Decomposition of the compound Atwood machine
Lopes Coelho, R.
2017-11-01
Non-standard solving strategies for the compound Atwood machine problem have been proposed. The present strategy is based on a very simple idea. Taking an Atwood machine and replacing one of its bodies by another Atwood machine, we have a compound machine. As this operation can be repeated, we can construct any compound Atwood machine. This rule of construction is transferred to a mathematical model, whereby the equations of motion are obtained. The only difference between the machine and its model is that instead of pulleys and bodies, we have reference frames that move solidarily with these objects. This model provides us with the accelerations in the non-inertial frames of the bodies, which we will use to obtain the equations of motion. This approach to the problem will be justified by the Lagrange method and exemplified by machines with six and eight bodies.
Machine learning topological 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.
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....
Separable decompositions of bipartite mixed states
Li, Jun-Li; Qiao, Cong-Feng
2018-04-01
We present a practical scheme for the decomposition of a bipartite mixed state into a sum of direct products of local density matrices, using the technique developed in Li and Qiao (Sci. Rep. 8:1442, 2018). In the scheme, the correlation matrix which characterizes the bipartite entanglement is first decomposed into two matrices composed of the Bloch vectors of local states. Then, we show that the symmetries of Bloch vectors are consistent with that of the correlation matrix, and the magnitudes of the local Bloch vectors are lower bounded by the correlation matrix. Concrete examples for the separable decompositions of bipartite mixed states are presented for illustration.
Ultra-precision machining induced phase decomposition at surface of Zn-Al based alloy
International Nuclear Information System (INIS)
To, S.; Zhu, Y.H.; Lee, W.B.
2006-01-01
The microstructural changes and phase transformation of an ultra-precision machined Zn-Al based alloy were examined using X-ray diffraction and back-scattered electron microscopy techniques. Decomposition of the Zn-rich η phase and the related changes in crystal orientation was detected at the surface of the ultra-precision machined alloy specimen. The effects of the machining parameters, such as cutting speed and depth of cut, on the phase decomposition were discussed in comparison with the tensile and rolling induced microstrucutural changes and phase decomposition
Energy Technology Data Exchange (ETDEWEB)
Xing, Zhanqiang; Qu, Jianfeng; Chai, Yi; Tang, Qiu; Zhou, Yuming [Chongqing University, Chongqing (China)
2017-02-15
The gear vibration signal is nonlinear and non-stationary, gear fault diagnosis under variable conditions has always been unsatisfactory. To solve this problem, an intelligent fault diagnosis method based on Intrinsic time-scale decomposition (ITD)-Singular value decomposition (SVD) and Support vector machine (SVM) is proposed in this paper. The ITD method is adopted to decompose the vibration signal of gearbox into several Proper rotation components (PRCs). Subsequently, the singular value decomposition is proposed to obtain the singular value vectors of the proper rotation components and improve the robustness of feature extraction under variable conditions. Finally, the Support vector machine is applied to classify the fault type of gear. According to the experimental results, the performance of ITD-SVD exceeds those of the time-frequency analysis methods with EMD and WPT combined with SVD for feature extraction, and the classifier of SVM outperforms those for K-nearest neighbors (K-NN) and Back propagation (BP). Moreover, the proposed approach can accurately diagnose and identify different fault types of gear under variable conditions.
Learning Extended Finite State Machines
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.
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....
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.
SwingStates: adding state machines to the swing toolkit
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...
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.
Decomposition of thermal-equilibrium states
International Nuclear Information System (INIS)
Gu Lei
2010-01-01
It is shown that a thermal-equilibrium state can be decomposed into a tensor product of the operators in subspaces of single-particle energy. On the basis of this form, a straightforward derivation of the Fermi-Dirac and the Bose-Einstein distribution is performed. The derivation can be generalized for systems with weak interaction to obtain an approximate distribution in momentum.
Rama Krishna, K.; Ramachandran, K. I.
2018-02-01
Crack propagation is a major cause of failure in rotating machines. It adversely affects the productivity, safety, and the machining quality. Hence, detecting the crack’s severity accurately is imperative for the predictive maintenance of such machines. Fault diagnosis is an established concept in identifying the faults, for observing the non-linear behaviour of the vibration signals at various operating conditions. In this work, we find the classification efficiencies for both original and the reconstructed vibrational signals. The reconstructed signals are obtained using Variational Mode Decomposition (VMD), by splitting the original signal into three intrinsic mode functional components and framing them accordingly. Feature extraction, feature selection and feature classification are the three phases in obtaining the classification efficiencies. All the statistical features from the original signals and reconstructed signals are found out in feature extraction process individually. A few statistical parameters are selected in feature selection process and are classified using the SVM classifier. The obtained results show the best parameters and appropriate kernel in SVM classifier for detecting the faults in bearings. Hence, we conclude that better results were obtained by VMD and SVM process over normal process using SVM. This is owing to denoising and filtering the raw vibrational signals.
Decomposition of gene expression state space trajectories.
Directory of Open Access Journals (Sweden)
Jessica C Mar
2009-12-01
Full Text Available Representing and analyzing complex networks remains a roadblock to creating dynamic network models of biological processes and pathways. The study of cell fate transitions can reveal much about the transcriptional regulatory programs that underlie these phenotypic changes and give rise to the coordinated patterns in expression changes that we observe. The application of gene expression state space trajectories to capture cell fate transitions at the genome-wide level is one approach currently used in the literature. In this paper, we analyze the gene expression dataset of Huang et al. (2005 which follows the differentiation of promyelocytes into neutrophil-like cells in the presence of inducers dimethyl sulfoxide and all-trans retinoic acid. Huang et al. (2005 build on the work of Kauffman (2004 who raised the attractor hypothesis, stating that cells exist in an expression landscape and their expression trajectories converge towards attractive sites in this landscape. We propose an alternative interpretation that explains this convergent behavior by recognizing that there are two types of processes participating in these cell fate transitions-core processes that include the specific differentiation pathways of promyelocytes to neutrophils, and transient processes that capture those pathways and responses specific to the inducer. Using functional enrichment analyses, specific biological examples and an analysis of the trajectories and their core and transient components we provide a validation of our hypothesis using the Huang et al. (2005 dataset.
International Nuclear Information System (INIS)
Yin, Hao; Dong, Zhen; Chen, Yunlong; Ge, Jiafei; Lai, Loi Lei; Vaccaro, Alfredo; Meng, Anbo
2017-01-01
Highlights: • A secondary decomposition approach is applied in the data pre-processing. • The empirical mode decomposition is used to decompose the original time series. • IMF1 continues to be decomposed by applying wavelet packet decomposition. • Crisscross optimization algorithm is applied to train extreme learning machine. • The proposed SHD-CSO-ELM outperforms other pervious methods in the literature. - Abstract: Large-scale integration of wind energy into electric grid is restricted by its inherent intermittence and volatility. So the increased utilization of wind power necessitates its accurate prediction. The contribution of this study is to develop a new hybrid forecasting model for the short-term wind power prediction by using a secondary hybrid decomposition approach. In the data pre-processing phase, the empirical mode decomposition is used to decompose the original time series into several intrinsic mode functions (IMFs). A unique feature is that the generated IMF1 continues to be decomposed into appropriate and detailed components by applying wavelet packet decomposition. In the training phase, all the transformed sub-series are forecasted with extreme learning machine trained by our recently developed crisscross optimization algorithm (CSO). The final predicted values are obtained from aggregation. The results show that: (a) The performance of empirical mode decomposition can be significantly improved with its IMF1 decomposed by wavelet packet decomposition. (b) The CSO algorithm has satisfactory performance in addressing the premature convergence problem when applied to optimize extreme learning machine. (c) The proposed approach has great advantage over other previous hybrid models in terms of prediction accuracy.
Combined spatial/angular domain decomposition SN algorithms for shared memory parallel machines
International Nuclear Information System (INIS)
Hunter, M.A.; Haghighat, A.
1993-01-01
Several parallel processing algorithms on the basis of spatial and angular domain decomposition methods are developed and incorporated into a two-dimensional discrete ordinates transport theory code. These algorithms divide the spatial and angular domains into independent subdomains so that the flux calculations within each subdomain can be processed simultaneously. Two spatial parallel algorithms (Block-Jacobi, red-black), one angular parallel algorithm (η-level), and their combinations are implemented on an eight processor CRAY Y-MP. Parallel performances of the algorithms are measured using a series of fixed source RZ geometry problems. Some of the results are also compared with those executed on an IBM 3090/600J machine. (orig.)
Fourier decomposition of segmented magnets with radial magnetization in surface-mounted PM machines
Tiang, Tow Leong; Ishak, Dahaman; Lim, Chee Peng
2017-11-01
This paper presents a generic field model of radial magnetization (RM) pattern produced by multiple segmented magnets per rotor pole in surface-mounted permanent magnet (PM) machines. The magnetization vectors from either odd- or even-number of magnet blocks per pole are described. Fourier decomposition is first employed to derive the field model, and later integrated with the exact 2D analytical subdomain method to predict the magnetic field distributions and other motor global quantities. For the assessment purpose, a 12-slot/8-pole surface-mounted PM motor with two segmented magnets per pole is investigated by using the proposed field model. The electromagnetic performances of the PM machines are intensively predicted by the proposed magnet field model which include the magnetic field distributions, airgap flux density, phase back-EMF, cogging torque, and output torque during either open-circuit or on-load operating conditions. The analytical results are evaluated and compared with those obtained from both 2D and 3D finite element analyses (FEA) where an excellent agreement has been achieved.
PLA realizations for VLSI state machines
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.
Mode decomposition for a synchronous state and its applications
International Nuclear Information System (INIS)
Xiong Xiaohua; Wang Junwei; Zhang Yanbin; Zhou Tianshou
2007-01-01
Synchronization of coupled dynamical systems including periodic and chaotic systems is investigated both anlaytically and numerically. A novel method, mode decomposition, of treating the stability of a synchronous state is proposed based on the Floquet theory. A rigorous criterion is then derived, which can be applied to arbitrary coupled systems. Two typical numerical examples: coupled Van der Pol systems (corresponding to the case of coupled periodic oscillators) and coupled Lorenz systems (corresponding to the case of chaotic systems) are used to demonstrate the theoretical analysis
Torres, A. F.
2011-12-01
two excellent tools from the Learning Machine field know as the Wavelet Decomposition Analysis (WDA) and the Multivariate Relevance Vector Machine (MVRVM) to forecast the results obtained from the SEBAL algorithm using LandSat imagery and soil moisture maps. The predictive capability of this novel hybrid WDA-RVM actual evapotranspiration forecasting technique is tested by comparing the crop water requirements and delivered crop water in the Lower Sevier River Basin, Utah, for the period 2007-2011. This location was selected because of their success increasing the efficiency of water use and control along the entire irrigation system. Research is currently on going to assess the efficacy of the WDA-RVM technique along the irrigation season, which is required to enhance the water use efficiency and minimize climate change impact on the Sevier River Basin.
Dynamic thermal analysis of machines in running state
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 ...
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.
Solid-state resistor for pulsed power machines
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.
An Approach for Implementing State Machines with Online Testability
Directory of Open Access Journals (Sweden)
P. K. Lala
2010-01-01
Full Text Available During the last two decades, significant amount of research has been performed to simplify the detection of transient or soft errors in VLSI-based digital systems. This paper proposes an approach for implementing state machines that uses 2-hot code for state encoding. State machines designed using this approach allow online detection of soft errors in registers and output logic. The 2-hot code considerably reduces the number of required flip-flops and leads to relatively straightforward implementation of next state and output logic. A new way of designing output logic for online fault detection has also been presented.
The Design of Finite State Machine for Asynchronous Replication Protocol
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.
Bonan, G. B.; Wieder, W. R.
2012-12-01
Decomposition is a large term in the global carbon budget, but models of the earth system that simulate carbon cycle-climate feedbacks are largely untested with respect to litter decomposition. Here, we demonstrate a protocol to document model performance with respect to both long-term (10 year) litter decomposition and steady-state soil carbon stocks. First, we test the soil organic matter parameterization of the Community Land Model version 4 (CLM4), the terrestrial component of the Community Earth System Model, with data from the Long-term Intersite Decomposition Experiment Team (LIDET). The LIDET dataset is a 10-year study of litter decomposition at multiple sites across North America and Central America. We show results for 10-year litter decomposition simulations compared with LIDET for 9 litter types and 20 sites in tundra, grassland, and boreal, conifer, deciduous, and tropical forest biomes. We show additional simulations with DAYCENT, a version of the CENTURY model, to ask how well an established ecosystem model matches the observations. The results reveal large discrepancy between the laboratory microcosm studies used to parameterize the CLM4 litter decomposition and the LIDET field study. Simulated carbon loss is more rapid than the observations across all sites, despite using the LIDET-provided climatic decomposition index to constrain temperature and moisture effects on decomposition. Nitrogen immobilization is similarly biased high. Closer agreement with the observations requires much lower decomposition rates, obtained with the assumption that nitrogen severely limits decomposition. DAYCENT better replicates the observations, for both carbon mass remaining and nitrogen, without requirement for nitrogen limitation of decomposition. Second, we compare global observationally-based datasets of soil carbon with simulated steady-state soil carbon stocks for both models. The models simulations were forced with observationally-based estimates of annual
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: ...
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.
Control of discrete event systems modeled as hierarchical state machines
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.
Directory of Open Access Journals (Sweden)
Wei Li
2013-01-01
Full Text Available Belt conveyors are the equipment widely used in coal mines and other manufacturing factories, whose main components are a number of idlers. The faults of belt conveyors can directly influence the daily production. In this paper, a fault diagnosis method combining wavelet packet decomposition (WPD and support vector machine (SVM is proposed for monitoring belt conveyors with the focus on the detection of idler faults. Since the number of the idlers could be large, one acceleration sensor is applied to gather the vibration signals of several idlers in order to reduce the number of sensors. The vibration signals are decomposed with WPD, and the energy of each frequency band is extracted as the feature. Then, the features are employed to train an SVM to realize the detection of idler faults. The proposed fault diagnosis method is firstly tested on a testbed, and then an online monitoring and fault diagnosis system is designed for belt conveyors. An experiment is also carried out on a belt conveyor in service, and it is verified that the proposed system can locate the position of the faulty idlers with a limited number of sensors, which is important for operating belt conveyors in practices.
Directory of Open Access Journals (Sweden)
Hualou Liang
2008-04-01
Full Text Available We propose an empirical mode decomposition (EMD- based method to extract features from the multichannel recordings of local field potential (LFP, collected from the middle temporal (MT visual cortex in a macaque monkey, for decoding its bistable structure-from-motion (SFM perception. The feature extraction approach consists of three stages. First, we employ EMD to decompose nonstationary single-trial time series into narrowband components called intrinsic mode functions (IMFs with time scales dependent on the data. Second, we adopt unsupervised K-means clustering to group the IMFs and residues into several clusters across all trials and channels. Third, we use the supervised common spatial patterns (CSP approach to design spatial filters for the clustered spatiotemporal signals. We exploit the support vector machine (SVM classifier on the extracted features to decode the reported perception on a single-trial basis. We demonstrate that the CSP feature of the cluster in the gamma frequency band outperforms the features in other frequency bands and leads to the best decoding performance. We also show that the EMD-based feature extraction can be useful for evoked potential estimation. Our proposed feature extraction approach may have potential for many applications involving nonstationary multivariable time series such as brain-computer interfaces (BCI.
Directory of Open Access Journals (Sweden)
Yingni Zhai
2014-10-01
Full Text Available Purpose: A decomposition heuristics based on multi-bottleneck machines for large-scale job shop scheduling problems (JSP is proposed.Design/methodology/approach: In the algorithm, a number of sub-problems are constructed by iteratively decomposing the large-scale JSP according to the process route of each job. And then the solution of the large-scale JSP can be obtained by iteratively solving the sub-problems. In order to improve the sub-problems' solving efficiency and the solution quality, a detection method for multi-bottleneck machines based on critical path is proposed. Therewith the unscheduled operations can be decomposed into bottleneck operations and non-bottleneck operations. According to the principle of “Bottleneck leads the performance of the whole manufacturing system” in TOC (Theory Of Constraints, the bottleneck operations are scheduled by genetic algorithm for high solution quality, and the non-bottleneck operations are scheduled by dispatching rules for the improvement of the solving efficiency.Findings: In the process of the sub-problems' construction, partial operations in the previous scheduled sub-problem are divided into the successive sub-problem for re-optimization. This strategy can improve the solution quality of the algorithm. In the process of solving the sub-problems, the strategy that evaluating the chromosome's fitness by predicting the global scheduling objective value can improve the solution quality.Research limitations/implications: In this research, there are some assumptions which reduce the complexity of the large-scale scheduling problem. They are as follows: The processing route of each job is predetermined, and the processing time of each operation is fixed. There is no machine breakdown, and no preemption of the operations is allowed. The assumptions should be considered if the algorithm is used in the actual job shop.Originality/value: The research provides an efficient scheduling method for the
An Embeddable Virtual Machine for State Space Generation
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
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.
SwingStates: Adding state machines to Java and the Swing toolkit
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...
Directory of Open Access Journals (Sweden)
Daniel Marcsa
2015-01-01
Full Text Available The analysis and design of electromechanical devices involve the solution of large sparse linear systems, and require therefore high performance algorithms. In this paper, the primal Domain Decomposition Method (DDM with parallel forward-backward and with parallel Preconditioned Conjugate Gradient (PCG solvers are introduced in two-dimensional parallel time-stepping finite element formulation to analyze rotating machine considering the electromagnetic field, external circuit and rotor movement. The proposed parallel direct and the iterative solver with two preconditioners are analyzed concerning its computational efficiency and number of iterations of the solver with different preconditioners. Simulation results of a rotating machine is also presented.
State Machine Framework And Its Use For Driving LHC Operational states
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...
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.
Decomposition of toluene in a steady-state atmospheric-pressure glow discharge
International Nuclear Information System (INIS)
Trushkin, A. N.; Grushin, M. E.; Kochetov, I. V.; Trushkin, N. I.; Akishev, Yu. S.
2013-01-01
Results are presented from experimental studies of decomposition of toluene (C 6 H 5 CH 3 ) in a polluted air flow by means of a steady-state atmospheric pressure glow discharge at different water vapor contents in the working gas. The experimental results on the degree of C 6 H 5 CH 3 removal are compared with the results of computer simulations conducted in the framework of the developed kinetic model of plasma chemical decomposition of toluene in the N 2 : O 2 : H 2 O gas mixture. A substantial influence of the gas flow humidity on toluene decomposition in the atmospheric pressure glow discharge is demonstrated. The main mechanisms of the influence of humidity on C 6 H 5 CH 3 decomposition are determined. The existence of two stages in the process of toluene removal, which differ in their duration and the intensity of plasma chemical decomposition of C 6 H 5 CH 3 is established. Based on the results of computer simulations, the composition of the products of plasma chemical reactions at the output of the reactor is analyzed as a function of the specific energy deposition and gas flow humidity. The existence of a catalytic cycle in which hydroxyl radical OH acts a catalyst and which substantially accelerates the recombination of oxygen atoms and suppression of ozone generation when the plasma-forming gas contains water vapor is established.
Decomposition of toluene in a steady-state atmospheric-pressure glow discharge
Trushkin, A. N.; Grushin, M. E.; Kochetov, I. V.; Trushkin, N. I.; Akishev, Yu. S.
2013-02-01
Results are presented from experimental studies of decomposition of toluene (C6H5CH3) in a polluted air flow by means of a steady-state atmospheric pressure glow discharge at different water vapor contents in the working gas. The experimental results on the degree of C6H5CH3 removal are compared with the results of computer simulations conducted in the framework of the developed kinetic model of plasma chemical decomposition of toluene in the N2: O2: H2O gas mixture. A substantial influence of the gas flow humidity on toluene decomposition in the atmospheric pressure glow discharge is demonstrated. The main mechanisms of the influence of humidity on C6H5CH3 decomposition are determined. The existence of two stages in the process of toluene removal, which differ in their duration and the intensity of plasma chemical decomposition of C6H5CH3 is established. Based on the results of computer simulations, the composition of the products of plasma chemical reactions at the output of the reactor is analyzed as a function of the specific energy deposition and gas flow humidity. The existence of a catalytic cycle in which hydroxyl radical OH acts a catalyst and which substantially accelerates the recombination of oxygen atoms and suppression of ozone generation when the plasma-forming gas contains water vapor is established.
On a decomposition theorem for density operators of a pure quantum state
International Nuclear Information System (INIS)
Giannoni, M.J.
1979-03-01
Conditions for the existence of a decomposition of a hermitian projector rho into two hermitian and time reversal invariant operators r/rho 0 and chi under the form rho=esup(i,chi)rho 0 esup(-i,chi) are investigated. Sufficient conditions are given, and an explicit construction of a decomposition is performed when they are fulfilled. A stronger theorem of existence and unicity is studied. All the proofs are valid for any p-body reduced density operator of a pure state of a system of bosons as well as fermions. The decomposition studied in this work has already been used in Nuclear Physics, and may be of interest in other fields of Physics
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.
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.
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.
Wang, Deyun; Wei, Shuai; Luo, Hongyuan; Yue, Chenqiang; Grunder, Olivier
2017-02-15
The randomness, non-stationarity and irregularity of air quality index (AQI) series bring the difficulty of AQI forecasting. To enhance forecast accuracy, a novel hybrid forecasting model combining two-phase decomposition technique and extreme learning machine (ELM) optimized by differential evolution (DE) algorithm is developed for AQI forecasting in this paper. In phase I, the complementary ensemble empirical mode decomposition (CEEMD) is utilized to decompose the AQI series into a set of intrinsic mode functions (IMFs) with different frequencies; in phase II, in order to further handle the high frequency IMFs which will increase the forecast difficulty, variational mode decomposition (VMD) is employed to decompose the high frequency IMFs into a number of variational modes (VMs). Then, the ELM model optimized by DE algorithm is applied to forecast all the IMFs and VMs. Finally, the forecast value of each high frequency IMF is obtained through adding up the forecast results of all corresponding VMs, and the forecast series of AQI is obtained by aggregating the forecast results of all IMFs. To verify and validate the proposed model, two daily AQI series from July 1, 2014 to June 30, 2016 collected from Beijing and Shanghai located in China are taken as the test cases to conduct the empirical study. The experimental results show that the proposed hybrid model based on two-phase decomposition technique is remarkably superior to all other considered models for its higher forecast accuracy. Copyright © 2016 Elsevier B.V. All rights reserved.
Directory of Open Access Journals (Sweden)
Shuyu Dai
2018-01-01
Full Text Available Daily peak load forecasting is an important part of power load forecasting. The accuracy of its prediction has great influence on the formulation of power generation plan, power grid dispatching, power grid operation and power supply reliability of power system. Therefore, it is of great significance to construct a suitable model to realize the accurate prediction of the daily peak load. A novel daily peak load forecasting model, CEEMDAN-MGWO-SVM (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, is proposed in this paper. Firstly, the model uses the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN algorithm to decompose the daily peak load sequence into multiple sub sequences. Then, the model of modified grey wolf optimization and support vector machine (MGWO-SVM is adopted to forecast the sub sequences. Finally, the forecasting sequence is reconstructed and the forecasting result is obtained. Using CEEMDAN can realize noise reduction for non-stationary daily peak load sequence, which makes the daily peak load sequence more regular. The model adopts the grey wolf optimization algorithm improved by introducing the population dynamic evolution operator and the nonlinear convergence factor to enhance the global search ability and avoid falling into the local optimum, which can better optimize the parameters of the SVM algorithm for improving the forecasting accuracy of daily peak load. In this paper, three cases are used to test the forecasting accuracy of the CEEMDAN-MGWO-SVM model. We choose the models EEMD-MGWO-SVM (Ensemble Empirical Mode Decomposition and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, MGWO-SVM (Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm, GWO-SVM (Support Vector Machine Optimized by Grey Wolf Optimization Algorithm, SVM (Support Vector
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.
Logic synthesis for FPGA-based finite state machines
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.
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...
Support vector machines for nuclear reactor state estimation
Energy Technology Data Exchange (ETDEWEB)
Zavaljevski, N.; Gross, K. C.
2000-02-14
Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm.
Support vector machines for nuclear reactor state estimation
International Nuclear Information System (INIS)
Zavaljevski, N.; Gross, K. C.
2000-01-01
Validation of nuclear power reactor signals is often performed by comparing signal prototypes with the actual reactor signals. The signal prototypes are often computed based on empirical data. The implementation of an estimation algorithm which can make predictions on limited data is an important issue. A new machine learning algorithm called support vector machines (SVMS) recently developed by Vladimir Vapnik and his coworkers enables a high level of generalization with finite high-dimensional data. The improved generalization in comparison with standard methods like neural networks is due mainly to the following characteristics of the method. The input data space is transformed into a high-dimensional feature space using a kernel function, and the learning problem is formulated as a convex quadratic programming problem with a unique solution. In this paper the authors have applied the SVM method for data-based state estimation in nuclear power reactors. In particular, they implemented and tested kernels developed at Argonne National Laboratory for the Multivariate State Estimation Technique (MSET), a nonlinear, nonparametric estimation technique with a wide range of applications in nuclear reactors. The methodology has been applied to three data sets from experimental and commercial nuclear power reactor applications. The results are promising. The combination of MSET kernels with the SVM method has better noise reduction and generalization properties than the standard MSET algorithm
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.
ATC calculation with steady-state security constraints using Benders decomposition
International Nuclear Information System (INIS)
Shaaban, M.; Yan, Z.; Ni, Y.; Wu, F.; Li, W.; Liu, H.
2003-01-01
Available transfer capability (ATC) is an important indicator of the usable amount of transmission capacity accessible by assorted parties for commercial trading, ATC calculation is nontrivial when steady-state security constraints are included. In hie paper, Benders decomposition method is proposed to partition the AC problem with steady-state security constraints into a base case master problem and a series of subproblems relevant to various contingencies to include their impacts on ATC. The mathematical model is formulated and the two solution schemes are presented. Computer testing on the 4-bus system and IEEE 30-bus system shows the effectiveness of the proposed method and the solution schemes. (Author)
International Nuclear Information System (INIS)
Sun Bin; Zhou Yunlong; Zhao Peng; Guan Yuebo
2007-01-01
Aiming at the non-stationary characteristics of differential pressure fluctuation signals of gas-liquid two-phase flow, and the slow convergence of learning and liability of dropping into local minima for BP neural networks, flow regime identification method based on Singular Value Decomposition (SVD) and Least Square Support Vector Machine (LS-SVM) is presented. First of all, the Empirical Mode Decomposition (EMD) method is used to decompose the differential pressure fluctuation signals of gas-liquid two-phase flow into a number of stationary Intrinsic Mode Functions (IMFs) components from which the initial feature vector matrix is formed. By applying the singular vale decomposition technique to the initial feature vector matrixes, the singular values are obtained. Finally, the singular values serve as the flow regime characteristic vector to be LS-SVM classifier and flow regimes are identified by the output of the classifier. The identification result of four typical flow regimes of air-water two-phase flow in horizontal pipe has shown that this method achieves a higher identification rate. (authors)
Directory of Open Access Journals (Sweden)
Maria Grazia De Giorgi
2014-08-01
Full Text Available A high penetration of wind energy into the electricity market requires a parallel development of efficient wind power forecasting models. Different hybrid forecasting methods were applied to wind power prediction, using historical data and numerical weather predictions (NWP. A comparative study was carried out for the prediction of the power production of a wind farm located in complex terrain. The performances of Least-Squares Support Vector Machine (LS-SVM with Wavelet Decomposition (WD were evaluated at different time horizons and compared to hybrid Artificial Neural Network (ANN-based methods. It is acknowledged that hybrid methods based on LS-SVM with WD mostly outperform other methods. A decomposition of the commonly known root mean square error was beneficial for a better understanding of the origin of the differences between prediction and measurement and to compare the accuracy of the different models. A sensitivity analysis was also carried out in order to underline the impact that each input had in the network training process for ANN. In the case of ANN with the WD technique, the sensitivity analysis was repeated on each component obtained by the decomposition.
DeSilva, J .H. S. R.; Udinwe, V.; Sideris, P. J.; Smart, M. C.; Krause, F. C.; Hwang, C.; Smith, K. A.; Greenbaum, S. G.
2012-01-01
Solid electrolyte interphase (SEI) formation in lithium ion cells prepared with advanced electrolytes is investigated by solid state multinuclear (7Li, 19F, 31P) magnetic resonance (NMR) measurements of electrode materials harvested from cycled cells subjected to an accelerated aging protocol. The electrolyte composition is varied to include the addition of fluorinated carbonates and triphenyl phosphate (TPP, a flame retardant). In addition to species associated with LiPF6 decomposition, cathode NMR spectra are characterized by the presence of compounds originating from the TPP additive. Substantial amounts of LiF are observed in the anodes as well as compounds originating from the fluorinated carbonates.
International Nuclear Information System (INIS)
Baddock, J.A.; Oades, J.M.; Nelson, P.N.; Skene, T.M.; Golchin, A.; Clarke, P.
