Localization of cask and plug remote handling system in ITER using multiple video cameras

International Nuclear Information System (INIS)

Ferreira, João; Vale, Alberto; Ribeiro, Isabel

2013-01-01

Highlights: ► Localization of cask and plug remote handling system with video cameras and markers. ► Video cameras already installed on the building for remote operators. ► Fiducial markers glued or painted on cask and plug remote handling system. ► Augmented reality contents on the video streaming as an aid for remote operators. ► Integration with other localization systems for enhanced robustness and precision. -- Abstract: The cask and plug remote handling system (CPRHS) provides the means for the remote transfer of in-vessel components and remote handling equipment between the Hot Cell building and the Tokamak building in ITER. Different CPRHS typologies will be autonomously guided following predefined trajectories. Therefore, the localization of any CPRHS in operation must be continuously known in real time to provide the feedback for the control system and also for the human supervision. This paper proposes a localization system that uses the video streaming captured by the multiple cameras already installed in the ITER scenario to estimate with precision the position and the orientation of any CPRHS. In addition, an augmented reality system can be implemented using the same video streaming and the libraries for the localization system. The proposed localization system was tested in a mock-up scenario with a scale 1:25 of the divertor level of Tokamak building

AIR POLLUITON INDEX PREDICTION USING MULTIPLE NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Zainal Ahmad

2017-05-01

Full Text Available Air quality monitoring and forecasting tools are necessary for the purpose of taking precautionary measures against air pollution, such as reducing the effect of a predicted air pollution peak on the surrounding population and ecosystem. In this study a single Feed-forward Artificial Neural Network (FANN is shown to be able to predict the Air Pollution Index (API with a Mean Squared Error (MSE and coefficient determination, R2, of 0.1856 and 0.7950 respectively. However, due to the non-robust nature of single FANN, a selective combination of Multiple Neural Networks (MNN is introduced using backward elimination and a forward selection method. The results show that both selective combination methods can improve the robustness and performance of the API prediction with the MSE and R2 of 0.1614 and 0.8210 respectively. This clearly shows that it is possible to reduce the number of networks combined in MNN for API prediction, without losses of any information in terms of the performance of the final API prediction model.

Global exponential stability for reaction-diffusion recurrent neural networks with multiple time varying delays

International Nuclear Information System (INIS)

Lou, X.; Cui, B.

2008-01-01

In this paper we consider the problem of exponential stability for recurrent neural networks with multiple time varying delays and reaction-diffusion terms. The activation functions are supposed to be bounded and globally Lipschitz continuous. By means of Lyapunov functional, sufficient conditions are derived, which guarantee global exponential stability of the delayed neural network. Finally, a numerical example is given to show the correctness of our analysis. (author)

Multiple Time Series Forecasting Using Quasi-Randomized Functional Link Neural Networks

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Thierry Moudiki

2018-03-01

Full Text Available We are interested in obtaining forecasts for multiple time series, by taking into account the potential nonlinear relationships between their observations. For this purpose, we use a specific type of regression model on an augmented dataset of lagged time series. Our model is inspired by dynamic regression models (Pankratz 2012, with the response variable’s lags included as predictors, and is known as Random Vector Functional Link (RVFL neural networks. The RVFL neural networks have been successfully applied in the past, to solving regression and classification problems. The novelty of our approach is to apply an RVFL model to multivariate time series, under two separate regularization constraints on the regression parameters.

Neural Computations in a Dynamical System with Multiple Time Scales

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Yuanyuan Mi

2016-09-01

Full Text Available Neural systems display rich short-term dynamics at various levels, e.g., spike-frequencyadaptation (SFA at single neurons, and short-term facilitation (STF and depression (STDat neuronal synapses. These dynamical features typically covers a broad range of time scalesand exhibit large diversity in different brain regions. It remains unclear what the computationalbenefit for the brain to have such variability in short-term dynamics is. In this study, we proposethat the brain can exploit such dynamical features to implement multiple seemingly contradictorycomputations in a single neural circuit. To demonstrate this idea, we use continuous attractorneural network (CANN as a working model and include STF, SFA and STD with increasing timeconstants in their dynamics. Three computational tasks are considered, which are persistent activity,adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, andhence cannot be implemented by a single dynamical feature or any combination with similar timeconstants. However, with properly coordinated STF, SFA and STD, we show that the network isable to implement the three computational tasks concurrently. We hope this study will shed lighton the understanding of how the brain orchestrates its rich dynamics at various levels to realizediverse cognitive functions.

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

Science.gov (United States)

Smith, James A.

1992-01-01

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI 1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

A comparative study between the use of artificial neural networks and multiple linear regression for caustic concentration prediction in a stage of alumina production

Directory of Open Access Journals (Sweden)

Giovanni Leopoldo Rozza

2015-09-01

Full Text Available With world becoming each day a global village, enterprises continuously seek to optimize their internal processes to hold or improve their competitiveness and make better use of natural resources. In this context, decision support tools are an underlying requirement. Such tools are helpful on predicting operational issues, avoiding cost risings, loss of productivity, work-related accident leaves or environmental disasters. This paper has its focus on the prediction of spent liquor caustic concentration of Bayer process for alumina production. Caustic concentration measuring is essential to keep it at expected levels, otherwise quality issues might arise. The organization requests caustic concentration by chemical analysis laboratory once a day, such information is not enough to issue preventive actions to handle process inefficiencies that will be known only after new measurement on the next day. Thereby, this paper proposes using Multiple Linear Regression and Artificial Neural Networks techniques a mathematical model to predict the spent liquor´s caustic concentration. Hence preventive actions will occur in real time. Such models were built using software tool for numerical computation (MATLAB and a statistical analysis software package (SPSS. The models output (predicted caustic concentration were compared with the real lab data. We found evidence suggesting superior results with use of Artificial Neural Networks over Multiple Linear Regression model. The results demonstrate that replacing laboratorial analysis by the forecasting model to support technical staff on decision making could be feasible.

Influence of neural adaptation on dynamics and equilibrium state of neural activities in a ring neural network

Science.gov (United States)

Takiyama, Ken

2017-12-01

How neural adaptation affects neural information processing (i.e. the dynamics and equilibrium state of neural activities) is a central question in computational neuroscience. In my previous works, I analytically clarified the dynamics and equilibrium state of neural activities in a ring-type neural network model that is widely used to model the visual cortex, motor cortex, and several other brain regions. The neural dynamics and the equilibrium state in the neural network model corresponded to a Bayesian computation and statistically optimal multiple information integration, respectively, under a biologically inspired condition. These results were revealed in an analytically tractable manner; however, adaptation effects were not considered. Here, I analytically reveal how the dynamics and equilibrium state of neural activities in a ring neural network are influenced by spike-frequency adaptation (SFA). SFA is an adaptation that causes gradual inhibition of neural activity when a sustained stimulus is applied, and the strength of this inhibition depends on neural activities. I reveal that SFA plays three roles: (1) SFA amplifies the influence of external input in neural dynamics; (2) SFA allows the history of the external input to affect neural dynamics; and (3) the equilibrium state corresponds to the statistically optimal multiple information integration independent of the existence of SFA. In addition, the equilibrium state in a ring neural network model corresponds to the statistically optimal integration of multiple information sources under biologically inspired conditions, independent of the existence of SFA.

A neuromorphic implementation of multiple spike-timing synaptic plasticity rules for large-scale neural networks

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Runchun Mark Wang

2015-05-01

Full Text Available We present a neuromorphic implementation of multiple synaptic plasticity learning rules, which include both Spike Timing Dependent Plasticity (STDP and Spike Timing Dependent Delay Plasticity (STDDP. We present a fully digital implementation as well as a mixed-signal implementation, both of which use a novel dynamic-assignment time-multiplexing approach and support up to 2^26 (64M synaptic plasticity elements. Rather than implementing dedicated synapses for particular types of synaptic plasticity, we implemented a more generic synaptic plasticity adaptor array that is separate from the neurons in the neural network. Each adaptor performs synaptic plasticity according to the arrival times of the pre- and post-synaptic spikes assigned to it, and sends out a weighted and/or delayed pre-synaptic spike to the target synapse in the neural network. This strategy provides great flexibility for building complex large-scale neural networks, as a neural network can be configured for multiple synaptic plasticity rules without changing its structure. We validate the proposed neuromorphic implementations with measurement results and illustrate that the circuits are capable of performing both STDP and STDDP. We argue that it is practical to scale the work presented here up to 2^36 (64G synaptic adaptors on a current high-end FPGA platform.

Handling of multiassembly sealed baskets between reactor storage and a remote handling facility

International Nuclear Information System (INIS)

Massey, J.V.; Kessler, J.H.; McSherry, A.J.

1989-06-01

The storage of multiple fuel assemblies in sealed (welded) dry storage baskets is gaining increasing use to augment at-reactor fuel storage capacity. Since this increasing use will place a significant number of such baskets on reactor sites, some initial downstream planning for their future handling scenarios for retrieving multi-assembly sealed baskets (MSBs) from onsite storage and transferring and shipping the fuel (and/or the baskets) to a federally operated remote handling facility (RHF). Numerous options or at-reactor and away-from-reactor handling were investigated. Materials handling flowsheets were developed along with conceptual designs for the equipment and tools required to handle and open the MSBs. The handling options were evaluated and compared to a reference case, fuel handling sequence (i.e., fuel assemblies are taken from the fuel pool, shipped to a receiving and handling facility and placed into interim storage). The main parameters analyzed are throughout, radiation dose burden and cost. In addition to evaluating the handling of MSBs, this work also evaluated handling consolidated fuel canisters (CFCs). In summary, the handling of MSBs and CFCs in the store, ship and bury fuel cycle was found to be feasible and, under some conditions, to offer significant benefits in terms of throughput, cost and safety. 14 refs., 20 figs., 24 tabs

Rock property estimates using multiple seismic attributes and neural networks; Pegasus Field, West Texas

Energy Technology Data Exchange (ETDEWEB)

Schuelke, J.S.; Quirein, J.A.; Sarg, J.F.

1998-12-31

This case study shows the benefit of using multiple seismic trace attributes and the pattern recognition capabilities of neural networks to predict reservoir architecture and porosity distribution in the Pegasus Field, West Texas. The study used the power of neural networks to integrate geologic, borehole and seismic data. Illustrated are the improvements between the new neural network approach and the more traditional method of seismic trace inversion for porosity estimation. Comprehensive statistical methods and interpretational/subjective measures are used in the prediction of porosity from seismic attributes. A 3-D volume of seismic derived porosity estimates for the Devonian reservoir provide a very detailed estimate of porosity, both spatially and vertically, for the field. The additional reservoir porosity detail provided, between the well control, allows for optimal placement of horizontal wells and improved field development. 6 refs., 2 figs.

Sequential interrogation of multiple FBG sensors using LPG modulation and an artificial neural network

International Nuclear Information System (INIS)

Basu, Mainak; Ghorai, S K

2015-01-01

Interrogating multiple fiber Bragg gratings (FBG) requires highly sensitive spectrum scanning equipment such as optical spectrum analyzers, tunable filters, acousto-optic tunable filters etc, which are expensive, bulky and time consuming. In this paper, we present a new approach for multiple FBG sensor interrogation using long-period gratings and an artificial neural network. The reflection spectra of the multiplexed FBGs are modulated by two long period gratings separately and the modulated optical intensities were detected by two photodetectors. The outputs of the detectors are then used as input in a previously trained artificial neural network to interrogate the FBG sensors. Simulations have been performed to determine the strain and wavelength shift using two and four sensors. The interrogation system has also been demonstrated experimentally for two sensors using simply supported beams in the range of 0–350 μstrain. The proposed interrogation scheme has been found to identify the perturbed FBG, and to determine strain and wavelength shift with reasonable accuracy. (paper)

Multiple-failure signal validation in nuclear power plants using artificial neural networks

International Nuclear Information System (INIS)

Fantoni, P.F.; Mazzola, A.

1996-01-01

The possibility of using a neural network to validate process signals during normal and abnormal plant conditions is explored. In boiling water reactor plants, signal validation is used to generate reliable thermal limits calculation and to supply reliable inputs to other computerized systems that support the operator during accident scenarios. The way that autoassociative neural networks can promptly detect faulty process signal measurements and produce a best estimate of the actual process values even in multifailure situations is shown. A method was developed to train the network for multiple sensor-failure detection, based on a random failure simulation algorithm. Noise was artificially added to the input to evaluate the network's ability to respond in a very low signal-to-noise ratio environment. Training and test data sets were simulated by the real-time transient simulator code APROS

Joint multiple fully connected convolutional neural network with extreme learning machine for hepatocellular carcinoma nuclei grading.

Science.gov (United States)

Li, Siqi; Jiang, Huiyan; Pang, Wenbo

2017-05-01

Accurate cell grading of cancerous tissue pathological image is of great importance in medical diagnosis and treatment. This paper proposes a joint multiple fully connected convolutional neural network with extreme learning machine (MFC-CNN-ELM) architecture for hepatocellular carcinoma (HCC) nuclei grading. First, in preprocessing stage, each grayscale image patch with the fixed size is obtained using center-proliferation segmentation (CPS) method and the corresponding labels are marked under the guidance of three pathologists. Next, a multiple fully connected convolutional neural network (MFC-CNN) is designed to extract the multi-form feature vectors of each input image automatically, which considers multi-scale contextual information of deep layer maps sufficiently. After that, a convolutional neural network extreme learning machine (CNN-ELM) model is proposed to grade HCC nuclei. Finally, a back propagation (BP) algorithm, which contains a new up-sample method, is utilized to train MFC-CNN-ELM architecture. The experiment comparison results demonstrate that our proposed MFC-CNN-ELM has superior performance compared with related works for HCC nuclei grading. Meanwhile, external validation using ICPR 2014 HEp-2 cell dataset shows the good generalization of our MFC-CNN-ELM architecture. Copyright © 2017 Elsevier Ltd. All rights reserved.

