methods under all priors. Furthermore, the GLMs strongly implicate temperature and precipitation as driving forces of this flu season and nearly unanimously identified a single root state, which exhibits the most tropical climate during a typical flu season in the U.S. The GLM, however, appears to be highly susceptible to sampling bias compared with the other methods, which casts doubt on whether its reconstructions should be favored over those created by alternate methods. We report that a BSSVS approach with a Poisson prior demonstrates less bias toward sample size under certain conditions than the GLMs or primitive models, and believe that the connection between reconstruction method and sampling bias warrants further investigation.

  10. Machine Protection

    International Nuclear Information System (INIS)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012

  11. Machine Learning

    Energy Technology Data Exchange (ETDEWEB)

    Chikkagoudar, Satish; Chatterjee, Samrat; Thomas, Dennis G.; Carroll, Thomas E.; Muller, George

    2017-04-21

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

  12. Machine Protection

    CERN Document Server

    Zerlauth, Markus; Wenninger, Jörg

    2012-01-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  13. Machine Protection

    Energy Technology Data Exchange (ETDEWEB)

    Zerlauth, Markus; Schmidt, Rüdiger; Wenninger, Jörg [European Organization for Nuclear Research, Geneva (Switzerland)

    2012-07-01

    The present architecture of the machine protection system is being recalled and the performance of the associated systems during the 2011 run will be briefly summarized. An analysis of the causes of beam dumps as well as an assessment of the dependability of the machine protection systems (MPS) itself is being presented. Emphasis will be given to events that risked exposing parts of the machine to damage. Further improvements and mitigations of potential holes in the protection systems will be evaluated along with their impact on the 2012 run. The role of rMPP during the various operational phases (commissioning, intensity ramp up, MDs...) will be discussed along with a proposal for the intensity ramp up for the start of beam operation in 2012.

  14. The open XXX spin chain in the SoV framework: scalar product of separate states

    Science.gov (United States)

    Kitanine, N.; Maillet, J. M.; Niccoli, G.; Terras, V.

    2017-06-01

    We consider the XXX open spin-1/2 chain with the most general non-diagonal boundary terms, that we solve by means of the quantum separation of variables (SoV) approach. We compute the scalar products of separate states, a class of states which notably contains all the eigenstates of the model. As usual for models solved by SoV, these scalar products can be expressed as some determinants with a non-trivial dependance in terms of the inhomogeneity parameters that have to be introduced for the method to be applicable. We show that these determinants can be transformed into alternative ones in which the homogeneous limit can easily be taken. These new representations can be considered as generalizations of the well-known determinant representation for the scalar products of the Bethe states of the periodic chain. In the particular case where a constraint is applied on the boundary parameters, such that the transfer matrix spectrum and eigenstates can be characterized in terms of polynomial solutions of a usual T-Q equation, the scalar product that we compute here corresponds to the scalar product between two off-shell Bethe-type states. If in addition one of the states is an eigenstate, the determinant representation can be simplified, hence leading in this boundary case to direct analogues of algebraic Bethe ansatz determinant representations of the scalar products for the periodic chain.

  15. The open XXX spin chain in the SoV framework: scalar product of separate states

    International Nuclear Information System (INIS)

    Kitanine, N; Maillet, J M; Niccoli, G; Terras, V

    2017-01-01

    We consider the XXX open spin-1/2 chain with the most general non-diagonal boundary terms, that we solve by means of the quantum separation of variables (SoV) approach. We compute the scalar products of separate states, a class of states which notably contains all the eigenstates of the model. As usual for models solved by SoV, these scalar products can be expressed as some determinants with a non-trivial dependance in terms of the inhomogeneity parameters that have to be introduced for the method to be applicable. We show that these determinants can be transformed into alternative ones in which the homogeneous limit can easily be taken. These new representations can be considered as generalizations of the well-known determinant representation for the scalar products of the Bethe states of the periodic chain. In the particular case where a constraint is applied on the boundary parameters, such that the transfer matrix spectrum and eigenstates can be characterized in terms of polynomial solutions of a usual T - Q equation, the scalar product that we compute here corresponds to the scalar product between two off-shell Bethe-type states. If in addition one of the states is an eigenstate, the determinant representation can be simplified, hence leading in this boundary case to direct analogues of algebraic Bethe ansatz determinant representations of the scalar products for the periodic chain. (paper)

  16. An analysis of local government health policy against state priorities and a social determinants framework.

    Science.gov (United States)

    Browne, Geoffrey R; Davern, Melanie T; Giles-Corti, Billie

    2016-04-01

    Victorian local governments are required to develop Municipal Public Health and Wellbeing Plans that incorporate state-level health planning priorities and address the social determinants of health. This paper describes a novel method for evaluating councils' performance against these requirements. Deductive content analysis was used to categorise all actions in 14 local government MPHWPs against Victorian state priorities as well as against social determinants of health policy areas. More than 1,000 actions were identified. However, fewer than half directly addressed a state priority, with many actions addressing policy areas known to be broader determinants of health. In particular, there was a marked focus on leisure and culture, and on building social cohesion through changes to living and working conditions. Councils are working beyond state priorities and there was a clear emphasis on addressing the diverse upstream 'causes of the causes' of health, rather than health promotion behaviour change programs. The approach for data analysis and presentation provides a useful method for rapid appraisal of health and wellbeing actions relative to councils', and the State's, responsibility and efficacy in public health. © 2015 Public Health Association of Australia.

  17. State Regulatory Authority (SRA) Coordination of Safety, Security, and Safeguards of Nuclear Facilities: A Framework for Analysis

    International Nuclear Information System (INIS)

    Mladineo, S.; Frazar, S.; Kurzrok, A.; Martikka, E.; Hack, T.; Wiander, T.

    2013-01-01

    In November 2012 the International Atomic Energy Agency (IAEA) sponsored a Technical Meeting on the Interfaces and Synergies in Safety, Security, and Safeguards for the Development of a Nuclear Power Program. The goal of the meeting was to explore whether and how safeguards, safety, and security systems could be coordinated or integrated to support more effective and efficient nonproliferation infrastructures. While no clear consensus emerged, participants identified practical challenges to and opportunities for integrating the three disciplines’ regulations and implementation activities. Simultaneously, participants also recognized that independent implementation of safeguards, safety, and security systems may be more effective or efficient at times. This paper will explore the development of a framework for conducting an assessment of safety-security-safeguards integration within a State. The goal is to examine State regulatory structures to identify conflicts and gaps that hinder management of the three disciplines at nuclear facilities. Such an analysis could be performed by a State Regulatory Authority (SRA) to provide a self-assessment or as part of technical cooperation either with a newcomer State, or to a State with a fully developed SRA.

  18. Teletherapy machine

    International Nuclear Information System (INIS)

    Panyam, Vinatha S.; Rakshit, Sougata; Kulkarni, M.S.; Pradeepkumar, K.S.

    2017-01-01

    Radiation Standards Section (RSS), RSSD, BARC is the national metrology institute for ionizing radiation. RSS develops and maintains radiation standards for X-ray, beta, gamma and neutron radiations. In radiation dosimetry, traceability, accuracy and consistency of radiation measurements is very important especially in radiotherapy where the success of patient treatment is dependent on the accuracy of the dose delivered to the tumour. Cobalt teletherapy machines have been used in the treatment of cancer since the early 1950s and India had its first cobalt teletherapy machine installed at the Cancer Institute, Chennai in 1956

  19. The ground state energy of 3He droplet in the LOCV framework

    International Nuclear Information System (INIS)

    Modarres, M.; Motahari, S.; Rajabi, A.

    2012-01-01

    The (extended) lowest order constrained variational method was used to calculate the ground state energy of liquid helium 3 ( 3 He) droplets at zero temperature. Different types of density distribution profiles, such as the Gaussian, the Quasi-Gaussian and the Woods-Saxon were used. It was shown that at least, on average, near 20 3 He atoms are needed to get the bound state for 3 He liquid droplet. Depending on the choice of the density profiles and the atomic radius of 3 He, the above estimate can increase to 300. Our calculated ground state energy and the number of atoms in liquid 3 He droplet were compared with those of Variational Monte Carlo method, Diffusion Monte Carlo method and Density Functional Theory, for which a reasonable agreement was found.

  20. A framework for estimating health state utility values within a discrete choice experiment: modeling risky choices.

    Science.gov (United States)

    Robinson, Angela; Spencer, Anne; Moffatt, Peter

    2015-04-01

    There has been recent interest in using the discrete choice experiment (DCE) method to derive health state utilities for use in quality-adjusted life year (QALY) calculations, but challenges remain. We set out to develop a risk-based DCE approach to derive utility values for health states that allowed 1) utility values to be anchored directly to normal health and death and 2) worse than dead health states to be assessed in the same manner as better than dead states. Furthermore, we set out to estimate alternative models of risky choice within a DCE model. A survey was designed that incorporated a risk-based DCE and a "modified" standard gamble (SG). Health state utility values were elicited for 3 EQ-5D health states assuming "standard" expected utility (EU) preferences. The DCE model was then generalized to allow for rank-dependent expected utility (RDU) preferences, thereby allowing for probability weighting. A convenience sample of 60 students was recruited and data collected in small groups. Under the assumption of "standard" EU preferences, the utility values derived within the DCE corresponded fairly closely to the mean results from the modified SG. Under the assumption of RDU preferences, the utility values estimated are somewhat lower than under the assumption of standard EU, suggesting that the latter may be biased upward. Applying the correct model of risky choice is important whether a modified SG or a risk-based DCE is deployed. It is, however, possible to estimate a probability weighting function within a DCE and estimate "unbiased" utility values directly, which is not possible within a modified SG. We conclude by setting out the relative strengths and weaknesses of the 2 approaches in this context. © The Author(s) 2014.

  1. The principles and values of the social state of law as a legal and political framework for resolving conflicts

    International Nuclear Information System (INIS)

    Valencia Hernandez, Javier Gonzaga

    2008-01-01

    The social state of law is the legal politic framework proposed in the 1991 Constitution, in which Colombians expect to construct a new relationship with nature, based in principles and values such as life, prevalence of general interest over the individual, solidarity, protection of cultural and natural wealth, human dignity and civic participation. The environmental conflicts currently pose a new challenge for the jurists, given that for its comprehension, development and solution proposal it becomes necessary to have a general legal framework and rules of environmental law, as well as principles and values consecrated in the constitution and in other international instruments ratified by Colombia. The participation of an informed, trained and deliberative citizenship, in the resolution of environmental conflicts and in the decisions taken over the environment, will create a dynamic public opinion that will question governors, will manage jointly their own projects and will promote different values from those created from the consumer societies and the individual ownership in the actual states

  2. The "State of Exception" and Disaster Education: A Multilevel Conceptual Framework with Implications for Social Justice

    Science.gov (United States)

    Preston, John; Chadderton, Charlotte; Kitagawa, Kaori

    2014-01-01

    The term "state of exception" has been used by Italian political theorist Giorgio Agamben to explain the ways in which emergencies, crises and disasters are used by governments to suspend legal processes. In this paper, we innovatively apply Agamben's theory to the way in which countries prepare and educate the population for various…

  3. Conceptual framework for improved wind-related forest threat assessment in the Southeastern United States

    Science.gov (United States)

    Scott L. Goodrick; John A. Stanturf

    2010-01-01

    In the Southeastern United States, forests are subject to a variety of damage-causing wind phenomena that range in scale from very localized (downbursts and tornadoes) to broad spatial scales (hurricanes). Incorporating the threat of wind damage into forest management plans requires tools capable of assessing risk across this range of scales. Our conceptual approach...

  4. Scientific Framework for Stormwater Monitoring by the Washington State Department of Transportation

    Science.gov (United States)

    Sheibley, R.W.; Kelly, V.J.; Wagner, R.J.

    2009-01-01

    The Washington State Department of Transportation municipal stormwater monitoring program, in operation for about 8 years, never has received an external, objective assessment. In addition, the Washington State Department of Transportation would like to identify the standard operating procedures and quality assurance protocols that must be adopted so that their monitoring program will meet the requirements of the new National Pollutant Discharge Elimination System municipal stormwater permit. As a result, in March 2009, the Washington State Department of Transportation asked the U.S. Geological Survey to assess their pre-2009 municipal stormwater monitoring program. This report presents guidelines developed for the Washington State Department of Transportation to meet new permit requirements and regional/national stormwater monitoring standards to ensure that adequate processes and procedures are identified to collect high-quality, scientifically defensible municipal stormwater monitoring data. These include: (1) development of coherent vision and cooperation among all elements of the program; (2) a comprehensive approach for site selection; (3) an effective quality assurance program for field, laboratory, and data management; and (4) an adequate database and data management system.

  5. Reforming Investor–State Dispute Settlement: A (Comparative and International) Constitutional Law Framework

    NARCIS (Netherlands)

    Schill, S.W.

    As a result of the steep increase in investment arbitrations, and the problems this has brought to the fore, many reform efforts in international investment law focus on changes to investor–state dispute settlement (ISDS). Reform proposals, however, diverge widely (ranging from exiting the system

  6. Machine testning

    DEFF Research Database (Denmark)

    De Chiffre, Leonardo

    This document is used in connection with a laboratory exercise of 3 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercise includes a series of tests carried out by the student on a conventional and a numerically controled lathe, respectively. This document...

  7. 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...