1997-01-01
Solid-state 13 C nuclear magnetic resonance (NMR) spectroscopy has become an important tool for examining the chemical structure of natural organic materials and the chemical changes associated with decomposition. In this paper, solid-state 13 C NMR data pertaining to changes in the chemical composition of a diverse range of natural organic materials, including wood, peat, composts, forest litter layers, and organic materials in surface layers of mineral soils, were reviewed with the objective of deriving an index of the extent of decomposition of such organic materials based on changes in chemical composition. Chemical changes associated with the decomposition of wood varied considerably and were dependent on a strong interaction between the species of wood examined and the species composition of the microbial decomposer community, making the derivation of a single general index applicable to wood decomposition unlikely. For the remaining forms of natural organic residues, decomposition was almost always associated with an increased content of alkyl C and a decreased content of O-alkyl C. The concomitant increase and decrease in alkyl and O-alkyl C contents, respectively, suggested that the ratio of alkyl to O-alkyl carbon (A/O-A ratio) may provide a sensitive index of the extent of decomposition. Contrary to the traditional view that humic substances with an aromatic core accumulate as decomposition proceeds, changes in the aromatic region were variable and suggested a relationship with the activity of lignin-degrading fungi. The A/O-A ratio did appear to provide a sensitive index of extent of decomposition provided that its use was restricted to situations where the organic materials were derived from a common starting material. In addition, the potential for adsorption of highly decomposable materials on mineral soil surfaces and the impacts which such an adsorption may have on bioavailability required consideration when the A/O-A ratio was used to assess the
Russell, Matthew B.; Woodall, Christopher W.; D'Amato, Anthony W.; Fraver, Shawn; Bradford, John B.
2014-01-01
Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased temperatures and longer growing seasons associated with projected climate change will increase the decomposition rates (i.e., more rapid C cycling) of downed woody debris (DWD); however, the magnitude of this increase has not been previously addressed. Using DWD measurements collected from a national forest inventory of the eastern United States, we show that the residence time of DWD may decrease (i.e., more rapid decomposition) by as much as 13% over the next 200 years, depending on various future climate change scenarios and forest types. Although existing dynamic global vegetation models account for the decomposition process, they typically do not include the effect of a changing climate on DWD decomposition rates. We expect that an increased understanding of decomposition rates, as presented in this current work, will be needed to adequately quantify the fate of woody detritus in future forests. Furthermore, we hope these results will lead to improved models that incorporate climate change scenarios for depicting future dead wood dynamics in addition to a traditional emphasis on live-tree demographics.
Developing a PLC-friendly state machine model: lessons learned
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
Equivalence of restricted Boltzmann machines and tensor network 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.
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
Directory of Open Access Journals (Sweden)
Shuyu Dai
2018-04-01
Full Text Available For social development, energy is a crucial material whose consumption affects the stable and sustained development of the natural environment and economy. Currently, China has become the largest energy consumer in the world. Therefore, establishing an appropriate energy consumption prediction model and accurately forecasting energy consumption in China have practical significance, and can provide a scientific basis for China to formulate a reasonable energy production plan and energy-saving and emissions-reduction-related policies to boost sustainable development. For forecasting the energy consumption in China accurately, considering the main driving factors of energy consumption, a novel model, EEMD-ISFLA-LSSVM (Ensemble Empirical Mode Decomposition and Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm, is proposed in this article. The prediction accuracy of energy consumption is influenced by various factors. In this article, first considering population, GDP (Gross Domestic Product, industrial structure (the proportion of the second industry added value, energy consumption structure, energy intensity, carbon emissions intensity, total imports and exports and other influencing factors of energy consumption, the main driving factors of energy consumption are screened as the model input according to the sorting of grey relational degrees to realize feature dimension reduction. Then, the original energy consumption sequence of China is decomposed into multiple subsequences by Ensemble Empirical Mode Decomposition for de-noising. Next, the ISFLA-LSSVM (Least Squares Support Vector Machine Optimized by Improved Shuffled Frog Leaping Algorithm model is adopted to forecast each subsequence, and the prediction sequences are reconstructed to obtain the forecasting result. After that, the data from 1990 to 2009 are taken as the training set, and the data from 2010 to 2016 are taken as the test set to make an
Energy Technology Data Exchange (ETDEWEB)
Tipireddy, R.; Stinis, P.; Tartakovsky, A. M.
2017-12-01
We present a novel approach for solving steady-state stochastic partial differential equations (PDEs) with high-dimensional random parameter space. The proposed approach combines spatial domain decomposition with basis adaptation for each subdomain. The basis adaptation is used to address the curse of dimensionality by constructing an accurate low-dimensional representation of the stochastic PDE solution (probability density function and/or its leading statistical moments) in each subdomain. Restricting the basis adaptation to a specific subdomain affords finding a locally accurate solution. Then, the solutions from all of the subdomains are stitched together to provide a global solution. We support our construction with numerical experiments for a steady-state diffusion equation with a random spatially dependent coefficient. Our results show that highly accurate global solutions can be obtained with significantly reduced computational costs.
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.
Institute of Scientific and Technical Information of China (English)
FAN Hong-Yi; FAN Yue
2002-01-01
By virtue of the technique of integration within an ordered product of operators and the Schmidt decomposition of the entangled state |η〉, we reduce the general projection calculation in the theory of quantum teleportation to a as simple as possible form and present a general formalism for teleportating quantum states of continuous variable.
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
Using Pipelined XNOR Logic to Reduce SEU Risks in State Machines
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
International Nuclear Information System (INIS)
Zhang, Yachao; Liu, Kaipei; Qin, Liang; An, Xueli
2016-01-01
Highlights: • Variational mode decomposition is adopted to process original wind power series. • A novel combined model based on machine learning methods is established. • An improved differential evolution algorithm is proposed for weight adjustment. • Probabilistic interval prediction is performed by quantile regression averaging. - Abstract: Due to the increasingly significant energy crisis nowadays, the exploitation and utilization of new clean energy gains more and more attention. As an important category of renewable energy, wind power generation has become the most rapidly growing renewable energy in China. However, the intermittency and volatility of wind power has restricted the large-scale integration of wind turbines into power systems. High-precision wind power forecasting is an effective measure to alleviate the negative influence of wind power generation on the power systems. In this paper, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. Variational mode decomposition is firstly adopted to handle the instability of the raw wind power series, and the subseries can be reconstructed by measuring sample entropy of the decomposed modes. Then the base models can be established for each subseries respectively. On this basis, the combined model is developed based on the optimal virtual prediction scheme, the weight matrix of which is dynamically adjusted by a self-adaptive multi-strategy differential evolution algorithm. Besides, a probabilistic interval prediction model based on quantile regression averaging and variational mode decomposition-based hybrid models is presented to quantify the potential risks of the wind power series. The simulation results indicate that: (1) the normalized mean absolute errors of the proposed combined model from one-step to three-step forecasting are 4.34%, 6.49% and 7.76%, respectively, which are much lower than those of the base models and the hybrid
Identifying student stuck states in programmingassignments using machine learning
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...
Directory of Open Access Journals (Sweden)
Ateke Goshvarpour
2016-06-01
Full Text Available Introduction Automatic human emotion recognition is one of the most interesting topics in the field of affective computing. However, development of a reliable approach with a reasonable recognition rate is a challenging task. The main objective of the present study was to propose a robust method for discrimination of emotional responses thorough examination of heart rate variability (HRV. In the present study, considering the non-stationary and non-linear characteristics of HRV, empirical mode decomposition technique was utilized as a feature extraction approach. Materials and Methods In order to induce the emotional states, images indicating four emotional states, i.e., happiness, peacefulness, sadness, and fearfulness were presented. Simultaneously, HRV was recorded in 47 college students. The signals were decomposed into some intrinsic mode functions (IMFs. For each IMF and different IMF combinations, 17 standard and non-linear parameters were extracted. Wilcoxon test was conducted to assess the difference between IMF parameters in different emotional states. Afterwards, a probabilistic neural network was used to classify the features into emotional classes. Results Based on the findings, maximum classification rates were achieved when all IMFs were fed into the classifier. Under such circumstances, the proposed algorithm could discriminate the affective states with sensitivity, specificity, and correct classification rate of 99.01%, 100%, and 99.09%, respectively. In contrast, the lowest discrimination rates were attained by IMF1 frequency and its combinations. Conclusion The high performance of the present approach indicated that the proposed method is applicable for automatic emotion recognition.
Long-term decomposition of sugarcane harvest residues in Sao Paulo state, Brazil
International Nuclear Information System (INIS)
Fortes, Caio; Trivelin, Paulo Cesar Ocheuze; Vitti, Andre Cesar
2012-01-01
Crop residues returned to the soil are important to preserve fertility and sustainability. This research addressed the long-term decomposition of sugarcane post-harvest residues (trash) under reduced tillage, therefore field renewal was performed with herbicide followed by subsoiling and ratoons were deprived of interrow scarification. The trial was conducted in the northern Sao Paulo State, Brazil during four consecutive crops (2005–2008) where litter bags containing 15 N-labeled trash were disposed in the field attempting to simulate two distinct situations: the previous crop trash (PCT) or residues incorporated in the field after tillage, and post-harvest trash (PHT) or the remains of plant-cane harvest. Decomposition rates regarding dry matter (DM), carbon (C), root growth, plant nutrients (N, P, K, Ca, Mg and S), lignin (LIG) cellulose (CEL) and hemicellulose (HCEL) contents were assessed for PCT (2005 ndash;2008) and for PHT (2006–2008). There were significant reductions on DM and C:N ratio due to C losses and root growth within the litter bags over time. The DM from PCT and PHT decreased 96% and 73% after four and three crops, respectively, and the higher nutrients release were found for K, Ca and N. The LIG, CEL and HCEL concentrations in PCT decreased 60%, 29%, 70% after four crops and 47%, 35%, 70% from PHT after three crops, respectively. Trash decomposition was driven mainly by residues biochemical composition, root growth within the trash blanket and the climatic conditions during the crop cycles. -- Highlights: ► Degradation of sugarcane previous or post-harvest trash (PCT or PHT) was evaluated. ► Dry matter and C decreased due to microbial and root growth within trash blankets. ► C:N ratio of PCT linearly decreased 23% per year during four consecutive crops. ► Lignin, cellulose and hemicellulose concentration averagely declined 54, 41 and 70%. ► PCT and PHT are long-term sources of C, K, Ca and N to the soil-plant system.
International Nuclear Information System (INIS)
Kimmel, Anna V.; Sushko, Peter V.; Shluger, Alexander L.; Kuklja, Maija M.
2007-01-01
The authors have calculated the electronic structure of individual 1,1-diamino-2,2-dinitroethylene molecules (FOX-7) in the gas phase by means of density functional theory with the hybrid B3LYP functional and 6-31+G(d,p) basis set and considered their dissociation pathways. Positively and negatively charged states as well as the lowest excited states of the molecule were simulated. They found that charging and excitation can not only reduce the activation barriers for decomposition reactions but also change the dominating chemistry from endo- to exothermic type. In particular, they found that there are two competing primary initiation mechanisms of FOX-7 decomposition: C-NO 2 bond fission and C-NO 2 to CONO isomerization. Electronic excitation or charging of FOX-7 disfavors CONO formation and, thus, terminates this channel of decomposition. However, if CONO is formed from the neutral FOX-7 molecule, charge trapping and/or excitation results in spontaneous splitting of an NO group accompanied by the energy release. Intramolecular hydrogen transfer is found to be a rare event in FOX-7 unless free electrons are available in the vicinity of the molecule, in which case HONO formation is a feasible exothermic reaction with a relatively low energy barrier. The effect of charged and excited states on other possible reactions is also studied. Implications of the obtained results to FOX-7 decomposition in condensed state are discussed
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 ...
Machine Learning Applications to Resting-State Functional MR Imaging Analysis.
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.
M.B. Russell; C.W. Woodall; A.W. D' Amato; S. Fraver; J.B. Bradford
2014-01-01
Forest ecosystems play a critical role in mitigating greenhouse gas emissions. Forest carbon (C) is stored through photosynthesis and released via decomposition and combustion. Relative to C fixation in biomass, much less is known about C depletion through decomposition of woody debris, particularly under a changing climate. It is assumed that the increased...
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.
Engelbrecht, Nicolaas; Chiuta, Steven; Bessarabov, Dmitri G.
2018-05-01
The experimental evaluation of an autothermal microchannel reactor for H2 production from NH3 decomposition is described. The reactor design incorporates an autothermal approach, with added NH3 oxidation, for coupled heat supply to the endothermic decomposition reaction. An alternating catalytic plate arrangement is used to accomplish this thermal coupling in a cocurrent flow strategy. Detailed analysis of the transient operating regime associated with reactor start-up and steady-state results is presented. The effects of operating parameters on reactor performance are investigated, specifically, the NH3 decomposition flow rate, NH3 oxidation flow rate, and fuel-oxygen equivalence ratio. Overall, the reactor exhibits rapid response time during start-up; within 60 min, H2 production is approximately 95% of steady-state values. The recommended operating point for steady-state H2 production corresponds to an NH3 decomposition flow rate of 6 NL min-1, NH3 oxidation flow rate of 4 NL min-1, and fuel-oxygen equivalence ratio of 1.4. Under these flows, NH3 conversion of 99.8% and H2 equivalent fuel cell power output of 0.71 kWe is achieved. The reactor shows good heat utilization with a thermal efficiency of 75.9%. An efficient autothermal reactor design is therefore demonstrated, which may be upscaled to a multi-kW H2 production system for commercial implementation.
Directory of Open Access Journals (Sweden)
Kozyr-Chepurna Mariia A.
2017-09-01
Full Text Available The aim of the article is to practically approve the authors’ multi-level hierarchical approach to the strategic planning of industrial enterprise restructuring using the example of solving the problem of disaggregating the strategic plan for restructuring a machine-building enterprise of the electrical industry providing for organization of production of railroad freight cars at the enterprise. Besides, there demonstrated the effectiveness of the mechanisms of coordinating the plans for adjacent hierarchical and time periods included in the corresponding mathematical support. In the course of the practical approval, different variants of formulating the problem of decomposing the strategic plan into plans of lower hierarchical levels differing in terms of coordination of the plans of adjacent hierarchical levels and adjacent planning periods are considered, and the solutions of corresponding optimal planning problems are analyzed. It is shown that the developed methodological approach, which is based on the methods of statistical optimization, demonstrates quite satisfactory performance characteristics in solving the problem of coordinating the plans of adjacent time periods in the mode of sliding planning in the process of decomposition of the strategic plan into lower-level plans.
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...
A Hybrid Model Based on Wavelet Decomposition-Reconstruction in Track Irregularity State Forecasting
Directory of Open Access Journals (Sweden)
Chaolong Jia
2015-01-01
Full Text Available Wavelet is able to adapt to the requirements of time-frequency signal analysis automatically and can focus on any details of the signal and then decompose the function into the representation of a series of simple basis functions. It is of theoretical and practical significance. Therefore, this paper does subdivision on track irregularity time series based on the idea of wavelet decomposition-reconstruction and tries to find the best fitting forecast model of detail signal and approximate signal obtained through track irregularity time series wavelet decomposition, respectively. On this ideology, piecewise gray-ARMA recursive based on wavelet decomposition and reconstruction (PG-ARMARWDR and piecewise ANN-ARMA recursive based on wavelet decomposition and reconstruction (PANN-ARMARWDR models are proposed. Comparison and analysis of two models have shown that both these models can achieve higher accuracy.
A Tensor Decomposition-Based Approach for Detecting Dynamic Network States From EEG.
Mahyari, Arash Golibagh; Zoltowski, David M; Bernat, Edward M; Aviyente, Selin
2017-01-01
Functional connectivity (FC), defined as the statistical dependency between distinct brain regions, has been an important tool in understanding cognitive brain processes. Most of the current works in FC have focused on the assumption of temporally stationary networks. However, recent empirical work indicates that FC is dynamic due to cognitive functions. The purpose of this paper is to understand the dynamics of FC for understanding the formation and dissolution of networks of the brain. In this paper, we introduce a two-step approach to characterize the dynamics of functional connectivity networks (FCNs) by first identifying change points at which the network connectivity across subjects shows significant changes and then summarizing the FCNs between consecutive change points. The proposed approach is based on a tensor representation of FCNs across time and subjects yielding a four-mode tensor. The change points are identified using a subspace distance measure on low-rank approximations to the tensor at each time point. The network summarization is then obtained through tensor-matrix projections across the subject and time modes. The proposed framework is applied to electroencephalogram (EEG) data collected during a cognitive control task. The detected change-points are consistent with a priori known ERN interval. The results show significant connectivities in medial-frontal regions which are consistent with widely observed ERN amplitude measures. The tensor-based method outperforms conventional matrix-based methods such as singular value decomposition in terms of both change-point detection and state summarization. The proposed tensor-based method captures the topological structure of FCNs which provides more accurate change-point-detection and state summarization.
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....
State Machine Modeling of the Space Launch System Solid Rocket Boosters
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.
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.
Patel, Vinay Kumar; Bhattacharya, Shantanu
2017-09-01
The present study reports a facile solid state green synthesis process using the leaf extracts of Hibiscus rosa-sinensis to synthesize CuO nanorods with average diameters of 15-20 nm and lengths up to 100 nm. The as-synthesized CuO nanorods were characterized by x-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and selected area electron diffraction. The formation mechanism of CuO nanorods has been explained by involving the individual role of amide I (amino groups) and carboxylate groups under excess hydroxyl ions released from NaOH. The catalytic activity of CuO nanorods in thermal decomposition of potassium periodate microparticles (µ-KIO4) microparticles was studied by thermo gravimetric analysis measurement. The original size (~100 µm) of commercially procured potassium periodate was reduced to microscale length scale to about one-tenth by PEG200 assisted emulsion process. The CuO nanorods prepared by solid state green route were found to catalyze the thermal decomposition of µ-KIO4 with a reduction of 18 °C in the final thermal decomposition temperature of potassium periodate.
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
A rule-based approach to model checking of UML state machines
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.
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.
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.
Laser Beam Machining (LBM), State of the Art and New Opportunities
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
Constrained state-feedback control of an externally excited synchronous machine
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.
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...
Practical programmable circuits a guide to PLDs, state machines, and microcontrollers
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.
Directory of Open Access Journals (Sweden)
Aliakbar Dehno Khalaji
2015-06-01
Full Text Available In this paper, plate-like NiO nanoparticles were prepared by one-pot solid-state thermal decomposition of nickel (II Schiff base complex as new precursor. First, the nickel (II Schiff base precursor was prepared by solid-state grinding using nickel (II nitrate hexahydrate, Ni(NO32∙6H2O, and the Schiff base ligand N,N′-bis-(salicylidene benzene-1,4-diamine for 30 min without using any solvent, catalyst, template or surfactant. It was characterized by Fourier Transform Infrared spectroscopy (FT-IR and elemental analysis (CHN. The resultant solid was subsequently annealed in the electrical furnace at 450 °C for 3 h in air atmosphere. Nanoparticles of NiO were produced and characterized by X-ray powder diffraction (XRD at 2θ degree 0-140°, FT-IR spectroscopy, scanning electron microscopy (SEM and transmission electron microscopy (TEM. The XRD and FT-IR results showed that the product is pure and has good crystallinity with cubic structure because no characteristic peaks of impurity were observed, while the SEM and TEM results showed that the obtained product is tiny, aggregated with plate-like shape, narrow size distribution with an average size between 10-40 nm. Results show that the solid state thermal decomposition method is simple, environmentally friendly, safe and suitable for preparation of NiO nanoparticles. This method can also be used to synthesize nanoparticles of other metal oxides.
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.
Implementing finite state machines in a computer-based teaching system
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.
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.
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)
Scaling up liquid state machines to predict over address events from dynamic vision sensors.
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.
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.
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%.
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.
Fast implementation of the 1\\rightarrow3 orbital state quantum cloning machine
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.
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
Parallel algorithms for testing finite state machines:Generating UIO sequences
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...
Diagnostics of the Technical State of Bearings of Mining Machines Base Assemblies
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.
Distributed state machine supervision for long-baseline gravitational-wave detectors
International Nuclear Information System (INIS)
Rollins, Jameson Graef
2016-01-01
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.
Distributed state machine supervision for long-baseline gravitational-wave detectors
Energy Technology Data Exchange (ETDEWEB)
Rollins, Jameson Graef, E-mail: jameson.rollins@ligo.org [LIGO Laboratory, California Institute of Technology, Pasadena, California 91125 (United States)
2016-09-15
The Laser Interferometer Gravitational-wave Observatory (LIGO) consists of two identical yet independent, widely separated, long-baseline gravitational-wave detectors. Each Advanced LIGO detector consists of complex optical-mechanical systems isolated from the ground by multiple layers of active seismic isolation, all controlled by hundreds of fast, digital, feedback control systems. This article describes a novel state machine-based automation platform developed to handle the automation and supervisory control challenges of these detectors. The platform, called Guardian, consists of distributed, independent, state machine automaton nodes organized hierarchically for full detector control. User code is written in standard Python and the platform is designed to facilitate the fast-paced development process associated with commissioning the complicated Advanced LIGO instruments. While developed specifically for the Advanced LIGO detectors, Guardian is a generic state machine automation platform that is useful for experimental control at all levels, from simple table-top setups to large-scale multi-million dollar facilities.
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.
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.
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
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.
O’Donnell, Jonathan A.; Aiken, George R.; Butler, Kenna D.; Guillemette, Francois; Podgorski, David C.; Spencer, Robert G. M.
2016-01-01
The boreal region stores large amounts of organic carbon (C) in organic-soil horizons, which are vulnerable to destabilization via warming and disturbance. Decomposition of soil organic matter (SOM) contributes to the production and turnover of dissolved organic matter (DOM). While temperature is a primary control on rates of SOM and DOM cycling, little is known about temperature effects on DOM composition in soil leachate. Here we conducted a 30 day incubation to examine the effects of temperature (20 versus 5°C) and SOM decomposition state (moss versus fibric versus amorphous horizons) on DOM composition in organic soils of interior Alaska. We characterized DOM using bulk dissolved organic C (DOC) concentration, chemical fractionation, optical properties, and ultrahigh-resolution mass spectrometry. We observed an increase in DOC concentration and DOM aromaticity in the 20°C treatment compared to the 5°C treatment. Leachate from fibric horizons had higher DOC concentration than shallow moss or deep amorphous horizons. We also observed chemical shifts in DOM leachate over time, including increases in hydrophobic organic acids, polyphenols, and condensed aromatics and decreases in low-molecular weight hydrophilic compounds and aliphatics. We compared ultrahigh-resolution mass spectrometry and optical data and observed strong correlations between polyphenols, condensed aromatics, SUVA254, and humic-like fluorescence intensities. These findings suggest that biolabile DOM was preferentially mineralized, and the magnitude of this transformation was determined by kinetics (i.e., temperature) and substrate quality (i.e., soil horizon). With future warming, our findings indicate that organic soils may release higher concentrations of aromatic DOM to aquatic ecosystems.
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.
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.
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.
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.
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.
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.
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.
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
Directory of Open Access Journals (Sweden)
Batakliev Todor
2014-06-01
Full Text Available Catalytic ozone decomposition is of great significance because ozone is a toxic substance commonly found or generated in human environments (aircraft cabins, offices with photocopiers, laser printers, sterilizers. Considerable work has been done on ozone decomposition reported in the literature. This review provides a comprehensive summary of the literature, concentrating on analysis of the physico-chemical properties, synthesis and catalytic decomposition of ozone. This is supplemented by a review on kinetics and catalyst characterization which ties together the previously reported results. Noble metals and oxides of transition metals have been found to be the most active substances for ozone decomposition. The high price of precious metals stimulated the use of metal oxide catalysts and particularly the catalysts based on manganese oxide. It has been determined that the kinetics of ozone decomposition is of first order importance. A mechanism of the reaction of catalytic ozone decomposition is discussed, based on detailed spectroscopic investigations of the catalytic surface, showing the existence of peroxide and superoxide surface intermediates
Detecting Mental States by Machine Learning Techniques: The Berlin Brain-Computer Interface
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.
Heat-machine control by quantum-state preparation: from quantum engines to refrigerators.
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.
State-trait decomposition of Name Letter Test scores and relationships with global self-esteem.
Perinelli, Enrico; Alessandri, Guido; Donnellan, M Brent; Łaguna, Mariola
2018-06-01
The Name Letter Test (NLT) assesses the degree that participants show a preference for an individual's own initials. The NLT was often thought to measure implicit self-esteem, but recent literature reviews do not equivocally support this hypothesis. Several authors have argued that the NLT is most strongly associated with the state component of self-esteem. The current research uses a modified STARTS model to (a) estimate the percentage of stable and transient components of the NLT and (b) estimate the covariances between stable/transient components of the NLT and stable/transient components of self-esteem and positive and negative affect. Two longitudinal studies were conducted with different time lags: In Study 1, participants were assessed daily for 7 consecutive days, whereas in Study 2, participants were assessed weekly for 8 consecutive weeks. Participants also completed a battery of questionnaires including global self-esteem, positive affect, and negative affect. In both studies, the NLT showed (a) high stability across time, (b) a high percentage of stable variance, (c) no significant covariance with stable and transient factors for global self-esteem, and (d) a different pattern of correlations with stable and transient factors of affect than global self-esteem. Collectively, these results further undermine the claim that the NLT is a valid measure of implicit self-esteem. Future work is needed to identify theoretically grounded correlates of the NLT. (PsycINFO Database Record (c) 2018 APA, all rights reserved).
Chao, T.T.; Sanzolone, R.F.
1992-01-01
Sample decomposition is a fundamental and integral step in the procedure of geochemical analysis. It is often the limiting factor to sample throughput, especially with the recent application of the fast and modern multi-element measurement instrumentation. The complexity of geological materials makes it necessary to choose the sample decomposition technique that is compatible with the specific objective of the analysis. When selecting a decomposition technique, consideration should be given to the chemical and mineralogical characteristics of the sample, elements to be determined, precision and accuracy requirements, sample throughput, technical capability of personnel, and time constraints. This paper addresses these concerns and discusses the attributes and limitations of many techniques of sample decomposition along with examples of their application to geochemical analysis. The chemical properties of reagents as to their function as decomposition agents are also reviewed. The section on acid dissolution techniques addresses the various inorganic acids that are used individually or in combination in both open and closed systems. Fluxes used in sample fusion are discussed. The promising microwave-oven technology and the emerging field of automation are also examined. A section on applications highlights the use of decomposition techniques for the determination of Au, platinum group elements (PGEs), Hg, U, hydride-forming elements, rare earth elements (REEs), and multi-elements in geological materials. Partial dissolution techniques used for geochemical exploration which have been treated in detail elsewhere are not discussed here; nor are fire-assaying for noble metals and decomposition techniques for X-ray fluorescence or nuclear methods be discussed. ?? 1992.
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.
High-Density Liquid-State Machine Circuitry for Time-Series Forecasting.
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.
Classification of fMRI resting-state maps using machine learning techniques: A comparative study
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.
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.
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.
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.
FSM-F: Finite State Machine Based Framework for Denial of Service and Intrusion Detection in MANET.
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.
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.
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.
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...
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
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.
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
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.
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.
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
An integrated condition-monitoring method for a milling process using reduced decomposition features
International Nuclear Information System (INIS)
Liu, Jie; Wu, Bo; Hu, Youmin; Wang, Yan
2017-01-01
Complex and non-stationary cutting chatter affects productivity and quality in the milling process. Developing an effective condition-monitoring approach is critical to accurately identify cutting chatter. In this paper, an integrated condition-monitoring method is proposed, where reduced features are used to efficiently recognize and classify machine states in the milling process. In the proposed method, vibration signals are decomposed into multiple modes with variational mode decomposition, and Shannon power spectral entropy is calculated to extract features from the decomposed signals. Principal component analysis is adopted to reduce feature size and computational cost. With the extracted feature information, the probabilistic neural network model is used to recognize and classify the machine states, including stable, transition, and chatter states. Experimental studies are conducted, and results show that the proposed method can effectively detect cutting chatter during different milling operation conditions. This monitoring method is also efficient enough to satisfy fast machine state recognition and classification. (paper)
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.
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...
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...
Exact complexity: The spectral decomposition of intrinsic computation
International Nuclear Information System (INIS)
Crutchfield, James P.; Ellison, Christopher J.; Riechers, Paul M.
2016-01-01
We give exact formulae for a wide family of complexity measures that capture the organization of hidden nonlinear processes. The spectral decomposition of operator-valued functions leads to closed-form expressions involving the full eigenvalue spectrum of the mixed-state presentation of a process's ϵ-machine causal-state dynamic. Measures include correlation functions, power spectra, past-future mutual information, transient and synchronization informations, and many others. As a result, a direct and complete analysis of intrinsic computation is now available for the temporal organization of finitary hidden Markov models and nonlinear dynamical systems with generating partitions and for the spatial organization in one-dimensional systems, including spin systems, cellular automata, and complex materials via chaotic crystallography. - Highlights: • We provide exact, closed-form expressions for a hidden stationary process' intrinsic computation. • These include information measures such as the excess entropy, transient information, and synchronization information and the entropy-rate finite-length approximations. • The method uses an epsilon-machine's mixed-state presentation. • The spectral decomposition of the mixed-state presentation relies on the recent development of meromorphic functional calculus for nondiagonalizable operators.
DEFF Research Database (Denmark)
Mihalache, V.; Grivel, J. C.; Secu, M.
2018-01-01
. An improvement of ferromagnetism and intensity of defect-related PL emission was observed when annealing the products in which nanocrystalline cerium oxide coexists with Ce - oxicarbonate traces, Ce2O2CO3. The experimental results were explained based on the following considerations: room temperature......Four batches of cerium oxide powders (with nanocrystallite size of 6.9 nm–572 nm) were prepared from four precursor nanopowders by thermal decomposition of Ce-propionate and annealing in air between 250 °C–1200 °C for 10 min–240 min. Ceria formation reactions, structure, vibrational, luminescence...... and magnetic properties were investigated by differential scanning calorimetry, x-ray diffraction, electron microscopy, infrared spectroscopy, photoluminescence and SQUID. All the samples exhibit room temperature ferromagnetism, RTFM, (with coercivity, Hc, of 8 Oe - 121 Oe and saturation magnetization, Ms...