Novel global robust stability criteria for interval neural networks with multiple time-varying delays

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.

2005-01-01

This Letter is concerned with the problem of robust stability analysis for interval neural networks with multiple time-varying delays and parameter uncertainties. The parameter uncertainties are assumed to be bounded in given compact sets and the activation functions are supposed to be bounded and globally Lipschitz continuous. A sufficient condition is obtained by means of Lyapunov functionals, which guarantees the existence, uniqueness and global asymptotic stability of the delayed neural network for all admissible uncertainties. This condition is in terms of a linear matrix inequality (LMI), which can be easily checked by using recently developed algorithms in solving LMIs. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed method

New results for global robust stability of bidirectional associative memory neural networks with multiple time delays

International Nuclear Information System (INIS)

Senan, Sibel; Arik, Sabri

2009-01-01

This paper presents some new sufficient conditions for the global robust asymptotic stability of the equilibrium point for bidirectional associative memory (BAM) neural networks with multiple time delays. The results we obtain impose constraint conditions on the network parameters of neural system independently of the delay parameter, and they are applicable to all bounded continuous non-monotonic neuron activation functions. We also give some numerical examples to demonstrate the applicability and effectiveness of our results, and compare the results with the previous robust stability results derived in the literature.

Exploiting deep neural networks and head movements for binaural localisation of multiple speakers in reverberant conditions

DEFF Research Database (Denmark)

Ma, Ning; Brown, Guy J.; May, Tobias

2015-01-01

This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for binaural localisation of multiple speakers in reverberant conditions. DNNs are used to map binaural features, consisting of the complete crosscorrelation function (CCF) and interaural...

ANT Advanced Neural Tool

Energy Technology Data Exchange (ETDEWEB)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-07-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs.

ANT Advanced Neural Tool

International Nuclear Information System (INIS)

Labrador, I.; Carrasco, R.; Martinez, L.

1996-01-01

This paper describes a practical introduction to the use of Artificial Neural Networks. Artificial Neural Nets are often used as an alternative to the traditional symbolic manipulation and first order logic used in Artificial Intelligence, due the high degree of difficulty to solve problems that can not be handled by programmers using algorithmic strategies. As a particular case of Neural Net a Multilayer Perception developed by programming in C language on OS9 real time operating system is presented. A detailed description about the program structure and practical use are included. Finally, several application examples that have been treated with the tool are presented, and some suggestions about hardware implementations. (Author) 15 refs

Efficient Cancer Detection Using Multiple Neural Networks.

Science.gov (United States)

Shell, John; Gregory, William D

2017-01-01

The inspection of live excised tissue specimens to ascertain malignancy is a challenging task in dermatopathology and generally in histopathology. We introduce a portable desktop prototype device that provides highly accurate neural network classification of malignant and benign tissue. The handheld device collects 47 impedance data samples from 1 Hz to 32 MHz via tetrapolar blackened platinum electrodes. The data analysis was implemented with six different backpropagation neural networks (BNN). A data set consisting of 180 malignant and 180 benign breast tissue data files in an approved IRB study at the Aurora Medical Center, Milwaukee, WI, USA, were utilized as a neural network input. The BNN structure consisted of a multi-tiered consensus approach autonomously selecting four of six neural networks to determine a malignant or benign classification. The BNN analysis was then compared with the histology results with consistent sensitivity of 100% and a specificity of 100%. This implementation successfully relied solely on statistical variation between the benign and malignant impedance data and intricate neural network configuration. This device and BNN implementation provides a novel approach that could be a valuable tool to augment current medical practice assessment of the health of breast, squamous, and basal cell carcinoma and other excised tissue without requisite tissue specimen expertise. It has the potential to provide clinical management personnel with a fast non-invasive accurate assessment of biopsied or sectioned excised tissue in various clinical settings.

Indirect Encoding in Neuroevolutionary Ship Handling

Directory of Open Access Journals (Sweden)

Miroslaw Lacki

2018-03-01

Full Text Available In this paper the author compares the efficiency of two encoding schemes for artificial intelligence methods used in the neuroevolutionary ship maneuvering system. This may be also be seen as the ship handling system that simulates a learning process of a group of artificial helmsmen - autonomous control units, created with an artificial neural network. The helmsman observes input signals derived form an enfironment and calculates the values of required parameters of the vessel maneuvering in confined waters. In neuroevolution such units are treated as individuals in population of artificial neural networks, which through environmental sensing and evolutionary algorithms learn to perform given task efficiently. The main task of this project is to evolve a population of helmsmen with indirect encoding and compare results of simulation with direct encoding method.

Continuous Timescale Long-Short Term Memory Neural Network for Human Intent Understanding

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Zhibin Yu

2017-08-01

Full Text Available Understanding of human intention by observing a series of human actions has been a challenging task. In order to do so, we need to analyze longer sequences of human actions related with intentions and extract the context from the dynamic features. The multiple timescales recurrent neural network (MTRNN model, which is believed to be a kind of solution, is a useful tool for recording and regenerating a continuous signal for dynamic tasks. However, the conventional MTRNN suffers from the vanishing gradient problem which renders it impossible to be used for longer sequence understanding. To address this problem, we propose a new model named Continuous Timescale Long-Short Term Memory (CTLSTM in which we inherit the multiple timescales concept into the Long-Short Term Memory (LSTM recurrent neural network (RNN that addresses the vanishing gradient problem. We design an additional recurrent connection in the LSTM cell outputs to produce a time-delay in order to capture the slow context. Our experiments show that the proposed model exhibits better context modeling ability and captures the dynamic features on multiple large dataset classification tasks. The results illustrate that the multiple timescales concept enhances the ability of our model to handle longer sequences related with human intentions and hence proving to be more suitable for complex tasks, such as intention recognition.

A Self-Reconstructing Algorithm for Single and Multiple-Sensor Fault Isolation Based on Auto-Associative Neural Networks

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Hamidreza Mousavi

2017-01-01

Full Text Available Recently different approaches have been developed in the field of sensor fault diagnostics based on Auto-Associative Neural Network (AANN. In this paper we present a novel algorithm called Self reconstructing Auto-Associative Neural Network (S-AANN which is able to detect and isolate single faulty sensor via reconstruction. We have also extended the algorithm to be applicable in multiple fault conditions. The algorithm uses a calibration model based on AANN. AANN can reconstruct the faulty sensor using non-faulty sensors due to correlation between the process variables, and mean of the difference between reconstructed and original data determines which sensors are faulty. The algorithms are tested on a Dimerization process. The simulation results show that the S-AANN can isolate multiple faulty sensors with low computational time that make the algorithm appropriate candidate for online applications.

Trade-off between multiple constraints enables simultaneous formation of modules and hubs in neural systems.

Directory of Open Access Journals (Sweden)

Yuhan Chen

Full Text Available The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter α, and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of α, resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of α values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real

Handling missing data for the identification of charged particles in a multilayer detector: A comparison between different imputation methods

Energy Technology Data Exchange (ETDEWEB)

Riggi, S., E-mail: sriggi@oact.inaf.it [INAF - Osservatorio Astrofisico di Catania (Italy); Riggi, D. [Keras Strategy - Milano (Italy); Riggi, F. [Dipartimento di Fisica e Astronomia - Università di Catania (Italy); INFN, Sezione di Catania (Italy)

2015-04-21

Identification of charged particles in a multilayer detector by the energy loss technique may also be achieved by the use of a neural network. The performance of the network becomes worse when a large fraction of information is missing, for instance due to detector inefficiencies. Algorithms which provide a way to impute missing information have been developed over the past years. Among the various approaches, we focused on normal mixtures’ models in comparison with standard mean imputation and multiple imputation methods. Further, to account for the intrinsic asymmetry of the energy loss data, we considered skew-normal mixture models and provided a closed form implementation in the Expectation-Maximization (EM) algorithm framework to handle missing patterns. The method has been applied to a test case where the energy losses of pions, kaons and protons in a six-layers’ Silicon detector are considered as input neurons to a neural network. Results are given in terms of reconstruction efficiency and purity of the various species in different momentum bins.

New backpropagation algorithm with type-2 fuzzy weights for neural networks

CERN Document Server

Gaxiola, Fernando; Valdez, Fevrier

2016-01-01

In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed learning method architecture and the adaptation of type-2 fuzzy weights are presented. The proposed method is based on research of recent methods that handle weight adaptation and especially fuzzy weights. The internal operation of the neuron is changed to work with two internal calculations for the activation function to obtain two results as outputs of the proposed method. Simulation results and a comparative study among monolithic neural networks, neural network with type-1 fuzzy weights and neural network with type-2 fuzzy weights are presented to illustrate the advantages of the proposed method. The proposed approach is based on recent methods that handle adaptation of weights using fuzzy logic of type-1 and type-2. The proposed approach is applied to a cases of prediction for the Mackey-Glass (for ô=17) and Dow-Jones time series, and recognition of person with iris bi...

Predicting methionine and lysine contents in soybean meal and fish meal using a group method of data handling-type neural network

Energy Technology Data Exchange (ETDEWEB)

Mottaghitalab, M.; Nikkhah, N.; Darmani-Kuhi, H.; López, S.; France, J.

2015-07-01

Artificial neural network models offer an alternative to linear regression analysis for predicting the amino acid content of feeds from their chemical composition. A group method of data handling-type neural network (GMDH-type NN), with an evolutionary method of genetic algorithm, was used to predict methionine (Met) and lysine (Lys) contents of soybean meal (SBM) and fish meal (FM) from their proximate analyses (i.e. crude protein, crude fat, crude fibre, ash and moisture). A data set with 119 data lines for Met and 116 lines for Lys was used to develop GMDH-type NN models with two hidden layers. The data lines were divided into two groups to produce training and validation sets. The data sets were imported into the GEvoM software for training the networks. The predictive capability of the constructed models was evaluated by their abilities to estimate the validation data sets accurately. A quantitative examination of goodness of fit for the predictive models was made using a number of precision, concordance and bias statistics. The statistical performance of the models developed revealed close agreement between observed and predicted Met and Lys contents for SBM and FM. The results of this study clearly illustrate the validity of GMDH-type NN models to estimate accurately the amino acid content of poultry feed ingredients from their chemical composition . (Author)

Oxygen-controlled automated neural differentiation of mouse embryonic stem cells.

Science.gov (United States)

Mondragon-Teran, Paul; Tostoes, Rui; Mason, Chris; Lye, Gary J; Veraitch, Farlan S

2013-03-01

Automation and oxygen tension control are two tools that provide significant improvements to the reproducibility and efficiency of stem cell production processes. the aim of this study was to establish a novel automation platform capable of controlling oxygen tension during both the cell-culture and liquid-handling steps of neural differentiation processes. We built a bespoke automation platform, which enclosed a liquid-handling platform in a sterile, oxygen-controlled environment. An airtight connection was used to transfer cell culture plates to and from an automated oxygen-controlled incubator. Our results demonstrate that our system yielded comparable cell numbers, viabilities, metabolism profiles and differentiation efficiencies when compared with traditional manual processes. Interestingly, eliminating exposure to ambient conditions during the liquid-handling stage resulted in significant improvements in the yield of MAP2-positive neural cells, indicating that this level of control can improve differentiation processes. This article describes, for the first time, an automation platform capable of maintaining oxygen tension control during both the cell-culture and liquid-handling stages of a 2D embryonic stem cell differentiation process.

Daily Suspended Sediment Discharge Prediction Using Multiple Linear Regression and Artificial Neural Network

Science.gov (United States)

Uca; Toriman, Ekhwan; Jaafar, Othman; Maru, Rosmini; Arfan, Amal; Saleh Ahmar, Ansari

2018-01-01

Prediction of suspended sediment discharge in a catchments area is very important because it can be used to evaluation the erosion hazard, management of its water resources, water quality, hydrology project management (dams, reservoirs, and irrigation) and to determine the extent of the damage that occurred in the catchments. Multiple Linear Regression analysis and artificial neural network can be used to predict the amount of daily suspended sediment discharge. Regression analysis using the least square method, whereas artificial neural networks using Radial Basis Function (RBF) and feedforward multilayer perceptron with three learning algorithms namely Levenberg-Marquardt (LM), Scaled Conjugate Descent (SCD) and Broyden-Fletcher-Goldfarb-Shanno Quasi-Newton (BFGS). The number neuron of hidden layer is three to sixteen, while in output layer only one neuron because only one output target. The mean absolute error (MAE), root mean square error (RMSE), coefficient of determination (R2 ) and coefficient of efficiency (CE) of the multiple linear regression (MLRg) value Model 2 (6 input variable independent) has the lowest the value of MAE and RMSE (0.0000002 and 13.6039) and highest R2 and CE (0.9971 and 0.9971). When compared between LM, SCG and RBF, the BFGS model structure 3-7-1 is the better and more accurate to prediction suspended sediment discharge in Jenderam catchment. The performance value in testing process, MAE and RMSE (13.5769 and 17.9011) is smallest, meanwhile R2 and CE (0.9999 and 0.9998) is the highest if it compared with the another BFGS Quasi-Newton model (6-3-1, 9-10-1 and 12-12-1). Based on the performance statistics value, MLRg, LM, SCG, BFGS and RBF suitable and accurately for prediction by modeling the non-linear complex behavior of suspended sediment responses to rainfall, water depth and discharge. The comparison between artificial neural network (ANN) and MLRg, the MLRg Model 2 accurately for to prediction suspended sediment discharge (kg

Comparison of multiple linear regression and artificial neural network in developing the objective functions of the orthopaedic screws.