  8. A regional modeling framework of phosphorus sources and transport in streams of the southeastern United States

    Science.gov (United States)

    Garcia, Ana Maria.; Hoos, Anne B.; Terziotti, Silvia

    2011-01-01

    We applied the SPARROW model to estimate phosphorus transport from catchments to stream reaches and subsequent delivery to major receiving water bodies in the Southeastern United States (U.S.). We show that six source variables and five land-to-water transport variables are significant (p < 0.05) in explaining 67% of the variability in long-term log-transformed mean annual phosphorus yields. Three land-to-water variables are a subset of landscape characteristics that have been used as transport factors in phosphorus indices developed by state agencies and are identified through experimental research as influencing land-to-water phosphorus transport at field and plot scales. Two land-to-water variables – soil organic matter and soil pH – are associated with phosphorus sorption, a significant finding given that most state-developed phosphorus indices do not explicitly contain variables for sorption processes. Our findings for Southeastern U.S. streams emphasize the importance of accounting for phosphorus present in the soil profile to predict attainable instream water quality. Regional estimates of phosphorus associated with soil-parent rock were highly significant in explaining instream phosphorus yield variability. Model predictions associate 31% of phosphorus delivered to receiving water bodies to geology and the highest total phosphorus yields in the Southeast were catchments with already high background levels that have been impacted by human activity.

  9. 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.

  10. Energy Efficiency of Tunnel Boring Machines.

    OpenAIRE

    Grishenko, Vitaly

    2014-01-01

    Herrenknecht AG is a German world-leading Tunnel Boring Machines manufacturer showing strong awareness and concern regarding environmental issues. The company supports research on the Energy Efficiency (EE) of their products, aimed at the development of intelligent design for a green Tunnel Boring Machine. The aim of this project is to produce a ’status quo’ report on EE of three types of Tunnel Boring Machines (Hardrock, EPB and Mixshield TBM). In the framework of this research 39 projects a...

  11. The Case For Space: A Legislative Framework For An Independent United States Space Force

    Science.gov (United States)

    2018-04-01

    example of an organization created by competing bureaucratic interests, ARPA hampered and muddled early service efforts to think clearly about space.12...change the way we think and prepare for that eventuality.”54 As aptly stated recently by Melissa de Zwart, Dean of Law at the University of Adelaide in...NASA Bets on Private Companies to Exploit Moon’s Resources,” Phys.org, 9 February 2014, https://phys.org/news/2014-02-nasa-private-companies-exploit

  12. Transitioning From Paper to Digital: State Statutory and Regulatory Frameworks for Health Information Technology.

    Science.gov (United States)

    Schmit, Cason; Sunshine, Gregory; Pepin, Dawn; Ramanathan, Tara; Menon, Akshara; Penn, Matthew

    In all health system sectors, electronic health information (EHI) is created, used, released, and reused. We examined states' efforts to address EHI uses in law to provide an understanding of the EHI legal environment. Attorney researchers used WestlawNext to search for EHI-related statutes and regulations of the US states, US territories, and the District of Columbia in effect as of January 2014. The researchers independently catalogued provisions by the EHI use described in the law. Researchers resolved discrepancies through peer review meetings and recorded the consensus codes for each law. This study identified 2364 EHI-related laws representing 49 EHI uses in 54 jurisdictions. A total of 18 EHI uses were regulated by ≥10 jurisdictions. More than 750 laws addressed 2 or more EHI uses. Jurisdictions varied by the number of EHI laws in effect, with a mean of 44 laws. Texas had the most EHI laws (n = 145). Hawaii and South Carolina had the fewest (n = 14 each). The EHI legal landscape is complex. The large quantity and diversity of laws complicate legal analysis, likely delay implementation of public health solutions, and might be detrimental to the development of emerging health information technology. Research is needed to understand the effect of EHI-related laws.

  13. Cooperative Extension as a Framework for Health Extension: The Michigan State University Model.

    Science.gov (United States)

    Dwyer, Jeffrey W; Contreras, Dawn; Eschbach, Cheryl L; Tiret, Holly; Newkirk, Cathy; Carter, Erin; Cronk, Linda

    2017-10-01

    The Affordable Care Act charged the Agency for Healthcare Research and Quality to create the Primary Care Extension Program, but did not fund this effort. The idea to work through health extension agents to support health care delivery systems was based on the nationally known Cooperative Extension System (CES). Instead of creating new infrastructure in health care, the CES is an ideal vehicle for increasing health-related research and primary care delivery. The CES, a long-standing component of the land-grant university system, features a sustained infrastructure for providing education to communities. The Michigan State University (MSU) Model of Health Extension offers another means of developing a National Primary Care Extension Program that is replicable in part because of the presence of the CES throughout the United States. A partnership between the MSU College of Human Medicine and MSU Extension formed in 2014, emphasizing the promotion and support of human health research. The MSU Model of Health Extension includes the following strategies: building partnerships, preparing MSU Extension educators for participation in research, increasing primary care patient referrals and enrollment in health programs, and exploring innovative funding. Since the formation of the MSU Model of Health Extension, researchers and extension professionals have made 200+ connections, and grants have afforded savings in salary costs. The MSU College of Human Medicine and MSU Extension partnership can serve as a model to promote health partnerships nationwide between CES services within land-grant universities and academic health centers or community-based medical schools.

  14. An mHealth Framework to Improve Birth Outcomes in Benue State, Nigeria: A Study Protocol.

    Science.gov (United States)

    Ezeanolue, Echezona Edozie; Gbadamosi, Semiu Olatunde; Olawepo, John Olajide; Iwelunmor, Juliet; Sarpong, Daniel; Eze, Chuka; Ogidi, Amaka; Patel, Dina; Onoka, Chima

    2017-05-26

    The unprecedented coverage of mobile technology across the globe has led to an increase in the use of mobile health apps and related strategies to make health information available at the point of care. These strategies have the potential to improve birth outcomes, but are limited by the availability of Internet services, especially in resource-limited settings such as Nigeria. Our primary objective is to determine the feasibility of developing an integrated mobile health platform that is able to collect data from community-based programs, embed collected data into a smart card, and read the smart card using a mobile phone-based app without the need for Internet access. Our secondary objectives are to determine (1) the acceptability of the smart card among pregnant women and (2) the usability of the smart card by pregnant women and health facilities in rural Nigeria. We will leverage existing technology to develop a platform that integrates a database, smart card technology, and a mobile phone-based app to read the smart cards. We will recruit 300 pregnant women with one of the three conditions-HIV, hepatitis B virus infection, and sickle cell trait or disease-and four health facilities in their community. We will use Glasgow's Reach, Effectiveness, Adoption, Implementation, and Maintenance framework as a guide to assess the implementation, acceptability, and usability of the mHealth platform. We have recruited four health facilities and 300 pregnant women with at least one of the eligible conditions. Over the course of 3 months, we will complete the development of the mobile health platform and each participant will be offered a smart card; staff in each health facility will receive training on the use of the mobile health platform. Findings from this study could offer a new approach to making health data from pregnant women available at the point of delivery without the need for an Internet connection. This would allow clinicians to implement evidence

  15. Parallel Boltzmann machines : a mathematical model

    NARCIS (Netherlands)

    Zwietering, P.J.; Aarts, E.H.L.

    1991-01-01

    A mathematical model is presented for the description of parallel Boltzmann machines. The framework is based on the theory of Markov chains and combines a number of previously known results into one generic model. It is argued that parallel Boltzmann machines maximize a function consisting of a

  16. Attention: A Machine Learning Perspective

    DEFF Research Database (Denmark)

    Hansen, Lars Kai

    2012-01-01

    We review a statistical machine learning model of top-down task driven attention based on the notion of ‘gist’. In this framework we consider the task to be represented as a classification problem with two sets of features — a gist of coarse grained global features and a larger set of low...

  17. Quantum Logic Network for Cloning a State Near a Given One Based on Cavity QED

    International Nuclear Information System (INIS)

    Da-Wei, Zhang; Xiao-Qiang, Shao; Ai-Dong, Zhu

    2008-01-01

    A quantum logic network is constructed to simulate a cloning machine which copies states near a given one. Meanwhile, a scheme for implementing this cloning network based on the technique of cavity quantum electrodynamics (QED) is presented. It is easy to implement this network of cloning machine in the framework of cavity QED and feasible in the experiment. (general)

  18. Maximally Localized States and Quantum Corrections of Black Hole Thermodynamics in the Framework of a New Generalized Uncertainty Principle

    International Nuclear Information System (INIS)

    Zhang, Shao-Jun; Miao, Yan-Gang; Zhao, Ying-Jie

    2015-01-01

    As a generalized uncertainty principle (GUP) leads to the effects of the minimal length of the order of the Planck scale and UV/IR mixing, some significant physical concepts and quantities are modified or corrected correspondingly. On the one hand, we derive the maximally localized states—the physical states displaying the minimal length uncertainty associated with a new GUP proposed in our previous work. On the other hand, in the framework of this new GUP we calculate quantum corrections to the thermodynamic quantities of the Schwardzschild black hole, such as the Hawking temperature, the entropy, and the heat capacity, and give a remnant mass of the black hole at the end of the evaporation process. Moreover, we compare our results with that obtained in the frameworks of several other GUPs. In particular, we observe a significant difference between the situations with and without the consideration of the UV/IR mixing effect in the quantum corrections to the evaporation rate and the decay time. That is, the decay time can greatly be prolonged in the former case, which implies that the quantum correction from the UV/IR mixing effect may give rise to a radical rather than a tiny influence to the Hawking radiation.

  19. Measuring performance in off-patent drug markets: a methodological framework and empirical evidence from twelve EU Member States.

    Science.gov (United States)

    Kanavos, Panos

    2014-11-01

    This paper develops a methodological framework to help evaluate the performance of generic pharmaceutical policies post-patent expiry or after loss of exclusivity in non-tendering settings, comprising five indicators (generic availability, time delay to and speed of generic entry, number of generic competitors, price developments, and generic volume share evolution) and proposes a series of metrics to evaluate performance. The paper subsequently tests this framework across twelve EU Member States (MS) by using IMS data on 101 patent expired molecules over the 1998-2010 period. Results indicate that significant variation exists in generic market entry, price competition and generic penetration across the study countries. Size of a geographical market is not a predictor of generic market entry intensity or price decline. Regardless of geographic or product market size, many off patent molecules lack generic competitors two years after loss of exclusivity. The ranges in each of the five proposed indicators suggest, first, that there are numerous factors--including institutional ones--contributing to the success of generic entry, price decline and market penetration and, second, MS should seek a combination of supply and demand-side policies in order to maximise cost-savings from generics. Overall, there seems to be considerable potential for faster generic entry, uptake and greater generic competition, particularly for molecules at the lower end of the market. Copyright © 2014. Published by Elsevier Ireland Ltd.

  20. Predicting precompetitive state anxiety: using the 2 x 2 achievement goal framework.

    Science.gov (United States)

    Li, Chiung-Huang

    2013-10-01

    The goal was to examine the predictiveness of achievement goals for self-confidence, cognitive anxiety, and somatic anxiety using a prospective design. 160 high school athletes completed the 2 x 2 Achievement Goals Questionnaire for Sport after daily practice and the Revised Competitive State Anxiety Inventory-2 before an official competition. Using hierarchical regression analyses, mastery-approach goals were found as positive predictors of self-confidence and negative predictors of cognitive and somatic anxiety. In contrast, performance- and mastery-avoidance goals positively predicted cognitive and somatic anxiety. Also, performance-avoidance goals negatively predicted self-confidence. Generally, athletes who pursued task mastery and improvement of competence were less physically and cognitively anxious and more self-confident. However, athletes felt tense, worried, and had lower confidence when they endorsed avoidance forms of achievement goals.

  1. Web mining in soft computing framework: relevance, state of the art and future directions.

    Science.gov (United States)

    Pal, S K; Talwar, V; Mitra, P

    2002-01-01

    The paper summarizes the different characteristics of Web data, the basic components of Web mining and its different types, and the current state of the art. The reason for considering Web mining, a separate field from data mining, is explained. The limitations of some of the existing Web mining methods and tools are enunciated, and the significance of soft computing (comprising fuzzy logic (FL), artificial neural networks (ANNs), genetic algorithms (GAs), and rough sets (RSs) are highlighted. A survey of the existing literature on "soft Web mining" is provided along with the commercially available systems. The prospective areas of Web mining where the application of soft computing needs immediate attention are outlined with justification. Scope for future research in developing "soft Web mining" systems is explained. An extensive bibliography is also provided.

  2. Electric machines

    CERN Document Server

    Gross, Charles A

    2006-01-01

    BASIC ELECTROMAGNETIC CONCEPTSBasic Magnetic ConceptsMagnetically Linear Systems: Magnetic CircuitsVoltage, Current, and Magnetic Field InteractionsMagnetic Properties of MaterialsNonlinear Magnetic Circuit AnalysisPermanent MagnetsSuperconducting MagnetsThe Fundamental Translational EM MachineThe Fundamental Rotational EM MachineMultiwinding EM SystemsLeakage FluxThe Concept of Ratings in EM SystemsSummaryProblemsTRANSFORMERSThe Ideal n-Winding TransformerTransformer Ratings and Per-Unit ScalingThe Nonideal Three-Winding TransformerThe Nonideal Two-Winding TransformerTransformer Efficiency and Voltage RegulationPractical ConsiderationsThe AutotransformerOperation of Transformers in Three-Phase EnvironmentsSequence Circuit Models for Three-Phase Transformer AnalysisHarmonics in TransformersSummaryProblemsBASIC MECHANICAL CONSIDERATIONSSome General PerspectivesEfficiencyLoad Torque-Speed CharacteristicsMass Polar Moment of InertiaGearingOperating ModesTranslational SystemsA Comprehensive Example: The ElevatorP...

  3. Charging machine

    International Nuclear Information System (INIS)

    Medlin, J.B.

    1976-01-01

    A charging machine for loading fuel slugs into the process tubes of a nuclear reactor includes a tubular housing connected to the process tube, a charging trough connected to the other end of the tubular housing, a device for loading the charging trough with a group of fuel slugs, means for equalizing the coolant pressure in the charging trough with the pressure in the process tubes, means for pushing the group of fuel slugs into the process tube and a latch and a seal engaging the last object in the group of fuel slugs to prevent the fuel slugs from being ejected from the process tube when the pusher is removed and to prevent pressure liquid from entering the charging machine. 3 claims, 11 drawing figures

  4. Immobilizing Organic-Based Molecular Switches into Metal-Organic Frameworks: A Promising Strategy for Switching in Solid State.