Mao, H.; McGlynn, D. F.; Wu, Z.; Sive, B. C.
2017-12-01
A time scale decomposition technique, the Ensemble Empirical Mode Decomposition (EEMD), has been employed to decompose the time scales in long-term ozone measurement data at 24 US National Park Service sites. Time scales of interest include the annual cycle, variability by large scale climate oscillations, and the long-term trend. The implementation of policy regulations was found to have had a greater effect on sites nearest to urban regions. Ozone daily mean values increased until around the late 1990s followed by decreasing trends during the ensuing decades for sites in the East, southern California, and northwestern Washington. Sites in the Midwest did not experience a reversal of trends from positive to negative until the mid- to late 2000s. The magnitude of the annual amplitude decreased for nine sites and increased for three sites. Stronger decreases in the annual amplitude occurred in the East, with more sites in the East experiencing decreases in annual amplitude than in the West. The date of annual ozone peaks and minimums has changed for 12 sites in total, but those with a shift in peak date did not necessarily have a shift in the trough date. There appeared to be a link between peak dates occurring earlier and a decrease in the annual amplitude. This is likely related to a decrease in ozone titration due to NOx emission reductions. Furthermore, it was found that the shift in the Pacific Decadal Oscillation (PDO) regime from positive to negative in 1998-1999 resulting in an increase in occurrences of La Niña-like conditions had the effect of directing more polluted air masses from East Asia to higher latitudes over North America. This change in PDO regime was likely one main factor causing the increase in ozone concentrations on all time scales at an Alaskan site DENA-HQ.
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
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
Performance optimization of a CNC machine through exploration of the timed state space
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.
Reactive Goal Decomposition Hierarchies for On-Board Autonomy
Hartmann, L.
2002-01-01
As our experience grows, space missions and systems are expected to address ever more complex and demanding requirements with fewer resources (e.g., mass, power, budget). One approach to accommodating these higher expectations is to increase the level of autonomy to improve the capabilities and robustness of on- board systems and to simplify operations. The goal decomposition hierarchies described here provide a simple but powerful form of goal-directed behavior that is relatively easy to implement for space systems. A goal corresponds to a state or condition that an operator of the space system would like to bring about. In the system described here goals are decomposed into simpler subgoals until the subgoals are simple enough to execute directly. For each goal there is an activation condition and a set of decompositions. The decompositions correspond to different ways of achieving the higher level goal. Each decomposition contains a gating condition and a set of subgoals to be "executed" sequentially or in parallel. The gating conditions are evaluated in order and for the first one that is true, the corresponding decomposition is executed in order to achieve the higher level goal. The activation condition specifies global conditions (i.e., for all decompositions of the goal) that need to hold in order for the goal to be achieved. In real-time, parameters and state information are passed between goals and subgoals in the decomposition; a termination indication (success, failure, degree) is passed up when a decomposition finishes executing. The lowest level decompositions include servo control loops and finite state machines for generating control signals and sequencing i/o. Semaphores and shared memory are used to synchronize and coordinate decompositions that execute in parallel. The goal decomposition hierarchy is reactive in that the generated behavior is sensitive to the real-time state of the system and the environment. That is, the system is able to react
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
Mathematical modelling of the decomposition of explosives
International Nuclear Information System (INIS)
Smirnov, Lev P
2010-01-01
Studies on mathematical modelling of the molecular and supramolecular structures of explosives and the elementary steps and overall processes of their decomposition are analyzed. Investigations on the modelling of combustion and detonation taking into account the decomposition of explosives are also considered. It is shown that solution of problems related to the decomposition kinetics of explosives requires the use of a complex strategy based on the methods and concepts of chemical physics, solid state physics and theoretical chemistry instead of empirical approach.
Executable Architecture of Net Enabled Operations: State Machine of Federated Nodes
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
State of the art in nuclear telerobotics: focus on the man/machine connection
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.
Directory of Open Access Journals (Sweden)
Shuan-Feng Zhao
2017-01-01
Full Text Available In the driver fatigue monitoring technology, the essence is to capture and analyze the driver behavior information, such as eyes, face, heart, and EEG activity during driving. However, ECG and EEG monitoring are limited by the installation electrodes and are not commercially available. The most common fatigue detection method is the analysis of driver behavior, that is, to determine whether the driver is tired by recording and analyzing the behavior characteristics of steering wheel and brake. The driver usually adjusts his or her actions based on the observed road conditions. Obviously the road path information is directly contained in the vehicle driving state; if you want to judge the driver’s driving behavior by vehicle driving status information, the first task is to remove the road information from the vehicle driving state data. Therefore, this paper proposes an effective intrinsic mode function selection method for the approximate entropy of empirical mode decomposition considering the characteristics of the frequency distribution of road and vehicle information and the unsteady and nonlinear characteristics of the driver closed-loop driving system in vehicle driving state data. The objective is to extract the effective component of the driving behavior information and to weaken the road information component. Finally the effectiveness of the proposed method is verified by simulating driving experiments.
International Nuclear Information System (INIS)
Sampaio, E.V.S.; Salcedo, I.H.; Bettany, J.
1990-01-01
The decomposition of 14 C- 15 N labelled straw, incorporated at three depths in a Red-yellow Latosol from the humid, coastal zone of Pernambuco State, Brazil, was measured during two years. The straw was ground, mixed with soil portions and placed in 400-mesh bags and replaced into the original field sites at 10, 30 and 60cm depth. The decomposition was also followed in the laboratory using soil from the superficial layer. Straw carbon losses in the field reached 52% during the first month and about 80% after two years. In the first 4 months mineralization was faster in the superficial layer, with no differences thereafter. In the laboratory, mineralization was slower than in the field, reaching 34 and 50% after one month and two years, respectively. During the first month, most of the soil microbial biomass was apparently formed from straw derived material but the contribution from the straw decrease to 15-30% after two months and to less than 1% after two years. Straw N losses reached 25% in the first month and 40-50% after two years, with significant differences among soil depths in the first six months when losses were higher in the deeper layers. There were no plants to absorb the mineral N which accumulated in the soil to a concentration of up to 32μg/g soil. The contribution of straw N to this mineral N decreased with incubation period but was always less than 12%. The C:N ratio of straw derived material ( 14 C- 15 N) decreased from 22:1 to 8-10:1, at all depths. (author)
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.
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.
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.
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.
Nutrient Dynamics and Litter Decomposition in Leucaena ...
African Journals Online (AJOL)
Nutrient contents and rate of litter decomposition were investigated in Leucaena leucocephala plantation in the University of Agriculture, Abeokuta, Ogun State, Nigeria. Litter bag technique was used to study the pattern and rate of litter decomposition and nutrient release of Leucaena leucocephala. Fifty grams of oven-dried ...
Serna, Gabriel Ramom
2012-01-01
It is arguably the case that one of the most pressing issues in higher education finance is the increasing price of obtaining a college education, and, more specifically, rising tuition and fees. Because state support to public higher education and tuition and fees at publicly supported colleges and universities have been shown to share an inverse…
International Nuclear Information System (INIS)
Gao Ruorui; Zhang Yue; Yu Wei; Xiong Rui; Shi Jing
2012-01-01
MnFe 2 O 4 nano-particles with an average size of about 7 nm were synthesized by the thermal decomposition method. Based on the magnetic hysteresis loops measured at different temperatures the temperature-dependent saturation magnetization (M S ) and coercivity (H C ) are determined. It is shown that above 20 K the temperature-dependence of the M S and H C indicates the magnetic behaviors in the single-domain nano-particles, while below 20 K, the change of the M S and H C indicates the freezing of the spin-glass like state on the surfaces. By measuring the magnetization–temperature (M–T) curves under the zero-field-cooling (ZFC) and field-cooling procedures at different applied fields, superparamagnetism behavior is also studied. Even though in the ZFC M–T curves peaks can be observed below 160 K, superparamagnetism does not appear until the temperature goes above 300 K, which is related with the strong inter-particle interaction. - Highlights: ► MnFe 2 O 4 nano-particles with size of 7 nm were prepared. ► The surface spin-glass like state is frozen below 20 K. ► The peaks in ZFC magnetization–temperature curves are observed below 160 K. ► The inter-particle interaction inhibits the superparamagnetism at room temperature.
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.
AUTONOMOUS GAUSSIAN DECOMPOSITION
Energy Technology Data Exchange (ETDEWEB)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian [Department of Astronomy, University of Wisconsin, 475 North Charter Street, Madison, WI 53706 (United States); Heiles, Carl [Radio Astronomy Lab, UC Berkeley, 601 Campbell Hall, Berkeley, CA 94720 (United States); Hennebelle, Patrick [Laboratoire AIM, Paris-Saclay, CEA/IRFU/SAp-CNRS-Université Paris Diderot, F-91191 Gif-sur Yvette Cedex (France); Goss, W. M. [National Radio Astronomy Observatory, P.O. Box O, 1003 Lopezville, Socorro, NM 87801 (United States); Dickey, John, E-mail: rlindner@astro.wisc.edu [University of Tasmania, School of Maths and Physics, Private Bag 37, Hobart, TAS 7001 (Australia)
2015-04-15
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes.
AUTONOMOUS GAUSSIAN DECOMPOSITION
International Nuclear Information System (INIS)
Lindner, Robert R.; Vera-Ciro, Carlos; Murray, Claire E.; Stanimirović, Snežana; Babler, Brian; Heiles, Carl; Hennebelle, Patrick; Goss, W. M.; Dickey, John
2015-01-01
We present a new algorithm, named Autonomous Gaussian Decomposition (AGD), for automatically decomposing spectra into Gaussian components. AGD uses derivative spectroscopy and machine learning to provide optimized guesses for the number of Gaussian components in the data, and also their locations, widths, and amplitudes. We test AGD and find that it produces results comparable to human-derived solutions on 21 cm absorption spectra from the 21 cm SPectral line Observations of Neutral Gas with the EVLA (21-SPONGE) survey. We use AGD with Monte Carlo methods to derive the H i line completeness as a function of peak optical depth and velocity width for the 21-SPONGE data, and also show that the results of AGD are stable against varying observational noise intensity. The autonomy and computational efficiency of the method over traditional manual Gaussian fits allow for truly unbiased comparisons between observations and simulations, and for the ability to scale up and interpret the very large data volumes from the upcoming Square Kilometer Array and pathfinder telescopes
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...
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.)
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.)
Developing a software for tracking the memory states of the machines in the LHCb Filter Farm
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...
Machine Protection and High Energy Density States in Matter for High Energy Hadron Accelerators
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...
State of the art and future challenges for Machine Protection Systems
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...
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.
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.
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.
Directory of Open Access Journals (Sweden)
Serge Mazeres
2017-08-01
Full Text Available Background: We wanted to investigate the physical state of biological membranes in live cells under the most physiological conditions possible. Methods: For this we have been using laurdan, C-laurdan or M-laurdan to label a variety of cells, and a biphoton microscope equipped with both a thermostatic chamber and a spectral analyser. We also used a flow cytometer to quantify the 450/530 nm ratio of fluorescence emissions by whole cells. Results: We find that using all the information provided by spectral analysis to perform spectral decomposition dramatically improves the imaging resolution compared to using just two channels, as commonly used to calculate generalized polarisation (GP. Coupled to a new plugin called Fraction Mapper, developed to represent the fraction of light intensity in the first component in a stack of two images, we obtain very clear pictures of both the intra-cellular distribution of the probes, and the polarity of the cellular environments where the lipid probes are localised. Our results lead us to conclude that, in live cells kept at 37°C, laurdan, and M-laurdan to a lesser extent, have a strong tendency to accumulate in the very apolar environment of intra-cytoplasmic lipid droplets, but label the plasma membrane (PM of mammalian cells ineffectively. On the other hand, C-laurdan labels the PM very quickly and effectively, and does not detectably accumulate in lipid droplets. Conclusions: From using these probes on a variety of mammalian cell lines, as well as on cells from Drosophila and Dictyostelium discoideum, we conclude that, apart from the lipid droplets, which are very apolar, probes in intracellular membranes reveal a relatively polar and hydrated environment, suggesting a very marked dominance of liquid disordered states. PMs, on the other hand, are much more apolar, suggesting a strong dominance of liquid ordered state, which fits with their high sterol contents.
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.
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.
Directory of Open Access Journals (Sweden)
Ying Deng
2016-01-01
Full Text Available Cubic phase cobalt (Co, which can be used as a key component for composite materials given its excellent ductility and internal structure, is not easy to obtain at room temperature. In this study, oxalic acid and cobalt nitrate are used as raw materials to synthesize the cobalt oxalate precursor, which has a stable structure with a five-membered chelate ring. Cobalt oxalate microspheres, having a high internal energy content, were prepared by using mechanical solid-state reaction in the presence of a surfactant, which can produce spherical micelles. The thermal decomposition of the precursor was carried out by maintaining it in a nitrogen atmosphere at 450°C for 3 h. At the end of the procedure, 100 nm cubic phase-Co microspheres, stable at room temperature, were obtained. Isothermal and nonisothermal kinetic mechanisms of cobalt grain growth were investigated. The cubic-Co grain growth activation energy, Q, was calculated in this study to be 71.47 kJ/mol. The required reaction temperature was low, making the production process simple and suitable for industrial applications.
Chen, Weiwei; Okunade, Albert; Lubiani, Gregory G
2014-11-01
Economic theory suggests that income growth could lead to changes in consumption quantity and quality as the spending on a commodity changes. Similarly, the volume and quality of healthcare consumption could rise with incomes because of demographic changes, usage of innovative medical technologies, and other factors. Hospital healthcare spending is the largest component of aggregate US healthcare expenditures. The novel contribution of our paper is estimating and decomposing the income elasticity of hospital care expenditures (HOCEXP) into its quantity and quality components. By using a 1999-2008 panel dataset of the 50 US states, results from the seemingly unrelated regressions model estimation reveal the income elasticity of HOCEXP to be 0.427 (std. error=0.044), with about 0.391 (calculated std. error=0.044) arising from care quality improvements and 0.035 (std. error=0.050) emanating from the rise in usage volume. Our novel research findings suggest the following: (i) the quantity part of hospital expenditure is inelastic to income change; (ii) almost the entire income-induced rise in hospital expenditure comes from care quality changes; and (iii) the 0.427 income elasticity of HOCEXP, the largest component of total US healthcare expenditure, makes hospital care a normal commodity and a much stronger technical necessity than aggregate healthcare. Policy implications are discussed. Copyright © 2013 John Wiley & Sons, Ltd.
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)
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.
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.
Energy Technology Data Exchange (ETDEWEB)
Gao Ruorui [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Zhang Yue, E-mail: yue-zhang@mail.hust.edu.cn [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Department of Electric Science and Technology, Huazhong University of Science and Technology, Wuhan 430074 (China); Yu Wei [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Xiong Rui [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Key Laboratory for the Green Preparation and Application of Functional Materials of Ministry of Education, Hubei University, Wuhan 430062 (China); Shi Jing [Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan 430072 (China); Key Laboratory for the Green Preparation and Application of Functional Materials of Ministry of Education, Hubei University, Wuhan 430062 (China); International Center for Material Physics, Shen Yang 110015 (China)
2012-08-15
MnFe{sub 2}O{sub 4} nano-particles with an average size of about 7 nm were synthesized by the thermal decomposition method. Based on the magnetic hysteresis loops measured at different temperatures the temperature-dependent saturation magnetization (M{sub S}) and coercivity (H{sub C}) are determined. It is shown that above 20 K the temperature-dependence of the M{sub S} and H{sub C} indicates the magnetic behaviors in the single-domain nano-particles, while below 20 K, the change of the M{sub S} and H{sub C} indicates the freezing of the spin-glass like state on the surfaces. By measuring the magnetization-temperature (M-T) curves under the zero-field-cooling (ZFC) and field-cooling procedures at different applied fields, superparamagnetism behavior is also studied. Even though in the ZFC M-T curves peaks can be observed below 160 K, superparamagnetism does not appear until the temperature goes above 300 K, which is related with the strong inter-particle interaction. - Highlights: Black-Right-Pointing-Pointer MnFe{sub 2}O{sub 4} nano-particles with size of 7 nm were prepared. Black-Right-Pointing-Pointer The surface spin-glass like state is frozen below 20 K. Black-Right-Pointing-Pointer The peaks in ZFC magnetization-temperature curves are observed below 160 K. Black-Right-Pointing-Pointer The inter-particle interaction inhibits the superparamagnetism at room temperature.
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.
International Nuclear Information System (INIS)
Chen Xiangsong; Sun Weimin; Wang Fan; Goldman, T.
2011-01-01
We analyze the problem of spin decomposition for an interacting system from a natural perspective of constructing angular-momentum eigenstates. We split, from the total angular-momentum operator, a proper part which can be separately conserved for a stationary state. This part commutes with the total Hamiltonian and thus specifies the quantum angular momentum. We first show how this can be done in a gauge-dependent way, by seeking a specific gauge in which part of the total angular-momentum operator vanishes identically. We then construct a gauge-invariant operator with the desired property. Our analysis clarifies what is the most pertinent choice among the various proposals for decomposing the nucleon spin. A similar analysis is performed for extracting a proper part from the total Hamiltonian to construct energy eigenstates.
Data Processing And Machine Learning Methods For Multi-Modal Operator State Classification Systems
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.
Machine Shop Grinding Machines.
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…
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
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.
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.
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
Energy Technology Data Exchange (ETDEWEB)
Koch, R. (ed.); Hahn, M.; Koennemann, T.; Mangold, S.; Ouerfelli, I.; Preuss, V.; Schoepke, R.; Sonntag, B.
2000-07-01
The use of natural soils for low-cost removal of precipitated water and slightly polluted waste water has become an issue in legal procedures concerning water management. Although groundwater protection is given high priority, authorities do not have sufficient data on long-term effects and decomposition effects during passage to the soil. The Hydrology Department of Brandenburgische Technische Universitaet Cottbus, on behalf of the Thuringian Environmental Authority, investigated the state of the art of discharge of precipitated water into natural soils. The investigation is the first in a series of which the following effects are expected: Faster results by preliminary assessment of the applicability of the technique envisaged, use of this low-cost technology for waste water treatment in consideration of surface water protection. [German] Die Nutzung natuerlicher Boeden zur kostenguenstigen Beseitigung von Niederschlagswasser aber auch von schwachbelastetem Abwasser ist eine Variante der Abwasserbeseitigung, die zunehmend Antragsgegenstand in wasserrechtlichen Verfahren ist. Der Schutz des Grundwassers ist dabei in besonderem Masse zu beachten. Die Einschaetzung der Eignung dieser Variante ist Behoerden aufgrund unzureichender Aussagen zum Langzeitverhalten und zu Abbaueffekten waehrend der Bodenpassage derzeit nicht moeglich. Durch die THUeRINGER LANDESANSTALT FUeR UMWELT erhielt der Lehrstuhl Wassertechnik der Brandenburgischen Technischen Universitaet Cottbus den Auftrag, im Rahmen einer Literaturstudie, den gegenwaertigen Stand der schadlosen Versickerung von Niederschlagswasser in natuerlichen Boeden aufzuzeigen. Die Literaturrecherche bildet den ersten Schritt einer Untersuchungsreihe, von der folgende Wirkungen erwartet werden: Verfahrensbeschleunigung durch Bewertbarkeit der Eignung der Behandlungsvariante, Nutzung dieser kostenguenstigen Umwelttechnologie zur Behandlung von Abwasser unter Beachtung des Gewaesserschutzes. (orig.)
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
Faider, W.; Pasquiers, S.; Blin-Simiand, N.; Magne, L.
2013-03-01
A photo-triggered discharge is used to study the decomposition processes of acetaldehyde in a high-pressure (460 mbar) nitrogen plasma, for a concentration of CH3CHO ranging from 500 up to 5000 ppm. Results of chromatographic measurements are compared with predictions of a self-consistent discharge and plasma kinetic model, for the primary molecule and for a number of detected by-products: H2, CH4, C2H2, C2H4, C2H6, CO and CH3COCH3. The main by-products are H2, CH4 and CO. It is proposed that CH3CHO mainly decomposes owing to quenching collisions of metastable states of the nitrogen molecule. The estimated coefficients for the quenching of N_2(A\\,^{3}\\!\\Sigma ^{{+}}_{\\rm{u}}) is 4.2 × 10-11 cm3 s-1, assuming that the coefficient for the singlet states equals the one previously known for the quenching of N2(a‧) by ethene, i.e. 4.0 × 10-10 cm3 s-1. A value of 6.5 × 10-11 cm3 s-1 constitutes a maximum for N_2(A\\,^{3}\\!\\Sigma^{{+}}_{\\rm{u}}) and a minimum for N2(a‧). The most probable exit routes (and the branching ratios) for the dissociation process of CH3CHO are CH3 + HCO (45%), CH4 + CO (30%), CH2CO + H2 (17%) and CH3CO + H (8%), as regards A\\,^{3}\\!\\Sigma ^{{+}}_{\\rm{u}} . For singlet states, a break of the double C = O bond occurs and the branching ratios are 15% for both exit channels producing C2H2 and C2H4 together with the oxygen atom. The model predictions for concentration values of C2H6 and CH3COCH3 are in good accordance with measurements, supporting the proposed dissociation pathways that lead to the production of methyl and acetyl radicals.
Spectral Decomposition Algorithm (SDA)
National Aeronautics and Space Administration — Spectral Decomposition Algorithm (SDA) is an unsupervised feature extraction technique similar to PCA that was developed to better distinguish spectral features in...
Thermal decomposition of pyrite
International Nuclear Information System (INIS)
Music, S.; Ristic, M.; Popovic, S.
1992-01-01
Thermal decomposition of natural pyrite (cubic, FeS 2 ) has been investigated using X-ray diffraction and 57 Fe Moessbauer spectroscopy. X-ray diffraction analysis of pyrite ore from different sources showed the presence of associated minerals, such as quartz, szomolnokite, stilbite or stellerite, micas and hematite. Hematite, maghemite and pyrrhotite were detected as thermal decomposition products of natural pyrite. The phase composition of the thermal decomposition products depends on the terature, time of heating and starting size of pyrite chrystals. Hematite is the end product of the thermal decomposition of natural pyrite. (author) 24 refs.; 6 figs.; 2 tabs
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)
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.
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping.
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.
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
A PARALLEL NONOVERLAPPING DOMAIN DECOMPOSITION METHOD FOR STOKES PROBLEMS
Institute of Scientific and Technical Information of China (English)
Mei-qun Jiang; Pei-liang Dai
2006-01-01
A nonoverlapping domain decomposition iterative procedure is developed and analyzed for generalized Stokes problems and their finite element approximate problems in RN(N=2,3). The method is based on a mixed-type consistency condition with two parameters as a transmission condition together with a derivative-free transmission data updating technique on the artificial interfaces. The method can be applied to a general multi-subdomain decomposition and implemented on parallel machines with local simple communications naturally.
Human Machine Learning Symbiosis
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…
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
Multiresolution signal decomposition schemes
J. Goutsias (John); H.J.A.M. Heijmans (Henk)
1998-01-01
textabstract[PNA-R9810] Interest in multiresolution techniques for signal processing and analysis is increasing steadily. An important instance of such a technique is the so-called pyramid decomposition scheme. This report proposes a general axiomatic pyramid decomposition scheme for signal analysis
Decomposition of Sodium Tetraphenylborate
International Nuclear Information System (INIS)
Barnes, M.J.
1998-01-01
The chemical decomposition of aqueous alkaline solutions of sodium tetraphenylborate (NaTPB) has been investigated. The focus of the investigation is on the determination of additives and/or variables which influence NaTBP decomposition. This document describes work aimed at providing better understanding into the relationship of copper (II), solution temperature, and solution pH to NaTPB stability
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.
National Research Council Canada - National Science Library
Hendrickson, Scott A; Shackleford, Scott A
2005-01-01
...) at 142, 145, and 148 oC. Global rate constants and kinetic deuterium isotope effect (KDIE) data from the exothermic decomposition process suggest that homolytic C-H bond rupture, in one or both types of chemically non-equivalent methylene...
International Nuclear Information System (INIS)
Dziegielewski, J.O.
1976-01-01
The yields of particular groups in the potassium of benzylpenicillin and benzatine penicillin were determined by the NMR method. The total yields of groups are in agreement with the total radiation decomposition yields of the penicillin molecules, as determined by the spectrophotometric, polarimetric and iodometric methods. (author)
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
Azimuthal decomposition of optical modes
CSIR Research Space (South Africa)
Dudley, Angela L
2012-07-01
Full Text Available This presentation analyses the azimuthal decomposition of optical modes. Decomposition of azimuthal modes need two steps, namely generation and decomposition. An azimuthally-varying phase (bounded by a ring-slit) placed in the spatial frequency...
Han, Haixiang; Wei, Zheng; Barry, Matthew C; Filatov, Alexander S; Dikarev, Evgeny V
2017-05-02
Three heterometallic single-source precursors with a Li : Fe = 1 : 1 ratio for a LiFeO 2 oxide material are reported. Heterometallic compounds LiFeL 3 (L = tbaoac (1), ptac (2), and acac(3)) have been obtained on a large scale, in nearly quantitative yields by one-step reactions that employ readily available reagents. The heterometallic precursor LiFe(acac) 3 (3) with small, symmetric substituents on the ligand (acac = pentane-2,4-dionate), maintains a 1D polymeric structure in the solid state that limits its volatility and prevents solubility in non-coordinating solvents. The application of the unsymmetrical ligands, tbaoac (tert-butyl acetoacetate) and ptac (1,1,1-trifluoro-5,5-dimethyl-2,4-hexanedionate), that exhibit different bridging properties at the two ends of the ligand, allowed us to change the connectivity pattern within the heterometallic assembly. The latter was demonstrated by structural characterization of heterometallic complexes LiFe(tbaoac) 3 (1) and LiFe(ptac) 3 (2) that consist of discrete heterocyclic tetranuclear molecules Li 2 Fe 2 L 6 . The compounds are highly volatile and exhibit a congruent sublimation character. DART mass spectrometric investigation revealed the presence of heterometallic molecules in the gas phase. The positive mode spectra are dominated by the presence of [M - L] + peaks (M = Li 2 Fe 2 L 6 ). In accord with their discrete molecular structure, complexes 1 and 2 are highly soluble in nearly all common solvents. In order to test the retention of the heterometallic structure in solution, the diamagnetic analog of 1, LiMg(tbaoac) 3 (4), has been isolated. Its tetranuclear molecular structure was found to be isomorphous to that of the iron counterpart. 1 H and 7 Li NMR spectroscopy unambiguously confirmed the presence of heterometallic molecules in solutions of non-coordinating solvents. The heterometallic precursor 1 was shown to exhibit clean thermal decomposition in air that results in phase-pure
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...
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
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
Cellular decomposition in vikalloys
International Nuclear Information System (INIS)
Belyatskaya, I.S.; Vintajkin, E.Z.; Georgieva, I.Ya.; Golikov, V.A.; Udovenko, V.A.
1981-01-01
Austenite decomposition in Fe-Co-V and Fe-Co-V-Ni alloys at 475-600 deg C is investigated. The cellular decomposition in ternary alloys results in the formation of bcc (ordered) and fcc structures, and in quaternary alloys - bcc (ordered) and 12R structures. The cellular 12R structure results from the emergence of stacking faults in the fcc lattice with irregular spacing in four layers. The cellular decomposition results in a high-dispersion structure and magnetic properties approaching the level of well-known vikalloys [ru
Daverman, Robert J
2007-01-01
Decomposition theory studies decompositions, or partitions, of manifolds into simple pieces, usually cell-like sets. Since its inception in 1929, the subject has become an important tool in geometric topology. The main goal of the book is to help students interested in geometric topology to bridge the gap between entry-level graduate courses and research at the frontier as well as to demonstrate interrelations of decomposition theory with other parts of geometric topology. With numerous exercises and problems, many of them quite challenging, the book continues to be strongly recommended to eve
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.
Improving Machining Accuracy of CNC Machines with Innovative Design Methods
Yemelyanov, N. V.; Yemelyanova, I. V.; Zubenko, V. L.
2018-03-01
The article considers achieving the machining accuracy of CNC machines by applying innovative methods in modelling and design of machining systems, drives and machine processes. The topological method of analysis involves visualizing the system as matrices of block graphs with a varying degree of detail between the upper and lower hierarchy levels. This approach combines the advantages of graph theory and the efficiency of decomposition methods, it also has visual clarity, which is inherent in both topological models and structural matrices, as well as the resiliency of linear algebra as part of the matrix-based research. The focus of the study is on the design of automated machine workstations, systems, machines and units, which can be broken into interrelated parts and presented as algebraic, topological and set-theoretical models. Every model can be transformed into a model of another type, and, as a result, can be interpreted as a system of linear and non-linear equations which solutions determine the system parameters. This paper analyses the dynamic parameters of the 1716PF4 machine at the stages of design and exploitation. Having researched the impact of the system dynamics on the component quality, the authors have developed a range of practical recommendations which have enabled one to reduce considerably the amplitude of relative motion, exclude some resonance zones within the spindle speed range of 0...6000 min-1 and improve machining accuracy.
Directory of Open Access Journals (Sweden)
Jinlu Sheng
2016-07-01
Full Text Available To effectively extract the typical features of the bearing, a new method that related the local mean decomposition Shannon entropy and improved kernel principal component analysis model was proposed. First, the features are extracted by time–frequency domain method, local mean decomposition, and using the Shannon entropy to process the original separated product functions, so as to get the original features. However, the features been extracted still contain superfluous information; the nonlinear multi-features process technique, kernel principal component analysis, is introduced to fuse the characters. The kernel principal component analysis is improved by the weight factor. The extracted characteristic features were inputted in the Morlet wavelet kernel support vector machine to get the bearing running state classification model, bearing running state was thereby identified. Cases of test and actual were analyzed.