Science.gov (United States)

Hsu, Ching-Chi; Lin, Jinn; Chao, Ching-Kong

2011-12-01

Optimizing the orthopaedic screws can greatly improve their biomechanical performances. However, a methodical design optimization approach requires a long time to search the best design. Thus, the surrogate objective functions of the orthopaedic screws should be accurately developed. To our knowledge, there is no study to evaluate the strengths and limitations of the surrogate methods in developing the objective functions of the orthopaedic screws. Three-dimensional finite element models for both the tibial locking screws and the spinal pedicle screws were constructed and analyzed. Then, the learning data were prepared according to the arrangement of the Taguchi orthogonal array, and the verification data were selected with use of a randomized selection. Finally, the surrogate objective functions were developed by using either the multiple linear regression or the artificial neural network. The applicability and accuracy of those surrogate methods were evaluated and discussed. The multiple linear regression method could successfully construct the objective function of the tibial locking screws, but it failed to develop the objective function of the spinal pedicle screws. The artificial neural network method showed a greater capacity of prediction in developing the objective functions for the tibial locking screws and the spinal pedicle screws than the multiple linear regression method. The artificial neural network method may be a useful option for developing the objective functions of the orthopaedic screws with a greater structural complexity. The surrogate objective functions of the orthopaedic screws could effectively decrease the time and effort required for the design optimization process. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Acute stress evokes sexually dimorphic, stressor-specific patterns of neural activation across multiple limbic brain regions in adult rats.

Science.gov (United States)

Sood, Ankit; Chaudhari, Karina; Vaidya, Vidita A

2018-03-01

Stress enhances the risk for psychiatric disorders such as anxiety and depression. Stress responses vary across sex and may underlie the heightened vulnerability to psychopathology in females. Here, we examined the influence of acute immobilization stress (AIS) and a two-day short-term forced swim stress (FS) on neural activation in multiple cortical and subcortical brain regions, implicated as targets of stress and in the regulation of neuroendocrine stress responses, in male and female rats using Fos as a neural activity marker. AIS evoked a sex-dependent pattern of neural activation within the cingulate and infralimbic subdivisions of the medial prefrontal cortex (mPFC), lateral septum (LS), habenula, and hippocampal subfields. The degree of neural activation in the mPFC, LS, and habenula was higher in males. Female rats exhibited reduced Fos positive cell numbers in the dentate gyrus hippocampal subfield, an effect not observed in males. We addressed whether the sexually dimorphic neural activation pattern noted following AIS was also observed with the short-term stress of FS. In the paraventricular nucleus of the hypothalamus and the amygdala, FS similar to AIS resulted in robust increases in neural activation in both sexes. The pattern of neural activation evoked by FS was distinct across sexes, with a heightened neural activation noted in the prelimbic mPFC subdivision and hippocampal subfields in females and differed from the pattern noted with AIS. This indicates that the sex differences in neural activation patterns observed within stress-responsive brain regions are dependent on the nature of stressor experience.

Direct Adaptive Aircraft Control Using Dynamic Cell Structure Neural Networks

Science.gov (United States)

Jorgensen, Charles C.

1997-01-01

A Dynamic Cell Structure (DCS) Neural Network was developed which learns topology representing networks (TRNS) of F-15 aircraft aerodynamic stability and control derivatives. The network is integrated into a direct adaptive tracking controller. The combination produces a robust adaptive architecture capable of handling multiple accident and off- nominal flight scenarios. This paper describes the DCS network and modifications to the parameter estimation procedure. The work represents one step towards an integrated real-time reconfiguration control architecture for rapid prototyping of new aircraft designs. Performance was evaluated using three off-line benchmarks and on-line nonlinear Virtual Reality simulation. Flight control was evaluated under scenarios including differential stabilator lock, soft sensor failure, control and stability derivative variations, and air turbulence.

Neural network post-processing of grayscale optical correlator

Science.gov (United States)

Lu, Thomas T; Hughlett, Casey L.; Zhoua, Hanying; Chao, Tien-Hsin; Hanan, Jay C.

2005-01-01

In this paper we present the use of a radial basis function neural network (RBFNN) as a post-processor to assist the optical correlator to identify the objects and to reject false alarms. Image plane features near the correlation peaks are extracted and fed to the neural network for analysis. The approach is capable of handling large number of object variations and filter sets. Preliminary experimental results are presented and the performance is analyzed.

A comparison of multiple regression and neural network techniques for mapping in situ pCO2 data

International Nuclear Information System (INIS)

Lefevre, Nathalie; Watson, Andrew J.; Watson, Adam R.

2005-01-01

Using about 138,000 measurements of surface pCO 2 in the Atlantic subpolar gyre (50-70 deg N, 60-10 deg W) during 1995-1997, we compare two methods of interpolation in space and time: a monthly distribution of surface pCO 2 constructed using multiple linear regressions on position and temperature, and a self-organizing neural network approach. Both methods confirm characteristics of the region found in previous work, i.e. the subpolar gyre is a sink for atmospheric CO 2 throughout the year, and exhibits a strong seasonal variability with the highest undersaturations occurring in spring and summer due to biological activity. As an annual average the surface pCO 2 is higher than estimates based on available syntheses of surface pCO 2 . This supports earlier suggestions that the sink of CO 2 in the Atlantic subpolar gyre has decreased over the last decade instead of increasing as previously assumed. The neural network is able to capture a more complex distribution than can be well represented by linear regressions, but both techniques agree relatively well on the average values of pCO 2 and derived fluxes. However, when both techniques are used with a subset of the data, the neural network predicts the remaining data to a much better accuracy than the regressions, with a residual standard deviation ranging from 3 to 11 μatm. The subpolar gyre is a net sink of CO 2 of 0.13 Gt-C/yr using the multiple linear regressions and 0.15 Gt-C/yr using the neural network, on average between 1995 and 1997. Both calculations were made with the NCEP monthly wind speeds converted to 10 m height and averaged between 1995 and 1997, and using the gas exchange coefficient of Wanninkhof

Artificial Neural Networks and Instructional Technology.

Science.gov (United States)

Carlson, Patricia A.

1991-01-01

Artificial neural networks (ANN), part of artificial intelligence, are discussed. Such networks are fed sample cases (training sets), learn how to recognize patterns in the sample data, and use this experience in handling new cases. Two cognitive roles for ANNs (intelligent filters and spreading, associative memories) are examined. Prototypes…

Modelling of multiple short-length-scale stall cells in an axial compressor using evolved GMDH neural networks

International Nuclear Information System (INIS)

Amanifard, N.; Nariman-Zadeh, N.; Farahani, M.H.; Khalkhali, A.

2008-01-01

Over the past 15 years there have been several research efforts to capture the stall inception nature in axial flow compressors. However previous analytical models could not explain the formation of short-length-scale stall cells. This paper provides a new model based on evolved GMDH neural network for transient evolution of multiple short-length-scale stall cells in an axial compressor. Genetic Algorithms (GAs) are also employed for optimal design of connectivity configuration of such GMDH-type neural networks. In this way, low-pass filter (LPF) pressure trace near the rotor leading edge is modelled with respect to the variation of pressure coefficient, flow rate coefficient, and number of rotor rotations which are defined as inputs

Multiple simultaneous fault diagnosis via hierarchical and single artificial neural networks

International Nuclear Information System (INIS)

Eslamloueyan, R.; Shahrokhi, M.; Bozorgmehri, R.

2003-01-01

Process fault diagnosis involves interpreting the current status of the plant given sensor reading and process knowledge. There has been considerable work done in this area with a variety of approaches being proposed for process fault diagnosis. Neural networks have been used to solve process fault diagnosis problems in chemical process, as they are well suited for recognizing multi-dimensional nonlinear patterns. In this work, the use of Hierarchical Artificial Neural Networks in diagnosing the multi-faults of a chemical process are discussed and compared with that of Single Artificial Neural Networks. The lower efficiency of Hierarchical Artificial Neural Networks , in comparison to Single Artificial Neural Networks, in process fault diagnosis is elaborated and analyzed. Also, the concept of a multi-level selection switch is presented and developed to improve the performance of hierarchical artificial neural networks. Simulation results indicate that application of multi-level selection switch increase the performance of the hierarchical artificial neural networks considerably

Comparison of Multiple Linear Regressions and Neural Networks based QSAR models for the design of new antitubercular compounds.

Science.gov (United States)

Ventura, Cristina; Latino, Diogo A R S; Martins, Filomena

2013-01-01

The performance of two QSAR methodologies, namely Multiple Linear Regressions (MLR) and Neural Networks (NN), towards the modeling and prediction of antitubercular activity was evaluated and compared. A data set of 173 potentially active compounds belonging to the hydrazide family and represented by 96 descriptors was analyzed. Models were built with Multiple Linear Regressions (MLR), single Feed-Forward Neural Networks (FFNNs), ensembles of FFNNs and Associative Neural Networks (AsNNs) using four different data sets and different types of descriptors. The predictive ability of the different techniques used were assessed and discussed on the basis of different validation criteria and results show in general a better performance of AsNNs in terms of learning ability and prediction of antitubercular behaviors when compared with all other methods. MLR have, however, the advantage of pinpointing the most relevant molecular characteristics responsible for the behavior of these compounds against Mycobacterium tuberculosis. The best results for the larger data set (94 compounds in training set and 18 in test set) were obtained with AsNNs using seven descriptors (R(2) of 0.874 and RMSE of 0.437 against R(2) of 0.845 and RMSE of 0.472 in MLRs, for test set). Counter-Propagation Neural Networks (CPNNs) were trained with the same data sets and descriptors. From the scrutiny of the weight levels in each CPNN and the information retrieved from MLRs, a rational design of potentially active compounds was attempted. Two new compounds were synthesized and tested against M. tuberculosis showing an activity close to that predicted by the majority of the models. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Logisnet: A tool for multimethod, multiple soil layers slope stability analysis

Science.gov (United States)

Legorreta Paulin, G.; Bursik, M.

2009-05-01

Shallow landslides and slope failures have been studied from several points of view (inventory, heuristic, statistic, and deterministic). In particular, numerous methods embedded in Geographic Information Systems (GIS) applications have been developed to assess slope stability. However, little work has been done on the systematic comparison of different techniques and the incorporation of vertical contrasts of geotechnical properties in multiple soil layers. In this research, stability is modeled by using LOGISNET, an acronym for Multiple Logistic Regression, Geographic Information System, and Neural Network. The main purpose of LOGISNET is to provide government planners and decision makers a tool to assess landslide susceptibility. The system is fully operational for models handling an enhanced cartographic-hydrologic model (SINMAP) and multiple logistic regression. The enhanced implementation of SINMAP was tested at regional scale in the Highway 101 corridor in Del Norte County, California, and its susceptibility map was found to have improved factor of safety estimates based on comparison with landslide inventory maps. The enhanced SINMAP and multiple logistic regression subsystems have functions that allow the user to include vertical variation in geotechnical properties through summation of forces in specific soil layers acting on failure planes for a local or regional-scale mapping. The working group of LOGISNET foresees the development of an integrated tool system to handle and support the prognostic studies of slope instability, and communicate the results to the public through maps.

A Hybrid Fuzzy Time Series Approach Based on Fuzzy Clustering and Artificial Neural Network with Single Multiplicative Neuron Model

Directory of Open Access Journals (Sweden)

Ozge Cagcag Yolcu

2013-01-01

Full Text Available Particularly in recent years, artificial intelligence optimization techniques have been used to make fuzzy time series approaches more systematic and improve forecasting performance. Besides, some fuzzy clustering methods and artificial neural networks with different structures are used in the fuzzification of observations and determination of fuzzy relationships, respectively. In approaches considering the membership values, the membership values are determined subjectively or fuzzy outputs of the system are obtained by considering that there is a relation between membership values in identification of relation. This necessitates defuzzification step and increases the model error. In this study, membership values were obtained more systematically by using Gustafson-Kessel fuzzy clustering technique. The use of artificial neural network with single multiplicative neuron model in identification of fuzzy relation eliminated the architecture selection problem as well as the necessity for defuzzification step by constituting target values from real observations of time series. The training of artificial neural network with single multiplicative neuron model which is used for identification of fuzzy relation step is carried out with particle swarm optimization. The proposed method is implemented using various time series and the results are compared with those of previous studies to demonstrate the performance of the proposed method.

Controlled neural network application in track-match problem

International Nuclear Information System (INIS)

Baginyan, S.A.; Ososkov, G.A.

1993-01-01

Track-match problem of high energy physics (HEP) data handling is formulated in terms of incidence matrices. The corresponding Hopfield neural network is developed to solve this type of constraint satisfaction problems (CSP). A special concept of the controlled neural network is proposed as a basis of an algorithm for the effective CSP solution. Results of comparable calculations show the very high performance of this algorithm against conventional search procedures. 8 refs.; 1 fig.; 1 tab

Morphological neural networks

Energy Technology Data Exchange (ETDEWEB)

Ritter, G.X.; Sussner, P. [Univ. of Florida, Gainesville, FL (United States)

1996-12-31

The theory of artificial neural networks has been successfully applied to a wide variety of pattern recognition problems. In this theory, the first step in computing the next state of a neuron or in performing the next layer neural network computation involves the linear operation of multiplying neural values by their synaptic strengths and adding the results. Thresholding usually follows the linear operation in order to provide for nonlinearity of the network. In this paper we introduce a novel class of neural networks, called morphological neural networks, in which the operations of multiplication and addition are replaced by addition and maximum (or minimum), respectively. By taking the maximum (or minimum) of sums instead of the sum of products, morphological network computation is nonlinear before thresholding. As a consequence, the properties of morphological neural networks are drastically different than those of traditional neural network models. In this paper we consider some of these differences and provide some particular examples of morphological neural network.