    Science.gov (United States)

    Gui, Bo; Meng, Yi; Xie, Yang; Du, Ke; Sue, Andrew C-H; Wang, Cheng

    2018-01-01

    Organic-based molecular switches (OMS) are essential components for the ultimate miniaturization of nanoscale electronics and devices. For practical applications, it is often necessary for OMS to be incorporated into functional solid-state materials. However, the switching characteristics of OMS in solution are usually not transferrable to the solid state, presumably because of spatial confinement or inefficient conversion in densely packed solid phase. A promising way to circumvent this issue is harboring the functional OMS within the robust and porous environment of metal-organic frameworks (MOFs) as their organic components. In this feature article, recent research progress of OMS-based MOFs is briefly summarized. The switching behaviors of OMS under different stimuli (e.g., light, redox, pH, etc.) in the MOF state are first introduced. After that, the technological applications of these OMS-based MOFs in different areas, including CO 2 adsorption, gas separation, drug delivery, photodynamic therapy, and sensing, are outlined. Finally, perspectives and future challenges are discussed in the conclusion. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Genesis machines

    CERN Document Server

    Amos, Martyn

    2014-01-01

    Silicon chips are out. Today's scientists are using real, wet, squishy, living biology to build the next generation of computers. Cells, gels and DNA strands are the 'wetware' of the twenty-first century. Much smaller and more intelligent, these organic computers open up revolutionary possibilities. Tracing the history of computing and revealing a brave new world to come, Genesis Machines describes how this new technology will change the way we think not just about computers - but about life itself.

  6. The Framework for an Information Technology Strategic Roadmap for the United States Marine Corps: How Current Acquisitions Align to the Current Strategic Direction of the Department of Defense, Department of the Navy, and United States Marine Corps

    National Research Council Canada - National Science Library

    Garcia, Richard D; Sloan, Joshua K

    2008-01-01

    ... (IT) roadmap may comprise a "tipping point" for future warfighting effectiveness. This thesis begins the basis for a framework for an information technology strategic roadmap for the United States Marine Corps...

  7. Methodological framework for economical and controllable design of heat exchanger networks: Steady-state analysis, dynamic simulation, and optimization

    International Nuclear Information System (INIS)

    Masoud, Ibrahim T.; Abdel-Jabbar, Nabil; Qasim, Muhammad; Chebbi, Rachid

    2016-01-01

    Highlights: • HEN total annualized cost, heat recovery, and controllability are considered in the framework. • Steady-state and dynamic simulations are performed. • Effect of bypass on total annualized cost and controllability is reported. • Optimum bypass fractions are found from closed and open-loop efforts. - Abstract: The problem of interaction between economic design and control system design of heat exchanger networks (HENs) is addressed in this work. The controllability issues are incorporated in the classical design of HENs. A new methodological framework is proposed to account for both economics and controllability of HENs. Two classical design methods are employed, namely, Pinch and superstructure designs. Controllability measures such as relative gain array (RGA) and singular value decomposition (SVD) are used. The proposed framework also presents a bypass placement strategy for optimal control of the designed network. A case study is used to test the applicability of the framework and to assess both economics and controllability. The results indicate that the superstructure design is more economical and controllable compared to the Pinch design. The controllability of the designed HEN is evaluated using Aspen-HYSYS closed-loop dynamic simulator. In addition, a sensitivity analysis is performed to study the effect of bypass fractions on the total annualized cost and controllability of the designed HEN. The analysis shows that increasing any bypass fraction increases the total annualized cost. However, the trend with the total annualized cost was not observed with respect to the control effort manifested by minimizing the integral of the squared errors (ISE) between the controlled stream temperatures and their targets (set-points). An optimal ISE point is found at a certain bypass fraction, which does not correspond to the minimal total annualized cost. The bypass fractions are validated via open-loop simulation and the additional cooling and

  8. Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010.

    Science.gov (United States)

    de Bruijn, Berry; Cherry, Colin; Kiritchenko, Svetlana; Martin, Joel; Zhu, Xiaodan

    2011-01-01

    As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge. The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline. Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set. The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second). For all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.

  9. Estimating the value of life and injury for pedestrians using a stated preference framework.

    Science.gov (United States)

    Niroomand, Naghmeh; Jenkins, Glenn P

    2017-09-01

    The incidence of pedestrian death over the period 2010 to 2014 per 1000,000 in North Cyprus is about 2.5 times that of the EU, with 10.5 times more pedestrian road injuries than deaths. With the prospect of North Cyprus entering the EU, many investments need to be undertaken to improve road safety in order to reach EU benchmarks. We conducted a stated choice experiment to identify the preferences and tradeoffs of pedestrians in North Cyprus for improved walking times, pedestrian costs, and safety. The choice of route was examined using mixed logit models to obtain the marginal utilities associated with each attribute of the routes that consumers chose. These were used to estimate the individuals' willingness to pay (WTP) to save walking time and to avoid pedestrian fatalities and injuries. We then used the results to obtain community-wide estimates of the value of a statistical life (VSL) saved, the value of an injury (VI) prevented, and the value per hour of walking time saved. The estimate of the VSL was €699,434 and the estimate of VI was €20,077. These values are consistent, after adjusting for differences in incomes, with the median results of similar studies done for EU countries. The estimated value of time to pedestrians is €7.20 per person hour. The ratio of deaths to injuries is much higher for pedestrians than for road accidents, and this is completely consistent with the higher estimated WTP to avoid a pedestrian accident than to avoid a car accident. The value of time of €7.20 is quite high relative to the wages earned. Findings provide a set of information on the VRR for fatalities and injuries and the value of pedestrian time that is critical for conducing ex ante appraisals of investments to improve pedestrian safety. Copyright © 2017 National Safety Council and Elsevier Ltd. All rights reserved.

  10. Estimating the Value of Life, Injury, and Travel Time Saved Using a Stated Preference Framework.

    Science.gov (United States)

    Niroomand, Naghmeh; Jenkins, Glenn P

    2016-06-01

    The incidence of fatality over the period 2010-2014 from automobile accidents in North Cyprus is 2.75 times greater than the average for the EU. With the prospect of North Cyprus entering the EU, many investments will need to be undertaken to improve road safety in order to reach EU benchmarks. The objective of this study is to provide local estimates of the value of a statistical life and injury along with the value of time savings. These are among the parameter values needed for the evaluation of the change in the expected incidence of automotive accidents and time savings brought about by such projects. In this study we conducted a stated choice experiment to identify the preferences and tradeoffs of automobile drivers in North Cyprus for improved travel times, travel costs, and safety. The choice of route was examined using mixed logit models to obtain the marginal utilities associated with each attribute of the routes that consumers choose. These estimates were used to assess the individuals' willingness to pay (WTP) to avoid fatalities and injuries and to save travel time. We then used the results to obtain community-wide estimates of the value of a statistical life (VSL) saved, the value of injury (VI) prevented, and the value per hour of travel time saved. The estimates for the VSL range from €315,293 to €1,117,856 and the estimates of VI from € 5,603 to € 28,186. These values are consistent, after adjusting for differences in incomes, with the median results of similar studies done for EU countries. Copyright © 2016 Elsevier Ltd. All rights reserved.

  11. Regularized maximum correntropy machine

    KAUST Repository

    Wang, Jim Jing-Yan; Wang, Yunji; Jing, Bing-Yi; Gao, Xin

    2015-01-01

    In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

  12. Regularized maximum correntropy machine

    KAUST Repository

    Wang, Jim Jing-Yan

    2015-02-12

    In this paper we investigate the usage of regularized correntropy framework for learning of classifiers from noisy labels. The class label predictors learned by minimizing transitional loss functions are sensitive to the noisy and outlying labels of training samples, because the transitional loss functions are equally applied to all the samples. To solve this problem, we propose to learn the class label predictors by maximizing the correntropy between the predicted labels and the true labels of the training samples, under the regularized Maximum Correntropy Criteria (MCC) framework. Moreover, we regularize the predictor parameter to control the complexity of the predictor. The learning problem is formulated by an objective function considering the parameter regularization and MCC simultaneously. By optimizing the objective function alternately, we develop a novel predictor learning algorithm. The experiments on two challenging pattern classification tasks show that it significantly outperforms the machines with transitional loss functions.

  13. Electronic gaming machines and gambling disorder: A cross-cultural comparison between treatment-seeking subjects from Brazil and the United States.

    Science.gov (United States)

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

    2015-12-15

    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. 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. We found that the Brazilian sample was younger, predominantly male, less likely to be Caucasian, more likely to be partnered, tended to have a faster progression from recreational gambling to GD, and were more likely to endorse chasing losses. 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. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  14. New Theoretical Frameworks for Machine Learning

    Science.gov (United States)

    2008-09-15

    both the low-level research (paper writing, slide preparing, conference calling, coffee drinking) and high level research (idea sharing, reference...pointing, and again coffee drinking) a lot of fun. Particular gratitude goes to Alina Beygelzimer, John Langford, Ke Yang, Adam Kalai, Anupam Gupta, Jason...a hierarchical clustering such that desired clustering is close to some pruning of this tree (which a user could navigate), then we can develop a

  15. A Concrete Framework for Environment Machines

    DEFF Research Database (Denmark)

    Biernacka, Malgorzata; Danvy, Olivier

    2007-01-01

    We materialize the common understanding that calculi with explicit substitutions provide an intermediate step between an abstract specification of substitution in the lambda-calculus and its concrete implementations. To this end, we go back to Curien’s original calculus of closures (an early...

  16. Java online monitoring framework

    International Nuclear Information System (INIS)

    Ronan, M.; Kirkby, D.; Johnson, A.S.; Groot, D. de

    1997-10-01

    An online monitoring framework has been written in the Java Language Environment to develop applications for monitoring special purpose detectors during commissioning of the PEP-II Interaction Region. PEP-II machine parameters and signals from several of the commissioning detectors are logged through VxWorks/EPICS and displayed by Java display applications. Remote clients are able to monitor the machine and detector performance using graphical displays and analysis histogram packages. In this paper, the design and implementation of the object-oriented Java framework is described. Illustrations of data acquisition, display and histograming applications are also given

  17. 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...

  18. Resting State fMRI in the moving fetus: a robust framework for motion, bias field and spin history correction.

    Science.gov (United States)

    Ferrazzi, Giulio; Kuklisova Murgasova, Maria; Arichi, Tomoki; Malamateniou, Christina; Fox, Matthew J; Makropoulos, Antonios; Allsop, Joanna; Rutherford, Mary; Malik, Shaihan; Aljabar, Paul; Hajnal, Joseph V

    2014-11-01

    There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies. Copyright © 2014 Elsevier Inc. All rights reserved.

  19. Model Based Mission Assurance in a Model Based Systems Engineering (MBSE) Framework: State-of-the-Art Assessment

    Science.gov (United States)

    Cornford, Steven L.; Feather, Martin S.

    2016-01-01

    This report explores the current state of the art of Safety and Mission Assurance (S&MA) in projects that have shifted towards Model Based Systems Engineering (MBSE). Its goal is to provide insight into how NASA's Office of Safety and Mission Assurance (OSMA) should respond to this shift. In MBSE, systems engineering information is organized and represented in models: rigorous computer-based representations, which collectively make many activities easier to perform, less error prone, and scalable. S&MA practices must shift accordingly. The "Objective Structure Hierarchies" recently developed by OSMA provide the framework for understanding this shift. Although the objectives themselves will remain constant, S&MA practices (activities, processes, tools) to achieve them are subject to change. This report presents insights derived from literature studies and interviews. The literature studies gleaned assurance implications from reports of space-related applications of MBSE. The interviews with knowledgeable S&MA and MBSE personnel discovered concerns and ideas for how assurance may adapt. Preliminary findings and observations are presented on the state of practice of S&MA with respect to MBSE, how it is already changing, and how it is likely to change further. Finally, recommendations are provided on how to foster the evolution of S&MA to best fit with MBSE.

  20. Representational Machines

    DEFF Research Database (Denmark)

    Photography not only represents space. Space is produced photographically. Since its inception in the 19th century, photography has brought to light a vast array of represented subjects. Always situated in some spatial order, photographic representations have been operatively underpinned by social...... to the enterprises of the medium. This is the subject of Representational Machines: How photography enlists the workings of institutional technologies in search of establishing new iconic and social spaces. Together, the contributions to this edited volume span historical epochs, social environments, technological...... possibilities, and genre distinctions. Presenting several distinct ways of producing space photographically, this book opens a new and important field of inquiry for photography research....

  1. Shear machines

    International Nuclear Information System (INIS)

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

    1980-01-01

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

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

    Science.gov (United States)

    Laban, Shaban; El-Desouky, Aly

    2013-04-01

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

  3. State-space approaches for modelling and control in financial engineering systems theory and machine learning methods

    CERN Document Server

    Rigatos, Gerasimos G

    2017-01-01

    The book conclusively solves problems associated with the control and estimation of nonlinear and chaotic dynamics in financial systems when these are described in the form of nonlinear ordinary differential equations. It then addresses problems associated with the control and estimation of financial systems governed by partial differential equations (e.g. the Black–Scholes partial differential equation (PDE) and its variants). Lastly it an offers optimal solution to the problem of statistical validation of computational models and tools used to support financial engineers in decision making. The application of state-space models in financial engineering means that the heuristics and empirical methods currently in use in decision-making procedures for finance can be eliminated. It also allows methods of fault-free performance and optimality in the management of assets and capitals and methods assuring stability in the functioning of financial systems to be established. Covering the following key are...

  4. Toward a Multi-City Framework for Urban GHG Estimation in the United States: Methods, Uncertainties, and Future Goals

    Science.gov (United States)

    Mueller, K. L.; Callahan, W.; Davis, K. J.; Dickerson, R. R.; Duren, R. M.; Gurney, K. R.; Karion, A.; Keeling, R. F.; Kim, J.; Lauvaux, T.; Miller, C. E.; Shepson, P. B.; Turnbull, J. C.; Weiss, R. F.; Whetstone, J. R.