Photochemical decomposition of catecholamines
International Nuclear Information System (INIS)
Mol, N.J. de; Henegouwen, G.M.J.B. van; Gerritsma, K.W.
1979-01-01
During photochemical decomposition (lambda=254 nm) adrenaline, isoprenaline and noradrenaline in aqueous solution were converted to the corresponding aminochrome for 65, 56 and 35% respectively. In determining this conversion, photochemical instability of the aminochromes was taken into account. Irradiations were performed in such dilute solutions that the neglect of the inner filter effect is permissible. Furthermore, quantum yields for the decomposition of the aminochromes in aqueous solution are given. (Author)
Microbiological decomposition of bagasse after radiation pasteurization
International Nuclear Information System (INIS)
Ito, Hitoshi; Ishigaki, Isao
1987-01-01
Microbiological decomposition of bagasse was studied for upgrading to animal feeds after radiation pasteurization. Solid-state culture media of bagasse were prepared with addition of some amount of inorganic salts for nitrogen source, and after irradiation, fungi were infected for cultivation. In this study, many kind of cellulosic fungi such as Pleurotus ostreatus, P. flavellatus, Verticillium sp., Coprinus cinereus, Lentinus edodes, Aspergillus niger, Trichoderma koningi, T. viride were used for comparison of decomposition of crude fibers. In alkali nontreated bagasse, P. ostreatus, P. flavellatus, C. cinereus and Verticillium sp. could decompose crude fibers from 25 to 34 % after one month of cultivation, whereas other fungi such as A. niger, T. koningi, T. viride, L. edodes decomposed below 10 %. On the contrary, alkali treatment enhanced the decomposition of crude fiber by A. niger, T. koningi and T. viride to be 29 to 47 % as well as Pleurotus species or C. cinereus. Other species of mushrooms such as L. edodes had a little ability of decomposition even after alkali treatment. Radiation treatment with 10 kGy could not enhance the decomposition of bagasse compared with steam treatment, whereas higher doses of radiation treatment enhanced a little of decomposition of crude fibers by microorganisms. (author)
Microbiological decomposition of bagasse after radiation pasteurization
Energy Technology Data Exchange (ETDEWEB)
Ito, Hitoshi; Ishigaki, Isao
1987-11-01
Microbiological decomposition of bagasse was studied for upgrading to animal feeds after radiation pasteurization. Solid-state culture media of bagasse were prepared with addition of some amount of inorganic salts for nitrogen source, and after irradiation, fungi were infected for cultivation. In this study, many kind of cellulosic fungi such as Pleurotus ostreatus, P. flavellatus, Verticillium sp., Coprinus cinereus, Lentinus edodes, Aspergillus niger, Trichoderma koningi, T. viride were used for comparison of decomposition of crude fibers. In alkali nontreated bagasse, P. ostreatus, P. flavellatus, C. cinereus and Verticillium sp. could decompose crude fibers from 25 to 34 % after one month of cultivation, whereas other fungi such as A. niger, T. koningi, T. viride, L. edodes decomposed below 10 %. On the contrary, alkali treatment enhanced the decomposition of crude fiber by A. niger, T. koningi and T. viride to be 29 to 47 % as well as Pleurotus species or C. cinereus. Other species of mushrooms such as L. edodes had a little ability of decomposition even after alkali treatment. Radiation treatment with 10 kGy could not enhance the decomposition of bagasse compared with steam treatment, whereas higher doses of radiation treatment enhanced a little of decomposition of crude fibers by microorganisms.
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.
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
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
Decomposing Nekrasov decomposition
Energy Technology Data Exchange (ETDEWEB)
Morozov, A. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); Institute for Information Transmission Problems,19-1 Bolshoy Karetniy, Moscow, 127051 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Zenkevich, Y. [ITEP,25 Bolshaya Cheremushkinskaya, Moscow, 117218 (Russian Federation); National Research Nuclear University MEPhI,31 Kashirskoe highway, Moscow, 115409 (Russian Federation); Institute for Nuclear Research of Russian Academy of Sciences,6a Prospekt 60-letiya Oktyabrya, Moscow, 117312 (Russian Federation)
2016-02-16
AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.
Decomposing Nekrasov decomposition
International Nuclear Information System (INIS)
Morozov, A.; Zenkevich, Y.
2016-01-01
AGT relations imply that the four-point conformal block admits a decomposition into a sum over pairs of Young diagrams of essentially rational Nekrasov functions — this is immediately seen when conformal block is represented in the form of a matrix model. However, the q-deformation of the same block has a deeper decomposition — into a sum over a quadruple of Young diagrams of a product of four topological vertices. We analyze the interplay between these two decompositions, their properties and their generalization to multi-point conformal blocks. In the latter case we explain how Dotsenko-Fateev all-with-all (star) pair “interaction” is reduced to the quiver model nearest-neighbor (chain) one. We give new identities for q-Selberg averages of pairs of generalized Macdonald polynomials. We also translate the slicing invariance of refined topological strings into the language of conformal blocks and interpret it as abelianization of generalized Macdonald polynomials.
Symmetric Tensor Decomposition
DEFF Research Database (Denmark)
Brachat, Jerome; Comon, Pierre; Mourrain, Bernard
2010-01-01
We present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a sum of rank-1 symmetric tensors, extending the algorithm of Sylvester devised in 1886 for binary forms. We recall the correspondence between the decomposition of a homogeneous polynomial in n variables...... of polynomial equations of small degree in non-generic cases. We propose a new algorithm for symmetric tensor decomposition, based on this characterization and on linear algebra computations with Hankel matrices. The impact of this contribution is two-fold. First it permits an efficient computation...... of the decomposition of any tensor of sub-generic rank, as opposed to widely used iterative algorithms with unproved global convergence (e.g. Alternate Least Squares or gradient descents). Second, it gives tools for understanding uniqueness conditions and for detecting the rank....
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.
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.
AstroML: "better, faster, cheaper" towards state-of-the-art data mining and machine learning
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).
Machining dynamics fundamentals, applications and practices
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.
International Nuclear Information System (INIS)
Macasek, F.; Buriova, E.
2004-01-01
In this presentation authors present the results of analysis of decomposition products of [ 18 ]fluorodexyglucose. It is concluded that the coupling of liquid chromatography - mass spectrometry with electrospray ionisation is a suitable tool for quantitative analysis of FDG radiopharmaceutical, i.e. assay of basic components (FDG, glucose), impurities (Kryptofix) and decomposition products (gluconic and glucuronic acids etc.); 2-[ 18 F]fluoro-deoxyglucose (FDG) is sufficiently stable and resistant towards autoradiolysis; the content of radiochemical impurities (2-[ 18 F]fluoro-gluconic and 2-[ 18 F]fluoro-glucuronic acids in expired FDG did not exceed 1%
Multiresolution signal decomposition transforms, subbands, and wavelets
Akansu, Ali N
1992-01-01
This book provides an in-depth, integrated, and up-to-date exposition of the topic of signal decomposition techniques. Application areas of these techniques include speech and image processing, machine vision, information engineering, High-Definition Television, and telecommunications. The book will serve as the major reference for those entering the field, instructors teaching some or all of the topics in an advanced graduate course and researchers needing to consult an authoritative source.n The first book to give a unified and coherent exposition of multiresolutional signal decompos
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.
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.
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)
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
Generalized decompositions of dynamic systems and vector Lyapunov functions
Ikeda, M.; Siljak, D. D.
1981-10-01
The notion of decomposition is generalized to provide more freedom in constructing vector Lyapunov functions for stability analysis of nonlinear dynamic systems. A generalized decomposition is defined as a disjoint decomposition of a system which is obtained by expanding the state-space of a given system. An inclusion principle is formulated for the solutions of the expansion to include the solutions of the original system, so that stability of the expansion implies stability of the original system. Stability of the expansion can then be established by standard disjoint decompositions and vector Lyapunov functions. The applicability of the new approach is demonstrated using the Lotka-Volterra equations.
Thermal decomposition of zirconium compounds with some aromatic hydroxycarboxylic acids
Energy Technology Data Exchange (ETDEWEB)
Koshel, A V; Malinko, L A; Karlysheva, K F; Sheka, I A; Shchepak, N I [AN Ukrainskoj SSR, Kiev. Inst. Obshchej i Neorganicheskoj Khimii
1980-02-01
By the thermogravimetry method investigated are processes of thermal decomposition of different zirconium compounds with mandelic, parabromomandelic, salicylic and sulphosalicylic acids. For identification of decomposition products the specimens have been kept at the temperature of effects up to the constant weight. Taken are IR-spectra, rentgenoarams, carried out is elementary analysis of decomposition products. It is stated that thermal decomposition of the investigated compounds passes in stages; the final product of thermolysis is ZrO/sub 2/. Nonhydrolized compounds are stable at heating in the air up to 200-265 deg. Hydroxy compounds begin to decompose at lower temperature (80-100 deg).
Energy Technology Data Exchange (ETDEWEB)
Laite, Alvaro Afonso Furtado; Bajay, Sergio Valdir; Pereira, Andre Flavio Soares [Universidade Estadual de Campinas, SP (Brazil). Faculdade de Engenharia Mecanica. Dept. de Energia]|[Universidade Estadual de Campinas, SP (Brazil). Nucleo Interdisciplinar de Planejamento Energetico (NIPE)]. E-mails: afurtado@fem.unicamp.br; bajay@fem.unicamp.br; apereira@fem.unicamp.br
2006-07-01
Long-term demand forecasts (up to 2025) are presented in this paper for the main energy forms consumed in the residential, trade and services, rural, transport, and industrial sectors in the State of Sao Paulo. They were obtained with the help of a flexible forecasting model based on the structural decomposition of the demand, for three alternative scenarios concerning the growth of the state economy. These three state-wise scenarios are related to initially nation-wide defined scenarios, through assumptions concerning the evolution on the ratio between the state GDP and the national GDP. (author)
Thermal decomposition of 2-methylbenzoates of rare earth elements
International Nuclear Information System (INIS)
Brzyska, W.; Szubartowski, L.
1980-01-01
The conditions of thermal decomposition of La, Ce(3), Pr, Nd, Sm and Y 2-methylbenzoates were examined. On the basis of obtained results it was stated that hydrated 2-methylbenzoates were subjected to dehydration passing into anhydrated salts and then they decomposed into oxides. The activation energy of dehydration and decomposition reactions of lanthanons, La and Y 2-methylbenzoates was determined. (author)
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.)
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.)
Distributed Model Predictive Control via Dual Decomposition
DEFF Research Database (Denmark)
Biegel, Benjamin; Stoustrup, Jakob; Andersen, Palle
2014-01-01
This chapter presents dual decomposition as a means to coordinate a number of subsystems coupled by state and input constraints. Each subsystem is equipped with a local model predictive controller while a centralized entity manages the subsystems via prices associated with the coupling constraints...
Thermic decomposition of biphenyl; Decomposition thermique du biphenyle
Energy Technology Data Exchange (ETDEWEB)
Lutz, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1966-03-01
Liquid and vapour phase pyrolysis of very pure biphenyl obtained by methods described in the text was carried out at 400 C in sealed ampoules, the fraction transformed being always less than 0.1 per cent. The main products were hydrogen, benzene, terphenyls, and a deposit of polyphenyls strongly adhering to the walls. Small quantities of the lower aliphatic hydrocarbons were also found. The variation of the yields of these products with a) the pyrolysis time, b) the state (gas or liquid) of the biphenyl, and c) the pressure of the vapour was measured. Varying the area and nature of the walls showed that in the absence of a liquid phase, the pyrolytic decomposition takes place in the adsorbed layer, and that metallic walls promote the reaction more actively than do those of glass (pyrex or silica). A mechanism is proposed to explain the results pertaining to this decomposition in the adsorbed phase. The adsorption seems to obey a Langmuir isotherm, and the chemical act which determines the overall rate of decomposition is unimolecular. (author) [French] Du biphenyle tres pur, dont la purification est decrite, est pyrolyse a 400 C en phase vapeur et en phase liquide dans des ampoules scellees sous vide, a des taux de decomposition n'ayant jamais depasse 0,1 pour cent. Les produits provenant de la pyrolyse sont essentiellement: l' hydrogene, le benzene, les therphenyles, et un depot de polyphenyles adherant fortement aux parois. En plus il se forme de faibles quantites d'hydrocarbures aliphatiques gazeux. On indique la variation des rendements des differents produits avec la duree de pyrolyse, l'etat gazeux ou liquide du biphenyle, et la pression de la vapeur. Variant la superficie et la nature des parois, on montre qu'en absence de liquide la pyrolyse se fait en phase adsorbee. La pyrolyse est plus active au contact de parois metalliques que de celles de verres (pyrex ou silice). A partir des resultats experimentaux un mecanisme de degradation du biphenyle en phase
The Machine within the Machine
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 ...
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.
Soft computing in machine learning
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...
Thermal decomposition process of silver behenate
International Nuclear Information System (INIS)
Liu Xianhao; Lu Shuxia; Zhang Jingchang; Cao Weiliang
2006-01-01
The thermal decomposition processes of silver behenate have been studied by infrared spectroscopy (IR), X-ray diffraction (XRD), combined thermogravimetry-differential thermal analysis-mass spectrometry (TG-DTA-MS), transmission electron microscopy (TEM) and UV-vis spectroscopy. The TG-DTA and the higher temperature IR and XRD measurements indicated that complicated structural changes took place while heating silver behenate, but there were two distinct thermal transitions. During the first transition at 138 deg. C, the alkyl chains of silver behenate were transformed from an ordered into a disordered state. During the second transition at about 231 deg. C, a structural change took place for silver behenate, which was the decomposition of silver behenate. The major products of the thermal decomposition of silver behenate were metallic silver and behenic acid. Upon heating up to 500 deg. C, the final product of the thermal decomposition was metallic silver. The combined TG-MS analysis showed that the gas products of the thermal decomposition of silver behenate were carbon dioxide, water, hydrogen, acetylene and some small molecule alkenes. TEM and UV-vis spectroscopy were used to investigate the process of the formation and growth of metallic silver nanoparticles
Optimization of pocket machining strategy in HSM
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 ...
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.
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.
Abstract quantum computing machines and quantum computational logics
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.
Formal modeling of virtual machines
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.
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)
Inverse scale space decomposition
DEFF Research Database (Denmark)
Schmidt, Marie Foged; Benning, Martin; Schönlieb, Carola-Bibiane
2018-01-01
We investigate the inverse scale space flow as a decomposition method for decomposing data into generalised singular vectors. We show that the inverse scale space flow, based on convex and even and positively one-homogeneous regularisation functionals, can decompose data represented...... by the application of a forward operator to a linear combination of generalised singular vectors into its individual singular vectors. We verify that for this decomposition to hold true, two additional conditions on the singular vectors are sufficient: orthogonality in the data space and inclusion of partial sums...... of the subgradients of the singular vectors in the subdifferential of the regularisation functional at zero. We also address the converse question of when the inverse scale space flow returns a generalised singular vector given that the initial data is arbitrary (and therefore not necessarily in the range...
Cacciatori, Sergio L; Marrani, Alessio
2013-01-01
By exploiting a "mixed" non-symmetric Freudenthal-Rozenfeld-Tits magic square, two types of coset decompositions are analyzed for the non-compact special K\\"ahler symmetric rank-3 coset E7(-25)/[(E6(-78) x U(1))/Z_3], occurring in supergravity as the vector multiplets' scalar manifold in N=2, D=4 exceptional Maxwell-Einstein theory. The first decomposition exhibits maximal manifest covariance, whereas the second (triality-symmetric) one is of Iwasawa type, with maximal SO(8) covariance. Generalizations to conformal non-compact, real forms of non-degenerate, simple groups "of type E7" are presented for both classes of coset parametrizations, and relations to rank-3 simple Euclidean Jordan algebras and normed trialities over division algebras are also discussed.
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
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
Energy Technology Data Exchange (ETDEWEB)
Kanemoto, Shigeru; Watanabe, Masaya [The University of Aizu, Aizuwakamatsu (Japan); Yusa, Noritaka [Tohoku University, Sendai (Japan)
2014-08-15
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology.
Advanced Machine learning Algorithm Application for Rotating Machine Health Monitoring
International Nuclear Information System (INIS)
Kanemoto, Shigeru; Watanabe, Masaya; Yusa, Noritaka
2014-01-01
The present paper tries to evaluate the applicability of conventional sound analysis techniques and modern machine learning algorithms to rotating machine health monitoring. These techniques include support vector machine, deep leaning neural network, etc. The inner ring defect and misalignment anomaly sound data measured by a rotating machine mockup test facility are used to verify the above various kinds of algorithms. Although we cannot find remarkable difference of anomaly discrimination performance, some methods give us the very interesting eigen patterns corresponding to normal and abnormal states. These results will be useful for future more sensitive and robust anomaly monitoring technology
The Machine Scoring of Writing
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.…
Making molecular machines work
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
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
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.
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.
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.
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
Clustering via Kernel Decomposition
DEFF Research Database (Denmark)
Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan
2006-01-01
Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....
Danburite decomposition by sulfuric acid
International Nuclear Information System (INIS)
Mirsaidov, U.; Mamatov, E.D.; Ashurov, N.A.
2011-01-01
Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by sulfuric acid. The process of decomposition of danburite concentrate by sulfuric acid was studied. The chemical nature of decomposition process of boron containing ore was determined. The influence of temperature on the rate of extraction of boron and iron oxides was defined. The dependence of decomposition of boron and iron oxides on process duration, dosage of H 2 SO 4 , acid concentration and size of danburite particles was determined. The kinetics of danburite decomposition by sulfuric acid was studied as well. The apparent activation energy of the process of danburite decomposition by sulfuric acid was calculated. The flowsheet of danburite processing by sulfuric acid was elaborated.
Thermal decomposition of lutetium propionate
DEFF Research Database (Denmark)
Grivel, Jean-Claude
2010-01-01
The thermal decomposition of lutetium(III) propionate monohydrate (Lu(C2H5CO2)3·H2O) in argon was studied by means of thermogravimetry, differential thermal analysis, IR-spectroscopy and X-ray diffraction. Dehydration takes place around 90 °C. It is followed by the decomposition of the anhydrous...... °C. Full conversion to Lu2O3 is achieved at about 1000 °C. Whereas the temperatures and solid reaction products of the first two decomposition steps are similar to those previously reported for the thermal decomposition of lanthanum(III) propionate monohydrate, the final decomposition...... of the oxycarbonate to the rare-earth oxide proceeds in a different way, which is here reminiscent of the thermal decomposition path of Lu(C3H5O2)·2CO(NH2)2·2H2O...
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...
Machine rates for selected forest harvesting machines
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...
Machine learning for healthcare technologies
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.
Domain decomposition methods for fluid dynamics
International Nuclear Information System (INIS)
Clerc, S.
1995-01-01
A domain decomposition method for steady-state, subsonic fluid dynamics calculations, is proposed. The method is derived from the Schwarz alternating method used for elliptic problems, extended to non-linear hyperbolic problems. Particular emphasis is given on the treatment of boundary conditions. Numerical results are shown for a realistic three-dimensional two-phase flow problem with the FLICA-4 code for PWR cores. (from author). 4 figs., 8 refs
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...
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
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.
Yang, Yi-Bo; Chen, Ying; Draper, Terrence; Liang, Jian; Liu, Keh-Fei
2018-03-01
We report the results on the proton mass decomposition and also on the related quark and glue momentum fractions. The results are based on overlap valence fermions on four ensembles of Nf = 2 + 1 DWF configurations with three lattice spacings and volumes, and several pion masses including the physical pion mass. With 1-loop pertur-bative calculation and proper normalization of the glue operator, we find that the u, d, and s quark masses contribute 9(2)% to the proton mass. The quark energy and glue field energy contribute 31(5)% and 37(5)% respectively in the MS scheme at µ = 2 GeV. The trace anomaly gives the remaining 23(1)% contribution. The u, d, s and glue momentum fractions in the MS scheme are consistent with the global analysis at µ = 2 GeV.
Erbium hydride decomposition kinetics.
Energy Technology Data Exchange (ETDEWEB)
Ferrizz, Robert Matthew
2006-11-01
Thermal desorption spectroscopy (TDS) is used to study the decomposition kinetics of erbium hydride thin films. The TDS results presented in this report are analyzed quantitatively using Redhead's method to yield kinetic parameters (E{sub A} {approx} 54.2 kcal/mol), which are then utilized to predict hydrogen outgassing in vacuum for a variety of thermal treatments. Interestingly, it was found that the activation energy for desorption can vary by more than 7 kcal/mol (0.30 eV) for seemingly similar samples. In addition, small amounts of less-stable hydrogen were observed for all erbium dihydride films. A detailed explanation of several approaches for analyzing thermal desorption spectra to obtain kinetic information is included as an appendix.
Domain decomposition methods and parallel computing
International Nuclear Information System (INIS)
Meurant, G.
1991-01-01
In this paper, we show how to efficiently solve large linear systems on parallel computers. These linear systems arise from discretization of scientific computing problems described by systems of partial differential equations. We show how to get a discrete finite dimensional system from the continuous problem and the chosen conjugate gradient iterative algorithm is briefly described. Then, the different kinds of parallel architectures are reviewed and their advantages and deficiencies are emphasized. We sketch the problems found in programming the conjugate gradient method on parallel computers. For this algorithm to be efficient on parallel machines, domain decomposition techniques are introduced. We give results of numerical experiments showing that these techniques allow a good rate of convergence for the conjugate gradient algorithm as well as computational speeds in excess of a billion of floating point operations per second. (author). 5 refs., 11 figs., 2 tabs., 1 inset
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.
Decomposition methods for unsupervised learning
DEFF Research Database (Denmark)
Mørup, Morten
2008-01-01
This thesis presents the application and development of decomposition methods for Unsupervised Learning. It covers topics from classical factor analysis based decomposition and its variants such as Independent Component Analysis, Non-negative Matrix Factorization and Sparse Coding...... methods and clustering problems is derived both in terms of classical point clustering but also in terms of community detection in complex networks. A guiding principle throughout this thesis is the principle of parsimony. Hence, the goal of Unsupervised Learning is here posed as striving for simplicity...... in the decompositions. Thus, it is demonstrated how a wide range of decomposition methods explicitly or implicitly strive to attain this goal. Applications of the derived decompositions are given ranging from multi-media analysis of image and sound data, analysis of biomedical data such as electroencephalography...
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.
International Nuclear Information System (INIS)
Liu, Hui; Tian, Hong-qi; Li, Yan-fei
2015-01-01
Highlights: • A hybrid architecture is proposed for the wind speed forecasting. • Four algorithms are used for the wind speed multi-scale decomposition. • The extreme learning machines are employed for the wind speed forecasting. • All the proposed hybrid models can generate the accurate results. - Abstract: Realization of accurate wind speed forecasting is important to guarantee the safety of wind power utilization. In this paper, a new hybrid forecasting architecture is proposed to realize the wind speed accurate forecasting. In this architecture, four different hybrid models are presented by combining four signal decomposing algorithms (e.g., Wavelet Decomposition/Wavelet Packet Decomposition/Empirical Mode Decomposition/Fast Ensemble Empirical Mode Decomposition) and Extreme Learning Machines. The originality of the study is to investigate the promoted percentages of the Extreme Learning Machines by those mainstream signal decomposing algorithms in the multiple step wind speed forecasting. The results of two forecasting experiments indicate that: (1) the method of Extreme Learning Machines is suitable for the wind speed forecasting; (2) by utilizing the decomposing algorithms, all the proposed hybrid algorithms have better performance than the single Extreme Learning Machines; (3) in the comparisons of the decomposing algorithms in the proposed hybrid architecture, the Fast Ensemble Empirical Mode Decomposition has the best performance in the three-step forecasting results while the Wavelet Packet Decomposition has the best performance in the one and two step forecasting results. At the same time, the Wavelet Packet Decomposition and the Fast Ensemble Empirical Mode Decomposition are better than the Wavelet Decomposition and the Empirical Mode Decomposition in all the step predictions, respectively; and (4) the proposed algorithms are effective in the wind speed accurate predictions
Skrdla, Peter J; Robertson, Rebecca T
2005-06-02
Many solid-state reactions and phase transformations performed under isothermal conditions give rise to asymmetric, sigmoidally shaped conversion-time (x-t) profiles. The mathematical treatment of such curves, as well as their physical interpretation, is often challenging. In this work, the functional form of a Maxwell-Boltzmann (M-B) distribution is used to describe the distribution of activation energies for the reagent solids, which, when coupled with an integrated first-order rate expression, yields a novel semiempirical equation that may offer better success in the modeling of solid-state kinetics. In this approach, the Arrhenius equation is used to relate the distribution of activation energies to a corresponding distribution of rate constants for the individual molecules in the reagent solids. This distribution of molecular rate constants is then correlated to the (observable) reaction time in the derivation of the model equation. In addition to providing a versatile treatment for asymmetric, sigmoidal reaction curves, another key advantage of our equation over other models is that the start time of conversion is uniquely defined at t = 0. We demonstrate the ability of our simple, two-parameter equation to successfully model the experimental x-t data for the polymorphic transformation of a pharmaceutical compound under crystallization slurry (i.e., heterogeneous) conditions. Additionally, we use a modification of this equation to model the kinetics of a historically significant, homogeneous solid-state reaction: the thermal decomposition of AgMnO4 crystals. The potential broad applicability of our statistical (i.e., dispersive) kinetic approach makes it a potentially attractive alternative to existing models/approaches.
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).
Machine learning and medical imaging
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...
Gas hydrates forming and decomposition conditions analysis
Directory of Open Access Journals (Sweden)
А. М. Павленко
2017-07-01
Full Text Available The concept of gas hydrates has been defined; their brief description has been given; factors that affect the formation and decomposition of the hydrates have been reported; their distribution, structure and thermodynamic conditions determining the gas hydrates formation disposition in gas pipelines have been considered. Advantages and disadvantages of the known methods for removing gas hydrate plugs in the pipeline have been analyzed, the necessity of their further studies has been proved. In addition to the negative impact on the process of gas extraction, the hydrates properties make it possible to outline the following possible fields of their industrial use: obtaining ultrahigh pressures in confined spaces at the hydrate decomposition; separating hydrocarbon mixtures by successive transfer of individual components through the hydrate given the mode; obtaining cold due to heat absorption at the hydrate decomposition; elimination of the open gas fountain by means of hydrate plugs in the bore hole of the gushing gasser; seawater desalination, based on the hydrate ability to only bind water molecules into the solid state; wastewater purification; gas storage in the hydrate state; dispersion of high temperature fog and clouds by means of hydrates; water-hydrates emulsion injection into the productive strata to raise the oil recovery factor; obtaining cold in the gas processing to cool the gas, etc.
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....
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.)
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.
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
The efficacy of support vector machines (SVM)
Indian Academy of Sciences (India)
(2006) by applying an SVM statistical learning machine on the time-scale wavelet decomposition methods. We used the data of 108 events in central Japan with magnitude ranging from 3 to 7.4 recorded at KiK-net network stations, for a source–receiver distance of up to 150 km during the period 1998–2011. We applied a ...
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...
Directory of Open Access Journals (Sweden)
Chun Wang
2017-01-01
Full Text Available A novel multiobjective memetic algorithm based on decomposition (MOMAD is proposed to solve multiobjective flexible job shop scheduling problem (MOFJSP, which simultaneously minimizes makespan, total workload, and critical workload. Firstly, a population is initialized by employing an integration of different machine assignment and operation sequencing strategies. Secondly, multiobjective memetic algorithm based on decomposition is presented by introducing a local search to MOEA/D. The Tchebycheff approach of MOEA/D converts the three-objective optimization problem to several single-objective optimization subproblems, and the weight vectors are grouped by K-means clustering. Some good individuals corresponding to different weight vectors are selected by the tournament mechanism of a local search. In the experiments, the influence of three different aggregation functions is first studied. Moreover, the effect of the proposed local search is investigated. Finally, MOMAD is compared with eight state-of-the-art algorithms on a series of well-known benchmark instances and the experimental results show that the proposed algorithm outperforms or at least has comparative performance to the other algorithms.
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.
Environmentally Friendly Machining
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.
Danburite decomposition by hydrochloric acid
International Nuclear Information System (INIS)
Mamatov, E.D.; Ashurov, N.A.; Mirsaidov, U.
2011-01-01
Present article is devoted to decomposition of danburite of Ak-Arkhar Deposit of Tajikistan by hydrochloric acid. The interaction of boron containing ores of Ak-Arkhar Deposit of Tajikistan with mineral acids, including hydrochloric acid was studied. The optimal conditions of extraction of valuable components from danburite composition were determined. The chemical composition of danburite of Ak-Arkhar Deposit was determined as well. The kinetics of decomposition of calcined danburite by hydrochloric acid was studied. The apparent activation energy of the process of danburite decomposition by hydrochloric acid was calculated.
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 ...
Entanglement and tensor product decomposition for two fermions
International Nuclear Information System (INIS)
Caban, P; Podlaski, K; Rembielinski, J; Smolinski, K A; Walczak, Z
2005-01-01
The problem of the choice of tensor product decomposition in a system of two fermions with the help of Bogoliubov transformations of creation and annihilation operators is discussed. The set of physical states of the composite system is restricted by the superselection rule forbidding the superposition of fermions and bosons. It is shown that the Wootters concurrence is not the proper entanglement measure in this case. The explicit formula for the entanglement of formation is found. This formula shows that the entanglement of a given state depends on the tensor product decomposition of a Hilbert space. It is shown that the set of separable states is narrower than in the two-qubit case. Moreover, there exist states which are separable with respect to all tensor product decompositions of the Hilbert space. (letter to the editor)
Spectral decomposition of tent maps using symmetry considerations
International Nuclear Information System (INIS)
Ordonez, G.E.; Driebe, D.J.