Research on Handling Stability of Steering-by-wire System

Directory of Open Access Journals (Sweden)

Yuan Ying

2017-01-01

Full Text Available The main function of steer-by-wire (SBW system are improving steering characteristics, security and stability of the vehicle. In this paper, the variable steering ratio of SBW system is analyzed, and the method of steering ratio based on fuzzy control and neural network are researched. In order to solve the actual working condition, the wheel angle may not reach the expected value, this paper establishes a twodegree-of-freedom (2-DOF vehicle model, and a Matlab/Simulink simulation model, in which a control strategy based on PID controller is put forward to control the front wheel steering angle. Simulation results show that proposed control strategy based on fuzzy neural network can effectively reduce lateral deviation and improve the handling stability and comfortability of the vehicle.

Kernel Temporal Differences for Neural Decoding

Science.gov (United States)

Bae, Jihye; Sanchez Giraldo, Luis G.; Pohlmeyer, Eric A.; Francis, Joseph T.; Sanchez, Justin C.; Príncipe, José C.

2015-01-01

We study the feasibility and capability of the kernel temporal difference (KTD)(λ) algorithm for neural decoding. KTD(λ) is an online, kernel-based learning algorithm, which has been introduced to estimate value functions in reinforcement learning. This algorithm combines kernel-based representations with the temporal difference approach to learning. One of our key observations is that by using strictly positive definite kernels, algorithm's convergence can be guaranteed for policy evaluation. The algorithm's nonlinear functional approximation capabilities are shown in both simulations of policy evaluation and neural decoding problems (policy improvement). KTD can handle high-dimensional neural states containing spatial-temporal information at a reasonable computational complexity allowing real-time applications. When the algorithm seeks a proper mapping between a monkey's neural states and desired positions of a computer cursor or a robot arm, in both open-loop and closed-loop experiments, it can effectively learn the neural state to action mapping. Finally, a visualization of the coadaptation process between the decoder and the subject shows the algorithm's capabilities in reinforcement learning brain machine interfaces. PMID:25866504

Global robust stability of neural networks with multiple discrete delays and distributed delays

International Nuclear Information System (INIS)

Gao Ming; Cui Baotong

2009-01-01

The problem of global robust stability is investigated for a class of uncertain neural networks with both multiple discrete time-varying delays and distributed time-varying delays. The uncertainties are assumed to be of norm-bounded form and the activation functions are supposed to be bounded and globally Lipschitz continuous. Based on the Lyapunov stability theory and linear matrix inequality technique, some robust stability conditions guaranteeing the global robust convergence of the equilibrium point are derived. The proposed LMI-based criteria are computationally efficient as they can be easily checked by using recently developed algorithms in solving LMIs. Two examples are given to show the effectiveness of the proposed results.

Switched-Observer-Based Adaptive Neural Control of MIMO Switched Nonlinear Systems With Unknown Control Gains.

Science.gov (United States)

Long, Lijun; Zhao, Jun

2017-07-01

In this paper, the problem of adaptive neural output-feedback control is addressed for a class of multi-input multioutput (MIMO) switched uncertain nonlinear systems with unknown control gains. Neural networks (NNs) are used to approximate unknown nonlinear functions. In order to avoid the conservativeness caused by adoption of a common observer for all subsystems, an MIMO NN switched observer is designed to estimate unmeasurable states. A new switched observer-based adaptive neural control technique for the problem studied is then provided by exploiting the classical average dwell time (ADT) method and the backstepping method and the Nussbaum gain technique. It effectively handles the obstacle about the coexistence of multiple Nussbaum-type function terms, and improves the classical ADT method, since the exponential decline property of Lyapunov functions for individual subsystems is no longer satisfied. It is shown that the technique proposed is able to guarantee semiglobal uniformly ultimately boundedness of all the signals in the closed-loop system under a class of switching signals with ADT, and the tracking errors converge to a small neighborhood of the origin. The effectiveness of the approach proposed is illustrated by its application to a two inverted pendulum system.

Elastic tracking versus neural network tracking for very high multiplicity problems

International Nuclear Information System (INIS)

Harlander, M.; Gyulassy, M.

1991-04-01

A new Elastic Tracking (ET) algorithm is proposed for finding tracks in very high multiplicity and noisy environments. It is based on a dynamical reinterpretation and generalization of the Radon transform and is related to elastic net algorithms for geometrical optimization. ET performs an adaptive nonlinear fit to noisy data with a variable number of tracks. Its numerics is more efficient than that of the traditional Radon or Hough transform method because it avoids binning of phase space and the costly search for valid minima. Spurious local minima are avoided in ET by introducing a time-dependent effective potential. The method is shown to be very robust to noise and measurement error and extends tracking capabilities to much higher track densities than possible via local road finding or even the novel Denby-Peterson neural network tracking algorithms. 12 refs., 2 figs

DeepFix: A Fully Convolutional Neural Network for Predicting Human Eye Fixations.

Science.gov (United States)

Kruthiventi, Srinivas S S; Ayush, Kumar; Babu, R Venkatesh

2017-09-01

Understanding and predicting the human visual attention mechanism is an active area of research in the fields of neuroscience and computer vision. In this paper, we propose DeepFix, a fully convolutional neural network, which models the bottom-up mechanism of visual attention via saliency prediction. Unlike classical works, which characterize the saliency map using various hand-crafted features, our model automatically learns features in a hierarchical fashion and predicts the saliency map in an end-to-end manner. DeepFix is designed to capture semantics at multiple scales while taking global context into account, by using network layers with very large receptive fields. Generally, fully convolutional nets are spatially invariant-this prevents them from modeling location-dependent patterns (e.g., centre-bias). Our network handles this by incorporating a novel location-biased convolutional layer. We evaluate our model on multiple challenging saliency data sets and show that it achieves the state-of-the-art results.

A comparative study of two neural networks for document retrieval

International Nuclear Information System (INIS)

Hui, S.C.; Goh, A.

1997-01-01

In recent years there has been specific interest in adopting advanced computer techniques in the field of document retrieval. This interest is generated by the fact that classical methods such as the Boolean search, the vector space model or even probabilistic retrieval cannot handle the increasing demands of end-users in satisfying their needs. The most recent attempt is the application of the neural network paradigm as a means of providing end-users with a more powerful retrieval mechanism. Neural networks are not only good pattern matchers but also highly versatile and adaptable. In this paper, we demonstrate how to apply two neural networks, namely Adaptive Resonance Theory and Fuzzy Kohonen Neural Network, for document retrieval. In addition, a comparison of these two neural networks based on performance is also given

The multiple roles of Fatty Acid Handling Proteins in brain

Directory of Open Access Journals (Sweden)

Valentine SF Moullé

2012-09-01

Full Text Available Lipids are essential components of a living organism as energy source but also as constituent of the membrane lipid bilayer. In addition fatty acid (FA derivatives interact with many signaling pathways. FAs have amphipathic properties and therefore require being associated to protein for both transport and intracellular trafficking. Here we will focus on several fatty acid handling proteins, among which the fatty acid translocase/CD36 (FAT/CD36, members of fatty acid transport proteins (FATPs, and lipid chaperones fatty acid-binding proteins (FABPs. A decade of extensive studies has helped decipher the mechanism of action of these proteins in peripheral tissue with high lipid metabolism. However, considerably less information is available regarding their role in the brain, despite the high lipid content of this tissue. This review will primarily focus on the recent studies that have highlighted the crucial role of lipid handling proteins in brain FA transport, neuronal differentiation and development, cognitive processes and brain diseases. Finally a special focus will be made on the recent studies that have revealed the role of FAT/CD36 in brain lipid sensing and nervous control of energy balance.

The use of artificial neural network analysis and multiple regression for trap quality evaluation: a case study of the Northern Kuqa Depression of Tarim Basin in western China

Energy Technology Data Exchange (ETDEWEB)

Guangren Shi; Xingxi Zhou; Guangya Zhang; Xiaofeng Shi; Honghui Li [Research Institute of Petroleum Exploration and Development, Beijing (China)

2004-03-01

Artificial neural network analysis is found to be far superior to multiple regression when applied to the evaluation of trap quality in the Northern Kuqa Depression, a gas-rich depression of Tarim Basin in western China. This is because this technique can correlate the complex and non-linear relationship between trap quality and related geological factors, whereas multiple regression can only describe a linear relationship. However, multiple regression can work as an auxiliary tool, as it is suited to high-speed calculations and can indicate the degree of dependence between the trap quality and its related geological factors which artificial neural network analysis cannot. For illustration, we have investigated 30 traps in the Northern Kuqa Depression. For each of the traps, the values of 14 selected geological factors were all known. While geologists were also able to assign individual trap quality values to 27 traps, they were less certain about the values for the other three traps. Multiple regression and artificial neural network analysis were, therefore, respectively used to ascertain these values. Data for the 27 traps were used as known sample data, while the three traps were used as prediction candidates. Predictions from artificial neural network analysis are found to agree with exploration results: where simulation predicted high trap quality, commercial quality flows were afterwards found, and where low trap quality is indicated, no such discoveries have yet been made. On the other hand, multiple regression results indicate the order of dependence of the trap quality on geological factors, which reconciles with what geologists have commonly recognized. We can conclude, therefore, that the application of artificial neural network analysis with the aid of multiple regression to trap evaluation in the Northern Kuqa Depression has been quite successful. To ensure the precision of the above mentioned geological factors and their related parameters for each

Tracking Control Based on Recurrent Neural Networks for Nonlinear Systems with Multiple Inputs and Unknown Deadzone

Directory of Open Access Journals (Sweden)

J. Humberto Pérez-Cruz

2012-01-01

Full Text Available This paper deals with the problem of trajectory tracking for a broad class of uncertain nonlinear systems with multiple inputs each one subject to an unknown symmetric deadzone. On the basis of a model of the deadzone as a combination of a linear term and a disturbance-like term, a continuous-time recurrent neural network is directly employed in order to identify the uncertain dynamics. By using a Lyapunov analysis, the exponential convergence of the identification error to a bounded zone is demonstrated. Subsequently, by a proper control law, the state of the neural network is compelled to follow a bounded reference trajectory. This control law is designed in such a way that the singularity problem is conveniently avoided and the exponential convergence to a bounded zone of the difference between the state of the neural identifier and the reference trajectory can be proven. Thus, the exponential convergence of the tracking error to a bounded zone and the boundedness of all closed-loop signals can be guaranteed. One of the main advantages of the proposed strategy is that the controller can work satisfactorily without any specific knowledge of an upper bound for the unmodeled dynamics and/or the disturbance term.

Stimulation and recording electrodes for neural prostheses

CERN Document Server

Pour Aryan, Naser; Rothermel, Albrecht

2015-01-01

This book provides readers with basic principles of the electrochemistry of the electrodes used in modern, implantable neural prostheses. The authors discuss the boundaries and conditions in which the electrodes continue to function properly for long time spans, which are required when designing neural stimulator devices for long-term in vivo applications. Two kinds of electrode materials, titanium nitride and iridium are discussed extensively, both qualitatively and quantitatively. The influence of the counter electrode on the safety margins and electrode lifetime in a two electrode system is explained. Electrode modeling is handled in a final chapter.

A Neural Network Model to Learn Multiple Tasks under Dynamic Environments

Science.gov (United States)

Tsumori, Kenji; Ozawa, Seiichi

When environments are dynamically changed for agents, the knowledge acquired in an environment might be useless in future. In such dynamic environments, agents should be able to not only acquire new knowledge but also modify old knowledge in learning. However, modifying all knowledge acquired before is not efficient because the knowledge once acquired may be useful again when similar environment reappears and some knowledge can be shared among different environments. To learn efficiently in such environments, we propose a neural network model that consists of the following modules: resource allocating network, long-term & short-term memory, and environment change detector. We evaluate the model under a class of dynamic environments where multiple function approximation tasks are sequentially given. The experimental results demonstrate that the proposed model possesses stable incremental learning, accurate environmental change detection, proper association and recall of old knowledge, and efficient knowledge transfer.

Safety issues in robotic handling of nuclear weapon parts

International Nuclear Information System (INIS)

Drotning, W.; Wapman, W.; Fahrenholtz, J.

1993-01-01

Robotic systems are being developed by the Intelligent Systems and Robotics Center at Sandia National Laboratories to perform automated handling tasks with radioactive weapon parts. These systems will reduce the occupational radiation exposure to workers by automating operations that are currently performed manually. The robotic systems at Sandia incorporate several levels of mechanical, electrical, and software safety for handling hazardous materials. For example, tooling used by the robot to handle radioactive parts has been designed with mechanical features that allow the robot to release its payload only at designated locations in the robotic workspace. In addition, software processes check for expected and unexpected situations throughout the operations. Incorporation of features such as these provides multiple levels of safety for handling hazardous or valuable payloads with automated intelligent systems

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

OpenAIRE

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational app...

Assessment of the expected construction company’s net profit using neural network and multiple regression models

Directory of Open Access Journals (Sweden)

H.H. Mohamad

2013-09-01

This research aims to develop a mathematical model for assessing the expected net profit of any construction company. To achieve the research objective, four steps were performed. First, the main factors affecting firms’ net profit were identified. Second, pertinent data regarding the net profit factors were collected. Third, two different net profit models were developed using the Multiple Regression (MR and the Neural Network (NN techniques. The validity of the proposed models was also investigated. Finally, the results of both MR and NN models were compared to investigate the predictive capabilities of the two models.