    2017-12-01

    City and State governments are increasingly interested in mitigating greenhouse gas (GHG) emissions to improve sustainability within their jurisdictions. Estimation of urban GHG emissions remains an active research area with many sources of uncertainty. To support the effort of improving measurement of trace gas emissions in city environments, several federal agencies along with academic, research, and private entities have been working within a handful of domestic metropolitan areas to improve both (1) the assessment of GHG emissions accuracy using a variety of measurement technologies, and (2) the tools that can better assess GHG inventory data at urban mitigation scales based upon these measurements. The National Institute of Standards and Technology (NIST) activities have focused on three areas, or testbeds: Indianapolis (INFLUX experiment), Los Angeles (the LA Megacities project), and the Northeastern Corridor areas encompassing Washington and Baltimore (the NEC/BW GHG Measurements project). These cities represent diverse meteorological, terrain, demographic, and emissions characteristics having a broad range of complexities. To date this research has involved multiple measurement systems and integrated observing approaches, all aimed at advancing development of a robust, science-base upon which higher accuracy quantification approaches can rest. Progress toward such scientifically robust, widely-accepted emissions quantification methods will rely upon continuous performance assessment. Such assessment is challenged by the complexities of cities themselves (e.g., population, urban form) along with the many variables impacting a city's technological ability to estimate its GHG emissions (e.g., meteorology, density of observations). We present the different NIST testbeds and a proposal to initiate conceptual development of a reference framework supporting the comparison of multi-city GHG emissions estimates. Such a reference framework has potential to provide

  5. 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.

  6. 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.

  7. Machine Protection

    CERN Document Server

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an ...

  8. Gaussian processes for machine learning.

    Science.gov (United States)

    Seeger, Matthias

    2004-04-01

    Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to infinite (countably or continuous) index sets. GPs have been applied in a large number of fields to a diverse range of ends, and very many deep theoretical analyses of various properties are available. This paper gives an introduction to Gaussian processes on a fairly elementary level with special emphasis on characteristics relevant in machine learning. It draws explicit connections to branches such as spline smoothing models and support vector machines in which similar ideas have been investigated. Gaussian process models are routinely used to solve hard machine learning problems. They are attractive because of their flexible non-parametric nature and computational simplicity. Treated within a Bayesian framework, very powerful statistical methods can be implemented which offer valid estimates of uncertainties in our predictions and generic model selection procedures cast as nonlinear optimization problems. Their main drawback of heavy computational scaling has recently been alleviated by the introduction of generic sparse approximations.13,78,31 The mathematical literature on GPs is large and often uses deep concepts which are not required to fully understand most machine learning applications. In this tutorial paper, we aim to present characteristics of GPs relevant to machine learning and to show up precise connections to other "kernel machines" popular in the community. Our focus is on a simple presentation, but references to more detailed sources are provided.

  9. Model-based machine learning.

    Science.gov (United States)

    Bishop, Christopher M

    2013-02-13

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

  10. Sequential Indentation Tests to Investigate the Influence of Confining Stress on Rock Breakage by Tunnel Boring Machine Cutter in a Biaxial State

    Science.gov (United States)

    Liu, Jie; Cao, Ping; Han, Dongya

    2016-04-01

    The influence of confining stress on rock breakage by a tunnel boring machine cutter was investigated by conducting sequential indentation tests in a biaxial state. Combined with morphology measurements of breaking grooves and an analysis of surface and internal crack propagation between nicks, the effects of maximum confining stress and minimum stress on indentation efficiency, crack propagation and chip formation were investigated. Indentation tests and morphology measurements show that increasing a maximum confining stress will result in increased consumed energy in indentations, enlarged groove volumes and promoted indentation efficiency when the corresponding minimum confining stress is fixed. The energy consumed in indentations will increase with increase in minimum confining stress, however, because of the decreased groove volumes as the minimum confining stress increases, the efficiency will decrease. Observations of surface crack propagation show that more intensive fractures will be induced as the maximum confining stress increases, whereas the opposite occurs for an increase of minimum confining stress. An observation of the middle section, cracks and chips shows that as the maximum confining stress increases, chips tend to form in deeper parts when the minimum confining stress is fixed, whereas they tend to formed in shallower parts as the minimum confining stress increases when the maximum confining stress is fixed.

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

    Science.gov (United States)

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

    2017-08-01

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

  12. Reducing Deadline Miss Rate for Grid Workloads running in Virtual Machines: a deadline-aware and adaptive approach

    CERN Document Server

    Khalid, Omer; Anthony, Richard; Petridis, Miltos

    2011-01-01

    This thesis explores three major areas of research; integration of virutalization into sci- entific grid infrastructures, evaluation of the virtualization overhead on HPC grid job’s performance, and optimization of job execution times to increase their throughput by reducing job deadline miss rate. Integration of the virtualization into the grid to deploy on-demand virtual machines for jobs in a way that is transparent to the end users and have minimum impact on the existing system poses a significant challenge. This involves the creation of virtual machines, decompression of the operating system image, adapting the virtual environ- ment to satisfy software requirements of the job, constant update of the job state once it’s running with out modifying batch system or existing grid middleware, and finally bringing the host machine back to a consistent state. To facilitate this research, an existing and in production pilot job framework has been modified to deploy virtual machines on demand on the grid using...

  13. 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...

  14. State-of-the-Art: Research Theoretical Framework of Information Systems Implementation Research in the Health Sector in Sub-Saharan Africa

    DEFF Research Database (Denmark)

    Tetteh, Godwin Kofi

    2014-01-01

    This study is about the state-of-the-art of reference theories and theoretical framework of information systems implementation research in the health industry in the Sub-Saharan countries from a process perspective. A process – variance framework, Poole et al, (2000), Markus & Robey, (1988......) and Shaw & Jarvenpaa, (1997) is employed to examine reference theories employed in research conducted on information systems implementation in the health sector in the Sub-Saharan region and published between 2003 and 2013. Using a number of key words and searching on a number of databases, EBSCO, CSA...... the process theoretical framework to enhance our insight into successful information systems implementation in the region. It is our optimism that the process based theoretical framework will be useful for, information system practitioners and organisational managers and researchers in the health sector...

  15. Machine Protection

    International Nuclear Information System (INIS)

    Schmidt, R

    2014-01-01

    The protection of accelerator equipment is as old as accelerator technology and was for many years related to high-power equipment. Examples are the protection of powering equipment from overheating (magnets, power converters, high-current cables), of superconducting magnets from damage after a quench and of klystrons. The protection of equipment from beam accidents is more recent. It is related to the increasing beam power of high-power proton accelerators such as ISIS, SNS, ESS and the PSI cyclotron, to the emission of synchrotron light by electron–positron accelerators and FELs, and to the increase of energy stored in the beam (in particular for hadron colliders such as LHC). Designing a machine protection system requires an excellent understanding of accelerator physics and operation to anticipate possible failures that could lead to damage. Machine protection includes beam and equipment monitoring, a system to safely stop beam operation (e.g. dumping the beam or stopping the beam at low energy) and an interlock system providing the glue between these systems. The most recent accelerator, the LHC, will operate with about 3 × 10 14 protons per beam, corresponding to an energy stored in each beam of 360 MJ. This energy can cause massive damage to accelerator equipment in case of uncontrolled beam loss, and a single accident damaging vital parts of the accelerator could interrupt operation for years. This article provides an overview of the requirements for protection of accelerator equipment and introduces the various protection systems. Examples are mainly from LHC, SNS and ESS

  16. 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.

  17. A comparative analysis of Science-Technology-Society standards in elementary, middle and high school state science curriculum frameworks

    Science.gov (United States)

    Tobias, Karen Marie

    An analysis of curriculum frameworks from the fifty states to ascertain the compliance with the National Science Education Standards for integrating Science-Technology-Society (STS) themes is reported within this dissertation. Science standards for all fifty states were analyzed to determine if the STS criteria were integrated at the elementary, middle, and high school levels of education. The analysis determined the compliance level for each state, then compared each educational level to see if the compliance was similar across the levels. Compliance is important because research shows that using STS themes in the science classroom increases the student's understanding of the concepts, increases the student's problem solving skills, increases the student's self-efficacy with respect to science, and students instructed using STS themes score well on science high stakes tests. The two hypotheses for this study are: (1) There is no significant difference in the degree of compliance to Science-Technology-Society themes (derived from National Science Education Standards) between the elementary, middle, and high school levels. (2) There is no significant difference in the degree of compliance to Science-Technology-Society themes (derived from National Science Education Standards) between the elementary, middle, and high school level when examined individually. The Analysis of Variance F ratio was used to determine the variance between and within the three educational levels. This analysis addressed hypothesis one. The Analysis of Variance results refused to reject the null hypothesis, meaning there is significant difference in the compliance to STS themes between the elementary, middle and high school educational levels. The Chi-Square test was the statistical analysis used to compare the educational levels for each individual criterion. This analysis addressed hypothesis two. The Chi-Squared results showed that none of the states were equally compliant with each

  18. A platform independent framework for Statecharts code generation

    International Nuclear Information System (INIS)

    Andolfato, L.; Chiozzi, G.; Migliorini, N.; Morales, C.

    2012-01-01

    Control systems for telescopes and their instruments are reactive systems very well suited to be modelled using Statecharts formalism. The World Wide Web Consortium is working on a new standard called SCXML that specifies XML notation to describe Statecharts and provides a well defined operational semantic for run-time interpretation of the SCXML models. This paper presents a generic application framework for reactive non realtime systems based on interpreted Statecharts. The framework consists of a model to text transformation tool and an SCXML interpreter. The tool generates from UML state machine models the SCXML representation of the state machines as well as the application skeletons for the supported software platforms. An abstraction layer propagates the events from the middle-ware to the SCXML interpreter facilitating the support for different software platforms. This project benefits from the positive experience gained in several years of development of coordination and monitoring applications for the telescope control software domain using Model Driven Development technologies. (authors)

  19. Metal-organic frameworks with dynamic interlocked components

    Science.gov (United States)

    Vukotic, V. Nicholas; Harris, Kristopher J.; Zhu, Kelong; Schurko, Robert W.; Loeb, Stephen J.

    2012-06-01

    The dynamics of mechanically interlocked molecules such as rotaxanes and catenanes have been studied in solution as examples of rudimentary molecular switches and machines, but in this medium, the molecules are randomly dispersed and their motion incoherent. As a strategy for achieving a higher level of molecular organization, we have constructed a metal-organic framework material using a [2]rotaxane as the organic linker and binuclear Cu(II) units as the nodes. Activation of the as-synthesized material creates a void space inside the rigid framework that allows the soft macrocyclic ring of the [2]rotaxane to rotate rapidly, unimpeded by neighbouring molecular components. Variable-temperature 13C and 2H solid-state NMR experiments are used to characterize the nature and rate of the dynamic processes occurring inside this unique material. These results provide a blueprint for the future creation of solid-state molecular switches and molecular machines based on mechanically interlocked molecules.

  20. Generalized hydrogeologic framework and groundwater budget for a groundwater availability study for the glacial aquifer system of the United States

    Science.gov (United States)

    Reeves, Howard W.; Bayless, E. Randall; Dudley, Robert W.; Feinstein, Daniel T.; Fienen, Michael N.; Hoard, Christopher J.; Hodgkins, Glenn A.; Qi, Sharon L.; Roth, Jason L.; Trost, Jared J.

    2017-12-14

    The glacial aquifer system groundwater availability study seeks to quantify (1) the status of groundwater resources in the glacial aquifer system, (2) how these resources have changed over time, and (3) likely system response to future changes in anthropogenic and environmental conditions. The glacial aquifer system extends from Maine to Alaska, although the focus of this report is the part of the system in the conterminous United States east of the Rocky Mountains. The glacial sand and gravel principal aquifer is the largest source of public and self-supplied industrial supply for any principal aquifer and also is an important source for irrigation supply. Despite its importance for water supply, water levels in the glacial aquifer system are generally stable varying with climate and only locally from pumping. The hydrogeologic framework developed for this study includes the information from waterwell records and classification of material types from surficial geologic maps into likely aquifers dominated by sand and gravel deposits. Generalized groundwater budgets across the study area highlight the variation in recharge and discharge primarily driven by climate.

  1. Ecosystem Health Assessment at County-Scale Using the Pressure-State-Response Framework on the Loess Plateau, China

    Directory of Open Access Journals (Sweden)

    Delin Liu

    2016-12-01

    Full Text Available Assessing ecosystem health is helpful to determine reasonable eco-environmental restoration and resource management strategies. Based on a pressure-state-response (PSR framework, a set of comprehensive indicators including natural, social and economic aspects was proposed and applied for assessing the ecosystem health of Yuanzhou County, Loess Plateau, Ningxia Province, China. The basic data used to calculate the values of the assessment indicators include Landsat TM image and socio-economic data, and remote sensing (RS and the geographic information system (GIS were used to process image data. The results showed that the ecosystem health conditions of most townships in Yuanzhou County were at the moderately healthy level, three townships were at the healthy level, and only two townships were at the unhelathy level; the areas (percentage at the unhealthy, moderately healthy and healthy levels were 443.91 km2 (12.66%, 2438.75 km2 (69.54% and 624.50 km2 (17.81%, respectively. The results could provide useful information for local residents and the government to take measures to improve the health conditions of their township ecosystem.