1996-01-01
The spectral decompostion of the Frobenius-Perron operator of maps composed of many tents is determined from symmetry considerations. The eigenstates involve Euler as well as Bernoulli polynomials. The authors have introduced some new techniques, based on symmetry considerations, enabling the construction of spectral decompositions in a much simpler way than previous construction algorithms, Here we utilize these techniques to construct the spectral decomposition for one- dimensional maps of the unit interval composed of many tents. The construction uses the knowledge of the spectral decomposition of the r-adic map, which involves Bernoulli polynomials and their duals. It will be seen that the spectral decomposition of the tent maps involves both Bernoulli polynomials and Euler polynomials along with the appropriate dual 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.
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.
NRSA enzyme decomposition model data
U.S. Environmental Protection Agency — Microbial enzyme activities measured at more than 2000 US streams and rivers. These enzyme data were then used to predict organic matter decomposition and microbial...
Some nonlinear space decomposition algorithms
Energy Technology Data Exchange (ETDEWEB)
Tai, Xue-Cheng; Espedal, M. [Univ. of Bergen (Norway)
1996-12-31
Convergence of a space decomposition method is proved for a general convex programming problem. The space decomposition refers to methods that decompose a space into sums of subspaces, which could be a domain decomposition or a multigrid method for partial differential equations. Two algorithms are proposed. Both can be used for linear as well as nonlinear elliptic problems and they reduce to the standard additive and multiplicative Schwarz methods for linear elliptic problems. Two {open_quotes}hybrid{close_quotes} algorithms are also presented. They converge faster than the additive one and have better parallelism than the multiplicative method. Numerical tests with a two level domain decomposition for linear, nonlinear and interface elliptic problems are presented for the proposed algorithms.
Analysis of machining and machine tools
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...
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
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.
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...
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)
Distributed Dynamic State Estimation with Extended Kalman Filter
Energy Technology Data Exchange (ETDEWEB)
Du, Pengwei; Huang, Zhenyu; Sun, Yannan; Diao, Ruisheng; Kalsi, Karanjit; Anderson, Kevin K.; Li, Yulan; Lee, Barry
2011-08-04
Increasing complexity associated with large-scale renewable resources and novel smart-grid technologies necessitates real-time monitoring and control. Our previous work applied the extended Kalman filter (EKF) with the use of phasor measurement data (PMU) for dynamic state estimation. However, high computation complexity creates significant challenges for real-time applications. In this paper, the problem of distributed dynamic state estimation is investigated. One domain decomposition method is proposed to utilize decentralized computing resources. The performance of distributed dynamic state estimation is tested on a 16-machine, 68-bus test system.
Real interest parity decomposition
Directory of Open Access Journals (Sweden)
Alex Luiz Ferreira
2009-09-01
Full Text Available The aim of this paper is to investigate the general causes of real interest rate differentials (rids for a sample of emerging markets for the period of January 1996 to August 2007. To this end, two methods are applied. The first consists of breaking the variance of rids down into relative purchasing power pariety and uncovered interest rate parity and shows that inflation differentials are the main source of rids variation; while the second method breaks down the rids and nominal interest rate differentials (nids into nominal and real shocks. Bivariate autoregressive models are estimated under particular identification conditions, having been adequately treated for the identified structural breaks. Impulse response functions and error variance decomposition result in real shocks as being the likely cause of rids.O objetivo deste artigo é investigar as causas gerais dos diferenciais da taxa de juros real (rids para um conjunto de países emergentes, para o período de janeiro de 1996 a agosto de 2007. Para tanto, duas metodologias são aplicadas. A primeira consiste em decompor a variância dos rids entre a paridade do poder de compra relativa e a paridade de juros a descoberto e mostra que os diferenciais de inflação são a fonte predominante da variabilidade dos rids; a segunda decompõe os rids e os diferenciais de juros nominais (nids em choques nominais e reais. Sob certas condições de identificação, modelos autorregressivos bivariados são estimados com tratamento adequado para as quebras estruturais identificadas e as funções de resposta ao impulso e a decomposição da variância dos erros de previsão são obtidas, resultando em evidências favoráveis a que os choques reais são a causa mais provável dos rids.
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.
Long-term litter decomposition controlled by manganese redox cycling.
Keiluweit, Marco; Nico, Peter; Harmon, Mark E; Mao, Jingdong; Pett-Ridge, Jennifer; Kleber, Markus
2015-09-22
Litter decomposition is a keystone ecosystem process impacting nutrient cycling and productivity, soil properties, and the terrestrial carbon (C) balance, but the factors regulating decomposition rate are still poorly understood. Traditional models assume that the rate is controlled by litter quality, relying on parameters such as lignin content as predictors. However, a strong correlation has been observed between the manganese (Mn) content of litter and decomposition rates across a variety of forest ecosystems. Here, we show that long-term litter decomposition in forest ecosystems is tightly coupled to Mn redox cycling. Over 7 years of litter decomposition, microbial transformation of litter was paralleled by variations in Mn oxidation state and concentration. A detailed chemical imaging analysis of the litter revealed that fungi recruit and redistribute unreactive Mn(2+) provided by fresh plant litter to produce oxidative Mn(3+) species at sites of active decay, with Mn eventually accumulating as insoluble Mn(3+/4+) oxides. Formation of reactive Mn(3+) species coincided with the generation of aromatic oxidation products, providing direct proof of the previously posited role of Mn(3+)-based oxidizers in the breakdown of litter. Our results suggest that the litter-decomposing machinery at our coniferous forest site depends on the ability of plants and microbes to supply, accumulate, and regenerate short-lived Mn(3+) species in the litter layer. This observation indicates that biogeochemical constraints on bioavailability, mobility, and reactivity of Mn in the plant-soil system may have a profound impact on litter decomposition rates.
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.
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.
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...
On the hadron mass decomposition
Lorcé, Cédric
2018-02-01
We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force.
On the hadron mass decomposition
Energy Technology Data Exchange (ETDEWEB)
Lorce, Cedric [Universite Paris-Saclay, Centre de Physique Theorique, Ecole Polytechnique, CNRS, Palaiseau (France)
2018-02-15
We argue that the standard decompositions of the hadron mass overlook pressure effects, and hence should be interpreted with great care. Based on the semiclassical picture, we propose a new decomposition that properly accounts for these pressure effects. Because of Lorentz covariance, we stress that the hadron mass decomposition automatically comes along with a stability constraint, which we discuss for the first time. We show also that if a hadron is seen as made of quarks and gluons, one cannot decompose its mass into more than two contributions without running into trouble with the consistency of the physical interpretation. In particular, the so-called quark mass and trace anomaly contributions appear to be purely conventional. Based on the current phenomenological values, we find that in average quarks exert a repulsive force inside nucleons, balanced exactly by the gluon attractive force. (orig.)
National Research Council Canada - National Science Library
Shackelford, S. A; Menapace, J. A; Goldman, J. F
2007-01-01
... decomposition process. Using IDSC-based KDIE comparisons with the DNNC-d2, DNNC-d4, and DNNC-d6 isotopomers, a more detailed chemical structure/mechanistic relationship emerged by entering the interior of the DNNC molecule...
National Research Council Canada - National Science Library
Shackelford, S. A; Menapace, J. A; Goldman, J. F
2007-01-01
... thermochemical decomposition process. Using IDSC-based KDIE comparisons with the DNNC-d2, DNNC-d4, and DNNC-d6 isotopomers, a more detailed chemical structure/mechanistic relationship emerged by entering the interior of the DNNC molecule...
Humanizing machines: Anthropomorphization of slot machines increases gambling.
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).
Abstract decomposition theorem and applications
Grossberg, R; Grossberg, Rami; Lessmann, Olivier
2005-01-01
Let K be an Abstract Elementary Class. Under the asusmptions that K has a nicely behaved forking-like notion, regular types and existence of some prime models we establish a decomposition theorem for such classes. The decomposition implies a main gap result for the class K. The setting is general enough to cover \\aleph_0-stable first-order theories (proved by Shelah in 1982), Excellent Classes of atomic models of a first order tehory (proved Grossberg and Hart 1987) and the class of submodels of a large sequentially homogenuus \\aleph_0-stable model (which is new).
Thermal decomposition of biphenyl (1963); Decomposition thermique du biphenyle (1963)
Energy Technology Data Exchange (ETDEWEB)
Clerc, M [Commissariat a l' Energie Atomique, Saclay (France). Centre d' Etudes Nucleaires
1962-06-15
The rates of formation of the decomposition products of biphenyl; hydrogen, methane, ethane, ethylene, as well as triphenyl have been measured in the vapour and liquid phases at 460 deg. C. The study of the decomposition products of biphenyl at different temperatures between 400 and 460 deg. C has provided values of the activation energies of the reactions yielding the main products of pyrolysis in the vapour phase. Product and Activation energy: Hydrogen 73 {+-} 2 kCal/Mole; Benzene 76 {+-} 2 kCal/Mole; Meta-triphenyl 53 {+-} 2 kCal/Mole; Biphenyl decomposition 64 {+-} 2 kCal/Mole; The rate of disappearance of biphenyl is only very approximately first order. These results show the major role played at the start of the decomposition by organic impurities which are not detectable by conventional physico-chemical analysis methods and the presence of which accelerates noticeably the decomposition rate. It was possible to eliminate these impurities by zone-melting carried out until the initial gradient of the formation curves for the products became constant. The composition of the high-molecular weight products (over 250) was deduced from the mean molecular weight and the dosage of the aromatic C - H bonds by infrared spectrophotometry. As a result the existence in tars of hydrogenated tetra, penta and hexaphenyl has been demonstrated. (author) [French] Les vitesses de formation des produits de decomposition du biphenyle: hydrogene, methane, ethane, ethylene, ainsi que des triphenyles, ont ete mesurees en phase vapeur et en phase liquide a 460 deg. C. L'etude des produits de decomposition du biphenyle a differentes temperatures comprises entre 400 et 460 deg. C, a fourni les valeurs des energies d'activation des reactions conduisant aux principaux produits de la pyrolyse en phase vapeur. Produit et Energie d'activation: Hydrogene 73 {+-} 2 kcal/Mole; Benzene 76 {+-} 2 kcal/Mole; Metatriphenyle, 53 {+-} 2 kcal/Mole; Decomposition du biphenyle 64 {+-} 2 kcal/Mole; La
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
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…
Deep Restricted Kernel Machines Using Conjugate Feature Duality.
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.
Universality of Schmidt decomposition and particle identity
Sciara, Stefania; Lo Franco, Rosario; Compagno, Giuseppe
2017-03-01
Schmidt decomposition is a widely employed tool of quantum theory which plays a key role for distinguishable particles in scenarios such as entanglement characterization, theory of measurement and state purification. Yet, its formulation for identical particles remains controversial, jeopardizing its application to analyze general many-body quantum systems. Here we prove, using a newly developed approach, a universal Schmidt decomposition which allows faithful quantification of the physical entanglement due to the identity of particles. We find that it is affected by single-particle measurement localization and state overlap. We study paradigmatic two-particle systems where identical qubits and qutrits are located in the same place or in separated places. For the case of two qutrits in the same place, we show that their entanglement behavior, whose physical interpretation is given, differs from that obtained before by different methods. Our results are generalizable to multiparticle systems and open the way for further developments in quantum information processing exploiting particle identity as a resource.
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.)
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.
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
Lie bialgebras with triangular decomposition
International Nuclear Information System (INIS)
Andruskiewitsch, N.; Levstein, F.
1992-06-01
Lie bialgebras originated in a triangular decomposition of the underlying Lie algebra are discussed. The explicit formulas for the quantization of the Heisenberg Lie algebra and some motion Lie algebras are given, as well as the algebra of rational functions on the quantum Heisenberg group and the formula for the universal R-matrix. (author). 17 refs
Decomposition of metal nitrate solutions
International Nuclear Information System (INIS)
Haas, P.A.; Stines, W.B.
1982-01-01
Oxides in powder form are obtained from aqueous solutions of one or more heavy metal nitrates (e.g. U, Pu, Th, Ce) by thermal decomposition at 300 to 800 deg C in the presence of about 50 to 500% molar concentration of ammonium nitrate to total metal. (author)
Probability inequalities for decomposition integrals
Czech Academy of Sciences Publication Activity Database
Agahi, H.; Mesiar, Radko
2017-01-01
Roč. 315, č. 1 (2017), s. 240-248 ISSN 0377-0427 Institutional support: RVO:67985556 Keywords : Decomposition integral * Superdecomposition integral * Probability inequalities Subject RIV: BA - General Mathematics OBOR OECD: Statistics and probability Impact factor: 1.357, year: 2016 http://library.utia.cas.cz/separaty/2017/E/mesiar-0470959.pdf
Thermal decomposition of ammonium hexachloroosmate
DEFF Research Database (Denmark)
Asanova, T I; Kantor, Innokenty; Asanov, I. P.
2016-01-01
Structural changes of (NH4)2[OsCl6] occurring during thermal decomposition in a reduction atmosphere have been studied in situ using combined energy-dispersive X-ray absorption spectroscopy (ED-XAFS) and powder X-ray diffraction (PXRD). According to PXRD, (NH4)2[OsCl6] transforms directly to meta...
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.
Chemical physics of decomposition of energetic materials. Problems and prospects
International Nuclear Information System (INIS)
Smirnov, Lev P
2004-01-01
The review is concerned with analysis of the results obtained in the kinetic and mechanistic studies on decomposition of energetic materials (explosives, powders and solid propellants). It is shown that the state-of-the art in this field is inadequate to the potential of modern chemical kinetics and chemical physics. Unsolved problems are outlined and ways of their solution are proposed.
Thermodynamic anomaly in magnesium hydroxide decomposition
International Nuclear Information System (INIS)
Reis, T.A.
1983-08-01
The Origin of the discrepancy in the equilibrium water vapor pressure measurements for the reaction Mg(OH) 2 (s) = MgO(s) + H 2 O(g) when determined by Knudsen effusion and static manometry at the same temperature was investigated. For this reaction undergoing continuous thermal decomposition in Knudsen cells, Kay and Gregory observed that by extrapolating the steady-state apparent equilibrium vapor pressure measurements to zero-orifice, the vapor pressure was approx. 10 -4 of that previously established by Giauque and Archibald as the true thermodynamic equilibrium vapor pressure using statistical mechanical entropy calculations for the entropy of water vapor. This large difference in vapor pressures suggests the possibility of the formation in a Knudsen cell of a higher energy MgO that is thermodynamically metastable by about 48 kJ / mole. It has been shown here that experimental results are qualitatively independent of the type of Mg(OH) 2 used as a starting material, which confirms the inferences of Kay and Gregory. Thus, most forms of Mg(OH) 2 are considered to be the stable thermodynamic equilibrium form. X-ray diffraction results show that during the course of the reaction only the equilibrium NaCl-type MgO is formed, and no different phases result from samples prepared in Knudsen cells. Surface area data indicate that the MgO molar surface area remains constant throughout the course of the reaction at low decomposition temperatures, and no significant annealing occurs at less than 400 0 C. Scanning electron microscope photographs show no change in particle size or particle surface morphology. Solution calorimetric measurements indicate no inherent hgher energy content in the MgO from the solid produced in Knudsen cells. The Knudsen cell vapor pressure discrepancy may reflect the formation of a transient metastable MgO or Mg(OH) 2 -MgO solid solution during continuous thermal decomposition in Knudsen cells
Task Decomposition Module For Telerobot Trajectory Generation
Wavering, Albert J.; Lumia, Ron
1988-10-01
A major consideration in the design of trajectory generation software for a Flight Telerobotic Servicer (FTS) is that the FTS will be called upon to perform tasks which require a diverse range of manipulator behaviors and capabilities. In a hierarchical control system where tasks are decomposed into simpler and simpler subtasks, the task decomposition module which performs trajectory planning and execution should therefore be able to accommodate a wide range of algorithms. In some cases, it will be desirable to plan a trajectory for an entire motion before manipulator motion commences, as when optimizing over the entire trajectory. Many FTS motions, however, will be highly sensory-interactive, such as moving to attain a desired position relative to a non-stationary object whose position is periodically updated by a vision system. In this case, the time-varying nature of the trajectory may be handled either by frequent replanning using updated sensor information, or by using an algorithm which creates a less specific state-dependent plan that determines the manipulator path as the trajectory is executed (rather than a priori). This paper discusses a number of trajectory generation techniques from these categories and how they may be implemented in a task decompo-sition module of a hierarchical control system. The structure, function, and interfaces of the proposed trajectory gener-ation module are briefly described, followed by several examples of how different algorithms may be performed by the module. The proposed task decomposition module provides a logical structure for trajectory planning and execution, and supports a large number of published trajectory generation techniques.
International Nuclear Information System (INIS)
Zeng Lihui; Wang Nanping; Tian Gui
2012-01-01
In order to extract the information of peaks in different energy from the data of overlapping peaks in environmental gamma spectrometer, a spectrum data Gaussian decomposition software was designed based on least-square Gaussian fitting method. The interface of this software is friendly, it can complete the decomposition of overlapping peaks in gamma spectrometer quickly by the way of man-machines interactive. The result of field measured data decomposed by this software indicates that the Gaussian decomposition software can efficiently extract 137 Cs spectra from over lapping peaks, which has significance to estimate the human nuclide contamination in the environment. (authors)
International Nuclear Information System (INIS)
Zeng Lihui; Wang Nanping Tian Gui
2011-01-01
In order to extract the information of peaks in different energy from the data of overlapping peaks in environmental gamma spectrometer, a spectrum data Gaussian decomposition soft is designed based on least- square Gaussian fitting method. The interface of this software is friendly, it can complete the decomposition of overlapping peaks in gamma spectrometer quickly by the way of man-machines interactive. The result that applied gamma spectrometry to data analysis in the field measurement indicates that the Gaussian decomposition soft can efficiently extract 137 Cs from overlapping peaks which has significance to assess the human nuclide contamination of environment. (authors)
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...
Quantum cloning machines and the applications
Energy Technology Data Exchange (ETDEWEB)
Fan, Heng, E-mail: hfan@iphy.ac.cn [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Collaborative Innovation Center of Quantum Matter, Beijing 100190 (China); Wang, Yi-Nan; Jing, Li [School of Physics, Peking University, Beijing 100871 (China); Yue, Jie-Dong [Beijing National Laboratory for Condensed Matter Physics, Institute of Physics, Chinese Academy of Sciences, Beijing 100190 (China); Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu [School of Physics, Peking University, Beijing 100871 (China)
2014-11-20
No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results.
Quantum cloning machines and the applications
International Nuclear Information System (INIS)
Fan, Heng; Wang, Yi-Nan; Jing, Li; Yue, Jie-Dong; Shi, Han-Duo; Zhang, Yong-Liang; Mu, Liang-Zhu
2014-01-01
No-cloning theorem is fundamental for quantum mechanics and for quantum information science that states an unknown quantum state cannot be cloned perfectly. However, we can try to clone a quantum state approximately with the optimal fidelity, or instead, we can try to clone it perfectly with the largest probability. Thus various quantum cloning machines have been designed for different quantum information protocols. Specifically, quantum cloning machines can be designed to analyze the security of quantum key distribution protocols such as BB84 protocol, six-state protocol, B92 protocol and their generalizations. Some well-known quantum cloning machines include universal quantum cloning machine, phase-covariant cloning machine, the asymmetric quantum cloning machine and the probabilistic quantum cloning machine. In the past years, much progress has been made in studying quantum cloning machines and their applications and implementations, both theoretically and experimentally. In this review, we will give a complete description of those important developments about quantum cloning and some related topics. On the other hand, this review is self-consistent, and in particular, we try to present some detailed formulations so that further study can be taken based on those results
Barbini, L.; Eltabach, M.; Hillis, A. J.; du Bois, J. L.
2018-03-01
In rotating machine diagnosis different spectral tools are used to analyse vibration signals. Despite the good diagnostic performance such tools are usually refined, computationally complex to implement and require oversight of an expert user. This paper introduces an intuitive and easy to implement method for vibration analysis: amplitude cyclic frequency decomposition. This method firstly separates vibration signals accordingly to their spectral amplitudes and secondly uses the squared envelope spectrum to reveal the presence of cyclostationarity in each amplitude level. The intuitive idea is that in a rotating machine different components contribute vibrations at different amplitudes, for instance defective bearings contribute a very weak signal in contrast to gears. This paper also introduces a new quantity, the decomposition squared envelope spectrum, which enables separation between the components of a rotating machine. The amplitude cyclic frequency decomposition and the decomposition squared envelope spectrum are tested on real word signals, both at stationary and varying speeds, using data from a wind turbine gearbox and an aircraft engine. In addition a benchmark comparison to the spectral correlation method is presented.
Czech Academy of Sciences Publication Activity Database
Jíša, Kamil; Nováková, Jana; Schwarze, Michael; Vondrová, Alena; Sklenák, Štěpán; Sobalík, Zdeněk
2009-01-01
Roč. 262, č. 1 (2009), s. 27-34 ISSN 0021-9517 R&D Projects: GA AV ČR 1ET400400413; GA AV ČR 1QS400400560 Institutional research plan: CEZ:AV0Z40400503 Keywords : Fe in FER * BEA a MFI * N2O decomposition Subject RIV: CF - Physical ; Theoretical Chemistry Impact factor: 5.288, year: 2009
Investigating hydrogel dosimeter decomposition by chemical methods
International Nuclear Information System (INIS)
Jordan, Kevin
2015-01-01
The chemical oxidative decomposition of leucocrystal violet micelle hydrogel dosimeters was investigated using the reaction of ferrous ions with hydrogen peroxide or sodium bicarbonate with hydrogen peroxide. The second reaction is more effective at dye decomposition in gelatin hydrogels. Additional chemical analysis is required to determine the decomposition products
A Fast SVD-Hidden-nodes based Extreme Learning Machine for Large-Scale Data Analytics.
Deng, Wan-Yu; Bai, Zuo; Huang, Guang-Bin; Zheng, Qing-Hua
2016-05-01
Big dimensional data is a growing trend that is emerging in many real world contexts, extending from web mining, gene expression analysis, protein-protein interaction to high-frequency financial data. Nowadays, there is a growing consensus that the increasing dimensionality poses impeding effects on the performances of classifiers, which is termed as the "peaking phenomenon" in the field of machine intelligence. To address the issue, dimensionality reduction is commonly employed as a preprocessing step on the Big dimensional data before building the classifiers. In this paper, we propose an Extreme Learning Machine (ELM) approach for large-scale data analytic. In contrast to existing approaches, we embed hidden nodes that are designed using singular value decomposition (SVD) into the classical ELM. These SVD nodes in the hidden layer are shown to capture the underlying characteristics of the Big dimensional data well, exhibiting excellent generalization performances. The drawback of using SVD on the entire dataset, however, is the high computational complexity involved. To address this, a fast divide and conquer approximation scheme is introduced to maintain computational tractability on high volume data. The resultant algorithm proposed is labeled here as Fast Singular Value Decomposition-Hidden-nodes based Extreme Learning Machine or FSVD-H-ELM in short. In FSVD-H-ELM, instead of identifying the SVD hidden nodes directly from the entire dataset, SVD hidden nodes are derived from multiple random subsets of data sampled from the original dataset. Comprehensive experiments and comparisons are conducted to assess the FSVD-H-ELM against other state-of-the-art algorithms. The results obtained demonstrated the superior generalization performance and efficiency of the FSVD-H-ELM. Copyright © 2016 Elsevier Ltd. All rights reserved.
International Nuclear Information System (INIS)
Ismail, I.M.K.; Hawkins, T.
2005-01-01
Recently, interest in aluminium hydride (alane) as a rocket propulsion ingredient has been renewed due to improvements in its manufacturing process and an increase in thermal stability. When alane is added to solid propellant formulations, rocket performance is enhanced and the specific impulse increases. Preliminary work was performed at AFRL on the characterization and evaluation of two alane samples. Decomposition kinetics were determined from gravimetric TGA data and volumetric vacuum thermal stability (VTS) results. Chemical analysis showed the samples had 88.30% (by weight) aluminium and 9.96% hydrogen. The average density, as measured by helium pycnometery, was 1.486 g/cc. Scanning electron microscopy showed that the particles were mostly composed of sharp edged crystallographic polyhedral such as simple cubes, cubic octahedrons and hexagonal prisms. Thermogravimetric analysis was utilized to investigate the decomposition kinetics of alane in argon atmosphere and to shed light on the mechanism of alane decomposition. Two kinetic models were successfully developed and used to propose a mechanism for the complete decomposition of alane and to predict its shelf-life during storage. Alane decomposes in two steps. The slowest (rate-determining) step is solely controlled by solid state nucleation of aluminium crystals; the fastest step is due to growth of the crystals. Thus, during decomposition, hydrogen gas is liberated and the initial polyhedral AlH 3 crystals yield a final mix of amorphous aluminium and aluminium crystals. After establishing the kinetic model, prediction calculations indicated that alane can be stored in inert atmosphere at temperatures below 10 deg. C for long periods of time (e.g., 15 years) without significant decomposition. After 15 years of storage, the kinetic model predicts ∼0.1% decomposition, but storage at higher temperatures (e.g. 30 deg. C) is not recommended
Reaction mechanism of reductive decomposition of FGD gypsum with anthracite
International Nuclear Information System (INIS)
Zheng, Da; Lu, Hailin; Sun, Xiuyun; Liu, Xiaodong; Han, Weiqing; Wang, Lianjun
2013-01-01
Highlights: • The reaction mechanism was different if the molar ratio of C/CaSO 4 was different. • The yield of CaO rises with an increase in temperature. • The optimal ratio of C/CaSO 4 = 1.2:1. • The decomposition process is mainly apparent solid–solid reaction with liquid-phase involved. - Abstract: The process of decomposition reaction between flue gas desulfurization (FGD) gypsum and anthracite is complex, which depends on the reaction conditions and atmosphere. In this study, thermogravimetric analysis with Fourier transform infrared spectroscopy (TGA-FTIR), X-ray diffraction (XRD), scanning electron microscopy (SEM) and the experiment in a tubular reactor were used to characterize the decomposition reaction in a nitrogen atmosphere under different conditions. The reaction mechanism analysis showed that the decomposition reaction process and mechanism were different when the molar proportion of C/CaSO 4 was changed. The experiment results showed that appropriate increase in the C/CaSO 4 proportion and higher temperatures were suitable for the formation of the main production of CaO, which can help us to understand the solid state reaction mechanism better. Via kinetic analysis of the reaction between anthracite and FGD gypsum under the optimal molar ratio of C/CaSO 4 , the mechanism model of the reaction was confirmed and the decomposition process was a two-step reaction which was in accordance with apparent solid–solid reaction
Optimization and Assessment of Wavelet Packet Decompositions with Evolutionary Computation
Directory of Open Access Journals (Sweden)
Schell Thomas
2003-01-01
Full Text Available In image compression, the wavelet transformation is a state-of-the-art component. Recently, wavelet packet decomposition has received quite an interest. A popular approach for wavelet packet decomposition is the near-best-basis algorithm using nonadditive cost functions. In contrast to additive cost functions, the wavelet packet decomposition of the near-best-basis algorithm is only suboptimal. We apply methods from the field of evolutionary computation (EC to test the quality of the near-best-basis results. We observe a phenomenon: the results of the near-best-basis algorithm are inferior in terms of cost-function optimization but are superior in terms of rate/distortion performance compared to EC methods.
Benders’ Decomposition for Curriculum-Based Course Timetabling
DEFF Research Database (Denmark)
Bagger, Niels-Christian F.; Sørensen, Matias; Stidsen, Thomas R.
2018-01-01
feasibility. We compared our algorithm with other approaches from the literature for a total of 32 data instances. We obtained a lower bound on 23 of the instances, which were at least as good as the lower bounds obtained by the state-of-the-art, and on eight of these, our lower bounds were higher. On two......In this paper we applied Benders’ decomposition to the Curriculum-Based Course Timetabling (CBCT) problem. The objective of the CBCT problem is to assign a set of lectures to time slots and rooms. Our approach was based on segmenting the problem into time scheduling and room allocation problems...... of the instances, our lower bound was an improvement of the currently best-known. Lastly, we compared our decomposition to the model without the decomposition on an additional six instances, which are much larger than the other 32. To our knowledge, this was the first time that lower bounds were calculated...
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
Magnet management in electric machines
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.
Global sensitivity analysis by polynomial dimensional decomposition
Energy Technology Data Exchange (ETDEWEB)
Rahman, Sharif, E-mail: rahman@engineering.uiowa.ed [College of Engineering, The University of Iowa, Iowa City, IA 52242 (United States)
2011-07-15
This paper presents a polynomial dimensional decomposition (PDD) method for global sensitivity analysis of stochastic systems subject to independent random input following arbitrary probability distributions. The method involves Fourier-polynomial expansions of lower-variate component functions of a stochastic response by measure-consistent orthonormal polynomial bases, analytical formulae for calculating the global sensitivity indices in terms of the expansion coefficients, and dimension-reduction integration for estimating the expansion coefficients. Due to identical dimensional structures of PDD and analysis-of-variance decomposition, the proposed method facilitates simple and direct calculation of the global sensitivity indices. Numerical results of the global sensitivity indices computed for smooth systems reveal significantly higher convergence rates of the PDD approximation than those from existing methods, including polynomial chaos expansion, random balance design, state-dependent parameter, improved Sobol's method, and sampling-based methods. However, for non-smooth functions, the convergence properties of the PDD solution deteriorate to a great extent, warranting further improvements. The computational complexity of the PDD method is polynomial, as opposed to exponential, thereby alleviating the curse of dimensionality to some extent.