Neural Networks for Segregation of Multiple Objects: Visual Figure-Ground Separation and Auditory Pitch Perception.

Science.gov (United States)

Wyse, Lonce

An important component of perceptual object recognition is the segmentation into coherent perceptual units of the "blooming buzzing confusion" that bombards the senses. The work presented herein develops neural network models of some key processes of pre-attentive vision and audition that serve this goal. A neural network model, called an FBF (Feature -Boundary-Feature) network, is proposed for automatic parallel separation of multiple figures from each other and their backgrounds in noisy images. Figure-ground separation is accomplished by iterating operations of a Boundary Contour System (BCS) that generates a boundary segmentation of a scene, and a Feature Contour System (FCS) that compensates for variable illumination and fills-in surface properties using boundary signals. A key new feature is the use of the FBF filling-in process for the figure-ground separation of connected regions, which are subsequently more easily recognized. The new CORT-X 2 model is a feed-forward version of the BCS that is designed to detect, regularize, and complete boundaries in up to 50 percent noise. It also exploits the complementary properties of on-cells and off -cells to generate boundary segmentations and to compensate for boundary gaps during filling-in. In the realm of audition, many sounds are dominated by energy at integer multiples, or "harmonics", of a fundamental frequency. For such sounds (e.g., vowels in speech), the individual frequency components fuse, so that they are perceived as one sound source with a pitch at the fundamental frequency. Pitch is integral to separating auditory sources, as well as to speaker identification and speech understanding. A neural network model of pitch perception called SPINET (SPatial PItch NETwork) is developed and used to simulate a broader range of perceptual data than previous spectral models. The model employs a bank of narrowband filters as a simple model of basilar membrane mechanics, spectral on-center off-surround competitive

Project conservation and handling for the multiple uses and the development of the swamps of Colombia

International Nuclear Information System (INIS)

Sanchez Paez, Heliodoro

1998-01-01

In Colombia the swamps are distributed in the Atlantic and Pacific coasts; they are located in areas with conditions of pluvial precipitation that varies from annual 200 mm in the Guajira department; nine species of mangroves have registered for Colombia, which are related following a zonation starting from the tide line, still when this pattern not always stays and it depends on the influence of diverse factors and local conditions. The project conservation and handling for the multiple use and the development of the swamps of Colombia, had as objectives; to watch and to control the parameters of salinity, level of water and flow to laminate and of strengthening the generation of alternative productive social and environmentally appropriate for the sustainable use, guaranteeing their conservation and preservation of the swamps. The project was developed in two phases, one of diagnostic and planning and the other of dynamics of growth, phenology and natural regeneration

ERROR HANDLING IN INTEGRATION WORKFLOWS

Directory of Open Access Journals (Sweden)

Alexey M. Nazarenko

2017-01-01

Full Text Available Simulation experiments performed while solving multidisciplinary engineering and scientific problems require joint usage of multiple software tools. Further, when following a preset plan of experiment or searching for optimum solu- tions, the same sequence of calculations is run multiple times with various simulation parameters, input data, or conditions while overall workflow does not change. Automation of simulations like these requires implementing of a workflow where tool execution and data exchange is usually controlled by a special type of software, an integration environment or plat- form. The result is an integration workflow (a platform-dependent implementation of some computing workflow which, in the context of automation, is a composition of weakly coupled (in terms of communication intensity typical subtasks. These compositions can then be decomposed back into a few workflow patterns (types of subtasks interaction. The pat- terns, in their turn, can be interpreted as higher level subtasks.This paper considers execution control and data exchange rules that should be imposed by the integration envi- ronment in the case of an error encountered by some integrated software tool. An error is defined as any abnormal behavior of a tool that invalidates its result data thus disrupting the data flow within the integration workflow. The main requirementto the error handling mechanism implemented by the integration environment is to prevent abnormal termination of theentire workflow in case of missing intermediate results data. Error handling rules are formulated on the basic pattern level and on the level of a composite task that can combine several basic patterns as next level subtasks. The cases where workflow behavior may be different, depending on user's purposes, when an error takes place, and possible error handling op- tions that can be specified by the user are also noted in the work.

Mockup of an automated material transport system for remote handling

International Nuclear Information System (INIS)

Porter, M.L.

1992-01-01

An Automated Material Transport System (AMTS) was identified for transport of samples within a Material and Process Control Laboratory (MPCL). The MPCL was designed with a dry sample handling laboratory and a wet chemistry analysis laboratory. Each laboratory contained several processing gloveboxes. The function of the AMTS was to automate the handling of materials, multiple process samples, and bulky items between process stations with a minimum of operator intervention and with minimum o[ waiting periods and nonproductive activities. This paper discusses the system design features, capabilities and results of initial testing. The overall performance of the AMTS is very good. No major problems or concerns were identified. System commands are simple and logical making the system user friendly. Operating principle and design of individual components is simple. With the addition of various track modules, the system can be configured in most any configuration. The AMTS lends itself very well for integration with other automated systems or products. The AMTS is suited for applications involving light payloads which require multiple sample and material handling, lot tracking, and system integration with other products

One weird trick for parallelizing convolutional neural networks

OpenAIRE

Krizhevsky, Alex

2014-01-01

I present a new way to parallelize the training of convolutional neural networks across multiple GPUs. The method scales significantly better than all alternatives when applied to modern convolutional neural networks.

Neural Dynamics of Multiple Object Processing in Mild Cognitive Impairment and Alzheimer's Disease: Future Early Diagnostic Biomarkers?

Science.gov (United States)

Bagattini, Chiara; Mazza, Veronica; Panizza, Laura; Ferrari, Clarissa; Bonomini, Cristina; Brignani, Debora

2017-01-01

The aim of this study was to investigate the behavioral and electrophysiological dynamics of multiple object processing (MOP) in mild cognitive impairment (MCI) and Alzheimer's disease (AD), and to test whether its neural signatures may represent reliable diagnostic biomarkers. Behavioral performance and event-related potentials [N2pc and contralateral delay activity (CDA)] were measured in AD, MCI, and healthy controls during a MOP task, which consisted in enumerating a variable number of targets presented among distractors. AD patients showed an overall decline in accuracy for both small and large target quantities, whereas in MCI patients, only enumeration of large quantities was impaired. N2pc, a neural marker of attentive individuation, was spared in both AD and MCI patients. In contrast, CDA, which indexes visual short term memory abilities, was altered in both groups of patients, with a non-linear pattern of amplitude modulation along the continuum of the disease: a reduction in AD and an increase in MCI. These results indicate that AD pathology shows a progressive decline in MOP, which is associated to the decay of visual short-term memory mechanisms. Crucially, CDA may be considered as a useful neural signature both to distinguish between healthy and pathological aging and to characterize the different stages along the AD continuum, possibly becoming a reliable candidate for an early diagnostic biomarker of AD pathology.

Adaptive Graph Convolutional Neural Networks

OpenAIRE

Li, Ruoyu; Wang, Sheng; Zhu, Feiyun; Huang, Junzhou

2018-01-01

Graph Convolutional Neural Networks (Graph CNNs) are generalizations of classical CNNs to handle graph data such as molecular data, point could and social networks. Current filters in graph CNNs are built for fixed and shared graph structure. However, for most real data, the graph structures varies in both size and connectivity. The paper proposes a generalized and flexible graph CNN taking data of arbitrary graph structure as input. In that way a task-driven adaptive graph is learned for eac...

Stability and bifurcation in a simplified four-neuron BAM neural network with multiple delays

Directory of Open Access Journals (Sweden)

2006-01-01

Full Text Available We first study the distribution of the zeros of a fourth-degree exponential polynomial. Then we apply the obtained results to a simplified bidirectional associated memory (BAM neural network with four neurons and multiple time delays. By taking the sum of the delays as the bifurcation parameter, it is shown that under certain assumptions the steady state is absolutely stable. Under another set of conditions, there are some critical values of the delay, when the delay crosses these critical values, the Hopf bifurcation occurs. Furthermore, some explicit formulae determining the stability and the direction of periodic solutions bifurcating from Hopf bifurcations are obtained by applying the normal form theory and center manifold reduction. Numerical simulations supporting the theoretical analysis are also included.

Efficient organ localization using multi-label convolutional neural networks in thorax-abdomen CT scans

Science.gov (United States)

Efrain Humpire-Mamani, Gabriel; Arindra Adiyoso Setio, Arnaud; van Ginneken, Bram; Jacobs, Colin

2018-04-01

Automatic localization of organs and other structures in medical images is an important preprocessing step that can improve and speed up other algorithms such as organ segmentation, lesion detection, and registration. This work presents an efficient method for simultaneous localization of multiple structures in 3D thorax-abdomen CT scans. Our approach predicts the location of multiple structures using a single multi-label convolutional neural network for each orthogonal view. Each network takes extra slices around the current slice as input to provide extra context. A sigmoid layer is used to perform multi-label classification. The output of the three networks is subsequently combined to compute a 3D bounding box for each structure. We used our approach to locate 11 structures of interest. The neural network was trained and evaluated on a large set of 1884 thorax-abdomen CT scans from patients undergoing oncological workup. Reference bounding boxes were annotated by human observers. The performance of our method was evaluated by computing the wall distance to the reference bounding boxes. The bounding boxes annotated by the first human observer were used as the reference standard for the test set. Using the best configuration, we obtained an average wall distance of 3.20~+/-~7.33 mm in the test set. The second human observer achieved 1.23~+/-~3.39 mm. For all structures, the results were better than those reported in previously published studies. In conclusion, we proposed an efficient method for the accurate localization of multiple organs. Our method uses multiple slices as input to provide more context around the slice under analysis, and we have shown that this improves performance. This method can easily be adapted to handle more organs.

Vibration analysis in nuclear power plant using neural networks

International Nuclear Information System (INIS)

Loskiewicz-Buczak, A.; Alguindigue, I.E.

1993-01-01

Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. This paper documents the authors' work on the design of a vibration monitoring methodology enhanced by neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to handle data which may be distorted or noisy. This paper describes three neural networks-based methods for the automation of some of the activities related to motion and vibration monitoring in engineering systems

Basic thinking patterns and working methods for multiple DFX

DEFF Research Database (Denmark)

Andreasen, Mogens Myrup; Mortensen, Niels Henrik

1997-01-01

This paper attempts to describe the theory and methodologies behind DFX and linking multiple DFX's together. The contribution is an articulation of basic thinking patterns and description of some working methods for handling multiple DFX.......This paper attempts to describe the theory and methodologies behind DFX and linking multiple DFX's together. The contribution is an articulation of basic thinking patterns and description of some working methods for handling multiple DFX....

Learning of N-layers neural network

Directory of Open Access Journals (Sweden)

Vladimír Konečný

2005-01-01

Full Text Available In the last decade we can observe increasing number of applications based on the Artificial Intelligence that are designed to solve problems from different areas of human activity. The reason why there is so much interest in these technologies is that the classical way of solutions does not exist or these technologies are not suitable because of their robustness. They are often used in applications like Business Intelligence that enable to obtain useful information for high-quality decision-making and to increase competitive advantage.One of the most widespread tools for the Artificial Intelligence are the artificial neural networks. Their high advantage is relative simplicity and the possibility of self-learning based on set of pattern situations.For the learning phase is the most commonly used algorithm back-propagation error (BPE. The base of BPE is the method minima of error function representing the sum of squared errors on outputs of neural net, for all patterns of the learning set. However, while performing BPE and in the first usage, we can find out that it is necessary to complete the handling of the learning factor by suitable method. The stability of the learning process and the rate of convergence depend on the selected method. In the article there are derived two functions: one function for the learning process management by the relative great error function value and the second function when the value of error function approximates to global minimum.The aim of the article is to introduce the BPE algorithm in compact matrix form for multilayer neural networks, the derivation of the learning factor handling method and the presentation of the results.

Exploiting Deep Neural Networks and Head Movements for Robust Binaural Localization of Multiple Sources in Reverberant Environments

DEFF Research Database (Denmark)

Ma, Ning; May, Tobias; Brown, Guy J.

2017-01-01

This paper presents a novel machine-hearing system that exploits deep neural networks (DNNs) and head movements for robust binaural localization of multiple sources in reverberant environments. DNNs are used to learn the relationship between the source azimuth and binaural cues, consisting...... of the complete cross-correlation function (CCF) and interaural level differences (ILDs). In contrast to many previous binaural hearing systems, the proposed approach is not restricted to localization of sound sources in the frontal hemifield. Due to the similarity of binaural cues in the frontal and rear...

Global existence of periodic solutions in a simplified four-neuron BAM neural network model with multiple delays

Directory of Open Access Journals (Sweden)

2006-01-01

Full Text Available We consider a simplified bidirectional associated memory (BAM neural network model with four neurons and multiple time delays. The global existence of periodic solutions bifurcating from Hopf bifurcations is investigated by applying the global Hopf bifurcation theorem due to Wu and Bendixson's criterion for high-dimensional ordinary differential equations due to Li and Muldowney. It is shown that the local Hopf bifurcation implies the global Hopf bifurcation after the second critical value of the sum of two delays. Numerical simulations supporting the theoretical analysis are also included.

Flexible microelectrode array for interfacing with the surface of neural ganglia

Science.gov (United States)

Sperry, Zachariah J.; Na, Kyounghwan; Parizi, Saman S.; Chiel, Hillel J.; Seymour, John; Yoon, Euisik; Bruns, Tim M.