  2. Ex-ante assessment of the Spanish Occupational Health and Safety Strategy (2007–2012) using a State Space framework

    International Nuclear Information System (INIS)

    Carnero, María del Carmen; Pedregal, Diego José

    2013-01-01

    Spain is above the EU-27, EU-25 and EU-15 average in the number of serious work accidents, and it is estimated that Spain is five years behind the rest of Europe in reducing such accidents. To address the problem of the high number of occupational accidents, in 2007 the Spanish Occupational Health and Safety Strategy (SOHSS, 2007–2012) was launched, with the general aim of achieving a continuous and significant reduction in work accidents, approaching the EU average in occupational accidents and illness. This article is an attempt to assess the extent to which the aims of the SOHSS have been satisfied by predicting incidence rates for different levels of accident severity (slight, serious and fatal), accidents that do not require sick leave, and commuting accidents (slight, serious and fatal). With these objectives in mind, both Univariate and Multivariate Unobserved Components models are used in an enhanced State Space framework in order to deal with the irregular sampling interval of the data from 1998 to 2010.

  3. 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.

  4. 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...

  5. The Machine / Job Features Mechanism

    Energy Technology Data Exchange (ETDEWEB)

    Alef, M. [KIT, Karlsruhe; Cass, T. [CERN; Keijser, J. J. [NIKHEF, Amsterdam; McNab, A. [Manchester U.; Roiser, S. [CERN; Schwickerath, U. [CERN; Sfiligoi, I. [Fermilab

    2017-11-22

    Within the HEPiX virtualization group and the Worldwide LHC Computing Grid’s Machine/Job Features Task Force, a mechanism has been developed which provides access to detailed information about the current host and the current job to the job itself. This allows user payloads to access meta information, independent of the current batch system or virtual machine model. The information can be accessed either locally via the filesystem on a worker node, or remotely via HTTP(S) from a webserver. This paper describes the final version of the specification from 2016 which was published as an HEP Software Foundation technical note, and the design of the implementations of this version for batch and virtual machine platforms. We discuss early experiences with these implementations and how they can be exploited by experiment frameworks.

  6. Traditional machining processes research advances

    CERN Document Server

    2015-01-01

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

  7. Relation between sedimentary framework and hydrogeology in the Guarani Aquifer System in São Paulo state, Brazil

    Science.gov (United States)

    Hirata, Ricardo; Gesicki, Ana; Sracek, Ondra; Bertolo, Reginaldo; Giannini, Paulo César; Aravena, Ramón

    2011-04-01

    This paper presents the results of a new investigation of the Guarani Aquifer System (SAG) in São Paulo state. New data were acquired about sedimentary framework, flow pattern, and hydrogeochemistry. The flow direction in the north of the state is towards the southwest and not towards the west as expected previously. This is linked to the absence of SAG outcrop in the northeast of São Paulo state. Both the underlying Pirambóia Formation and the overlying Botucatu Formation possess high porosity (18.9% and 19.5%, respectively), which was not modified significantly by diagenetic changes. Investigation of sediments confirmed a zone of chalcedony cement close to the SAG outcrop and a zone of calcite cement in the deep confined zone. The main events in the SAG post-sedimentary history were: (1) adhesion of ferrugineous coatings on grains, (2) infiltration of clays in eodiagenetic stage, (3) regeneration of coatings with formation of smectites, (4) authigenic overgrowth of quartz and K-feldspar in advanced eodiagenetic stage, (5) bitumen cementation of Pirambóia Formation in mesodiagenetic stage, (6) cementation by calcite in mesodiagenetic and telodiagenetic stages in Pirambóia Formation, (7) formation of secondary porosity by dissolution of unstable minerals after appearance of hydraulic gradient and penetration of the meteoric water caused by the uplift of the Serra do Mar coastal range in the Late Cretaceous, (8) authigenesis of kaolinite and amorphous silica in unconfined zone of the SAG and cation exchange coupled with the dissolution of calcite at the transition between unconfined and confined zone, and (9) authigenesis of analcime in the confined SAG zone. The last two processes are still under operation. The deep zone of the SAG comprises an alkaline pH, Na-HCO 3 groundwater type with old water and enriched δ 13C values (-18.8) close to the SAG outcrop. This is consistent with a conceptual geochemical model of the SAG, suggesting dissolution of calcite

  8. The Milling Assistant, Case-Based Reasoning, and machining strategy: A report on the development of automated numerical control programming systems at New Mexico State University

    Energy Technology Data Exchange (ETDEWEB)

    Burd, W. [Sandia National Labs., Albuquerque, NM (United States); Culler, D.; Eskridge, T.; Cox, L.; Slater, T. [New Mexico State Univ., Las Cruces, NM (United States)

    1993-08-01

    The Milling Assistant (MA) programming system demonstrates the automated development of tool paths for Numerical Control (NC) machine tools. By integrating a Case-Based Reasoning decision processor with a commercial CAD/CAM software, intelligent tool path files for milled and point-to-point features can be created. The operational system is capable of reducing the time required to program a variety of parts and improving product quality by collecting and utilizing ``best of practice`` machining strategies.

  9. Popular conceptions of nationhood in old and new European member states: Partial support for the ethnic-civic framework

    NARCIS (Netherlands)

    Janmaat, J.G.

    2006-01-01

    One of the most influential theories in the study of nationalism has been the ethnic-East/civic-West framework developed by Hans Kohn. Using the 2002 Eurobarometer survey on national identity and building on earlier survey studies, this article examines whether the Kohn framework is valid at the

  10. Machine-learned and codified synthesis parameters of oxide materials

    Science.gov (United States)

    Kim, Edward; Huang, Kevin; Tomala, Alex; Matthews, Sara; Strubell, Emma; Saunders, Adam; McCallum, Andrew; Olivetti, Elsa

    2017-09-01

    Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.

  11. Addiction Machines

    Directory of Open Access Journals (Sweden)

    James Godley

    2011-10-01

    Full Text Available Entry into the crypt William Burroughs shared with his mother opened and shut around a failed re-enactment of William Tell’s shot through the prop placed upon a loved one’s head. The accidental killing of his wife Joan completed the installation of the addictation machine that spun melancholia as manic dissemination. An early encryptment to which was added the audio portion of abuse deposited an undeliverable message in WB. Wil- liam could never tell, although his corpus bears the in- scription of this impossibility as another form of pos- sibility. James Godley is currently a doctoral candidate in Eng- lish at SUNY Buffalo, where he studies psychoanalysis, Continental philosophy, and nineteenth-century litera- ture and poetry (British and American. His work on the concept of mourning and “the dead” in Freudian and Lacanian approaches to psychoanalytic thought and in Gothic literature has also spawned an essay on zombie porn. Since entering the Academy of Fine Arts Karlsruhe in 2007, Valentin Hennig has studied in the classes of Sil- via Bächli, Claudio Moser, and Corinne Wasmuht. In 2010 he spent a semester at the Dresden Academy of Fine Arts. His work has been shown in group exhibi- tions in Freiburg and Karlsruhe.

  12. Machine musicianship

    Science.gov (United States)

    Rowe, Robert

    2002-05-01

    The training of musicians begins by teaching basic musical concepts, a collection of knowledge commonly known as musicianship. Computer programs designed to implement musical skills (e.g., to make sense of what they hear, perform music expressively, or compose convincing pieces) can similarly benefit from access to a fundamental level of musicianship. Recent research in music cognition, artificial intelligence, and music theory has produced a repertoire of techniques that can make the behavior of computer programs more musical. Many of these were presented in a recently published book/CD-ROM entitled Machine Musicianship. For use in interactive music systems, we are interested in those which are fast enough to run in real time and that need only make reference to the material as it appears in sequence. This talk will review several applications that are able to identify the tonal center of musical material during performance. Beyond this specific task, the design of real-time algorithmic listening through the concurrent operation of several connected analyzers is examined. The presentation includes discussion of a library of C++ objects that can be combined to perform interactive listening and a demonstration of their capability.

  13. Tobacco control and the World Trade Organization: mapping member states' positions after the framework convention on tobacco control.

    Science.gov (United States)

    Eckhardt, Jappe; Holden, Chris; Callard, Cynthia D

    2016-11-01

    To note the frequency of discussions and disputes about tobacco control measures at the World Trade Organization (WTO) before and after the coming into force of the Framework Convention on Tobacco Control (FCTC). To review trends or patterns in the positions taken by members of the WTO with respect to tobacco control measures. To discuss possible explanations for these observed trends/patterns. We gathered data on tobacco-related disputes in the WTO since its establishment in 1995 and its forerunner, the General Agreement on Tariffs and Trade (GATT), prior-FCTC and post-FCTC. We also looked at debates on tobacco control measures within the WTO more broadly. To this end, we classified and coded the positions of WTO member states during discussions on tobacco control and the FCTC, from 1995 until 2013, within the Technical Barriers to Trade (TBT) Committee and the Trade-Related Aspects of Intellectual Property Rights (TRIPS) Council. There is a growing interest within the WTO for tobacco-related issues and opposition to tobacco control measures is moving away from high-income countries towards low(er) income countries. The growing prominence of tobacco issues in the WTO can be attributed at least in part to the fact that during the past decade tobacco firms have been marginalised from the domestic policy-making process in many countries, which has forced them to look for other ways and forums to influence decision-making. Furthermore, the finding that almost all recent opposition within the WTO to stronger tobacco regulations came from developing countries is consistent with a relative shift of transnational tobacco companies' lobbying efforts from developed to developing countries. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  14. 23 February 2010 - Polish Under Secretary of State, Ministry of Science and Higher Education, J. Szwed visiting CERN installations with sLHC Project Office T. Kurtyka and Machine Protection and Electrical Integrity Group Leader A. Siemko.

    CERN Multimedia

    Michel Blanc

    2010-01-01

    Tirage 1002023-01: In LHCb experimental area with Machine Protection and Electrical Integrity Group Leader A. Siemko; Mission Counselor M. Cichucka; Counselor to the Minister M. Klimkiewicz, Under Secretary of State J. Szwed; LHCb Collaboration, national group leader, Henryk Niewodniczanski Institut of Nuclear Physics G. Polok, Collaboration Spokesperson A. Golutvin and Delegate to CERN Council A. Zalewska. Tirage 28: Visiting the Computing Centre with IT Department Head F. Hemmer Tirage 49: In CMS Control centre, buiding 354 with Collaboration Spokesperson G. Tonelli and CMS Collaboration, national group leader, University of Warsaw J. Krolikowski. Tirage 62: visiting ALICE exhibition area and counting room with Collaboration Spokesperson J. Schukraft. Tirage 82-99: Under Secretary of State address to the Polish Community Tirage 82: Machine Protection and Electrical Integrity Group Leader A. Siemko Tirage 83: Polish Delegate to CERN Council A. Zalewska. Tirage 85: Directorate Office E. Rondio Tirage 86: ATLA...

  15. A multi-modal geological investigation framework for subsurface modeling and kinematic monitoring of a slow-moving landslide complex in Colorado, United States

    Science.gov (United States)

    Lowry, B. W.; Zhou, W.; Smartgeo

    2010-12-01

    The Muddy Creek landslide complex is a large area of active and reactivating landslides that impact the operation of both a state highway and Paonia Reservoir in Gunnison County, Colorado, United States. Historically, the monitoring of this slide has been investigated using disparate techniques leading to protracted analysis and project knowledge attrition. We present an integrated, data-driven investigation framework that supports continued kinematic monitoring, document cataloging, and subsurface modeling of the landslide complex. A geospatial information system (GIS) was integrated with a visual programming based subsurface model to facilitate modular integration of monitoring data with borehole information. Subsurface modeling was organized by material type and activity state based on multiple sources of kinematic measurement. The framework is constructed to modularly integrate remotely sensed imagery and other spatial datasets such as ASTER, InSAR, and LiDAR derived elevation products as more precise datasets become available. The framework allows for terrestrial LiDAR survey error estimation, borehole siting, and placement of wireless sensor (GPS, accelerometers, geophysical ) networks for optimized spatial relevance and utility. Coordinated spatial referencing within the GIS facilitates geotechnical and hydrogeological modeling input generation and common display of modeling outputs. Kinematic data fusion techniques are accomplished with integration of instrumentation, surficial feature tracking, subsurface classification, and 3D interpolation. The framework includes dynamic decision support including landslide dam failure estimates, back-flooding scenario planning that can be accessed by multiple agencies and stakeholders.

  16. Game-powered machine learning.

    Science.gov (United States)

    Barrington, Luke; Turnbull, Douglas; Lanckriet, Gert

    2012-04-24

    Searching for relevant content in a massive amount of multimedia information is facilitated by accurately annotating each image, video, or song with a large number of relevant semantic keywords, or tags. We introduce game-powered machine learning, an integrated approach to annotating multimedia content that combines the effectiveness of human computation, through online games, with the scalability of machine learning. We investigate this framework for labeling music. First, a socially-oriented music annotation game called Herd It collects reliable music annotations based on the "wisdom of the crowds." Second, these annotated examples are used to train a supervised machine learning system. Third, the machine learning system actively directs the annotation games to collect new data that will most benefit future model iterations. Once trained, the system can automatically annotate a corpus of music much larger than what could be labeled using human computation alone. Automatically annotated songs can be retrieved based on their semantic relevance to text-based queries (e.g., "funky jazz with saxophone," "spooky electronica," etc.). Based on the results presented in this paper, we find that actively coupling annotation games with machine learning provides a reliable and scalable approach to making searchable massive amounts of multimedia data.