Salient Object Detection via Structured Matrix Decomposition.
Peng, Houwen; Li, Bing; Ling, Haibin; Hu, Weiming; Xiong, Weihua; Maybank, Stephen J
2016-05-04
Low-rank recovery models have shown potential for salient object detection, where a matrix is decomposed into a low-rank matrix representing image background and a sparse matrix identifying salient objects. Two deficiencies, however, still exist. First, previous work typically assumes the elements in the sparse matrix are mutually independent, ignoring the spatial and pattern relations of image regions. Second, when the low-rank and sparse matrices are relatively coherent, e.g., when there are similarities between the salient objects and background or when the background is complicated, it is difficult for previous models to disentangle them. To address these problems, we propose a novel structured matrix decomposition model with two structural regularizations: (1) a tree-structured sparsity-inducing regularization that captures the image structure and enforces patches from the same object to have similar saliency values, and (2) a Laplacian regularization that enlarges the gaps between salient objects and the background in feature space. Furthermore, high-level priors are integrated to guide the matrix decomposition and boost the detection. We evaluate our model for salient object detection on five challenging datasets including single object, multiple objects and complex scene images, and show competitive results as compared with 24 state-of-the-art methods in terms of seven performance metrics.
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
Dictionary-Based Tensor Canonical Polyadic Decomposition
Cohen, Jeremy Emile; Gillis, Nicolas
2018-04-01
To ensure interpretability of extracted sources in tensor decomposition, we introduce in this paper a dictionary-based tensor canonical polyadic decomposition which enforces one factor to belong exactly to a known dictionary. A new formulation of sparse coding is proposed which enables high dimensional tensors dictionary-based canonical polyadic decomposition. The benefits of using a dictionary in tensor decomposition models are explored both in terms of parameter identifiability and estimation accuracy. Performances of the proposed algorithms are evaluated on the decomposition of simulated data and the unmixing of hyperspectral images.
Decomposition of diesel oil by various microorganisms
Energy Technology Data Exchange (ETDEWEB)
Suess, A; Netzsch-Lehner, A
1969-01-01
Previous experiments demonstrated the decomposition of diesel oil in different soils. In this experiment the decomposition of /sup 14/C-n-Hexadecane labelled diesel oil by special microorganisms was studied. The results were as follows: (1) In the experimental soils the microorganisms Mycoccus ruber, Mycobacterium luteum and Trichoderma hamatum are responsible for the diesel oil decomposition. (2) By adding microorganisms to the soil an increase of the decomposition rate was found only in the beginning of the experiments. (3) Maximum decomposition of diesel oil was reached 2-3 weeks after incubation.
Variance decomposition in stochastic simulators.
Le Maître, O P; Knio, O M; Moraes, A
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Le Maître, O. P.; Knio, O. M.; Moraes, A.
2015-06-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Energy Technology Data Exchange (ETDEWEB)
Le Maître, O. P., E-mail: olm@limsi.fr [LIMSI-CNRS, UPR 3251, Orsay (France); Knio, O. M., E-mail: knio@duke.edu [Department of Mechanical Engineering and Materials Science, Duke University, Durham, North Carolina 27708 (United States); Moraes, A., E-mail: alvaro.moraesgutierrez@kaust.edu.sa [King Abdullah University of Science and Technology, Thuwal (Saudi Arabia)
2015-06-28
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Variance decomposition in stochastic simulators
Le Maî tre, O. P.; Knio, O. M.; Moraes, Alvaro
2015-01-01
This work aims at the development of a mathematical and computational approach that enables quantification of the inherent sources of stochasticity and of the corresponding sensitivities in stochastic simulations of chemical reaction networks. The approach is based on reformulating the system dynamics as being generated by independent standardized Poisson processes. This reformulation affords a straightforward identification of individual realizations for the stochastic dynamics of each reaction channel, and consequently a quantitative characterization of the inherent sources of stochasticity in the system. By relying on the Sobol-Hoeffding decomposition, the reformulation enables us to perform an orthogonal decomposition of the solution variance. Thus, by judiciously exploiting the inherent stochasticity of the system, one is able to quantify the variance-based sensitivities associated with individual reaction channels, as well as the importance of channel interactions. Implementation of the algorithms is illustrated in light of simulations of simplified systems, including the birth-death, Schlögl, and Michaelis-Menten models.
Single Directional SMO Algorithm for Least Squares Support Vector Machines
Directory of Open Access Journals (Sweden)
Xigao Shao
2013-01-01
Full Text Available Working set selection is a major step in decomposition methods for training least squares support vector machines (LS-SVMs. In this paper, a new technique for the selection of working set in sequential minimal optimization- (SMO- type decomposition methods is proposed. By the new method, we can select a single direction to achieve the convergence of the optimality condition. A simple asymptotic convergence proof for the new algorithm is given. Experimental comparisons demonstrate that the classification accuracy of the new method is not largely different from the existing methods, but the training speed is faster than existing ones.
Prediction Model of Machining Failure Trend Based on Large Data Analysis
Li, Jirong
2017-12-01
The mechanical processing has high complexity, strong coupling, a lot of control factors in the machining process, it is prone to failure, in order to improve the accuracy of fault detection of large mechanical equipment, research on fault trend prediction requires machining, machining fault trend prediction model based on fault data. The characteristics of data processing using genetic algorithm K mean clustering for machining, machining feature extraction which reflects the correlation dimension of fault, spectrum characteristics analysis of abnormal vibration of complex mechanical parts processing process, the extraction method of the abnormal vibration of complex mechanical parts processing process of multi-component spectral decomposition and empirical mode decomposition Hilbert based on feature extraction and the decomposition results, in order to establish the intelligent expert system for the data base, combined with large data analysis method to realize the machining of the Fault trend prediction. The simulation results show that this method of fault trend prediction of mechanical machining accuracy is better, the fault in the mechanical process accurate judgment ability, it has good application value analysis and fault diagnosis in the machining process.
Excimer laser decomposition of silicone
International Nuclear Information System (INIS)
Laude, L.D.; Cochrane, C.; Dicara, Cl.; Dupas-Bruzek, C.; Kolev, K.
2003-01-01
Excimer laser irradiation of silicone foils is shown in this work to induce decomposition, ablation and activation of such materials. Thin (100 μm) laminated silicone foils are irradiated at 248 nm as a function of impacting laser fluence and number of pulsed irradiations at 1 s intervals. Above a threshold fluence of 0.7 J/cm 2 , material starts decomposing. At higher fluences, this decomposition develops and gives rise to (i) swelling of the irradiated surface and then (ii) emission of matter (ablation) at a rate that is not proportioned to the number of pulses. Taking into consideration the polymer structure and the foil lamination process, these results help defining the phenomenology of silicone ablation. The polymer decomposition results in two parts: one which is organic and volatile, and another part which is inorganic and remains, forming an ever thickening screen to light penetration as the number of light pulses increases. A mathematical model is developed that accounts successfully for this physical screening effect
Singular value decomposition based feature extraction technique for physiological signal analysis.
Chang, Cheng-Ding; Wang, Chien-Chih; Jiang, Bernard C
2012-06-01
Multiscale entropy (MSE) is one of the popular techniques to calculate and describe the complexity of the physiological signal. Many studies use this approach to detect changes in the physiological conditions in the human body. However, MSE results are easily affected by noise and trends, leading to incorrect estimation of MSE values. In this paper, singular value decomposition (SVD) is adopted to replace MSE to extract the features of physiological signals, and adopt the support vector machine (SVM) to classify the different physiological states. A test data set based on the PhysioNet website was used, and the classification results showed that using SVD to extract features of the physiological signal could attain a classification accuracy rate of 89.157%, which is higher than that using the MSE value (71.084%). The results show the proposed analysis procedure is effective and appropriate for distinguishing different physiological states. This promising result could be used as a reference for doctors in diagnosis of congestive heart failure (CHF) disease.
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.
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)
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...
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...
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
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
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.
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.
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.
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.
Mechanistic Aspects of the Thermal Decomposition of Dicyclopentadienyl Titanium(IV) Dibenzyl
Boekel, C.P.; Teuben, J.H.; Liefde Meijer, H.J. de
1975-01-01
The thermal decomposition of dicyclopentadienyltitanium(IV) dibenzyl in the solid state and in hydrocarbon solvents has been investigated. The compound decomposes via intermolecular abstraction of hydrogen atoms from the cyclopentadienyl rings with quantitative formation of toluene. The reaction was
International Nuclear Information System (INIS)
Jin Jing; Wei Biao; Feng Peng; Tang Yuelin; Zhou Mi
2010-01-01
Based on the interdependent relationship between fission neutrons ( 252 Cf) and fission chain ( 235 U system), the paper presents the time-frequency feature analysis and recognition in fission neutron signal based on support vector machine (SVM) through the analysis on signal characteristics and the measuring principle of the 252 Cf fission neutron signal. The time-frequency characteristics and energy features of the fission neutron signal are extracted by using wavelet decomposition and de-noising wavelet packet decomposition, and then applied to training and classification by means of support vector machine based on statistical learning theory. The results show that, it is effective to obtain features of nuclear signal via wavelet decomposition and de-noising wavelet packet decomposition, and the latter can reflect the internal characteristics of the fission neutron system better. With the training accomplished, the SVM classifier achieves an accuracy rate above 70%, overcoming the lack of training samples, and verifying the effectiveness of the algorithm. (authors)
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.
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.
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...
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...
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…
Faddeev wave function decomposition using bipolar harmonics
International Nuclear Information System (INIS)
Friar, J.L.; Tomusiak, E.L.; Gibson, B.F.; Payne, G.L.
1981-01-01
The standard partial wave (channel) representation for the Faddeev solution to the Schroedinger equation for the ground state of 3 nucleons is written in terms of functions which couple the interacting pair and spectator angular momenta to give S, P, and D waves. For each such coupling there are three terms, one for each of the three cyclic permutations of the nucleon coordinates. A series of spherical harmonic identities is developed which allows writing the Faddeev solution in terms of a basis set of 5 bipolar harmonics: 1 for S waves; 1 for P waves; and 3 for D waves. The choice of a D-wave basis is largely arbitrary, and specific choices correspond to the decomposition schemes of Derrick and Blatt, Sachs, Gibson and Schiff, and Bolsterli and Jezak. The bipolar harmonic form greatly simplifies applications which utilize the wave function, and we specifically discuss the isoscalar charge (or mass) density and the 3 He Coulomb energy
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.
Asynchronized synchronous machines
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
Kosambi and Proper Orthogonal Decomposition
Indian Academy of Sciences (India)
any meteorological station, or process and control pa- rameters in a chemical plant. All these are .... The power spectrum of turbulent kinetic energy is usu- ally expressed in ... to have been inspired by Kelvin's tidal machine, with templates and ...
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.
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
Dolomite decomposition under CO2
International Nuclear Information System (INIS)
Guerfa, F.; Bensouici, F.; Barama, S.E.; Harabi, A.; Achour, S.
2004-01-01
Full text.Dolomite (MgCa (CO 3 ) 2 is one of the most abundant mineral species on the surface of the planet, it occurs in sedimentary rocks. MgO, CaO and Doloma (Phase mixture of MgO and CaO, obtained from the mineral dolomite) based materials are attractive steel-making refractories because of their potential cost effectiveness and world wide abundance more recently, MgO is also used as protective layers in plasma screen manufacture ceel. The crystal structure of dolomite was determined as rhombohedral carbonates, they are layers of Mg +2 and layers of Ca +2 ions. It dissociates depending on the temperature variations according to the following reactions: MgCa (CO 3 ) 2 → MgO + CaO + 2CO 2 .....MgCa (CO 3 ) 2 → MgO + Ca + CaCO 3 + CO 2 .....This latter reaction may be considered as a first step for MgO production. Differential thermal analysis (DTA) are used to control dolomite decomposition and the X-Ray Diffraction (XRD) was used to elucidate thermal decomposition of dolomite according to the reaction. That required samples were heated to specific temperature and holding times. The average particle size of used dolomite powders is 0.3 mm, as where, the heating temperature was 700 degree celsius, using various holding times (90 and 120 minutes). Under CO 2 dolomite decomposed directly to CaCO 3 accompanied by the formation of MgO, no evidence was offered for the MgO formation of either CaO or MgCO 3 , under air, simultaneous formation of CaCO 3 , CaO and accompanied dolomite decomposition
Spectral Tensor-Train Decomposition
DEFF Research Database (Denmark)
Bigoni, Daniele; Engsig-Karup, Allan Peter; Marzouk, Youssef M.
2016-01-01
The accurate approximation of high-dimensional functions is an essential task in uncertainty quantification and many other fields. We propose a new function approximation scheme based on a spectral extension of the tensor-train (TT) decomposition. We first define a functional version of the TT...... adaptive Smolyak approach. The method is also used to approximate the solution of an elliptic PDE with random input data. The open source software and examples presented in this work are available online (http://pypi.python.org/pypi/TensorToolbox/)....
Learning Activity Packets for Milling Machines. Unit I--Introduction to Milling Machines.
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…
Advances in independent component analysis and learning machines
Bingham, Ella; Laaksonen, Jorma; Lampinen, Jouko
2015-01-01
In honour of Professor Erkki Oja, one of the pioneers of Independent Component Analysis (ICA), this book reviews key advances in the theory and application of ICA, as well as its influence on signal processing, pattern recognition, machine learning, and data mining. Examples of topics which have developed from the advances of ICA, which are covered in the book are: A unifying probabilistic model for PCA and ICA Optimization methods for matrix decompositions Insights into the FastICA algorithmUnsupervised deep learning Machine vision and image retrieval A review of developments in the t
Decomposition of Multi-player Games
Zhao, Dengji; Schiffel, Stephan; Thielscher, Michael
Research in General Game Playing aims at building systems that learn to play unknown games without human intervention. We contribute to this endeavour by generalising the established technique of decomposition from AI Planning to multi-player games. To this end, we present a method for the automatic decomposition of previously unknown games into independent subgames, and we show how a general game player can exploit a successful decomposition for game tree search.
Constructive quantum Shannon decomposition from Cartan involutions
International Nuclear Information System (INIS)
Drury, Byron; Love, Peter
2008-01-01
The work presented here extends upon the best known universal quantum circuit, the quantum Shannon decomposition proposed by Shende et al (2006 IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 25 1000). We obtain the basis of the circuit's design in a pair of Cartan decompositions. This insight gives a simple constructive factoring algorithm in terms of the Cartan involutions corresponding to these decompositions
Constructive quantum Shannon decomposition from Cartan involutions
Energy Technology Data Exchange (ETDEWEB)
Drury, Byron; Love, Peter [Department of Physics, 370 Lancaster Ave., Haverford College, Haverford, PA 19041 (United States)], E-mail: plove@haverford.edu
2008-10-03
The work presented here extends upon the best known universal quantum circuit, the quantum Shannon decomposition proposed by Shende et al (2006 IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst. 25 1000). We obtain the basis of the circuit's design in a pair of Cartan decompositions. This insight gives a simple constructive factoring algorithm in terms of the Cartan involutions corresponding to these decompositions.
Shigeri, Yasushi; Matsui, Tatsunobu; Watanabe, Kunihiko
2009-11-01
In order to develop a practical method for the decomposition of intact chicken feathers, a moderate thermophile strain, Meiothermus ruber H328, having strong keratinolytic activity, was used in a bio-type garbage-treatment machine working with an acidulocomposting process. The addition of strain H328 cells (15 g) combined with acidulocomposting in the garbage machine resulted in 70% degradation of intact chicken feathers (30 g) within 14 d. This degradation efficiency is comparable to a previous result employing the strain as a single bacterium in flask culture, and it indicates that strain H328 can promote intact feather degradation activity in a garbage machine currently on the market.
Decomposition in pelagic marine ecosytems
International Nuclear Information System (INIS)
Lucas, M.I.
1986-01-01
During the decomposition of plant detritus, complex microbial successions develop which are dominated in the early stages by a number of distinct bacterial morphotypes. The microheterotrophic community rapidly becomes heterogenous and may include cyanobacteria, fungi, yeasts and bactivorous protozoans. Microheterotrophs in the marine environment may have a biomass comparable to that of all other heterotrophs and their significance as a resource to higher trophic orders, and in the regeneration of nutrients, particularly nitrogen, that support 'regenerated' primary production, has aroused both attention and controversy. Numerous methods have been employed to measure heterotrophic bacterial production and activity. The most widely used involve estimates of 14 C-glucose uptake; the frequency of dividing cells; the incorporation of 3 H-thymidine and exponential population growth in predator-reduced filtrates. Recent attempts to model decomposition processes and C and N fluxes in pelagic marine ecosystems are described. This review examines the most sensitive components and predictions of the models with particular reference to estimates of bacterial production, net growth yield and predictions of N cycling determined by 15 N methodology. Directed estimates of nitrogen (and phosphorus) flux through phytoplanktonic and bacterioplanktonic communities using 15 N (and 32 P) tracer methods are likely to provide more realistic measures of nitrogen flow through planktonic communities
Infrared multiphoton absorption and decomposition
International Nuclear Information System (INIS)
Evans, D.K.; McAlpine, R.D.
1984-01-01
The discovery of infrared laser induced multiphoton absorption (IRMPA) and decomposition (IRMPD) by Isenor and Richardson in 1971 generated a great deal of interest in these phenomena. This interest was increased with the discovery by Ambartzumian, Letokhov, Ryadbov and Chekalin that isotopically selective IRMPD was possible. One of the first speculations about these phenomena was that it might be possible to excite a particular mode of a molecule with the intense infrared laser beam and cause decomposition or chemical reaction by channels which do not predominate thermally, thus providing new synthetic routes for complex chemicals. The potential applications to isotope separation and novel chemistry stimulated efforts to understand the underlying physics and chemistry of these processes. At ICOMP I, in 1977 and at ICOMP II in 1980, several authors reviewed the current understandings of IRMPA and IRMPD as well as the particular aspect of isotope separation. There continues to be a great deal of effort into understanding IRMPA and IRMPD and we will briefly review some aspects of these efforts with particular emphasis on progress since ICOMP II. 31 references
Decomposition of Diethylstilboestrol in Soil
DEFF Research Database (Denmark)
Gregers-Hansen, Birte
1964-01-01
The rate of decomposition of DES-monoethyl-1-C14 in soil was followed by measurement of C14O2 released. From 1.6 to 16% of the added C14 was recovered as C14O2 during 3 months. After six months as much as 12 to 28 per cent was released as C14O2.Determination of C14 in the soil samples after the e...... not inhibit the CO2 production from the soil.Experiments with γ-sterilized soil indicated that enzymes present in the soil are able to attack DES.......The rate of decomposition of DES-monoethyl-1-C14 in soil was followed by measurement of C14O2 released. From 1.6 to 16% of the added C14 was recovered as C14O2 during 3 months. After six months as much as 12 to 28 per cent was released as C14O2.Determination of C14 in the soil samples after...
Live Replication of Paravirtual Machines
Stodden, Daniel
2009-01-01
Virtual machines offer a fair degree of system state encapsulation, which promotes practical advances in fault tolerance, system debugging, profiling and security applications. This work investigates deterministic replay and semi-active replication for system paravirtualization, a software discipline trading guest kernel binar compatibility for reduced dependency on costly trap-and-emulate techniques. A primary contribution is evidence that trace capturing under a piecewise deterministic exec...
Evaluation of Polarimetric SAR Decomposition for Classifying Wetland Vegetation Types
Directory of Open Access Journals (Sweden)
Sang-Hoon Hong
2015-07-01
Full Text Available The Florida Everglades is the largest subtropical wetland system in the United States and, as with subtropical and tropical wetlands elsewhere, has been threatened by severe environmental stresses. It is very important to monitor such wetlands to inform management on the status of these fragile ecosystems. This study aims to examine the applicability of TerraSAR-X quadruple polarimetric (quad-pol synthetic aperture radar (PolSAR data for classifying wetland vegetation in the Everglades. We processed quad-pol data using the Hong & Wdowinski four-component decomposition, which accounts for double bounce scattering in the cross-polarization signal. The calculated decomposition images consist of four scattering mechanisms (single, co- and cross-pol double, and volume scattering. We applied an object-oriented image analysis approach to classify vegetation types with the decomposition results. We also used a high-resolution multispectral optical RapidEye image to compare statistics and classification results with Synthetic Aperture Radar (SAR observations. The calculated classification accuracy was higher than 85%, suggesting that the TerraSAR-X quad-pol SAR signal had a high potential for distinguishing different vegetation types. Scattering components from SAR acquisition were particularly advantageous for classifying mangroves along tidal channels. We conclude that the typical scattering behaviors from model-based decomposition are useful for discriminating among different wetland vegetation types.
Asynchronous Task-Based Polar Decomposition on Manycore Architectures
Sukkari, Dalal
2016-10-25
This paper introduces the first asynchronous, task-based implementation of the polar decomposition on manycore architectures. Based on a new formulation of the iterative QR dynamically-weighted Halley algorithm (QDWH) for the calculation of the polar decomposition, the proposed implementation replaces the original and hostile LU factorization for the condition number estimator by the more adequate QR factorization to enable software portability across various architectures. Relying on fine-grained computations, the novel task-based implementation is also capable of taking advantage of the identity structure of the matrix involved during the QDWH iterations, which decreases the overall algorithmic complexity. Furthermore, the artifactual synchronization points have been severely weakened compared to previous implementations, unveiling look-ahead opportunities for better hardware occupancy. The overall QDWH-based polar decomposition can then be represented as a directed acyclic graph (DAG), where nodes represent computational tasks and edges define the inter-task data dependencies. The StarPU dynamic runtime system is employed to traverse the DAG, to track the various data dependencies and to asynchronously schedule the computational tasks on the underlying hardware resources, resulting in an out-of-order task scheduling. Benchmarking experiments show significant improvements against existing state-of-the-art high performance implementations (i.e., Intel MKL and Elemental) for the polar decomposition on latest shared-memory vendors\\' systems (i.e., Intel Haswell/Broadwell/Knights Landing, NVIDIA K80/P100 GPUs and IBM Power8), while maintaining high numerical accuracy.
High Performance Polar Decomposition on Distributed Memory Systems
Sukkari, Dalal E.
2016-08-08
The polar decomposition of a dense matrix is an important operation in linear algebra. It can be directly calculated through the singular value decomposition (SVD) or iteratively using the QR dynamically-weighted Halley algorithm (QDWH). The former is difficult to parallelize due to the preponderant number of memory-bound operations during the bidiagonal reduction. We investigate the latter scenario, which performs more floating-point operations but exposes at the same time more parallelism, and therefore, runs closer to the theoretical peak performance of the system, thanks to more compute-bound matrix operations. Profiling results show the performance scalability of QDWH for calculating the polar decomposition using around 9200 MPI processes on well and ill-conditioned matrices of 100K×100K problem size. We study then the performance impact of the QDWH-based polar decomposition as a pre-processing step toward calculating the SVD itself. The new distributed-memory implementation of the QDWH-SVD solver achieves up to five-fold speedup against current state-of-the-art vendor SVD implementations. © Springer International Publishing Switzerland 2016.
Dynamic mode decomposition for compressive system identification
Bai, Zhe; Kaiser, Eurika; Proctor, Joshua L.; Kutz, J. Nathan; Brunton, Steven L.
2017-11-01
Dynamic mode decomposition has emerged as a leading technique to identify spatiotemporal coherent structures from high-dimensional data. In this work, we integrate and unify two recent innovations that extend DMD to systems with actuation and systems with heavily subsampled measurements. When combined, these methods yield a novel framework for compressive system identification, where it is possible to identify a low-order model from limited input-output data and reconstruct the associated full-state dynamic modes with compressed sensing, providing interpretability of the state of the reduced-order model. When full-state data is available, it is possible to dramatically accelerate downstream computations by first compressing the data. We demonstrate this unified framework on simulated data of fluid flow past a pitching airfoil, investigating the effects of sensor noise, different types of measurements (e.g., point sensors, Gaussian random projections, etc.), compression ratios, and different choices of actuation (e.g., localized, broadband, etc.). This example provides a challenging and realistic test-case for the proposed method, and results indicate that the dominant coherent structures and dynamics are well characterized even with heavily subsampled data.
Multi-Label Classification by Semi-Supervised Singular Value Decomposition.
Jing, Liping; Shen, Chenyang; Yang, Liu; Yu, Jian; Ng, Michael K
2017-10-01
Multi-label problems arise in various domains, including automatic multimedia data categorization, and have generated significant interest in computer vision and machine learning community. However, existing methods do not adequately address two key challenges: exploiting correlations between labels and making up for the lack of labelled data or even missing labelled data. In this paper, we proposed to use a semi-supervised singular value decomposition (SVD) to handle these two challenges. The proposed model takes advantage of the nuclear norm regularization on the SVD to effectively capture the label correlations. Meanwhile, it introduces manifold regularization on mapping to capture the intrinsic structure among data, which provides a good way to reduce the required labelled data with improving the classification performance. Furthermore, we designed an efficient algorithm to solve the proposed model based on the alternating direction method of multipliers, and thus, it can efficiently deal with large-scale data sets. Experimental results for synthetic and real-world multimedia data sets demonstrate that the proposed method can exploit the label correlations and obtain promising and better label prediction results than the state-of-the-art methods.
Quantum machine learning for quantum anomaly detection
Liu, Nana; Rebentrost, Patrick
2018-04-01
Anomaly detection is used for identifying data that deviate from "normal" data patterns. Its usage on classical data finds diverse applications in many important areas such as finance, fraud detection, medical diagnoses, data cleaning, and surveillance. With the advent of quantum technologies, anomaly detection of quantum data, in the form of quantum states, may become an important component of quantum applications. Machine-learning algorithms are playing pivotal roles in anomaly detection using classical data. Two widely used algorithms are the kernel principal component analysis and the one-class support vector machine. We find corresponding quantum algorithms to detect anomalies in quantum states. We show that these two quantum algorithms can be performed using resources that are logarithmic in the dimensionality of quantum states. For pure quantum states, these resources can also be logarithmic in the number of quantum states used for training the machine-learning algorithm. This makes these algorithms potentially applicable to big quantum data applications.
High speed operation of permanent magnet machines
El-Refaie, Ayman M.
This work proposes methods to extend the high-speed operating capabilities of both the interior PM (IPM) and surface PM (SPM) machines. For interior PM machines, this research has developed and presented the first thorough analysis of how a new bi-state magnetic material can be usefully applied to the design of IPM machines. Key elements of this contribution include identifying how the unique properties of the bi-state magnetic material can be applied most effectively in the rotor design of an IPM machine by "unmagnetizing" the magnet cavity center posts rather than the outer bridges. The importance of elevated rotor speed in making the best use of the bi-state magnetic material while recognizing its limitations has been identified. For surface PM machines, this research has provided, for the first time, a clear explanation of how fractional-slot concentrated windings can be applied to SPM machines in order to achieve the necessary conditions for optimal flux weakening. A closed-form analytical procedure for analyzing SPM machines designed with concentrated windings has been developed. Guidelines for designing SPM machines using concentrated windings in order to achieve optimum flux weakening are provided. Analytical and numerical finite element analysis (FEA) results have provided promising evidence of the scalability of the concentrated winding technique with respect to the number of poles, machine aspect ratio, and output power rating. Useful comparisons between the predicted performance characteristics of SPM machines equipped with concentrated windings and both SPM and IPM machines designed with distributed windings are included. Analytical techniques have been used to evaluate the impact of the high pole number on various converter performance metrics. Both analytical techniques and FEA have been used for evaluating the eddy-current losses in the surface magnets due to the stator winding subharmonics. Techniques for reducing these losses have been
A stochastic model for an urea decomposition system
Directory of Open Access Journals (Sweden)
VSS Yadavalli
2005-12-01
Full Text Available Availability is an important measure in describing the performance of a system. The availability of a decomposition process in an urea production system in the fertilizer industry is considered in this paper. The system contains four subsystems and is supported by a standby unit. An estimation study of the steady state availability of the system is performed and illustrated by means of a numerical example.
Pattern recognition & machine learning
Anzai, Y
1992-01-01
This is the first text to provide a unified and self-contained introduction to visual pattern recognition and machine learning. It is useful as a general introduction to artifical intelligence and knowledge engineering, and no previous knowledge of pattern recognition or machine learning is necessary. Basic for various pattern recognition and machine learning methods. Translated from Japanese, the book also features chapter exercises, keywords, and summaries.
Support vector machines applications
Guo, Guodong
2014-01-01
Support vector machines (SVM) have both a solid mathematical background and good performance in practical applications. This book focuses on the recent advances and applications of the SVM in different areas, such as image processing, medical practice, computer vision, pattern recognition, machine learning, applied statistics, business intelligence, and artificial intelligence. The aim of this book is to create a comprehensive source on support vector machine applications, especially some recent advances.
International Nuclear Information System (INIS)
Seo, Yeong Seop; Choe, Byeong Do; Bang, Meong Sung
2005-08-01
This book gives descriptions of machine material with classification of machine material and selection of machine material, structure and connection of material, coagulation of metal and crystal structure, equilibrium diagram, properties of metal material, elasticity and plasticity, biopsy of metal, material test and nondestructive test. It also explains steel material such as heat treatment of steel, cast iron and cast steel, nonferrous metal materials, non metallic materials, and new materials.
Introduction to machine learning
Baştanlar, Yalın; Özuysal, Mustafa
2014-01-01
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning app...