2018-06-01

Objective. The dorsal root ganglia (DRG) are promising nerve structures for sensory neural interfaces because they provide centralized access to primary afferent cell bodies and spinal reflex circuitry. In order to harness this potential, new electrode technologies are needed which take advantage of the unique properties of DRG, specifically the high density of neural cell bodies at the dorsal surface. Here we report initial in vivo results from the development of a flexible non-penetrating polyimide electrode array interfacing with the surface of ganglia. Approach. Multiple layouts of a 64-channel iridium electrode (420 µm2) array were tested, with pitch as small as 25 µm. The buccal ganglia of invertebrate sea slug Aplysia californica were used to develop handling and recording techniques with ganglionic surface electrode arrays (GSEAs). We also demonstrated the GSEA’s capability to record single- and multi-unit activity from feline lumbosacral DRG related to a variety of sensory inputs, including cutaneous brushing, joint flexion, and bladder pressure. Main results. We recorded action potentials from a variety of Aplysia neurons activated by nerve stimulation, and units were observed firing simultaneously on closely spaced electrode sites. We also recorded single- and multi-unit activity associated with sensory inputs from feline DRG. We utilized spatial oversampling of action potentials on closely-spaced electrode sites to estimate the location of neural sources at between 25 µm and 107 µm below the DRG surface. We also used the high spatial sampling to demonstrate a possible spatial sensory map of one feline’s DRG. We obtained activation of sensory fibers with low-amplitude stimulation through individual or groups of GSEA electrode sites. Significance. Overall, the GSEA has been shown to provide a variety of information types from ganglia neurons and to have significant potential as a tool for neural mapping and interfacing.

Fuel handling machine and auxiliary systems for a fuel handling cell

International Nuclear Information System (INIS)

Suikki, M.

2013-10-01

This working report is an update for as well as a supplement to an earlier fuel handling machine design (Kukkola and Roennqvist 2006). A focus in the earlier design proposal was primarily on the selection of a mechanical structure and operating principle for the fuel handling machine. This report introduces not only a fuel handling machine design but also auxiliary fuel handling cell equipment and its operation. An objective of the design work was to verify the operating principles of and space allocations for fuel handling cell equipment. The fuel handling machine is a remote controlled apparatus capable of handling intensely radiating fuel assemblies in the fuel handling cell of an encapsulation plant. The fuel handling cell is air tight space radiation-shielded with massive concrete walls. The fuel handling machine is based on a bridge crane capable of traveling in the handling cell along wall tracks. The bridge crane has its carriage provided with a carousel type turntable having mounted thereon both fixed and telescopic masts. The fixed mast has a gripper movable on linear guides for the transfer of fuel assemblies. The telescopic mast has a manipulator arm capable of maneuvering equipment present in the fuel handling cell, as well as conducting necessary maintenance and cleaning operations or rectifying possible fault conditions. The auxiliary fuel handling cell systems consist of several subsystems. The subsystems include a service manipulator, a tool carrier for manipulators, a material hatch, assisting winches, a vacuum cleaner, as well as a hose reel. With the exception of the vacuum cleaner, the devices included in the fuel handling cell's auxiliary system are only used when the actual encapsulation process is not ongoing. The malfunctions of mechanisms or actuators responsible for the motion actions of a fuel handling machine preclude in a worst case scenario the bringing of the fuel handling cell and related systems to a condition appropriate for

Fuel handling machine and auxiliary systems for a fuel handling cell

Energy Technology Data Exchange (ETDEWEB)

Suikki, M. [Optimik Oy, Turku (Finland)

2013-10-15

This working report is an update for as well as a supplement to an earlier fuel handling machine design (Kukkola and Roennqvist 2006). A focus in the earlier design proposal was primarily on the selection of a mechanical structure and operating principle for the fuel handling machine. This report introduces not only a fuel handling machine design but also auxiliary fuel handling cell equipment and its operation. An objective of the design work was to verify the operating principles of and space allocations for fuel handling cell equipment. The fuel handling machine is a remote controlled apparatus capable of handling intensely radiating fuel assemblies in the fuel handling cell of an encapsulation plant. The fuel handling cell is air tight space radiation-shielded with massive concrete walls. The fuel handling machine is based on a bridge crane capable of traveling in the handling cell along wall tracks. The bridge crane has its carriage provided with a carousel type turntable having mounted thereon both fixed and telescopic masts. The fixed mast has a gripper movable on linear guides for the transfer of fuel assemblies. The telescopic mast has a manipulator arm capable of maneuvering equipment present in the fuel handling cell, as well as conducting necessary maintenance and cleaning operations or rectifying possible fault conditions. The auxiliary fuel handling cell systems consist of several subsystems. The subsystems include a service manipulator, a tool carrier for manipulators, a material hatch, assisting winches, a vacuum cleaner, as well as a hose reel. With the exception of the vacuum cleaner, the devices included in the fuel handling cell's auxiliary system are only used when the actual encapsulation process is not ongoing. The malfunctions of mechanisms or actuators responsible for the motion actions of a fuel handling machine preclude in a worst case scenario the bringing of the fuel handling cell and related systems to a condition appropriate for

Fuel handling grapple for nuclear reactor plants

International Nuclear Information System (INIS)

Rousar, D.L.

1992-01-01

This patent describes a fuel handling system for nuclear reactor plants. It comprises: a reactor vessel having an openable top and removable cover and containing therein, submerged in water substantially filling the reactor vessel, a fuel core including a multiplicity of fuel bundles formed of groups of sealed tube elements enclosing fissionable fuel assembled into units, the fuel handling system consisting essentially of the combination of: a fuel bundle handling platform movable over the open top of the reactor vessel; a fuel bundle handling mast extendable downward from the platform with a lower end projecting into the open top reactor vessel to the fuel core submerged in water; a grapple head mounted on the lower end of the mast provided with grapple means comprising complementary hooks which pivot inward toward each other to securely grasp a bail handle of a nuclear reactor fuel bundle and pivot backward away from each other to release a bail handle; the grapple means having a hollow cylindrical support shaft fixed within the grapple head with hollow cylindrical sleeves rotatably mounted and fixed in longitudinal axial position on the support shaft and each sleeve having complementary hooks secured thereto whereby each hook pivots with the rotation of the sleeve secured thereto; and the hollow cylindrical support shaft being provided with complementary orifices on opposite sides of its hollow cylindrical and intermediate to the sleeves mounted thereon whereby the orifices on both sides of the hollow cylindrical support shaft are vertically aligned providing a direct in-line optical viewing path downward there-through and a remote operator positioned above the grapple means can observe from overhead the area immediately below the grapple hooks

Structured Memory for Neural Turing Machines

OpenAIRE

Zhang, Wei; Yu, Yang; Zhou, Bowen

2015-01-01

Neural Turing Machines (NTM) contain memory component that simulates "working memory" in the brain to store and retrieve information to ease simple algorithms learning. So far, only linearly organized memory is proposed, and during experiments, we observed that the model does not always converge, and overfits easily when handling certain tasks. We think memory component is key to some faulty behaviors of NTM, and better organization of memory component could help fight those problems. In this...

A Dynamic Programming-Based Heuristic for the Shift Design Problem in Airport Ground Handling

DEFF Research Database (Denmark)

Clausen, Tommy

We consider the heterogeneous shift design problem for a workforce with multiple skills, where work shifts are created to cover a given demand as well as possible while minimizing cost and satisfying a flexible set of constraints. We focus mainly on applications within airport ground handling whe...... programming that allows flexibility in modeling the workforce. Parameters allow a planner to determine the level of demand coverage that best fulfills the requirements of the organization. Results are presented from several diverse real-life ground handling instances.......We consider the heterogeneous shift design problem for a workforce with multiple skills, where work shifts are created to cover a given demand as well as possible while minimizing cost and satisfying a flexible set of constraints. We focus mainly on applications within airport ground handling where...

Comparison of multiple linear regression, partial least squares and artificial neural networks for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids.

Science.gov (United States)

Fragkaki, A G; Farmaki, E; Thomaidis, N; Tsantili-Kakoulidou, A; Angelis, Y S; Koupparis, M; Georgakopoulos, C

2012-09-21

The comparison among different modelling techniques, such as multiple linear regression, partial least squares and artificial neural networks, has been performed in order to construct and evaluate models for prediction of gas chromatographic relative retention times of trimethylsilylated anabolic androgenic steroids. The performance of the quantitative structure-retention relationship study, using the multiple linear regression and partial least squares techniques, has been previously conducted. In the present study, artificial neural networks models were constructed and used for the prediction of relative retention times of anabolic androgenic steroids, while their efficiency is compared with that of the models derived from the multiple linear regression and partial least squares techniques. For overall ranking of the models, a novel procedure [Trends Anal. Chem. 29 (2010) 101-109] based on sum of ranking differences was applied, which permits the best model to be selected. The suggested models are considered useful for the estimation of relative retention times of designer steroids for which no analytical data are available. Copyright © 2012 Elsevier B.V. All rights reserved.

Ship Benchmark Shaft and Engine Gain FDI Using Neural Network

DEFF Research Database (Denmark)

Bendtsen, Jan Dimon; Izadi-Zamanabadi, Roozbeh

2002-01-01

threshold value. In the paper a method for determining this threshold based on the neural network model is proposed, which can be used for a design strategy to handle residual sensitivity to input variations. The proposed method is used for successful FDI of a diesel engine gain fault in a ship propulsion...

Integrated Neural Flight and Propulsion Control System

Science.gov (United States)

Kaneshige, John; Gundy-Burlet, Karen; Norvig, Peter (Technical Monitor)

2001-01-01

This paper describes an integrated neural flight and propulsion control system. which uses a neural network based approach for applying alternate sources of control power in the presence of damage or failures. Under normal operating conditions, the system utilizes conventional flight control surfaces. Neural networks are used to provide consistent handling qualities across flight conditions and for different aircraft configurations. Under damage or failure conditions, the system may utilize unconventional flight control surface allocations, along with integrated propulsion control, when additional control power is necessary for achieving desired flight control performance. In this case, neural networks are used to adapt to changes in aircraft dynamics and control allocation schemes. Of significant importance here is the fact that this system can operate without emergency or backup flight control mode operations. An additional advantage is that this system can utilize, but does not require, fault detection and isolation information or explicit parameter identification. Piloted simulation studies were performed on a commercial transport aircraft simulator. Subjects included both NASA test pilots and commercial airline crews. Results demonstrate the potential for improving handing qualities and significantly increasing survivability rates under various simulated failure conditions.

Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

International Nuclear Information System (INIS)

Treutterer, W.; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

2013-01-01

Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

Event detection and exception handling strategies in the ASDEX Upgrade discharge control system

Energy Technology Data Exchange (ETDEWEB)

Treutterer, W., E-mail: Wolfgang.Treutterer@ipp.mpg.de; Neu, G.; Rapson, C.; Raupp, G.; Zasche, D.; Zehetbauer, T.

2013-10-15

Highlights: •Event detection and exception handling is integrated in control system architecture. •Pulse control with local exception handling and pulse supervision with central exception handling are strictly separated. •Local exception handling limits the effect of an exception to a minimal part of the controlled system. •Central Exception Handling solves problems requiring coordinated action of multiple control components. -- Abstract: Thermonuclear plasmas are governed by nonlinear characteristics: plasma operation can be classified into scenarios with pronounced features like L and H-mode, ELMs or MHD activity. Transitions between them may be treated as events. Similarly, technical systems are also subject to events such as failure of measurement sensors, actuator saturation or violation of machine and plant operation limits. Such situations often are handled with a mixture of pulse abortion and iteratively improved pulse schedule reference programming. In case of protection-relevant events, however, the complexity of even a medium-sized device as ASDEX Upgrade requires a sophisticated and coordinated shutdown procedure rather than a simple stop of the pulse. The detection of events and their intelligent handling by the control system has been shown to be valuable also in terms of saving experiment time and cost. This paper outlines how ASDEX Upgrade's discharge control system (DCS) detects events and handles exceptions in two stages: locally and centrally. The goal of local exception handling is to limit the effect of an unexpected or asynchronous event to a minimal part of the controlled system. Thus, local exception handling facilitates robustness to failures but keeps the decision structures lean. A central state machine deals with exceptions requiring coordinated action of multiple control components. DCS implements the state machine by means of pulse schedule segments containing pre-programmed waveforms to define discharge goal and control

Neural tube closure depends on expression of Grainyhead-like 3 in multiple tissues.

Science.gov (United States)

De Castro, Sandra C P; Hirst, Caroline S; Savery, Dawn; Rolo, Ana; Lickert, Heiko; Andersen, Bogi; Copp, Andrew J; Greene, Nicholas D E

2018-03-15

Failure of neural tube closure leads to neural tube defects (NTDs), common congenital abnormalities in humans. Among the genes whose loss of function causes NTDs in mice, Grainyhead-like3 (Grhl3) is essential for spinal neural tube closure, with null mutants exhibiting fully penetrant spina bifida. During spinal neurulation Grhl3 is initially expressed in the surface (non-neural) ectoderm, subsequently in the neuroepithelial component of the neural folds and at the node-streak border, and finally in the hindgut endoderm. Here, we show that endoderm-specific knockout of Grhl3 causes late-arising spinal NTDs, preceded by increased ventral curvature of the caudal region which was shown previously to suppress closure of the spinal neural folds. This finding supports the hypothesis that diminished Grhl3 expression in the hindgut is the cause of spinal NTDs in the curly tail, carrying a hypomorphic Grhl3 allele. Complete loss of Grhl3 function produces a more severe phenotype in which closure fails earlier in neurulation, before the stage of onset of expression in the hindgut of wild-type embryos. This implicates additional tissues and NTD mechanisms in Grhl3 null embryos. Conditional knockout of Grhl3 in the neural plate and node-streak border has minimal effect on closure, suggesting that abnormal function of surface ectoderm, where Grhl3 transcripts are first detected, is primarily responsible for early failure of spinal neurulation in Grhl3 null embryos. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

Handling limited datasets with neural networks in medical applications: A small-data approach.