  17. SIMULATION FRAMEWORK FOR REGIONAL GEOLOGIC CO{sub 2} STORAGE ALONG ARCHES PROVINCE OF MIDWESTERN UNITED STATES

    Energy Technology Data Exchange (ETDEWEB)

    Sminchak, Joel

    2012-09-30

    This report presents final technical results for the project Simulation Framework for Regional Geologic CO{sub 2} Storage Infrastructure along Arches Province of the Midwest United States. The Arches Simulation project was a three year effort designed to develop a simulation framework for regional geologic carbon dioxide (CO{sub 2}) storage infrastructure along the Arches Province through development of a geologic model and advanced reservoir simulations of large-scale CO{sub 2} storage. The project included five major technical tasks: (1) compilation of geologic, hydraulic and injection data on Mount Simon, (2) development of model framework and parameters, (3) preliminary variable density flow simulations, (4) multi-phase model runs of regional storage scenarios, and (5) implications for regional storage feasibility. The Arches Province is an informal region in northeastern Indiana, northern Kentucky, western Ohio, and southern Michigan where sedimentary rock formations form broad arch and platform structures. In the province, the Mount Simon sandstone is an appealing deep saline formation for CO{sub 2} storage because of the intersection of reservoir thickness and permeability. Many CO{sub 2} sources are located in proximity to the Arches Province, and the area is adjacent to coal fired power plants along the Ohio River Valley corridor. Geophysical well logs, rock samples, drilling logs, and geotechnical tests were evaluated for a 500,000 km{sup 2} study area centered on the Arches Province. Hydraulic parameters and historical operational information was also compiled from Mount Simon wastewater injection wells in the region. This information was integrated into a geocellular model that depicts the parameters and conditions in a numerical array. The geologic and hydraulic data were integrated into a three-dimensional grid of porosity and permeability, which are key parameters regarding fluid flow and pressure buildup due to CO{sub 2} injection. Permeability data

  18. SIMULATION FRAMEWORK FOR REGIONAL GEOLOGIC CO{sub 2} STORAGE ALONG ARCHES PROVINCE OF MIDWESTERN UNITED STATES

    Energy Technology Data Exchange (ETDEWEB)

    Sminchak, Joel

    2012-09-30

    This report presents final technical results for the project Simulation Framework for Regional Geologic CO{sub 2} Storage Infrastructure along Arches Province of the Midwest United States. The Arches Simulation project was a three year effort designed to develop a simulation framework for regional geologic carbon dioxide (CO{sub 2}) storage infrastructure along the Arches Province through development of a geologic model and advanced reservoir simulations of large-scale CO{sub 2} storage. The project included five major technical tasks: (1) compilation of geologic, hydraulic and injection data on Mount Simon, (2) development of model framework and parameters, (3) preliminary variable density flow simulations, (4) multi-phase model runs of regional storage scenarios, and (5) implications for regional storage feasibility. The Arches Province is an informal region in northeastern Indiana, northern Kentucky, western Ohio, and southern Michigan where sedimentary rock formations form broad arch and platform structures. In the province, the Mount Simon sandstone is an appealing deep saline formation for CO{sub 2} storage because of the intersection of reservoir thickness and permeability. Many CO{sub 2} sources are located in proximity to the Arches Province, and the area is adjacent to coal fired power plants along the Ohio River Valley corridor. Geophysical well logs, rock samples, drilling logs, and geotechnical tests were evaluated for a 500,000 km{sup 2} study area centered on the Arches Province. Hydraulic parameters and historical operational information was also compiled from Mount Simon wastewater injection wells in the region. This information was integrated into a geocellular model that depicts the parameters and conditions in a numerical array. The geologic and hydraulic data were integrated into a three-dimensional grid of porosity and permeability, which are key parameters regarding fluid flow and pressure buildup due to CO{sub 2} injection. Permeability data

  19. 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).

  20. Managing virtual machines with Vac and Vcycle

    Science.gov (United States)

    McNab, A.; Love, P.; MacMahon, E.

    2015-12-01

    We compare the Vac and Vcycle virtual machine lifecycle managers and our experiences in providing production job execution services for ATLAS, CMS, LHCb, and the GridPP VO at sites in the UK, France and at CERN. In both the Vac and Vcycle systems, the virtual machines are created outside of the experiment's job submission and pilot framework. In the case of Vac, a daemon runs on each physical host which manages a pool of virtual machines on that host, and a peer-to-peer UDP protocol is used to achieve the desired target shares between experiments across the site. In the case of Vcycle, a daemon manages a pool of virtual machines on an Infrastructure-as-a-Service cloud system such as OpenStack, and has within itself enough information to create the types of virtual machines to achieve the desired target shares. Both systems allow unused shares for one experiment to temporarily taken up by other experiements with work to be done. The virtual machine lifecycle is managed with a minimum of information, gathered from the virtual machine creation mechanism (such as libvirt or OpenStack) and using the proposed Machine/Job Features API from WLCG. We demonstrate that the same virtual machine designs can be used to run production jobs on Vac and Vcycle/OpenStack sites for ATLAS, CMS, LHCb, and GridPP, and that these technologies allow sites to be operated in a reliable and robust way.

  1. Machine technology: a survey

    International Nuclear Information System (INIS)

    Barbier, M.M.

    1981-01-01

    An attempt was made to find existing machines that have been upgraded and that could be used for large-scale decontamination operations outdoors. Such machines are in the building industry, the mining industry, and the road construction industry. The road construction industry has yielded the machines in this presentation. A review is given of operations that can be done with the machines available

  2. 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…

  3. 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.)

  4. Developing a Framework for Seamless Prediction of Sub-Seasonal to Seasonal Extreme Precipitation Events in the United States.

    Science.gov (United States)

    Rosendahl, D. H.; Ćwik, P.; Martin, E. R.; Basara, J. B.; Brooks, H. E.; Furtado, J. C.; Homeyer, C. R.; Lazrus, H.; Mcpherson, R. A.; Mullens, E.; Richman, M. B.; Robinson-Cook, A.

    2017-12-01

    Extreme precipitation events cause significant damage to homes, businesses, infrastructure, and agriculture, as well as many injures and fatalities as a result of fast-moving water or waterborne diseases. In the USA, these natural hazard events claimed the lives of more than 300 people during 2015 - 2016 alone, with total damage reaching $24.4 billion. Prior studies of extreme precipitation events have focused on the sub-daily to sub-weekly timeframes. However, many decisions for planning, preparing and resilience-building require sub-seasonal to seasonal timeframes (S2S; 14 to 90 days), but adequate forecasting tools for prediction do not exist. Therefore, the goal of this newly funded project is an enhancement in understanding of the large-scale forcing and dynamics of S2S extreme precipitation events in the United States, and improved capability for modeling and predicting such events. Here, we describe the project goals, objectives, and research activities that will take place over the next 5 years. In this project, a unique team of scientists and stakeholders will identify and understand weather and climate processes connected with the prediction of S2S extreme precipitation events by answering these research questions: 1) What are the synoptic patterns associated with, and characteristic of, S2S extreme precipitation evens in the contiguous U.S.? 2) What role, if any, do large-scale modes of climate variability play in modulating these events? 3) How predictable are S2S extreme precipitation events across temporal scales? 4) How do we create an informative prediction of S2S extreme precipitation events for policymaking and planing? This project will use observational data, high-resolution radar composites, dynamical climate models and workshops that engage stakeholders (water resource managers, emergency managers and tribal environmental professionals) in co-production of knowledge. The overarching result of this project will be predictive models to reduce of

  5. 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.

  6. Development of master-slave energy management strategy based on fuzzy logic hysteresis state machine and differential power processing compensation for a PEMFC-LIB-SC hybrid tramway

    International Nuclear Information System (INIS)

    Peng, Fei; Zhao, Yuanzhe; Li, Xiaopeng; Liu, Zhixiang; Chen, Weirong; Liu, Yang; Zhou, Donghua

    2017-01-01

    Highlights: •A power system model for the PEMFC based commercial hybrid tramway was established. •An energy management strategy based on master FuHSM and slave DPPC was proposed. •The optimal OER operation of PEMFC subsystem was achieved. •The real-time EMS based HCM optimization was achieved. •The influence on system fuel economy and PEMFC performance degradation was verified. -- Abstract: A hybrid power system configuration based on proton exchange membrane fuel cell (PEMFC), lion-lithium battery (LIB) and supercapacitor (SC) was designed without grid connection for the hybrid tramway. To adapt to the rapid load power change and achieve higher fuel efficiency and optimal oxygen excess ratio (OER) operation of the PEMFC power subsystem, a master-slave energy management strategy based on fuzzy logic hysteresis state machine (FuHSM) and differential power processing compensation (DPPC) was proposed for the hybrid tramway, effectively taking into consideration of the dynamic response and optimum OER tracing of the integrated PEMFC subsystem. The master FuHSM controller was utilized to grantee the optimal power coordination of the multiple power sources and the slave DPPC controller was responsible for further compensating the load power demand to enhance the dynamic performance and bus voltage stability. Furthermore, the equivalent H 2 consumption minimization optimization considering characteristics of the proposed energy management strategy was realized by means of EIA-PSO algorithm to further improve the fuel economy of the overall hybrid power system. The results demonstrate that the proposed energy management strategy can guarantee the stability of the hybrid power system throughout the driving cycle. In addition, more efficient power coordination dynamics among the PEMFC, LIB and SC subsystems could be achieved without additional performance degradation of the integrated PEMFC subsystem, and the results of the comparisons with other control strategies

  7. 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...

  8. 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.

  9. 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

  10. Reduced graphene oxide-wrapped MoO3 composites prepared by using metal-organic frameworks as precursor for all-solid-state flexible supercapacitors.

    Science.gov (United States)

    Cao, Xiehong; Zheng, Bing; Shi, Wenhui; Yang, Jian; Fan, Zhanxi; Luo, Zhimin; Rui, Xianhong; Chen, Bo; Yan, Qingyu; Zhang, Hua

    2015-08-26

    Reduced graphene oxide-wrapped MoO3M (rGO/MoO3 ) is prepared by a novel and simple method that is developed by using a metal-organic framework as the precursor. After a two-step annealing process, the obtained rGO/MoO3 composite is used for a high-performance supercapacitor electrode. Moreover, an all-solid-state flexible supercapacitor is fabricated based on the rGO/MoO3 composite, which shows stable performance under different bending states. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. Nonlinear machine learning and design of reconfigurable digital colloids.

    Science.gov (United States)

    Long, Andrew W; Phillips, Carolyn L; Jankowksi, Eric; Ferguson, Andrew L

    2016-09-14

    Digital colloids, a cluster of freely rotating "halo" particles tethered to the surface of a central particle, were recently proposed as ultra-high density memory elements for information storage. Rational design of these digital colloids for memory storage applications requires a quantitative understanding of the thermodynamic and kinetic stability of the configurational states within which information is stored. We apply nonlinear machine learning to Brownian dynamics simulations of these digital colloids to extract the low-dimensional intrinsic manifold governing digital colloid morphology, thermodynamics, and kinetics. By modulating the relative size ratio between halo particles and central particles, we investigate the size-dependent configurational stability and transition kinetics for the 2-state tetrahedral (N = 4) and 30-state octahedral (N = 6) digital colloids. We demonstrate the use of this framework to guide the rational design of a memory storage element to hold a block of text that trades off the competing design criteria of memory addressability and volatility.

  12. The regulatory framework for storage and disposal of radioactive waste in the member states of the European Community

    International Nuclear Information System (INIS)

    Burholt, G.D.; Martin, A.

    1988-01-01

    The purpose of the study is to collate information and to summarise the present situation with regard to the regulatory framework for the storage and disposal of radioactive waste in each of the member countries of the European Community together with several important countries outside the Community. (author)

  13. Of Policy Entrepreneurship, bandwagoning and free-riding : EU member states and multilateral cooperation frameworks for Europe's southern neighbourhood

    NARCIS (Netherlands)

    Schumacher, T.; Bouris, D.; Olszewska, M.

    2016-01-01

    Over the past 25 years the EU and NATO have displayed considerable agency and thus influence as far as the development of institutionalised collective cooperation and/or foreign policy frameworks towards Europe’s southern neighbourhood is concerned. Against this backdrop, this article puts EU and

  14. Legal Framework for Social Enterprise : Lessons from a Comparative Study of Italy, Malaysia, South Korea, United Kingdom, and United States

    OpenAIRE

    Triponel, Anna; Agapitova, Natalia

    2017-01-01

    Social enterprises are emerging as a new area of public policy: several countries seek to stimulate private sector contribution to development outcomes, and social enterprises could be important players in that agenda. However, those seeking a middle ground between for-profit and non-profit sectors to enable social enterprise have found legal frameworks to be lacking. This has triggered a ...

  15. 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

  16. 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.

  17. 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

  18. A framework to expand public services to children with biomedical healthcare needs related to HIV in the Free State, South Africa.

    Science.gov (United States)

    Reid, Marianne; Botma, Yvonne

    2012-06-01

    The study undertook the development of a framework for expanding the public services available to children with biomedical healthcare needs related to HIV in South Africa. The study consisted of various component projects which were depicted as phases. The first phase was a descriptive quantitative analysis of healthcare services for children exposed to or infected by HIV, as rendered by the public health sector in the Free State Province. The second stage was informed by health policy research: a nominal group technique with stakeholders was used to identify strategies for expanding the healthcare services available to these children. The third phase consisted of workshops with stakeholders in order to devise and validate a framework for the expansion. The theory of change logic model served as the theoretical underpinning of the draft framework. Triangulated data from the literature and the preceding two phases of the study provided the empirical foundation. The problem identified was that of fragmented care delivered to children exposed to or infected with HIV, due to the 'over-verticalization' of programmes. A workshop was held during which the desired results, the possible factors that could influence the results, as well as the suggested strategies to expand and integrate the public services available to HIV-affected children were confirmed. Thus the framework was finalised during the validation workshop by the researchers in collaboration with the stakeholders.

  19. End of FY10 report - used fuel disposition technical bases and lessons learned : legal and regulatory framework for high-level waste disposition in the United States.