Machinability of advanced materials
Davim, J Paulo
2014-01-01
Machinability of Advanced Materials addresses the level of difficulty involved in machining a material, or multiple materials, with the appropriate tooling and cutting parameters. A variety of factors determine a material's machinability, including tool life rate, cutting forces and power consumption, surface integrity, limiting rate of metal removal, and chip shape. These topics, among others, and multiple examples comprise this research resource for engineering students, academics, and practitioners.
2014-01-01
This book presents a collection of examples illustrating the resent research advances in the machining of titanium alloys. These materials have excellent strength and fracture toughness as well as low density and good corrosion resistance; however, machinability is still poor due to their low thermal conductivity and high chemical reactivity with cutting tool materials. This book presents solutions to enhance machinability in titanium-based alloys and serves as a useful reference to professionals and researchers in aerospace, automotive and biomedical fields.
Decomposition kinetics of plutonium hydride
Energy Technology Data Exchange (ETDEWEB)
Haschke, J.M.; Stakebake, J.L.
1979-01-01
Kinetic data for decomposition of PuH/sub 1/ /sub 95/ provides insight into a possible mechanism for the hydriding and dehydriding reactions of plutonium. The fact that the rate of the hydriding reaction, K/sub H/, is proportional to P/sup 1/2/ and the rate of the dehydriding process, K/sub D/, is inversely proportional to P/sup 1/2/ suggests that the forward and reverse reactions proceed by opposite paths of the same mechanism. The P/sup 1/2/ dependence of hydrogen solubility in metals is characteristic of the dissociative absorption of hydrogen; i.e., the reactive species is atomic hydrogen. It is reasonable to assume that the rates of the forward and reverse reactions are controlled by the surface concentration of atomic hydrogen, (H/sub s/), that K/sub H/ = c'(H/sub s/), and that K/sub D/ = c/(H/sub s/), where c' and c are proportionality constants. For this surface model, the pressure dependence of K/sub D/ is related to (H/sub s/) by the reaction (H/sub s/) reversible 1/2H/sub 2/(g) and by its equilibrium constant K/sub e/ = (H/sub 2/)/sup 1/2//(H/sub s/). In the pressure range of ideal gas behavior, (H/sub s/) = K/sub e//sup -1/(RT)/sup -1/2/ and the decomposition rate is given by K/sub D/ = cK/sub e/(RT)/sup -1/2/P/sup 1/2/. For an analogous treatment of the hydriding process with this model, it can be readily shown that K/sub H/ = c'K/sub e//sup -1/(RT)/sup -1/2/P/sup 1/2/. The inverse pressure dependence and direct temperature dependence of the decomposition rate are correctly predicted by this mechanism which is most consistent with the observed behavior of the Pu--H system.
Three-photon polarization ququarts: polarization, entanglement and Schmidt decompositions
International Nuclear Information System (INIS)
Fedorov, M V; Miklin, N I
2015-01-01
We consider polarization states of three photons, propagating collinearly and having equal given frequencies but with arbitrary distributed horizontal or vertical polarizations of photons. A general form of such states is a superposition of four basic three-photon polarization modes, to be referred to as the three-photon polarization ququarts (TPPQ). All such states can be considered as consisting of one- and two-photon parts, which can be entangled with each other. The degrees of entanglement and polarization, as well as the Schmidt decomposition and Stokes vectors of TPPQ are found and discussed. (paper)
Stolarski, Tadeusz
1999-01-01
""Tribology in Machine Design is strongly recommended for machine designers, and engineers and scientists interested in tribology. It should be in the engineering library of companies producing mechanical equipment.""Applied Mechanics ReviewTribology in Machine Design explains the role of tribology in the design of machine elements. It shows how algorithms developed from the basic principles of tribology can be used in a range of practical applications within mechanical devices and systems.The computer offers today's designer the possibility of greater stringen
Boldea, Ion
2002-01-01
Often called the workhorse of industry, the advent of power electronics and advances in digital control are transforming the induction motor into the racehorse of industrial motion control. Now, the classic texts on induction machines are nearly three decades old, while more recent books on electric motors lack the necessary depth and detail on induction machines.The Induction Machine Handbook fills industry's long-standing need for a comprehensive treatise embracing the many intricate facets of induction machine analysis and design. Moving gradually from simple to complex and from standard to
Suzuki, Hideyuki; Imura, Jun-ichi; Horio, Yoshihiko; Aihara, Kazuyuki
2013-01-01
The chaotic Boltzmann machine proposed in this paper is a chaotic pseudo-billiard system that works as a Boltzmann machine. Chaotic Boltzmann machines are shown numerically to have computing abilities comparable to conventional (stochastic) Boltzmann machines. Since no randomness is required, efficient hardware implementation is expected. Moreover, the ferromagnetic phase transition of the Ising model is shown to be characterised by the largest Lyapunov exponent of the proposed system. In general, a method to relate probabilistic models to nonlinear dynamics by derandomising Gibbs sampling is presented. PMID:23558425
Hammond, P
1985-01-01
Containing approximately 200 problems (100 worked), the text covers a wide range of topics concerning electrical machines, placing particular emphasis upon electrical-machine drive applications. The theory is concisely reviewed and focuses on features common to all machine types. The problems are arranged in order of increasing levels of complexity and discussions of the solutions are included where appropriate to illustrate the engineering implications. This second edition includes an important new chapter on mathematical and computer simulation of machine systems and revised discussions o
Nanocomposites for Machining Tools
Directory of Open Access Journals (Sweden)
Daria Sidorenko
2017-10-01
Full Text Available Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials. A promising way to improve the performance characteristics of these materials is to design new nanocomposites based on them. The application of micromechanical modeling during the elaboration of composite materials for machining tools can reduce the financial and time costs for development of new tools, with enhanced performance. This article reviews the main groups of nanocomposites for machining tools and their performance.
Machine listening intelligence
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.
Lantz, Brett
2013-01-01
Written as a tutorial to explore and understand the power of R for machine learning. This practical guide that covers all of the need to know topics in a very systematic way. For each machine learning approach, each step in the process is detailed, from preparing the data for analysis to evaluating the results. These steps will build the knowledge you need to apply them to your own data science tasks.Intended for those who want to learn how to use R's machine learning capabilities and gain insight from your data. Perhaps you already know a bit about machine learning, but have never used R; or
Probabilistic machine learning and artificial intelligence.
Ghahramani, Zoubin
2015-05-28
How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.
Probabilistic machine learning and artificial intelligence
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.
Measurements for stresses in machine components
Yakovlev, V F
1964-01-01
Measurements for Stresses in Machine Components focuses on the state of stress and strain of components and members, which determines the service life and strength of machines and structures. This book is divided into four chapters. Chapter I describes the physical basis of several methods of measuring strains, which includes strain gauges, photoelasticity, X-ray diffraction, brittle coatings, and dividing grids. The basic concepts of the electric strain gauge method for measuring stresses inside machine components are covered in Chapter II. Chapter III elaborates on the results of experim
Preliminary assessment of the possibility of supporting the decomposition of biodegradable packaging
Directory of Open Access Journals (Sweden)
Niekraś Lidia
2017-01-01
Full Text Available This article presents a preliminary evaluation of the possibility of using grass biomass from a sports field as a compost ingredient which positively affects the degree of decomposition of the biodegradable wrappings. For 5 months the biodegradable bags were stored, both empty and filled with organic waste in the heap of grass clippings. After that period, fragments of the bags were observed under the microscope and then assessed the state of their decomposition. The results indicate that the biomass used favourably affected the process of bag degradation, however the speed of decomposition of the empty bags was quicker than the bags filled with the organic waste.
Are there intelligent Turing machines?
Bátfai, Norbert
2015-01-01
This paper introduces a new computing model based on the cooperation among Turing machines called orchestrated machines. Like universal Turing machines, orchestrated machines are also designed to simulate Turing machines but they can also modify the original operation of the included Turing machines to create a new layer of some kind of collective behavior. Using this new model we can define some interested notions related to cooperation ability of Turing machines such as the intelligence quo...
Spinodal decomposition in fluid mixtures
International Nuclear Information System (INIS)
Kawasaki, Kyozi; Koga, Tsuyoshi
1993-01-01
We study the late stage dynamics of spinodal decomposition in binary fluids by the computer simulation of the time-dependent Ginzburg-Landau equation. We obtain a temporary linear growth law of the characteristic length of domains in the late stage. This growth law has been observed in many real experiments of binary fluids and indicates that the domain growth proceeds by the flow caused by the surface tension of interfaces. We also find that the dynamical scaling law is satisfied in this hydrodynamic domain growth region. By comparing the scaling functions for fluids with that for the case without hydrodynamic effects, we find that the scaling functions for the two systems are different. (author)
Early stage litter decomposition across biomes
Ika Djukic; Sebastian Kepfer-Rojas; Inger Kappel Schmidt; Klaus Steenberg Larsen; Claus Beier; Björn Berg; Kris Verheyen; Adriano Caliman; Alain Paquette; Alba Gutiérrez-Girón; Alberto Humber; Alejandro Valdecantos; Alessandro Petraglia; Heather Alexander; Algirdas Augustaitis; Amélie Saillard; Ana Carolina Ruiz Fernández; Ana I. Sousa; Ana I. Lillebø; Anderson da Rocha Gripp; André-Jean Francez; Andrea Fischer; Andreas Bohner; Andrey Malyshev; Andrijana Andrić; Andy Smith; Angela Stanisci; Anikó Seres; Anja Schmidt; Anna Avila; Anne Probst; Annie Ouin; Anzar A. Khuroo; Arne Verstraeten; Arely N. Palabral-Aguilera; Artur Stefanski; Aurora Gaxiola; Bart Muys; Bernard Bosman; Bernd Ahrends; Bill Parker; Birgit Sattler; Bo Yang; Bohdan Juráni; Brigitta Erschbamer; Carmen Eugenia Rodriguez Ortiz; Casper T. Christiansen; E. Carol Adair; Céline Meredieu; Cendrine Mony; Charles A. Nock; Chi-Ling Chen; Chiao-Ping Wang; Christel Baum; Christian Rixen; Christine Delire; Christophe Piscart; Christopher Andrews; Corinna Rebmann; Cristina Branquinho; Dana Polyanskaya; David Fuentes Delgado; Dirk Wundram; Diyaa Radeideh; Eduardo Ordóñez-Regil; Edward Crawford; Elena Preda; Elena Tropina; Elli Groner; Eric Lucot; Erzsébet Hornung; Esperança Gacia; Esther Lévesque; Evanilde Benedito; Evgeny A. Davydov; Evy Ampoorter; Fabio Padilha Bolzan; Felipe Varela; Ferdinand Kristöfel; Fernando T. Maestre; Florence Maunoury-Danger; Florian Hofhansl; Florian Kitz; Flurin Sutter; Francisco Cuesta; Francisco de Almeida Lobo; Franco Leandro de Souza; Frank Berninger; Franz Zehetner; Georg Wohlfahrt; George Vourlitis; Geovana Carreño-Rocabado; Gina Arena; Gisele Daiane Pinha; Grizelle González; Guylaine Canut; Hanna Lee; Hans Verbeeck; Harald Auge; Harald Pauli; Hassan Bismarck Nacro; Héctor A. Bahamonde; Heike Feldhaar; Heinke Jäger; Helena C. Serrano; Hélène Verheyden; Helge Bruelheide; Henning Meesenburg; Hermann Jungkunst; Hervé Jactel; Hideaki Shibata; Hiroko Kurokawa; Hugo López Rosas; Hugo L. Rojas Villalobos; Ian Yesilonis; Inara Melece; Inge Van Halder; Inmaculada García Quirós; Isaac Makelele; Issaka Senou; István Fekete; Ivan Mihal; Ivika Ostonen; Jana Borovská; Javier Roales; Jawad Shoqeir; Jean-Christophe Lata; Jean-Paul Theurillat; Jean-Luc Probst; Jess Zimmerman; Jeyanny Vijayanathan; Jianwu Tang; Jill Thompson; Jiří Doležal; Joan-Albert Sanchez-Cabeza; Joël Merlet; Joh Henschel; Johan Neirynck; Johannes Knops; John Loehr; Jonathan von Oppen; Jónína Sigríður Þorláksdóttir; Jörg Löffler; José-Gilberto Cardoso-Mohedano; José-Luis Benito-Alonso; Jose Marcelo Torezan; Joseph C. Morina; Juan J. Jiménez; Juan Dario Quinde; Juha Alatalo; Julia Seeber; Jutta Stadler; Kaie Kriiska; Kalifa Coulibaly; Karibu Fukuzawa; Katalin Szlavecz; Katarína Gerhátová; Kate Lajtha; Kathrin Käppeler; Katie A. Jennings; Katja Tielbörger; Kazuhiko Hoshizaki; Ken Green; Lambiénou Yé; Laryssa Helena Ribeiro Pazianoto; Laura Dienstbach; Laura Williams; Laura Yahdjian; Laurel M. Brigham; Liesbeth van den Brink; Lindsey Rustad; al. et
2018-01-01
Through litter decomposition enormous amounts of carbon is emitted to the atmosphere. Numerous large-scale decomposition experiments have been conducted focusing on this fundamental soil process in order to understand the controls on the terrestrial carbon transfer to the atmosphere. However, previous studies were mostly based on site-specific litter and methodologies...
Climate history shapes contemporary leaf litter decomposition
Michael S. Strickland; Ashley D. Keiser; Mark A. Bradford
2015-01-01
Litter decomposition is mediated by multiple variables, of which climate is expected to be a dominant factor at global scales. However, like other organisms, traits of decomposers and their communities are shaped not just by the contemporary climate but also their climate history. Whether or not this affects decomposition rates is underexplored. Here we source...
The decomposition of estuarine macrophytes under different ...
African Journals Online (AJOL)
The aim of this study was to determine the decomposition characteristics of the most dominant submerged macrophyte and macroalgal species in the Great Brak Estuary. Laboratory experiments were conducted to determine the effect of different temperature regimes on the rate of decomposition of 3 macrophyte species ...
Decomposition and flame structure of hydrazinium nitroformate
Louwers, J.; Parr, T.; Hanson-Parr, D.
1999-01-01
The decomposition of hydrazinium nitroformate (HNF) was studied in a hot quartz cell and by dropping small amounts of HNF on a hot plate. The species formed during the decomposition were identified by ultraviolet-visible absorption experiments. These experiments reveal that first HONO is formed. The
Multilevel index decomposition analysis: Approaches and application
International Nuclear Information System (INIS)
Xu, X.Y.; Ang, B.W.
2014-01-01
With the growing interest in using the technique of index decomposition analysis (IDA) in energy and energy-related emission studies, such as to analyze the impacts of activity structure change or to track economy-wide energy efficiency trends, the conventional single-level IDA may not be able to meet certain needs in policy analysis. In this paper, some limitations of single-level IDA studies which can be addressed through applying multilevel decomposition analysis are discussed. We then introduce and compare two multilevel decomposition procedures, which are referred to as the multilevel-parallel (M-P) model and the multilevel-hierarchical (M-H) model. The former uses a similar decomposition procedure as in the single-level IDA, while the latter uses a stepwise decomposition procedure. Since the stepwise decomposition procedure is new in the IDA literature, the applicability of the popular IDA methods in the M-H model is discussed and cases where modifications are needed are explained. Numerical examples and application studies using the energy consumption data of the US and China are presented. - Highlights: • We discuss the limitations of single-level decomposition in IDA applied to energy study. • We introduce two multilevel decomposition models, study their features and discuss how they can address the limitations. • To extend from single-level to multilevel analysis, necessary modifications to some popular IDA methods are discussed. • We further discuss the practical significance of the multilevel models and present examples and cases to illustrate
Scikit-learn: Machine Learning in Python
Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu; Perrot, Matthieu
2011-01-01
International audience; Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic ...
Scikit-learn: Machine Learning in Python
Pedregosa, Fabian; Varoquaux, Gaël; Gramfort, Alexandre; Michel, Vincent; Thirion, Bertrand; Grisel, Olivier; Blondel, Mathieu; Louppe, Gilles; Prettenhofer, Peter; Weiss, Ron; Dubourg, Vincent; Vanderplas, Jake; Passos, Alexandre; Cournapeau, David; Brucher, Matthieu
2012-01-01
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings....
Interacting effects of insects and flooding on wood decomposition.
Directory of Open Access Journals (Sweden)
Michael D Ulyshen
Full Text Available Saproxylic arthropods are thought to play an important role in wood decomposition but very few efforts have been made to quantify their contributions to the process and the factors controlling their activities are not well understood. In the current study, mesh exclusion bags were used to quantify how arthropods affect loblolly pine (Pinus taeda L. decomposition rates in both seasonally flooded and unflooded forests over a 31-month period in the southeastern United States. Wood specific gravity (based on initial wood volume was significantly lower in bolts placed in unflooded forests and for those unprotected from insects. Approximately 20.5% and 13.7% of specific gravity loss after 31 months was attributable to insect activity in flooded and unflooded forests, respectively. Importantly, minimal between-treatment differences in water content and the results from a novel test carried out separately suggest the mesh bags had no significant impact on wood mass loss beyond the exclusion of insects. Subterranean termites (Isoptera: Rhinotermitidae: Reticulitermes spp. were 5-6 times more active below-ground in unflooded forests compared to flooded forests based on wooden monitoring stakes. They were also slightly more active above-ground in unflooded forests but these differences were not statistically significant. Similarly, seasonal flooding had no detectable effect on above-ground beetle (Coleoptera richness or abundance. Although seasonal flooding strongly reduced Reticulitermes activity below-ground, it can be concluded from an insignificant interaction between forest type and exclusion treatment that reduced above-ground decomposition rates in seasonally flooded forests were due largely to suppressed microbial activity at those locations. The findings from this study indicate that southeastern U.S. arthropod communities accelerate above-ground wood decomposition significantly and to a similar extent in both flooded and unflooded forests
Monge Palacios, Manuel
2018-01-22
Reactions of hydroxyl (OH) and hydroperoxyl (HO2) are important for governing the reactivity of combustion systems. We performed post-CCSD(T) ab initio calculations at the W3X-L//CCSD = FC/cc-pVTZ level to explore the triplet ground-state and singlet excited-state potential energy surfaces of the OH + HO2 → H2O + O2(3Σg−)/O2(1Δg) reactions. Using microcanonical and multistructural canonical transition state theories, we calculated the rate constant for the triplet and singlet channels over the temperature range 200–2500 K, represented by k(T) = 3.08 × 1012T0.07 exp(1151/RT) + 8.00 × 1012T0.32 exp(−6896/RT) and k(T) = 2.14 × 106T1.65 exp(−2180/RT) in cm3 mol−1 s−1, respectively. The branching ratios show that the yield of singlet excited oxygen is small (<0.5% below 1000 K). To ascertain the importance of singlet oxygen channel, our new kinetic information was implemented into the kinetic model for hydrogen combustion recently updated by Konnov (Combust. Flame, 2015, 162, 3755–3772). The updated kinetic model was used to perform H2O2 thermal decomposition simulations for comparison against shock tube experiments performed by Hong et al. (Proc. Combust. Inst., 2013, 34, 565–571), and to estimate flame speeds and ignition delay times in H2 mixtures. The simulation predicted a larger amount of O2(1Δg) in H2O2 decomposition than that predicted by Konnov\\'s original model. These differences in the O2(1Δg) yield are due to the use of a higher ab initio level and a more sophisticated methodology to compute the rate constant than those used in previous studies, thereby predicting a significantly larger rate constant. No effect was observed on the rate of the H2O2 decomposition and on the flame speeds and ignition delay times of different H2–oxidizer mixtures. However, if the oxidizer is seeded with O3, small differences appear in the flame speed. Given that O2(1Δg) is much more reactive than O2(3Σg−), we do not preclude an effect of the
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.
In situ study of glasses decomposition layer
International Nuclear Information System (INIS)
Zarembowitch-Deruelle, O.
1997-01-01
The aim of this work is to understand the involved mechanisms during the decomposition of glasses by water and the consequences on the morphology of the decomposition layer, in particular in the case of a nuclear glass: the R 7 T 7 . The chemical composition of this glass being very complicated, it is difficult to know the influence of the different elements on the decomposition kinetics and on the resulting morphology because several atoms have a same behaviour. Glasses with simplified composition (only 5 elements) have then been synthesized. The morphological and structural characteristics of these glasses have been given. They have then been decomposed by water. The leaching curves do not reflect the decomposition kinetics but the solubility of the different elements at every moment. The three steps of the leaching are: 1) de-alkalinization 2) lattice rearrangement 3) heavy elements solubilization. Two decomposition layer types have also been revealed according to the glass heavy elements rate. (O.M.)
Multilinear operators for higher-order decompositions.
Energy Technology Data Exchange (ETDEWEB)
Kolda, Tamara Gibson
2006-04-01
We propose two new multilinear operators for expressing the matrix compositions that are needed in the Tucker and PARAFAC (CANDECOMP) decompositions. The first operator, which we call the Tucker operator, is shorthand for performing an n-mode matrix multiplication for every mode of a given tensor and can be employed to concisely express the Tucker decomposition. The second operator, which we call the Kruskal operator, is shorthand for the sum of the outer-products of the columns of N matrices and allows a divorce from a matricized representation and a very concise expression of the PARAFAC decomposition. We explore the properties of the Tucker and Kruskal operators independently of the related decompositions. Additionally, we provide a review of the matrix and tensor operations that are frequently used in the context of tensor decompositions.
Improved Wind Speed Prediction Using Empirical Mode Decomposition
Directory of Open Access Journals (Sweden)
ZHANG, Y.
2018-05-01
Full Text Available Wind power industry plays an important role in promoting the development of low-carbon economic and energy transformation in the world. However, the randomness and volatility of wind speed series restrict the healthy development of the wind power industry. Accurate wind speed prediction is the key to realize the stability of wind power integration and to guarantee the safe operation of the power system. In this paper, combined with the Empirical Mode Decomposition (EMD, the Radial Basis Function Neural Network (RBF and the Least Square Support Vector Machine (SVM, an improved wind speed prediction model based on Empirical Mode Decomposition (EMD-RBF-LS-SVM is proposed. The prediction result indicates that compared with the traditional prediction model (RBF, LS-SVM, the EMD-RBF-LS-SVM model can weaken the random fluctuation to a certain extent and improve the short-term accuracy of wind speed prediction significantly. In a word, this research will significantly reduce the impact of wind power instability on the power grid, ensure the power grid supply and demand balance, reduce the operating costs in the grid-connected systems, and enhance the market competitiveness of the wind power.
Management intensity alters decomposition via biological pathways
Wickings, Kyle; Grandy, A. Stuart; Reed, Sasha; Cleveland, Cory
2011-01-01
Current conceptual models predict that changes in plant litter chemistry during decomposition are primarily regulated by both initial litter chemistry and the stage-or extent-of mass loss. Far less is known about how variations in decomposer community structure (e.g., resulting from different ecosystem management types) could influence litter chemistry during decomposition. Given the recent agricultural intensification occurring globally and the importance of litter chemistry in regulating soil organic matter storage, our objectives were to determine the potential effects of agricultural management on plant litter chemistry and decomposition rates, and to investigate possible links between ecosystem management, litter chemistry and decomposition, and decomposer community composition and activity. We measured decomposition rates, changes in litter chemistry, extracellular enzyme activity, microarthropod communities, and bacterial versus fungal relative abundance in replicated conventional-till, no-till, and old field agricultural sites for both corn and grass litter. After one growing season, litter decomposition under conventional-till was 20% greater than in old field communities. However, decomposition rates in no-till were not significantly different from those in old field or conventional-till sites. After decomposition, grass residue in both conventional- and no-till systems was enriched in total polysaccharides relative to initial litter, while grass litter decomposed in old fields was enriched in nitrogen-bearing compounds and lipids. These differences corresponded with differences in decomposer communities, which also exhibited strong responses to both litter and management type. Overall, our results indicate that agricultural intensification can increase litter decomposition rates, alter decomposer communities, and influence litter chemistry in ways that could have important and long-term effects on soil organic matter dynamics. We suggest that future
Preliminary assessment of the possibility of supporting the decomposition of biodegradable packaging
Niekraś Lidia; Moliszewska Ewa
2017-01-01
This article presents a preliminary evaluation of the possibility of using grass biomass from a sports field as a compost ingredient which positively affects the degree of decomposition of the biodegradable wrappings. For 5 months the biodegradable bags were stored, both empty and filled with organic waste in the heap of grass clippings. After that period, fragments of the bags were observed under the microscope and then assessed the state of their decomposition. The results indicate that the...
Microsoft Azure machine learning
Mund, Sumit
2015-01-01
The book is intended for those who want to learn how to use Azure Machine Learning. Perhaps you already know a bit about Machine Learning, but have never used ML Studio in Azure; or perhaps you are an absolute newbie. In either case, this book will get you up-and-running quickly.
Scarnati, James T.; Tice, Craig J.
1992-01-01
Describes how students can make and use Hooey Machines to learn how mechanical energy can be transferred from one object to another within a system. The Hooey Machine is made using a pencil, eight thumbtacks, one pushpin, tape, scissors, graph paper, and a plastic lid. (PR)
Nanocomposites for Machining Tools
DEFF Research Database (Denmark)
Sidorenko, Daria; Loginov, Pavel; Mishnaevsky, Leon
2017-01-01
Machining tools are used in many areas of production. To a considerable extent, the performance characteristics of the tools determine the quality and cost of obtained products. The main materials used for producing machining tools are steel, cemented carbides, ceramics and superhard materials...
International Nuclear Information System (INIS)
Anon.
1978-01-01
The design and operation of a nucleonic weighing machine fabricated for continuous weighing of material over conveyor belt are described. The machine uses a 40 mCi cesium-137 line source and a 10 litre capacity ionization chamber. It is easy to maintain as there are no moving parts. It can also be easily removed and reinstalled. (M.G.B.)
Cristallini, Achille
2016-07-01
A new and intriguing machine may be obtained replacing the moving pulley of a gun tackle with a fixed point in the rope. Its most important feature is the asymptotic efficiency. Here we obtain a satisfactory description of this machine by means of vector calculus and elementary trigonometry. The mathematical model has been compared with experimental data and briefly discussed.
Lantz, Brett
2015-01-01
Perhaps you already know a bit about machine learning but have never used R, or perhaps you know a little R but are new to machine learning. In either case, this book will get you up and running quickly. It would be helpful to have a bit of familiarity with basic programming concepts, but no prior experience is required.
The deleuzian abstract machines
DEFF Research Database (Denmark)
Werner Petersen, Erik
2005-01-01
To most people the concept of abstract machines is connected to the name of Alan Turing and the development of the modern computer. The Turing machine is universal, axiomatic and symbolic (E.g. operating on symbols). Inspired by Foucault, Deleuze and Guattari extended the concept of abstract...
Reliability analysis in intelligent machines
Mcinroy, John E.; Saridis, George N.
1990-01-01
Given an explicit task to be executed, an intelligent machine must be able to find the probability of success, or reliability, of alternative control and sensing strategies. By using concepts for information theory and reliability theory, new techniques for finding the reliability corresponding to alternative subsets of control and sensing strategies are proposed such that a desired set of specifications can be satisfied. The analysis is straightforward, provided that a set of Gaussian random state variables is available. An example problem illustrates the technique, and general reliability results are presented for visual servoing with a computed torque-control algorithm. Moreover, the example illustrates the principle of increasing precision with decreasing intelligence at the execution level of an intelligent machine.
Li, Zhen; P?rez-Osorio, Ailyn; Wang, Yu; Eckmann, Kaye; Glover, William A.; Allard, Marc W.; Brown, Eric W.; Chen, Yi
2017-01-01
Background In 2015, in addition to a United States multistate outbreak linked to contaminated ice cream, another outbreak linked to ice cream was reported in the Pacific Northwest of the United States. It was a hospital-acquired outbreak linked to milkshakes, made from contaminated ice cream mixes and milkshake maker, served to patients. Here we performed multiple analyses on isolates associated with this outbreak: pulsed-field gel electrophoresis (PFGE), whole genome single nucleotide polymo...
Salehi, Hadi; Das, Saptarshi; Chakrabartty, Shantanu; Biswas, Subir; Burgueño, Rigoberto
2017-04-01
This study proposes a novel strategy for damage identification in aircraft structures. The strategy was evaluated based on the simulation of the binary data generated from self-powered wireless sensors employing a pulse switching architecture. The energy-aware pulse switching communication protocol uses single pulses instead of multi-bit packets for information delivery resulting in discrete binary data. A system employing this energy-efficient technology requires dealing with time-delayed binary data due to the management of power budgets for sensing and communication. This paper presents an intelligent machine-learning framework based on combination of the low-rank matrix decomposition and pattern recognition (PR) methods. Further, data fusion is employed as part of the machine-learning framework to take into account the effect of data time delay on its interpretation. Simulated time-delayed binary data from self-powered sensors was used to determine damage indicator variables. Performance and accuracy of the damage detection strategy was examined and tested for the case of an aircraft horizontal stabilizer. Damage states were simulated on a finite element model by reducing stiffness in a region of the stabilizer's skin. The proposed strategy shows satisfactory performance to identify the presence and location of the damage, even with noisy and incomplete data. It is concluded that PR is a promising machine-learning algorithm for damage detection for time-delayed binary data from novel self-powered wireless sensors.
Precision machining commercialization
International Nuclear Information System (INIS)
1978-01-01
To accelerate precision machining development so as to realize more of the potential savings within the next few years of known Department of Defense (DOD) part procurement, the Air Force Materials Laboratory (AFML) is sponsoring the Precision Machining Commercialization Project (PMC). PMC is part of the Tri-Service Precision Machine Tool Program of the DOD Manufacturing Technology Five-Year Plan. The technical resources supporting PMC are provided under sponsorship of the Department of Energy (DOE). The goal of PMC is to minimize precision machining development time and cost risk for interested vendors. PMC will do this by making available the high precision machining technology as developed in two DOE contractor facilities, the Lawrence Livermore Laboratory of the University of California and the Union Carbide Corporation, Nuclear Division, Y-12 Plant, at Oak Ridge, Tennessee
Introduction to machine learning.