Science.gov (United States)

Shaikhina, Torgyn; Khovanova, Natalia A

2017-01-01

Single-centre studies in medical domain are often characterised by limited samples due to the complexity and high costs of patient data collection. Machine learning methods for regression modelling of small datasets (less than 10 observations per predictor variable) remain scarce. Our work bridges this gap by developing a novel framework for application of artificial neural networks (NNs) for regression tasks involving small medical datasets. In order to address the sporadic fluctuations and validation issues that appear in regression NNs trained on small datasets, the method of multiple runs and surrogate data analysis were proposed in this work. The approach was compared to the state-of-the-art ensemble NNs; the effect of dataset size on NN performance was also investigated. The proposed framework was applied for the prediction of compressive strength (CS) of femoral trabecular bone in patients suffering from severe osteoarthritis. The NN model was able to estimate the CS of osteoarthritic trabecular bone from its structural and biological properties with a standard error of 0.85MPa. When evaluated on independent test samples, the NN achieved accuracy of 98.3%, outperforming an ensemble NN model by 11%. We reproduce this result on CS data of another porous solid (concrete) and demonstrate that the proposed framework allows for an NN modelled with as few as 56 samples to generalise on 300 independent test samples with 86.5% accuracy, which is comparable to the performance of an NN developed with 18 times larger dataset (1030 samples). The significance of this work is two-fold: the practical application allows for non-destructive prediction of bone fracture risk, while the novel methodology extends beyond the task considered in this study and provides a general framework for application of regression NNs to medical problems characterised by limited dataset sizes. Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.

32P-postlabeling assay for carcinogen-DNA adducts: description of beta shielding apparatus and semi-automatic spotting and washing devices that facilitate the handling of multiple samples

International Nuclear Information System (INIS)

Reddy, M.V.; Blackburn, G.R.

1990-01-01

The utilization of the 32 P-postlabeling assay in combination with TLC for the sensitive detection and estimation of aromatic DNA adducts has been increasing. The procedure consists of 32 P-labeling of carcinogen-adducted 3'-nucleotides in the DNA digests using γ- 32 P ATP and polynucleotide kinase, separation of 32 P-labeled adducts by TLC, and their detection by autoradiography. During both 32 P-labeling and initial phases of TLC, a relatively high amount of γ- 32 P ATP is handled when 30 samples are processed simultaneously. We describe the design of acrylic shielding apparatus, semi-automatic TLC spotting devices, and devices for development and washing of multiple TLC plates, which not only provide substantial protection from exposure to 32 P beta radiation, but also allow quick and easy handling of a large number of samples. Specifically, the equipment includes: (i) a multi-tube carousel rack having 15 wells to hold capless Eppendorf tubes and a rotatable lid with an aperture to access individual tubes; (ii) a pipette shielder; (iii) two semi-automatic spotting devices to apply radioactive solutions to TLC plates; (iv) a multi-plate holder for TLC plates; and (v) a mechanical device for washing multiple TLC plates. Item (i) is small enough to be held in one-hand, vortexed, and centrifuged to mix the solutions in each tube while beta radiation is shielded. Items (iii) to (iv) aid in the automation of the assay. (author)

Designing neural networks that process mean values of random variables

International Nuclear Information System (INIS)

Barber, Michael J.; Clark, John W.

2014-01-01

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence

Designing neural networks that process mean values of random variables

Energy Technology Data Exchange (ETDEWEB)

Barber, Michael J. [AIT Austrian Institute of Technology, Innovation Systems Department, 1220 Vienna (Austria); Clark, John W. [Department of Physics and McDonnell Center for the Space Sciences, Washington University, St. Louis, MO 63130 (United States); Centro de Ciências Matemáticas, Universidade de Madeira, 9000-390 Funchal (Portugal)

2014-06-13

We develop a class of neural networks derived from probabilistic models posed in the form of Bayesian networks. Making biologically and technically plausible assumptions about the nature of the probabilistic models to be represented in the networks, we derive neural networks exhibiting standard dynamics that require no training to determine the synaptic weights, that perform accurate calculation of the mean values of the relevant random variables, that can pool multiple sources of evidence, and that deal appropriately with ambivalent, inconsistent, or contradictory evidence. - Highlights: • High-level neural computations are specified by Bayesian belief networks of random variables. • Probability densities of random variables are encoded in activities of populations of neurons. • Top-down algorithm generates specific neural network implementation of given computation. • Resulting “neural belief networks” process mean values of random variables. • Such networks pool multiple sources of evidence and deal properly with inconsistent evidence.

Multiple Improvements of Multiple Imputation Likelihood Ratio Tests

OpenAIRE

Chan, Kin Wai; Meng, Xiao-Li

2017-01-01

Multiple imputation (MI) inference handles missing data by first properly imputing the missing values $m$ times, and then combining the $m$ analysis results from applying a complete-data procedure to each of the completed datasets. However, the existing method for combining likelihood ratio tests has multiple defects: (i) the combined test statistic can be negative in practice when the reference null distribution is a standard $F$ distribution; (ii) it is not invariant to re-parametrization; ...

Cosmic-ray discrimination capabilities of DELTA E-E silicon nuclear telescopes using neural networks

CERN Document Server

Ambriola, M; Cafagna, F; Castellano, M; Ciacio, F; Circella, M; De Marzo, C N; Montaruli, T

2000-01-01

An isotope classifier of cosmic-ray events collected by space detectors has been implemented using a multi-layer perceptron neural architecture. In order to handle a great number of different isotopes a modular architecture of the 'mixture of experts' type is proposed. The performance of this classifier has been tested on simulated data and has been compared with a 'classical' classifying procedure. The quantitative comparison with traditional techniques shows that the neural approach has classification performances comparable - within 1% - with that of the classical one, with efficiency of the order of 98%. A possible hardware implementation of such a kind of neural architecture in future space missions is considered.

Automated processing of measuring information and control processes of eutrophication in water for household purpose, based on artificial neural networks

Directory of Open Access Journals (Sweden)

О.М. Безвесільна

2006-04-01

Full Text Available  The possibilities of application  informational-computer technologies for automated handling of a measuring information about development of seaweed (evtrofication in household reservoirs are considered. The input data’s for a research of processes evtrofication are videoimages of tests of water, which are used for the definition of geometric characteristics, number and biomass of seaweed. For handling a measuring information the methods of digital handling videoimages and mathematical means of artificial neural networks are offered.

Rare Neural Correlations Implement Robotic Conditioning with Delayed Rewards and Disturbances

Science.gov (United States)

Soltoggio, Andrea; Lemme, Andre; Reinhart, Felix; Steil, Jochen J.

2013-01-01

Neural conditioning associates cues and actions with following rewards. The environments in which robots operate, however, are pervaded by a variety of disturbing stimuli and uncertain timing. In particular, variable reward delays make it difficult to reconstruct which previous actions are responsible for following rewards. Such an uncertainty is handled by biological neural networks, but represents a challenge for computational models, suggesting the lack of a satisfactory theory for robotic neural conditioning. The present study demonstrates the use of rare neural correlations in making correct associations between rewards and previous cues or actions. Rare correlations are functional in selecting sparse synapses to be eligible for later weight updates if a reward occurs. The repetition of this process singles out the associating and reward-triggering pathways, and thereby copes with distal rewards. The neural network displays macro-level classical and operant conditioning, which is demonstrated in an interactive real-life human-robot interaction. The proposed mechanism models realistic conditioning in humans and animals and implements similar behaviors in neuro-robotic platforms. PMID:23565092

Feed forward neural networks modeling for K-P interactions

International Nuclear Information System (INIS)

El-Bakry, M.Y.

2003-01-01

Artificial intelligence techniques involving neural networks became vital modeling tools where model dynamics are difficult to track with conventional techniques. The paper make use of the feed forward neural networks (FFNN) to model the charged multiplicity distribution of K-P interactions at high energies. The FFNN was trained using experimental data for the multiplicity distributions at different lab momenta. Results of the FFNN model were compared to that generated using the parton two fireball model and the experimental data. The proposed FFNN model results showed good fitting to the experimental data. The neural network model performance was also tested at non-trained space and was found to be in good agreement with the experimental data

Vibration monitoring with artificial neural networks

International Nuclear Information System (INIS)

Alguindigue, I.

1991-01-01

Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural network to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected from operating machinery. Two neural networks algorithms were used in our project: the Recirculation algorithm for data compression and the Backpropagation algorithm to perform the actual classification of the patterns. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results to date are very encouraging

Strategy over operation: neural activation in subtraction and multiplication during fact retrieval and procedural strategy use in children.

Science.gov (United States)

Polspoel, Brecht; Peters, Lien; Vandermosten, Maaike; De Smedt, Bert

2017-09-01

Arithmetic development is characterized by strategy shifts between procedural strategy use and fact retrieval. This study is the first to explicitly investigate children's neural activation associated with the use of these different strategies. Participants were 26 typically developing 4th graders (9- to 10-year-olds), who, in a behavioral session, were asked to verbally report on a trial-by-trial basis how they had solved 100 subtraction and multiplication items. These items were subsequently presented during functional magnetic resonance imaging. An event-related design allowed us to analyze the brain responses during retrieval and procedural trials, based on the children's verbal reports. During procedural strategy use, and more specifically for the decomposition of operands strategy, activation increases were observed in the inferior and superior parietal lobes (intraparietal sulci), inferior to superior frontal gyri, bilateral areas in the occipital lobe, and insular cortex. For retrieval, in comparison to procedural strategy use, we observed increased activity in the bilateral angular and supramarginal gyri, left middle to inferior temporal gyrus, right superior temporal gyrus, and superior medial frontal gyrus. No neural differences were found between the two operations under study. These results are the first in children to provide direct evidence for alternate neural activation when different arithmetic strategies are used and further unravel that previously found effects of operation on brain activity reflect differences in arithmetic strategy use. Hum Brain Mapp 38:4657-4670, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Conditions Data Handling In The Multithreaded ATLAS Framework

CERN Document Server

Leggett, Charles; The ATLAS collaboration

2018-01-01

In preparation for Run 3 of the LHC, the ATLAS experiment is migrating its offline software to use a multithreaded framework, which will allow multiple events to be processed simultaneously. This implies that the handling of non-event, time-dependent (conditions) data, such as calibrations and geometry, must also be extended to allow for multiple versions of such data to exist simultaneously. This has now been implemented as part of the new ATLAS framework. The detector geometry is included in this scheme by having sets of time-dependent displacements on top of a static base geometry.

Compact holographic optical neural network system for real-time pattern recognition

Science.gov (United States)

Lu, Taiwei; Mintzer, David T.; Kostrzewski, Andrew A.; Lin, Freddie S.

1996-08-01

One of the important characteristics of artificial neural networks is their capability for massive interconnection and parallel processing. Recently, specialized electronic neural network processors and VLSI neural chips have been introduced in the commercial market. The number of parallel channels they can handle is limited because of the limited parallel interconnections that can be implemented with 1D electronic wires. High-resolution pattern recognition problems can require a large number of neurons for parallel processing of an image. This paper describes a holographic optical neural network (HONN) that is based on high- resolution volume holographic materials and is capable of performing massive 3D parallel interconnection of tens of thousands of neurons. A HONN with more than 16,000 neurons packaged in an attache case has been developed. Rotation- shift-scale-invariant pattern recognition operations have been demonstrated with this system. System parameters such as the signal-to-noise ratio, dynamic range, and processing speed are discussed.

A Neural Parametric Singing Synthesizer Modeling Timbre and Expression from Natural Songs

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Merlijn Blaauw

2017-12-01

Full Text Available We recently presented a new model for singing synthesis based on a modified version of the WaveNet architecture. Instead of modeling raw waveform, we model features produced by a parametric vocoder that separates the influence of pitch and timbre. This allows conveniently modifying pitch to match any target melody, facilitates training on more modest dataset sizes, and significantly reduces training and generation times. Nonetheless, compared to modeling waveform directly, ways of effectively handling higher-dimensional outputs, multiple feature streams and regularization become more important with our approach. In this work, we extend our proposed system to include additional components for predicting F0 and phonetic timings from a musical score with lyrics. These expression-related features are learned together with timbrical features from a single set of natural songs. We compare our method to existing statistical parametric, concatenative, and neural network-based approaches using quantitative metrics as well as listening tests.

Epidemiologic study of neural tube defects in Los Angeles County. I. Prevalence at birth based on multiple sources of case ascertainment

Energy Technology Data Exchange (ETDEWEB)

Sever, L.E. (Pacific Northwest Lab., Richland, WA); Sanders, M.; Monsen, R.

1982-01-01

Epidemiologic studies of the neural tube defects (NTDs), anencephalus and spina bifida, have for the most part been based on single sources of case ascertainment in past studies. The present investigation attempts total ascertainment of NTD cases in the newborn population of Los Angeles County residents for the period 1966 to 1972. Design of the study, sources of data, and estimates of prevalence rates based on single and multiple sources of case ascertainment are here discussed. Anencephalus cases totaled 448, spina bifida 442, and encephalocele 72, giving prevalence rates of 0.52, 0.51, and 0.08 per 1000 total births, respectively, for these neural tube defects - rates considered to be low. The Los Angeles County prevalence rates are compared with those of other recent North American studies and support is provided for earlier suggestions of low rates on the West Coast.