    Energy Technology Data Exchange (ETDEWEB)

    Weiner, Ruth F.; Blink, James A. (Lawrence Livermore National Laboratory, Livermore, CA); Rechard, Robert Paul; Perry, Frank (Los Alamos National Laboratory, Los Alamos, NM); Jenkins-Smith, Hank C. (University of Oklahoma, Norman, OK); Carter, Joe (Savannah River Nuclear Solutions, Aiken, SC); Nutt, Mark (Argonne National Laboratory, Argonne, IL); Cotton, Tom (Complex Systems Group, Washington DC)

    2010-09-01

    This report examines the current policy, legal, and regulatory framework pertaining to used nuclear fuel and high level waste management in the United States. The goal is to identify potential changes that if made could add flexibility and possibly improve the chances of successfully implementing technical aspects of a nuclear waste policy. Experience suggests that the regulatory framework should be established prior to initiating future repository development. Concerning specifics of the regulatory framework, reasonable expectation as the standard of proof was successfully implemented and could be retained in the future; yet, the current classification system for radioactive waste, including hazardous constituents, warrants reexamination. Whether or not consideration of multiple sites are considered simultaneously in the future, inclusion of mechanisms such as deliberate use of performance assessment to manage site characterization would be wise. Because of experience gained here and abroad, diversity of geologic media is not particularly necessary as a criterion in site selection guidelines for multiple sites. Stepwise development of the repository program that includes flexibility also warrants serious consideration. Furthermore, integration of the waste management system from storage, transportation, and disposition, should be examined and would be facilitated by integration of the legal and regulatory framework. Finally, in order to enhance acceptability of future repository development, the national policy should be cognizant of those policy and technical attributes that enhance initial acceptance, and those policy and technical attributes that maintain and broaden credibility.

  20. Conceptual Framework of Tourism Carrying Capacity for a Tourism City: Experiences from National Parks in the United States

    Institute of Scientific and Technical Information of China (English)

    Zheng Nasha; Zheng Xilai

    2010-01-01

    There is no universally-accepted definition of tourism carrying capacity(TCC).Numerical TCC focuses on use level and is considered as"a magic number"of the saturation point for tourism.There are several reasons why numerical tourism capacity is inadequate.Alternatively,tourism capacity can be defined in terms of limits of acceptable change,which shifts the focus from"how much use is too much"to"how much change is acceptable".This article proposes an improved conceptual framework for evaluating carrying capacity for the tourism city based on approaches used in US national parks,which consider the impact of human use on a city's economic,environmental/resource,and socio-cultural conditions.Based on the basic data of indicator values and relevant standards,the framework monitors the current indicators and predicts future indicator values; it can also be used to assess and predict TCC.

  1. The regulatory framework of accounting and accounting standard-setting bodies in the European Union member states

    OpenAIRE

    Mamić-Sačer, Ivana

    2015-01-01

    One of the principal features of accounting in the 21st century is harmonisation and stanardisation. Regulation of the European Parliament and European Council No. 1606/2002 harmonizes financial reporting for certain companies in the EU. However, national accounting principles are of great importance for financial reporting. The main purpose of this research was to investigate the application of generally accepted accounting principles, the regulatory accounting framework and the standard-set...

  2. Controlling Methane Emissions in the Natural Gas Sector. A Review of Federal and State Regulatory Frameworks Governing Production, Gathering, Processing, Transmission, and Distribution

    Energy Technology Data Exchange (ETDEWEB)

    Paranhos, Elizabeth [Energy Innovation Partners, Seoul (South Korea); Kozak, Tracy G. [Energy Innovation Partners, Seoul (South Korea); Boyd, William [Univ. of Colorado, Boulder, CO (United States); Bradbury, James [U.S. Department of Energy, Washington, DC (United States); Steinberg, D. C. [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Arent, D. J. [Joint Inst. for Strategic Energy Alaysis, Washington, DC (United States)

    2015-04-23

    This report provides an overview of the regulatory frameworks governing natural gas supply chain infrastructure siting, construction, operation, and maintenance. Information was drawn from a number of sources, including published analyses, government reports, in addition to relevant statutes, court decisions and regulatory language, as needed. The scope includes all onshore facilities that contribute to methane emissions from the natural gas sector, focusing on three areas of state and federal regulations: (1) natural gas pipeline infrastructure siting and transportation service (including gathering, transmission, and distribution pipelines), (2) natural gas pipeline safety, and (3) air emissions associated with the natural gas supply chain. In addition, the report identifies the incentives under current regulatory frameworks to invest in measures to reduce leakage, as well as the barriers facing investment in infrastructure improvement to reduce leakage. Policy recommendations regarding how federal or state authorities could regulate methane emissions are not provided; rather, existing frameworks are identified and some of the options for modifying existing regulations or adopting new regulations to reduce methane leakage are discussed.

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

    Directory of Open Access Journals (Sweden)

    Ruben Ruiz-Gonzalez

    2014-11-01

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

  4. Stochastic thermodynamics, fluctuation theorems and molecular machines

    International Nuclear Information System (INIS)

    Seifert, Udo

    2012-01-01

    Stochastic thermodynamics as reviewed here systematically provides a framework for extending the notions of classical thermodynamics such as work, heat and entropy production to the level of individual trajectories of well-defined non-equilibrium ensembles. It applies whenever a non-equilibrium process is still coupled to one (or several) heat bath(s) of constant temperature. Paradigmatic systems are single colloidal particles in time-dependent laser traps, polymers in external flow, enzymes and molecular motors in single molecule assays, small biochemical networks and thermoelectric devices involving single electron transport. For such systems, a first-law like energy balance can be identified along fluctuating trajectories. For a basic Markovian dynamics implemented either on the continuum level with Langevin equations or on a discrete set of states as a master equation, thermodynamic consistency imposes a local-detailed balance constraint on noise and rates, respectively. Various integral and detailed fluctuation theorems, which are derived here in a unifying approach from one master theorem, constrain the probability distributions for work, heat and entropy production depending on the nature of the system and the choice of non-equilibrium conditions. For non-equilibrium steady states, particularly strong results hold like a generalized fluctuation–dissipation theorem involving entropy production. Ramifications and applications of these concepts include optimal driving between specified states in finite time, the role of measurement-based feedback processes and the relation between dissipation and irreversibility. Efficiency and, in particular, efficiency at maximum power can be discussed systematically beyond the linear response regime for two classes of molecular machines, isothermal ones such as molecular motors, and heat engines such as thermoelectric devices, using a common framework based on a cycle decomposition of entropy production. (review article)

  5. 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)

  6. A Biopsychosocial Conceptual Framework of Postpartum Depression Risk in Immigrant and U.S.-born Latina Mothers in the United States.

    Science.gov (United States)

    Lara-Cinisomo, Sandraluz; Girdler, Susan S; Grewen, Karen; Meltzer-Brody, Samantha

    2016-01-01

    In this review, we offer a conceptual framework that identifies risk factors of postpartum depression (PPD) in immigrant and U.S.-born Latinas in the United States by focusing on psychosocial and neuroendocrine factors. Although the evidence of the impact psychosocial stressors have on the development of PPD has been well-documented, less is known about the biological etiology of PPD or how these complex stressors jointly increase the risk of PPD in immigrant and U.S.-born Latinas in the United States. Using PubMed, CINAHL, and Embase, we reviewed the literature from 2000 to 2015 regarding psychosocial and physiological risk factors associated with PPD to develop a conceptual model for Latinas. Our search yielded 16 relevant studies. Based on our review of the literature, we developed a biopsychosocial conceptual model of PPD for Latinas in the United States. We make arguments for an integrated model designed to assess psychosocial and physiological risk factors and PPD in a high-risk population. Our framework describes the hypothesized associations between culturally and contextually relevant psychosocial stressors, neurobiological factors (e.g., hypothalamic-pituitary-adrenal [HPA] axis response system and oxytocin signaling), and PPD in Latinas in the United States. Future studies should evaluate prospectively the impact psychosocial stressors identified here have on the development of PPD in both immigrant and U.S-born Latinas while examining neuroendocrine function, such as the HPA axis and oxytocin signaling. Our conceptual framework will allow for the reporting of main and indirect effects of psychosocial risk factors and biomarkers (e.g., HPA axis and oxytocin function) on PPD in foreign- and U.S.-born postpartum Latinas. Copyright © 2016 Jacobs Institute of Women's Health. Published by Elsevier Inc. All rights reserved.

  7. MITS machine operations

    International Nuclear Information System (INIS)

    Flinchem, J.

    1980-01-01

    This document contains procedures which apply to operations performed on individual P-1c machines in the Machine Interface Test System (MITS) at AiResearch Manufacturing Company's Torrance, California Facility

  8. Brain versus Machine Control.

    Directory of Open Access Journals (Sweden)

    Jose M Carmena

    2004-12-01

    Full Text Available Dr. Octopus, the villain of the movie "Spiderman 2", is a fusion of man and machine. Neuroscientist Jose Carmena examines the facts behind this fictional account of a brain- machine interface

  9. Applied machining technology

    CERN Document Server

    Tschätsch, Heinz

    2010-01-01

    Machining and cutting technologies are still crucial for many manufacturing processes. This reference presents all important machining processes in a comprehensive and coherent way. It includes many examples of concrete calculations, problems and solutions.

  10. Machining with abrasives

    CERN Document Server

    Jackson, Mark J

    2011-01-01

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

  11. A Driver Pressure State Impact Response (DPSIR) framework applied to an interdisciplinary coastal zone management workshop along the eastern Gulf of Thailand.

    Science.gov (United States)

    Hines, E.; Baldwin, C.; Jones, C.; Lewison, R. L.; Lieske, S.; Rudd, M.

    2016-02-01

    The flexibility of the Driver Pressure State Impact Response (DPSIR) framework is demonstrated through application to the coastal zone of east Gulf of Thailand during an inter-disciplinary multi-cultural workshop comprised of participants (including practitioners) from south-east Asian coastal countries, North America and Australia in January 2015. The benefits of the framework as identified by participants included systematic and critical thinking, and identification of data gaps and other needs, such as capacity building. We use four case studies that highlight cross-border social-ecological challenges in Thailand and Cambodia to demonstrate: a) participant learning, b) individuality and flexibility of approaches (e.g. scales considered), c) participants' feedback on its application, and d) its potential use to identify both data-gaps and low-hanging-fruit type actions.

  12. Statistical and machine learning approaches for network analysis

    CERN Document Server

    Dehmer, Matthias

    2012-01-01

    Explore the multidisciplinary nature of complex networks through machine learning techniques Statistical and Machine Learning Approaches for Network Analysis provides an accessible framework for structurally analyzing graphs by bringing together known and novel approaches on graph classes and graph measures for classification. By providing different approaches based on experimental data, the book uniquely sets itself apart from the current literature by exploring the application of machine learning techniques to various types of complex networks. Comprised of chapters written by internation

  13. 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.

  14. Qualification measurements of the voltage supply system as well as conceptionation of a state machine for the detector control of the ATLAS pixel detector; Qualifizierungsmessungen des Spannungsversorgungssystems sowie Konzeptionierung einer Zustandsmaschine fuer die Detektorkontrolle des ATLAS-Pixeldetektors

    Energy Technology Data Exchange (ETDEWEB)

    Schultes, Joachim

    2007-02-15

    The supply system and the control system of the ATLAS pixel detector represent important building blocks of the pixel detector. Corresponding studies of the supply system, which were performed within a comprehensive test system, the so-called system test, with nearly all final components and the effects on the pixel detector are object of this thesis. A further point of this thesis is the coordination and further development of the detector-control-system software under regardment of the different partial systems. A main topic represents thereby the conceptionation of the required state machine as interface for the users and the connection to the data acquisition system.

  15. Dictionary of machine terms

    International Nuclear Information System (INIS)

    1990-06-01

    This book has introduction of dictionary of machine terms, and a compilation committee and introductory remarks. It gives descriptions of the machine terms in alphabetical order from a to Z and also includes abbreviation of machine terms and symbol table, way to read mathematical symbols and abbreviation and terms of drawings.

  16. Mankind, machines and people

    Energy Technology Data Exchange (ETDEWEB)

    Hugli, A

    1984-01-01

    The following questions are addressed: is there a difference between machines and men, between human communication and communication with machines. Will we ever reach the point when the dream of artificial intelligence becomes a reality. Will thinking machines be able to replace the human spirit in all its aspects. Social consequences and philosophical aspects are addressed. 8 references.

  17. A Universal Reactive Machine

    DEFF Research Database (Denmark)

    Andersen, Henrik Reif; Mørk, Simon; Sørensen, Morten U.

    1997-01-01

    Turing showed the existence of a model universal for the set of Turing machines in the sense that given an encoding of any Turing machine asinput the universal Turing machine simulates it. We introduce the concept of universality for reactive systems and construct a CCS processuniversal...

  18. HTS machine laboratory prototype

    DEFF Research Database (Denmark)

    machine. The machine comprises six stationary HTS field windings wound from both YBCO and BiSCOO tape operated at liquid nitrogen temperature and enclosed in a cryostat, and a three phase armature winding spinning at up to 300 rpm. This design has full functionality of HTS synchronous machines. The design...

  19. 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…

  20. Modelisation de la conversion electromecanique des machines ...

    African Journals Online (AJOL)

    These implemented models would constitute the module of possible generators that one could couple with a model of wind power engine in order to study, within the framework of a virtual laboratory, the performances of wind-driven systems of electricity generation. Cet article présente les modèles de machines électriques ...

  1. Parallelization of TMVA Machine Learning Algorithms

    CERN Document Server

    Hajili, Mammad

    2017-01-01

    This report reflects my work on Parallelization of TMVA Machine Learning Algorithms integrated to ROOT Data Analysis Framework during summer internship at CERN. The report consists of 4 impor- tant part - data set used in training and validation, algorithms that multiprocessing applied on them, parallelization techniques and re- sults of execution time changes due to number of workers.

  2. Prototype-based models in machine learning

    NARCIS (Netherlands)

    Biehl, Michael; Hammer, Barbara; Villmann, Thomas

    2016-01-01

    An overview is given of prototype-based models in machine learning. In this framework, observations, i.e., data, are stored in terms of typical representatives. Together with a suitable measure of similarity, the systems can be employed in the context of unsupervised and supervised analysis of

  3. Assessing the state of pelagic fish communities within an ecosystem approach and the European Marine Strategy Framework Directive

    DEFF Research Database (Denmark)

    Shephard, Samuel; Rindorf, Anna; Dickey-Collas, Mark

    2014-01-01

    Pelagic fish are key elements in marine foodwebs and thus comprise an important part of overall ecosystem health. We develop a suite of ecological indicators that track pelagic fish community state and evaluate state of specific objectives against Good Environmental Status (GES) criteria. Indicator...