Baştanlar, Yalin; Ozuysal, Mustafa
2014-01-01
The machine learning field, which can be briefly defined as enabling computers make successful predictions using past experiences, has exhibited an impressive development recently with the help of the rapid increase in the storage capacity and processing power of computers. Together with many other disciplines, machine learning methods have been widely employed in bioinformatics. The difficulties and cost of biological analyses have led to the development of sophisticated machine learning approaches for this application area. In this chapter, we first review the fundamental concepts of machine learning such as feature assessment, unsupervised versus supervised learning and types of classification. Then, we point out the main issues of designing machine learning experiments and their performance evaluation. Finally, we introduce some supervised learning methods.
LHC Report: machine development
Rogelio Tomás García for the LHC team
2015-01-01
Machine development weeks are carefully planned in the LHC operation schedule to optimise and further study the performance of the machine. The first machine development session of Run 2 ended on Saturday, 25 July. Despite various hiccoughs, it allowed the operators to make great strides towards improving the long-term performance of the LHC. The main goals of this first machine development (MD) week were to determine the minimum beam-spot size at the interaction points given existing optics and collimation constraints; to test new beam instrumentation; to evaluate the effectiveness of performing part of the beam-squeezing process during the energy ramp; and to explore the limits on the number of protons per bunch arising from the electromagnetic interactions with the accelerator environment and the other beam. Unfortunately, a series of events reduced the machine availability for studies to about 50%. The most critical issue was the recurrent trip of a sextupolar corrector circuit –...
22 CFR 121.10 - Forgings, castings and machined bodies.
2010-04-01
... 22 Foreign Relations 1 2010-04-01 2010-04-01 false Forgings, castings and machined bodies. 121.10... STATES MUNITIONS LIST Enumeration of Articles § 121.10 Forgings, castings and machined bodies. Articles on the U.S. Munitions List include articles in a partially completed state (such as forgings...
Patched bimetallic surfaces are active catalysts for ammonia decomposition.
Guo, Wei; Vlachos, Dionisios G
2015-10-07
Ammonia decomposition is often used as an archetypical reaction for predicting new catalytic materials and understanding the very reason of why some reactions are sensitive on material's structure. Core-shell or surface-segregated bimetallic nanoparticles expose outstanding activity for many heterogeneously catalysed reactions but the reasons remain elusive owing to the difficulties in experimentally characterizing active sites. Here by performing multiscale simulations in ammonia decomposition on various nickel loadings on platinum (111), we show that the very high activity of core-shell structures requires patches of the guest metal to create and sustain dual active sites: nickel terraces catalyse N-H bond breaking and nickel edge sites drive atomic nitrogen association. The structure sensitivity on these active catalysts depends profoundly on reaction conditions due to kinetically competing relevant elementary reaction steps. We expose a remarkable difference in active sites between transient and steady-state studies and provide insights into optimal material design.
A Longitudinal Study on Human Outdoor Decomposition in Central Texas.
Suckling, Joanna K; Spradley, M Katherine; Godde, Kanya
2016-01-01
The development of a methodology that estimates the postmortem interval (PMI) from stages of decomposition is a goal for which forensic practitioners strive. A proposed equation (Megyesi et al. 2005) that utilizes total body score (TBS) and accumulated degree days (ADD) was tested using longitudinal data collected from human remains donated to the Forensic Anthropology Research Facility (FARF) at Texas State University-San Marcos. Exact binomial tests examined the rate of the equation to successfully predict ADD. Statistically significant differences were found between ADD estimated by the equation and the observed value for decomposition stage. Differences remained significant after carnivore scavenged donations were removed from analysis. Low success rates for the equation to predict ADD from TBS and the wide standard errors demonstrate the need to re-evaluate the use of this equation and methodology for PMI estimation in different environments; rather, multivariate methods and equations should be derived that are environmentally specific. © 2015 American Academy of Forensic Sciences.
LMDI decomposition approach: A guide for implementation
International Nuclear Information System (INIS)
Ang, B.W.
2015-01-01
Since it was first used by researchers to analyze industrial electricity consumption in the early 1980s, index decomposition analysis (IDA) has been widely adopted in energy and emission studies. Lately its use as the analytical component of accounting frameworks for tracking economy-wide energy efficiency trends has attracted considerable attention and interest among policy makers. The last comprehensive literature review of IDA was reported in 2000 which is some years back. After giving an update and presenting the key trends in the last 15 years, this study focuses on the implementation issues of the logarithmic mean Divisia index (LMDI) decomposition methods in view of their dominance in IDA in recent years. Eight LMDI models are presented and their origin, decomposition formulae, and strengths and weaknesses are summarized. Guidelines on the choice among these models are provided to assist users in implementation. - Highlights: • Guidelines for implementing LMDI decomposition approach are provided. • Eight LMDI decomposition models are summarized and compared. • The development of the LMDI decomposition approach is presented. • The latest developments of index decomposition analysis are briefly reviewed.
Thermal decomposition of beryllium perchlorate tetrahydrate
International Nuclear Information System (INIS)
Berezkina, L.G.; Borisova, S.I.; Tamm, N.S.; Novoselova, A.V.
1975-01-01
Thermal decomposition of Be(ClO 4 ) 2 x4H 2 O was studied by the differential flow technique in the helium stream. The kinetics was followed by an exchange reaction of the perchloric acid appearing by the decomposition with potassium carbonate. The rate of CO 2 liberation in this process was recorded by a heat conductivity detector. The exchange reaction yielding CO 2 is quantitative, it is not the limiting one and it does not distort the kinetics of the process of perchlorate decomposition. The solid products of decomposition were studied by infrared and NMR spectroscopy, roentgenography, thermography and chemical analysis. A mechanism suggested for the decomposition involves intermediate formation of hydroxyperchlorate: Be(ClO 4 ) 2 x4H 2 O → Be(OH)ClO 4 +HClO 4 +3H 2 O; Be(OH)ClO 4 → BeO+HClO 4 . Decomposition is accompained by melting of the sample. The mechanism of decomposition is hydrolytic. At room temperature the hydroxyperchlorate is a thick syrup-like compound crystallizing after long storing
Machine learning properties of materials and molecules with entropy-regularized kernels
Ceriotti, Michele; Bartók, Albert; CsáNyi, GáBor; de, Sandip
Application of machine-learning methods to physics, chemistry and materials science is gaining traction as a strategy to obtain accurate predictions of the properties of matter at a fraction of the typical cost of quantum mechanical electronic structure calculations. In this endeavor, one can leverage general-purpose frameworks for supervised-learning. It is however very important that the input data - for instance the positions of atoms in a molecule or solid - is processed into a form that reflects all the underlying physical symmetries of the problem, and that possesses the regularity properties that are required by machine-learning algorithms. Here we introduce a general strategy to build a representation of this kind. We will start from existing approaches to compare local environments (basically, groups of atoms), and combine them using techniques borrowed from optimal transport theory, discussing the relation between this idea and additive energy decompositions. We will present a few examples demonstrating the potential of this approach as a tool to predict molecular and materials' properties with an accuracy on par with state-of-the-art electronic structure methods. MARVEL NCCR (Swiss National Science Foundation) and ERC StG HBMAP (European Research Council, G.A. 677013).
Machine Learning and Radiology
Wang, Shijun; Summers, Ronald M.
2012-01-01
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. PMID:22465077
Machine learning and radiology.
Wang, Shijun; Summers, Ronald M
2012-07-01
In this paper, we give a short introduction to machine learning and survey its applications in radiology. We focused on six categories of applications in radiology: medical image segmentation, registration, computer aided detection and diagnosis, brain function or activity analysis and neurological disease diagnosis from fMR images, content-based image retrieval systems for CT or MRI images, and text analysis of radiology reports using natural language processing (NLP) and natural language understanding (NLU). This survey shows that machine learning plays a key role in many radiology applications. Machine learning identifies complex patterns automatically and helps radiologists make intelligent decisions on radiology data such as conventional radiographs, CT, MRI, and PET images and radiology reports. In many applications, the performance of machine learning-based automatic detection and diagnosis systems has shown to be comparable to that of a well-trained and experienced radiologist. Technology development in machine learning and radiology will benefit from each other in the long run. Key contributions and common characteristics of machine learning techniques in radiology are discussed. We also discuss the problem of translating machine learning applications to the radiology clinical setting, including advantages and potential barriers. Copyright © 2012. Published by Elsevier B.V.
Challenges for coexistence of machine to machine and human to human applications in mobile network
DEFF Research Database (Denmark)
Sanyal, R.; Cianca, E.; Prasad, Ramjee
2012-01-01
A key factor for the evolution of the mobile networks towards 4G is to bring to fruition high bandwidth per mobile node. Eventually, due to the advent of a new class of applications, namely, Machine-to-Machine, we foresee new challenges where bandwidth per user is no more the primal driver...... be evolved to address various nuances of the mobile devices used by man and machines. The bigger question is as follows. Is the state-of-the-art mobile network designed optimally to cater both the Human-to-Human and Machine-to-Machine applications? This paper presents the primary challenges....... As an immediate impact of the high penetration of M2M devices, we envisage a surge in the signaling messages for mobility and location management. The cell size will shrivel due to high tele-density resulting in even more signaling messages related to handoff and location updates. The mobile network should...
Thermal decomposition of lanthanide and actinide tetrafluorides
International Nuclear Information System (INIS)
Gibson, J.K.; Haire, R.G.
1988-01-01
The thermal stabilities of several lanthanide/actinide tetrafluorides have been studied using mass spectrometry to monitor the gaseous decomposition products, and powder X-ray diffraction (XRD) to identify solid products. The tetrafluorides, TbF 4 , CmF 4 , and AmF 4 , have been found to thermally decompose to their respective solid trifluorides with accompanying release of fluorine, while cerium tetrafluoride has been found to be significantly more thermally stable and to congruently sublime as CeF 4 prior to appreciable decomposition. The results of these studies are discussed in relation to other relevant experimental studies and the thermodynamics of the decomposition processes. 9 refs., 3 figs
Decomposition of lake phytoplankton. 1
International Nuclear Information System (INIS)
Hansen, L.; Krog, G.F.; Soendergaard, M.
1986-01-01
Short-time (24 h) and long-time (4-6 d) decomposition of phytoplankton cells were investigasted under in situ conditions in four Danish lakes. Carbon-14-labelled, dead algae were exposed to sterile or natural lake water and the dynamics of cell lysis and bacterial utilization of the leached products were followed. The lysis process was dominated by an initial fast water extraction. Within 2 to 4 h from 4 to 34% of the labelled carbon leached from the algal cells. After 24 h from 11 to 43% of the initial particulate carbon was found as dissolved carbon in the experiments with sterile lake water; after 4 to 6 d the leaching was from 67 to 78% of the initial 14 C. The leached compounds were utilized by bacteria. A comparison of the incubations using sterile and natural water showed that a mean of 71% of the lysis products was metabolized by microorganisms within 24 h. In two experiments the uptake rate equalled the leaching rate. (author)
Decomposition of lake phytoplankton. 2
International Nuclear Information System (INIS)
Hansen, L.; Krog, G.F.; Soendergaard, M.
1986-01-01
The lysis process of phytoplankton was followed in 24 h incubations in three Danish lakes. By means of gel-chromatography it was shown that the dissolved carbon leaching from different algal groups differed in molecular weight composition. Three distinct molecular weight classes (>10,000; 700 to 10,000 and < 700 Daltons) leached from blue-green algae in almost equal proportion. The lysis products of spring-bloom diatoms included only the two smaller size classes, and the molecules between 700 and 10,000 Daltons dominated. Measurements of cell content during decomposition of the diatoms revealed polysaccharides and low molecular weight compounds to dominate the lysis products. No proteins were leached during the first 24 h after cell death. By incubating the dead algae in natural lake water, it was possible to detect a high bacterial affinity towards molecules between 700 and 10,000 Daltons, although the other size classes were also utilized. Bacterial transformation of small molecules to larger molecules could be demonstrated. (author)
Wang, Fuan; Willner, Bilha; Willner, Itamar
2014-01-01
The base sequence in nucleic acids encodes substantial structural and functional information into the biopolymer. This encoded information provides the basis for the tailoring and assembly of DNA machines. A DNA machine is defined as a molecular device that exhibits the following fundamental features. (1) It performs a fuel-driven mechanical process that mimics macroscopic machines. (2) The mechanical process requires an energy input, "fuel." (3) The mechanical operation is accompanied by an energy consumption process that leads to "waste products." (4) The cyclic operation of the DNA devices, involves the use of "fuel" and "anti-fuel" ingredients. A variety of DNA-based machines are described, including the construction of "tweezers," "walkers," "robots," "cranes," "transporters," "springs," "gears," and interlocked cyclic DNA structures acting as reconfigurable catenanes, rotaxanes, and rotors. Different "fuels", such as nucleic acid strands, pH (H⁺/OH⁻), metal ions, and light, are used to trigger the mechanical functions of the DNA devices. The operation of the devices in solution and on surfaces is described, and a variety of optical, electrical, and photoelectrochemical methods to follow the operations of the DNA machines are presented. We further address the possible applications of DNA machines and the future perspectives of molecular DNA devices. These include the application of DNA machines as functional structures for the construction of logic gates and computing, for the programmed organization of metallic nanoparticle structures and the control of plasmonic properties, and for controlling chemical transformations by DNA machines. We further discuss the future applications of DNA machines for intracellular sensing, controlling intracellular metabolic pathways, and the use of the functional nanostructures for drug delivery and medical applications.
Thermal models of pulse electrochemical machining
International Nuclear Information System (INIS)
Kozak, J.
2004-01-01
Pulse electrochemical machining (PECM) provides an economical and effective method for machining high strength, heat-resistant materials into complex shapes such as turbine blades, die, molds and micro cavities. Pulse Electrochemical Machining involves the application of a voltage pulse at high current density in the anodic dissolution process. Small interelectrode gap, low electrolyte flow rate, gap state recovery during the pulse off-times lead to improved machining accuracy and surface finish when compared with ECM using continuous current. This paper presents a mathematical model for PECM and employs this model in a computer simulation of the PECM process for determination of the thermal limitation and energy consumption in PECM. The experimental results and discussion of the characteristics PECM are presented. (authors)
A study on optimal task decomposition of networked parallel computing using PVM
International Nuclear Information System (INIS)
Seong, Kwan Jae; Kim, Han Gyoo
1998-01-01
A numerical study is performed to investigate the effect of task decomposition on networked parallel processes using Parallel Virtual Machine (PVM). In our study, a PVM program distributed over a network of workstations is used in solving a finite difference version of a one dimensional heat equation, where natural choice of PVM programming structure would be the master-slave paradigm, with the aim of finding an optimal configuration resulting in least computing time including communication overhead among machines. Given a set of PVM tasks comprised of one master and five slave programs, it is found that there exists a pseudo-optimal number of machines, which does not necessarily coincide with the number of tasks, that yields the best performance when the network is under a light usage. Increasing the number of machines beyond this optimal one does not improve computing performance since increase in communication overhead among the excess number of machines offsets the decrease in CPU time obtained by distributing the PVM tasks among these machines. However, when the network traffic is heavy, the results exhibit a more random characteristic that is explained by the random nature of data transfer time
Fundamentals of machine design
Karaszewski, Waldemar
2011-01-01
A forum of researchers, educators and engineers involved in various aspects of Machine Design provided the inspiration for this collection of peer-reviewed papers. The resultant dissemination of the latest research results, and the exchange of views concerning the future research directions to be taken in this field will make the work of immense value to all those having an interest in the topics covered. The book reflects the cooperative efforts made in seeking out the best strategies for effecting improvements in the quality and the reliability of machines and machine parts and for extending
Conway, Drew
2012-01-01
If you're an experienced programmer interested in crunching data, this book will get you started with machine learning-a toolkit of algorithms that enables computers to train themselves to automate useful tasks. Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation. Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyz
Creativity in Machine Learning
Thoma, Martin
2016-01-01
Recent machine learning techniques can be modified to produce creative results. Those results did not exist before; it is not a trivial combination of the data which was fed into the machine learning system. The obtained results come in multiple forms: As images, as text and as audio. This paper gives a high level overview of how they are created and gives some examples. It is meant to be a summary of the current work and give people who are new to machine learning some starting points.
1988-01-01
A NASA-developed software package has played a part in technical education of students who major in Mechanical Engineering Technology at William Rainey Harper College. Professor Hack has been using (APT) Automatically Programmed Tool Software since 1969 in his CAD/CAM Computer Aided Design and Manufacturing curriculum. Professor Hack teaches the use of APT programming languages for control of metal cutting machines. Machine tool instructions are geometry definitions written in APT Language to constitute a "part program." The part program is processed by the machine tool. CAD/CAM students go from writing a program to cutting steel in the course of a semester.
Prepared by Thermal Hydro-decomposition
Prasoetsopha, N.; Pinitsoontorn, S.; Kamwanna, T.; Kurosaki, K.; Ohishi, Y.; Muta, H.; Yamanaka, S.
2014-06-01
The polycrystalline samples of Ca3Co4- x Ga x O9+ δ (0 ≤ x ≤ 0.15) were prepared by a simple thermal hydro-decomposition method. The high density ceramics were fabricated using a spark plasma sintering technique. The crystal structure of calcined powders was characterized by x-ray diffraction. The single phase of Ca3Co4- x Ga x O9+ δ was obtained. The scanning electron micrograph illustrated the grain alignment perpendicular to the direction of the pressure in the sintering process. The evidence from x-ray absorption near edge spectra were used to confirm the oxidation state of the Ga dopant. The thermoelectric properties of the misfit-layered of Ca3Co4- x Ga x O9+ δ were investigated. Seebeck coefficient tended to decrease with increasing Ga content due to the hole-doping effect. The electrical resistivity and thermal conductivity were monotonically decreased with increasing Ga content. The Ga doping of x = 0.15 showed the highest power factor of 3.99 × 10-4 W/mK2 at 1,023 K and the lowest thermal conductivity of 1.45 W/mK at 1,073 K. This resulted in the highest ZT of 0.29 at 1,073 K. From the optical absorption spectra, the electronic structure near the Fermi level show no significant change with Ga doping.
Decomposition Technique for Remaining Useful Life Prediction
Saha, Bhaskar (Inventor); Goebel, Kai F. (Inventor); Saxena, Abhinav (Inventor); Celaya, Jose R. (Inventor)
2014-01-01
The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation. Remaining life is computed by subtracting the instance when the extrapolated damage reaches the failure threshold from the instance when the prediction is made.
Rate of litter decomposition and microbial activity in an area of Caatinga
Directory of Open Access Journals (Sweden)
Patrícia Carneiro Souto
2013-12-01
Full Text Available In order to evaluate the decomposition of litter and microbial activity in an area of preserved Caatinga, an experiment was conducted in the Natural Heritage Private Reserve Tamanduá Farm in Santa Terezinha county, State of Paraiba. The decomposition rate was determined by using litter bags containing 30 g of litter, which were arranged on the soil surface in September 2003 and 20 bags were taken each month until September 2005. The collected material was oven dried and weighed to assess weight loss compared to initial weight. Microbial activity was estimated monthly by the quantification of carbon dioxide (CO2 released into the edaphic breathing process from the soil surface, and captured by KOH solution. Weight loss of litter after one year was 41.19% and, after two years, was 48.37%, indicating a faster decomposition in the first year. Data analysis showed the influence of season on litter decomposition and temperature on microbial activity.
A Decomposition Theorem for Finite Automata.
Santa Coloma, Teresa L.; Tucci, Ralph P.
1990-01-01
Described is automata theory which is a branch of theoretical computer science. A decomposition theorem is presented that is easier than the Krohn-Rhodes theorem. Included are the definitions, the theorem, and a proof. (KR)
Spatial domain decomposition for neutron transport problems
International Nuclear Information System (INIS)
Yavuz, M.; Larsen, E.W.
1989-01-01
A spatial Domain Decomposition method is proposed for modifying the Source Iteration (SI) and Diffusion Synthetic Acceleration (DSA) algorithms for solving discrete ordinates problems. The method, which consists of subdividing the spatial domain of the problem and performing the transport sweeps independently on each subdomain, has the advantage of being parallelizable because the calculations in each subdomain can be performed on separate processors. In this paper we describe the details of this spatial decomposition and study, by numerical experimentation, the effect of this decomposition on the SI and DSA algorithms. Our results show that the spatial decomposition has little effect on the convergence rates until the subdomains become optically thin (less than about a mean free path in thickness)
DEFF Research Database (Denmark)
De Chiffre, Leonardo
This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceabilit...... and uncertainty during coordinate measurements, 3) Digitalisation and Reverse Engineering. This document contains a short description of each step in the exercise and schemes with room for taking notes of the results.......This document is used in connection with three exercises of 2 hours duration as a part of the course GEOMETRICAL METROLOGY AND MACHINE TESTING. The exercises concern three aspects of coordinate measuring: 1) Measuring and verification of tolerances on coordinate measuring machines, 2) Traceability...
2012-01-01
The automation of visual inspection is becoming more and more important in modern industry as a consistent, reliable means of judging the quality of raw materials and manufactured goods . The Machine Vision Handbook equips the reader with the practical details required to engineer integrated mechanical-optical-electronic-software systems. Machine vision is first set in the context of basic information on light, natural vision, colour sensing and optics. The physical apparatus required for mechanized image capture – lenses, cameras, scanners and light sources – are discussed followed by detailed treatment of various image-processing methods including an introduction to the QT image processing system. QT is unique to this book, and provides an example of a practical machine vision system along with extensive libraries of useful commands, functions and images which can be implemented by the reader. The main text of the book is completed by studies of a wide variety of applications of machine vision in insp...
Energy Technology Data Exchange (ETDEWEB)
Ghosh, Jayanta; Bhattacharya, Atanu, E-mail: atanub@ipc.iisc.ernet.in
2016-01-13
Highlights: • Decomposition mechanisms of model energetic salt, guanidium triazolate, are explored. • Decomposition pathways are electronically nonadiabatic. • CASPT2, CASMP2 and CASSCF methodologies are employed. • N{sub 2} and NH{sub 3} are predicted to be the most possible initial decomposition products. - Abstract: Electronically nonadiabatic decomposition pathways of guanidium triazolate are explored theoretically. Nonadiabatically coupled potential energy surfaces are explored at the complete active space self-consistent field (CASSCF) level of theory. For better estimation of energies complete active space second order perturbation theories (CASPT2 and CASMP2) are also employed. Density functional theory (DFT) with B3LYP functional and MP2 level of theory are used to explore subsequent ground state decomposition pathways. In comparison with all possible stable decomposition products (such as, N{sub 2}, NH{sub 3}, HNC, HCN, NH{sub 2}CN and CH{sub 3}NC), only NH{sub 3} (with NH{sub 2}CN) and N{sub 2} are predicted to be energetically most accessible initial decomposition products. Furthermore, different conical intersections between the S{sub 1} and S{sub 0} surfaces, which are computed at the CASSCF(14,10)/6-31G(d) level of theory, are found to play an essential role in the excited state deactivation process of guanidium triazolate. This is the first report on the electronically nonadiabatic decomposition mechanisms of isolated guanidium triazolate salt.
Introduction to AC machine design
Lipo, Thomas A
2018-01-01
AC electrical machine design is a key skill set for developing competitive electric motors and generators for applications in industry, aerospace, and defense. This book presents a thorough treatment of AC machine design, starting from basic electromagnetic principles and continuing through the various design aspects of an induction machine. Introduction to AC Machine Design includes one chapter each on the design of permanent magnet machines, synchronous machines, and thermal design. It also offers a basic treatment of the use of finite elements to compute the magnetic field within a machine without interfering with the initial comprehension of the core subject matter. Based on the author's notes, as well as after years of classroom instruction, Introduction to AC Machine Design: * Brings to light more advanced principles of machine design--not just the basic principles of AC and DC machine behavior * Introduces electrical machine design to neophytes while also being a resource for experienced designers * ...
Joint Matrices Decompositions and Blind Source Separation
Czech Academy of Sciences Publication Activity Database
Chabriel, G.; Kleinsteuber, M.; Moreau, E.; Shen, H.; Tichavský, Petr; Yeredor, A.
2014-01-01
Roč. 31, č. 3 (2014), s. 34-43 ISSN 1053-5888 R&D Projects: GA ČR GA102/09/1278 Institutional support: RVO:67985556 Keywords : joint matrices decomposition * tensor decomposition * blind source separation Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 5.852, year: 2014 http://library.utia.cas.cz/separaty/2014/SI/tichavsky-0427607.pdf
Review on Thermal Decomposition of Ammonium Nitrate
Chaturvedi, Shalini; Dave, Pragnesh N.
2013-01-01
In this review data from the literature on thermal decomposition of ammonium nitrate (AN) and the effect of additives to their thermal decomposition are summarized. The effect of additives like oxides, cations, inorganic acids, organic compounds, phase-stablized CuO, etc., is discussed. The effect of an additive mainly occurs at the exothermic peak of pure AN in a temperature range of 200°C to 140°C.
Note on Symplectic SVD-Like Decomposition
Directory of Open Access Journals (Sweden)
AGOUJIL Said
2016-02-01
Full Text Available The aim of this study was to introduce a constructive method to compute a symplectic singular value decomposition (SVD-like decomposition of a 2n-by-m rectangular real matrix A, based on symplectic refectors.This approach used a canonical Schur form of skew-symmetric matrix and it allowed us to compute eigenvalues for the structured matrices as Hamiltonian matrix JAA^T.
International Nuclear Information System (INIS)
Farhadi, Saeid; Safabakhsh, Jalil
2012-01-01
Highlights: ► [Co(NH 3 ) 5 CO 3 ]NO 3 ·0.5H 2 O complex was used for preparing pure Co 3 O 4 nanoparticles. ► Co 3 O 4 nanoparticles were prepared at low temperature of 175 °C. ► Co 3 O 4 nanoparticles show a weak ferromagnetic behaviour at room temperature. ► The method is simple, low-cost and suitable for the production of Co 3 O 4 . - Abstract: Co 3 O 4 nanoparticles were easily prepared via the decomposition of the pentammine(carbonato)cobalt(III) nitrate precursor complex [Co(NH 3 ) 5 CO 3 ]NO 3 ·0.5H 2 O at low temperature (175 °C). The product was characterized by thermal analysis, X-ray diffraction (XRD), Fourier-transform infrared spectroscopy (FT-IR), UV–visible spectroscopy, transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), Raman spectroscopy, Brunauer–Emmett–Teller (BET) specific surface area measurements and magnetic measurements. The FT-IR, XRD, Raman and EDX results indicated that the synthesized Co 3 O 4 nanoparticles are highly pure and have a single phase. The TEM analysis revealed nearly uniform and quasi-spherical Co 3 O 4 nanoparticles with an average particle size of approximately 10 nm. The optical absorption spectrum of the Co 3 O 4 nanoparticles showed two direct band gaps of 2.18 and 3.52 eV with a red shift in comparison with previous reported values. The prepared Co 3 O 4 nanoparticles showed a weak ferromagnetic behaviour that could be attributed to uncompensated surface spins and/or finite-size effects. Using the present method, Co 3 O 4 nanoparticles can be produced without expensive organic solvents and complicated equipment. This simple, rapid, safe and low-cost synthetic route can be extended to the synthesis of other transition-metal oxides.
Metalworking and machining fluids
Erdemir, Ali; Sykora, Frank; Dorbeck, Mark
2010-10-12
Improved boron-based metal working and machining fluids. Boric acid and boron-based additives that, when mixed with certain carrier fluids, such as water, cellulose and/or cellulose derivatives, polyhydric alcohol, polyalkylene glycol, polyvinyl alcohol, starch, dextrin, in solid and/or solvated forms result in improved metalworking and machining of metallic work pieces. Fluids manufactured with boric acid or boron-based additives effectively reduce friction, prevent galling and severe wear problems on cutting and forming tools.
Superconducting machines. Chapter 4
International Nuclear Information System (INIS)
Appleton, A.D.
1977-01-01
A brief account is given of the principles of superconductivity and superconductors. The properties of Nb-Ti superconductors and the method of flux stabilization are described. The basic features of superconducting d.c. machines are illustrated by the use of these machines for ship propulsion, steel-mill drives, industrial drives, aluminium production, and other d.c. power supplies. Superconducting a.c. generators and their design parameters are discussed. (U.K.)
Romero García, Cristian
2017-01-01
[EN] In a world in which accessible information grows exponentially, the selection of the appropriate information turns out to be an extremely relevant problem. In this context, the idea of Machine Learning (ML), a subfield of Artificial Intelligence, emerged to face problems in data mining, pattern recognition, automatic prediction, among others. Quantum Machine Learning is an interdisciplinary research area combining quantum mechanics with methods of ML, in which quantum properties allow fo...
Forsythe, J Chris [Sandia Park, NM; Xavier, Patrick G [Albuquerque, NM; Abbott, Robert G [Albuquerque, NM; Brannon, Nathan G [Albuquerque, NM; Bernard, Michael L [Tijeras, NM; Speed, Ann E [Albuquerque, NM
2009-04-28
Digital technology utilizing a cognitive model based on human naturalistic decision-making processes, including pattern recognition and episodic memory, can reduce the dependency of human-machine interactions on the abilities of a human user and can enable a machine to more closely emulate human-like responses. Such a cognitive model can enable digital technology to use cognitive capacities fundamental to human-like communication and cooperation to interact with humans.