  4. Conceptual Framework for Developing Resilience Metrics for the Electricity, Oil, and Gas Sectors in the United States

    Energy Technology Data Exchange (ETDEWEB)

    Watson, Jean-Paul [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Guttromson, Ross [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Silva-Monroy, Cesar [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jeffers, Robert [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Katherine [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Ellison, James [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Rath, Charles [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Gearhart, Jared [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Jones, Dean [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Corbet, Tom [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Hanley, Charles [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Walker, La Tonya [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2014-09-01

    This report has been written for the Department of Energy’s Energy Policy and Systems Analysis Office to inform their writing of the Quadrennial Energy Review in the area of energy resilience. The topics of measuring and increasing energy resilience are addressed, including definitions, means of measuring, and analytic methodologies that can be used to make decisions for policy, infrastructure planning, and operations. A risk-based framework is presented which provides a standard definition of a resilience metric. Additionally, a process is identified which explains how the metrics can be applied. Research and development is articulated that will further accelerate the resilience of energy infrastructures.

  5. Using Machine Learning to Advance Personality Assessment and Theory.

    Science.gov (United States)

    Bleidorn, Wiebke; Hopwood, Christopher James

    2018-05-01

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

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

    Science.gov (United States)

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

    2016-09-01

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

  7. Cost-effectiveness Analysis in R Using a Multi-state Modeling Survival Analysis Framework: A Tutorial.

    Science.gov (United States)

    Williams, Claire; Lewsey, James D; Briggs, Andrew H; Mackay, Daniel F

    2017-05-01

    This tutorial provides a step-by-step guide to performing cost-effectiveness analysis using a multi-state modeling approach. Alongside the tutorial, we provide easy-to-use functions in the statistics package R. We argue that this multi-state modeling approach using a package such as R has advantages over approaches where models are built in a spreadsheet package. In particular, using a syntax-based approach means there is a written record of what was done and the calculations are transparent. Reproducing the analysis is straightforward as the syntax just needs to be run again. The approach can be thought of as an alternative way to build a Markov decision-analytic model, which also has the option to use a state-arrival extended approach. In the state-arrival extended multi-state model, a covariate that represents patients' history is included, allowing the Markov property to be tested. We illustrate the building of multi-state survival models, making predictions from the models and assessing fits. We then proceed to perform a cost-effectiveness analysis, including deterministic and probabilistic sensitivity analyses. Finally, we show how to create 2 common methods of visualizing the results-namely, cost-effectiveness planes and cost-effectiveness acceptability curves. The analysis is implemented entirely within R. It is based on adaptions to functions in the existing R package mstate to accommodate parametric multi-state modeling that facilitates extrapolation of survival curves.

  8. 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.

  9. 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

  10. Sample-Based Extreme Learning Machine with Missing Data

    Directory of Open Access Journals (Sweden)

    Hang Gao

    2015-01-01

    Full Text Available Extreme learning machine (ELM has been extensively studied in machine learning community during the last few decades due to its high efficiency and the unification of classification, regression, and so forth. Though bearing such merits, existing ELM algorithms cannot efficiently handle the issue of missing data, which is relatively common in practical applications. The problem of missing data is commonly handled by imputation (i.e., replacing missing values with substituted values according to available information. However, imputation methods are not always effective. In this paper, we propose a sample-based learning framework to address this issue. Based on this framework, we develop two sample-based ELM algorithms for classification and regression, respectively. Comprehensive experiments have been conducted in synthetic data sets, UCI benchmark data sets, and a real world fingerprint image data set. As indicated, without introducing extra computational complexity, the proposed algorithms do more accurate and stable learning than other state-of-the-art ones, especially in the case of higher missing ratio.

  11. Machine Learning Methods for Attack Detection in the Smart Grid.

    Science.gov (United States)

    Ozay, Mete; Esnaola, Inaki; Yarman Vural, Fatos Tunay; Kulkarni, Sanjeev R; Poor, H Vincent

    2016-08-01

    Attack detection problems in the smart grid are posed as statistical learning problems for different attack scenarios in which the measurements are observed in batch or online settings. In this approach, machine learning algorithms are used to classify measurements as being either secure or attacked. An attack detection framework is provided to exploit any available prior knowledge about the system and surmount constraints arising from the sparse structure of the problem in the proposed approach. Well-known batch and online learning algorithms (supervised and semisupervised) are employed with decision- and feature-level fusion to model the attack detection problem. The relationships between statistical and geometric properties of attack vectors employed in the attack scenarios and learning algorithms are analyzed to detect unobservable attacks using statistical learning methods. The proposed algorithms are examined on various IEEE test systems. Experimental analyses show that machine learning algorithms can detect attacks with performances higher than attack detection algorithms that employ state vector estimation methods in the proposed attack detection framework.

  12. Deep Restricted Kernel Machines Using Conjugate Feature Duality.

    Science.gov (United States)

    Suykens, Johan A K

    2017-08-01

    The aim of this letter is to propose a theory of deep restricted kernel machines offering new foundations for deep learning with kernel machines. From the viewpoint of deep learning, it is partially related to restricted Boltzmann machines, which are characterized by visible and hidden units in a bipartite graph without hidden-to-hidden connections and deep learning extensions as deep belief networks and deep Boltzmann machines. From the viewpoint of kernel machines, it includes least squares support vector machines for classification and regression, kernel principal component analysis (PCA), matrix singular value decomposition, and Parzen-type models. A key element is to first characterize these kernel machines in terms of so-called conjugate feature duality, yielding a representation with visible and hidden units. It is shown how this is related to the energy form in restricted Boltzmann machines, with continuous variables in a nonprobabilistic setting. In this new framework of so-called restricted kernel machine (RKM) representations, the dual variables correspond to hidden features. Deep RKM are obtained by coupling the RKMs. The method is illustrated for deep RKM, consisting of three levels with a least squares support vector machine regression level and two kernel PCA levels. In its primal form also deep feedforward neural networks can be trained within this framework.

  13. Evolutionary algorithm based optimization of hydraulic machines utilizing a state-of-the-art block coupled CFD solver and parametric geometry and mesh generation tools

    Science.gov (United States)

    S, Kyriacou; E, Kontoleontos; S, Weissenberger; L, Mangani; E, Casartelli; I, Skouteropoulou; M, Gattringer; A, Gehrer; M, Buchmayr

    2014-03-01

    An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure.

  14. Evolutionary algorithm based optimization of hydraulic machines utilizing a state-of-the-art block coupled CFD solver and parametric geometry and mesh generation tools

    International Nuclear Information System (INIS)

    Kyriacou S; Kontoleontos E; Weissenberger S; Mangani L; Casartelli E; Skouteropoulou I; Gattringer M; Gehrer A; Buchmayr M

    2014-01-01

    An efficient hydraulic optimization procedure, suitable for industrial use, requires an advanced optimization tool (EASY software), a fast solver (block coupled CFD) and a flexible geometry generation tool. EASY optimization software is a PCA-driven metamodel-assisted Evolutionary Algorithm (MAEA (PCA)) that can be used in both single- (SOO) and multiobjective optimization (MOO) problems. In MAEAs, low cost surrogate evaluation models are used to screen out non-promising individuals during the evolution and exclude them from the expensive, problem specific evaluation, here the solution of Navier-Stokes equations. For additional reduction of the optimization CPU cost, the PCA technique is used to identify dependences among the design variables and to exploit them in order to efficiently drive the application of the evolution operators. To further enhance the hydraulic optimization procedure, a very robust and fast Navier-Stokes solver has been developed. This incompressible CFD solver employs a pressure-based block-coupled approach, solving the governing equations simultaneously. This method, apart from being robust and fast, also provides a big gain in terms of computational cost. In order to optimize the geometry of hydraulic machines, an automatic geometry and mesh generation tool is necessary. The geometry generation tool used in this work is entirely based on b-spline curves and surfaces. In what follows, the components of the tool chain are outlined in some detail and the optimization results of hydraulic machine components are shown in order to demonstrate the performance of the presented optimization procedure

  15. Analytic energy gradient of excited electronic state within TDDFT/MMpol framework: Benchmark tests and parallel implementation.

    Science.gov (United States)

    Zeng, Qiao; Liang, WanZhen

    2015-10-07

    The time-dependent density functional theory (TDDFT) has become the most popular method to calculate the electronic excitation energies, describe the excited-state properties, and perform the excited-state geometric optimization of medium and large-size molecules due to the implementation of analytic excited-state energy gradient and Hessian in many electronic structure software packages. To describe the molecules in condensed phase, one usually adopts the computationally efficient hybrid Quantum Mechanics/Molecular Mechanics (QM/MM) models. Here, we extend our previous work on the energy gradient of TDDFT/MM excited state to account for the mutual polarization effects between QM and MM regions, which is believed to hold a crucial position in the potential energy surface of molecular systems when the photoexcitation-induced charge rearrangement in the QM region is drastic. The implementation of a simple polarizable TDDFT/MM (TDDFT/MMpol) model in Q-Chem/CHARMM interface with both the linear response and the state-specific features has been realized. Several benchmark tests and preliminary applications are exhibited to confirm our implementation and assess the effects of different treatment of environmental polarization on the excited-state properties, and the efficiency of parallel implementation is demonstrated as well.

  16. 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…

  17. Vapor-Phase Deposition and Modification of Metal-Organic Frameworks: State-of-the-Art and Future Directions.

    Science.gov (United States)

    Stassen, Ivo; De Vos, Dirk; Ameloot, Rob

    2016-10-04

    Materials processing, and thin-film deposition in particular, is decisive in the implementation of functional materials in industry and real-world applications. Vapor processing of materials plays a central role in manufacturing, especially in electronics. Metal-organic frameworks (MOFs) are a class of nanoporous crystalline materials on the brink of breakthrough in many application areas. Vapor deposition of MOF thin films will facilitate their implementation in micro- and nanofabrication research and industries. In addition, vapor-solid modification can be used for postsynthetic tailoring of MOF properties. In this context, we review the recent progress in vapor processing of MOFs, summarize the underpinning chemistry and principles, and highlight promising directions for future research. © 2016 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  18. State of the art of contaminated site management in The Netherlands: Policy framework and risk assessment tools

    International Nuclear Information System (INIS)

    Swartjes, F.A.; Rutgers, M.; Lijzen, J.P.A.; Janssen, P.J.C.M.; Otte, P.F.; Wintersen, A.; Brand, E.; Posthuma, L.

    2012-01-01

    This paper presents the policy framework of contaminated site management in The Netherlands and the corresponding risk assessment tools, including innovations that have taken place since an overview was published in 1999. According to the Dutch Soil Protection Act assessment framework, soils are subdivided into three quality classes: clean, slightly contaminated and seriously contaminated. Historic cases of slightly contaminated soils are managed in a sustainable way by re-use of soil material within a region on the basis of risk-based and land use specific Maximal Values and Background Values. In case of serious soil contamination remediation is in principle necessary and the urgency of remediation has to be determined based on site-specific risks for human health, the ecosystem and groundwater. The major risk assessment tools in The Netherlands are the CSOIL exposure model (human health risks and food safety), Species Sensitivity Distributions and the Soil Quality Triad (ecological risks), along with a procedure to assess the risks due to contaminant spreading to and in the groundwater. Following the principle ‘simple if possible, complex when necessary’, tiered approaches are used. Contaminated site practices are supported with web-based decision support systems. - Highlights: ► The Dutch Soil Protection Act distinguishes three quality classes: clean, slightly contaminated and seriously contaminated. ► Serious soil contamination in principle compels remediation and the determination of the urgency of remediation. ► The relevant protection targets in The Netherlands are human health, ecosystems, groundwater and food safety. ► Important risk assessment tools are the CSOIL and VOLASOIL exposure models, SSDs and the TRIAD approach. ► Under the principle ‘simple when possible, complex when necessary’ tiered approaches are used.

  19. Interconnection test framework for the CMS level-1 trigger system

    International Nuclear Information System (INIS)

    Hammer, J.; Magrans de Abril, M.; Wulz, C.E.

    2012-01-01

    The Level-1 Trigger Control and Monitoring System is a software package designed to configure, monitor and test the Level-1 Trigger System of the Compact Muon Solenoid (CMS) experiment at CERN's Large Hadron Collider. It is a large and distributed system that runs over 50 PCs and controls about 200 hardware units. The objective of this paper is to describe and evaluate the architecture of a distributed testing framework - the Interconnection Test Framework (ITF). This generic and highly flexible framework for creating and executing hardware tests within the Level-1 Trigger environment is meant to automate testing of the 13 major subsystems interconnected with more than 1000 links. Features include a web interface to create and execute tests, modeling using finite state machines, dependency management, automatic configuration, and loops. Furthermore, the ITF will replace the existing heterogeneous testing procedures and help reducing both maintenance and complexity of operation tasks. (authors)

  20. Excited state nuclear forces from the Tamm-Dancoff approximation to time-dependent density functional theory within the plane wave basis set framework

    Science.gov (United States)

    Hutter, Jürg

    2003-03-01

    An efficient formulation of time-dependent linear response density functional theory for the use within the plane wave basis set framework is presented. The method avoids the transformation of the Kohn-Sham matrix into the canonical basis and references virtual orbitals only through a projection operator. Using a Lagrangian formulation nuclear derivatives of excited state energies within the Tamm-Dancoff approximation are derived. The algorithms were implemented into a pseudo potential/plane wave code and applied to the calculation of adiabatic excitation energies, optimized geometries and vibrational frequencies of three low lying states of formaldehyde. An overall good agreement with other time-dependent density functional calculations, multireference configuration interaction calculations and experimental data was found.