Vibration control of uncertain multiple launch rocket system using radial basis function neural network

Science.gov (United States)

Li, Bo; Rui, Xiaoting

2018-01-01

Poor dispersion characteristics of rockets due to the vibration of Multiple Launch Rocket System (MLRS) have always restricted the MLRS development for several decades. Vibration control is a key technique to improve the dispersion characteristics of rockets. For a mechanical system such as MLRS, the major difficulty in designing an appropriate control strategy that can achieve the desired vibration control performance is to guarantee the robustness and stability of the control system under the occurrence of uncertainties and nonlinearities. To approach this problem, a computed torque controller integrated with a radial basis function neural network is proposed to achieve the high-precision vibration control for MLRS. In this paper, the vibration response of a computed torque controlled MLRS is described. The azimuth and elevation mechanisms of the MLRS are driven by permanent magnet synchronous motors and supposed to be rigid. First, the dynamic model of motor-mechanism coupling system is established using Lagrange method and field-oriented control theory. Then, in order to deal with the nonlinearities, a computed torque controller is designed to control the vibration of the MLRS when it is firing a salvo of rockets. Furthermore, to compensate for the lumped uncertainty due to parametric variations and un-modeled dynamics in the design of the computed torque controller, a radial basis function neural network estimator is developed to adapt the uncertainty based on Lyapunov stability theory. Finally, the simulated results demonstrate the effectiveness of the proposed control system and show that the proposed controller is robust with regard to the uncertainty.

Multiple POU-binding motifs, recognized by tissue-specific nuclear factors, are important for Dll1 gene expression in neural stem cells

International Nuclear Information System (INIS)

Nakayama, Kohzo; Nagase, Kazuko; Tokutake, Yuriko; Koh, Chang-Sung; Hiratochi, Masahiro; Ohkawara, Takeshi; Nakayama, Noriko

2004-01-01

We cloned the 5'-flanking region of the mouse homolog of the Delta gene (Dll1) and demonstrated that the sequence between nucleotide position -514 and -484 in the 5'-flanking region of Dll1 played a critical role in the regulation of its tissue-specific expression in neural stem cells (NSCs). Further, we showed that multiple POU-binding motifs, located within this short sequence of 30 bp, were essential for transcriptional activation of Dll1 and also that multiple tissue-specific nuclear factors recognized these POU-binding motifs in various combinations through differentiation of NSCs. Thus, POU-binding factors may play an important role in Dll1 expression in developing NSCs

Nuclear fuel handling apparatus

International Nuclear Information System (INIS)

Andrea, C.; Dupen, C.F.G.; Noyes, R.C.

1977-01-01

A fuel handling machine for a liquid metal cooled nuclear reactor in which a retractable handling tube and gripper are lowered into the reactor to withdraw a spent fuel assembly into the handling tube. The handling tube containing the fuel assembly immersed in liquid sodium is then withdrawn completely from the reactor into the outer barrel of the handling machine. The machine is then used to transport the spent fuel assembly directly to a remotely located decay tank. The fuel handling machine includes a decay heat removal system which continuously removes heat from the interior of the handling tube and which is capable of operating at its full cooling capacity at all times. The handling tube is supported in the machine from an articulated joint which enables it to readily align itself with the correct position in the core. An emergency sodium supply is carried directly by the machine to provide make up in the event of a loss of sodium from the handling tube during transport to the decay tank. 5 claims, 32 drawing figures

Weather forecasting based on hybrid neural model

Science.gov (United States)

Saba, Tanzila; Rehman, Amjad; AlGhamdi, Jarallah S.

2017-11-01

Making deductions and expectations about climate has been a challenge all through mankind's history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Current research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the specialty of climate anticipating frameworks. The study concentrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimum temperature, maximum temperature, mean temperature, average relative moistness, precipitation, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to both individual networks. Additionally, results are better than reported in the state of art, using a simple neural structure that reduces training time and complexity.

Structural reliability calculation method based on the dual neural network and direct integration method.

Science.gov (United States)

Li, Haibin; He, Yun; Nie, Xiaobo

2018-01-01

Structural reliability analysis under uncertainty is paid wide attention by engineers and scholars due to reflecting the structural characteristics and the bearing actual situation. The direct integration method, started from the definition of reliability theory, is easy to be understood, but there are still mathematics difficulties in the calculation of multiple integrals. Therefore, a dual neural network method is proposed for calculating multiple integrals in this paper. Dual neural network consists of two neural networks. The neural network A is used to learn the integrand function, and the neural network B is used to simulate the original function. According to the derivative relationships between the network output and the network input, the neural network B is derived from the neural network A. On this basis, the performance function of normalization is employed in the proposed method to overcome the difficulty of multiple integrations and to improve the accuracy for reliability calculations. The comparisons between the proposed method and Monte Carlo simulation method, Hasofer-Lind method, the mean value first-order second moment method have demonstrated that the proposed method is an efficient and accurate reliability method for structural reliability problems.

Occlusion Handling in Videos Object Tracking: A Survey

International Nuclear Information System (INIS)

Lee, B Y; Liew, L H; Cheah, W S; Wang, Y C

2014-01-01

Object tracking in video has been an active research since for decades. This interest is motivated by numerous applications, such as surveillance, human-computer interaction, and sports event monitoring. Many challenges related to tracking objects still remain, this can arise due to abrupt object motion, changing appearance patterns of objects and the scene, non-rigid object structures and most significant are occlusion of tracked object be it object-to-object or object-to-scene occlusions. Generally, occlusion in object tracking occur under three situations: self-occlusion, inter-object occlusion by background scene structure. Self-occlusion occurs most frequently while tracking articulated objects when one part of the object occludes another. Inter-object occlusion occurs when two objects being tracked occlude each other whereas occlusion by the background occurs when a structure in the background occludes the tracked objects. Typically, tracking methods handle occlusion by modelling the object motion using linear and non-linear dynamic models. The derived models will be used to continuously predicting the object location when a tracked object is occluded until the object reappears. Example of these method are Kalman filtering and Particle filtering trackers. Researchers have also utilised other features to resolved occlusion, for example, silhouette projections, colour histogram and optical flow. We will present some result from a previously conducted experiment when tracking single object using Kalman filter, Particle filter and Mean Shift trackers under various occlusion situation in this paper. We will also review various other occlusion handling methods that involved using multiple cameras. In a nutshell, the goal of this paper is to discuss in detail the problem of occlusion in object tracking and review the state of the art occlusion handling methods, classify them into different categories, and identify new trends. Moreover, we discuss the important

Occlusion Handling in Videos Object Tracking: A Survey

Science.gov (United States)

Lee, B. Y.; Liew, L. H.; Cheah, W. S.; Wang, Y. C.

2014-02-01

Object tracking in video has been an active research since for decades. This interest is motivated by numerous applications, such as surveillance, human-computer interaction, and sports event monitoring. Many challenges related to tracking objects still remain, this can arise due to abrupt object motion, changing appearance patterns of objects and the scene, non-rigid object structures and most significant are occlusion of tracked object be it object-to-object or object-to-scene occlusions. Generally, occlusion in object tracking occur under three situations: self-occlusion, inter-object occlusion by background scene structure. Self-occlusion occurs most frequently while tracking articulated objects when one part of the object occludes another. Inter-object occlusion occurs when two objects being tracked occlude each other whereas occlusion by the background occurs when a structure in the background occludes the tracked objects. Typically, tracking methods handle occlusion by modelling the object motion using linear and non-linear dynamic models. The derived models will be used to continuously predicting the object location when a tracked object is occluded until the object reappears. Example of these method are Kalman filtering and Particle filtering trackers. Researchers have also utilised other features to resolved occlusion, for example, silhouette projections, colour histogram and optical flow. We will present some result from a previously conducted experiment when tracking single object using Kalman filter, Particle filter and Mean Shift trackers under various occlusion situation in this paper. We will also review various other occlusion handling methods that involved using multiple cameras. In a nutshell, the goal of this paper is to discuss in detail the problem of occlusion in object tracking and review the state of the art occlusion handling methods, classify them into different categories, and identify new trends. Moreover, we discuss the important

Logistics management for storing multiple cask plug and remote handling systems in ITER

International Nuclear Information System (INIS)

Ventura, Rodrigo; Ferreira, João; Filip, Iulian; Vale, Alberto

2013-01-01

Highlights: ► We model the logistics management problem in ITER, taking into account casks of multiple typologies. ► We propose a method to determine the best position of the casks inside a given storage area. ► Our method obtains the sequence of operations required to retrieve or store an arbitrary cask, given its storage place. ► We illustrate our method with simulation results in an example scenario. -- Abstract: During operation, maintenance inside the reactor building at ITER (International Thermonuclear Experimental Reactor) has to be performed by remote handling, due to the presence of activated materials. Maintenance operations involve the transportation and storage of large, heavyweight casks from and to the tokamak building. The transportation is carried out by autonomous vehicles that lift and move beneath these casks. The storage of these casks face several challenges, since (1) the cask storage area is limited in space, and (2) all casks have to be accessible for transportation by the vehicles. In particular, casks in the storage area may block other casks, so that the former has to be moved to a temporary position to give way to the latter. This paper addresses the challenge of managing the logistics of cask storage, where casks may have different typologies. In particular, we propose an approach to (1) determine the best position of the casks inside the storage area, and to (2) obtain the sequence of operations required to retrieve and store an arbitrary cask from/to a given storage place. A combinatorial optimization approach is used to obtain solutions to both these problems. Simulation results illustrate the application of the proposed method to a simple scenario

Logistics management for storing multiple cask plug and remote handling systems in ITER

Energy Technology Data Exchange (ETDEWEB)

Ventura, Rodrigo, E-mail: rodrigo.ventura@isr.ist.utl.pt [Laboratório de Robótica e Sistemas em Engenharia e Ciência – Laboratório Associado, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Ferreira, João, E-mail: jftferreira@ipfn.ist.utl.pt [Instituto de Plasmas e Fusão Nuclear – Laboratório Associado, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal); Filip, Iulian, E-mail: ifilip@gmail.com [Faculty of Mechanical Engineering – Technical University Gheorghe Asachi of Iasi, 61 Dimitrie Mangeron Bldv., Iasi 700050 (Romania); Vale, Alberto, E-mail: avale@ipfn.ist.utl.pt [Instituto de Plasmas e Fusão Nuclear – Laboratório Associado, Instituto Superior Técnico, Universidade Técnica de Lisboa, Av. Rovisco Pais 1, 1049-001 Lisboa (Portugal)

2013-10-15

Highlights: ► We model the logistics management problem in ITER, taking into account casks of multiple typologies. ► We propose a method to determine the best position of the casks inside a given storage area. ► Our method obtains the sequence of operations required to retrieve or store an arbitrary cask, given its storage place. ► We illustrate our method with simulation results in an example scenario. -- Abstract: During operation, maintenance inside the reactor building at ITER (International Thermonuclear Experimental Reactor) has to be performed by remote handling, due to the presence of activated materials. Maintenance operations involve the transportation and storage of large, heavyweight casks from and to the tokamak building. The transportation is carried out by autonomous vehicles that lift and move beneath these casks. The storage of these casks face several challenges, since (1) the cask storage area is limited in space, and (2) all casks have to be accessible for transportation by the vehicles. In particular, casks in the storage area may block other casks, so that the former has to be moved to a temporary position to give way to the latter. This paper addresses the challenge of managing the logistics of cask storage, where casks may have different typologies. In particular, we propose an approach to (1) determine the best position of the casks inside the storage area, and to (2) obtain the sequence of operations required to retrieve and store an arbitrary cask from/to a given storage place. A combinatorial optimization approach is used to obtain solutions to both these problems. Simulation results illustrate the application of the proposed method to a simple scenario.

Small leucine rich proteoglycan family regulates multiple signalling pathways in neural development and maintenance.

Science.gov (United States)

Dellett, Margaret; Hu, Wanzhou; Papadaki, Vasiliki; Ohnuma, Shin-ichi

2012-04-01

The small leucine-rich repeat proteoglycan (SLRPs) family of proteins currently consists of five classes, based on their structural composition and chromosomal location. As biologically active components of the extracellular matrix (ECM), SLRPs were known to bind to various collagens, having a role in regulating fibril assembly, organization and degradation. More recently, as a function of their diverse proteins cores and glycosaminoglycan side chains, SLRPs have been shown to be able to bind various cell surface receptors, growth factors, cytokines and other ECM components resulting in the ability to influence various cellular functions. Their involvement in several signaling pathways such as Wnt, transforming growth factor-β and epidermal growth factor receptor also highlights their role as matricellular proteins. SLRP family members are expressed during neural development and in adult neural tissues, including ocular tissues. This review focuses on describing SLRP family members involvement in neural development with a brief summary of their role in non-neural ocular tissues and in response to neural injury. © 2012 The Authors Development, Growth & Differentiation © 2012 Japanese Society of Developmental Biologists.

Polymer SU-8 Based Microprobes for Neural Recording and Drug Delivery

Science.gov (United States)

Altuna, Ane; Fernandez, Luis; Berganzo, Javier

2015-06-01

This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a) minimize injury in neural tissue, b) obtain high-quality electrical signals and c) deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

POLYMER SU-8 BASED MICROPROBES FOR NEURAL RECORDING AND DRUG DELIVERY

Directory of Open Access Journals (Sweden)

Ane eAltuna

2015-06-01

Full Text Available This manuscript makes a reflection about SU-8 based microprobes for neural activity recording and drug delivery. By taking advantage of improvements in microfabrication technologies and using polymer SU-8 as the only structural material, we developed several microprobe prototypes aimed to: a minimize injury in neural tissue, b obtain high-quality electrical signals and c deliver drugs at a micrometer precision scale. Dedicated packaging tools have been developed in parallel to fulfill requirements concerning electric and fluidic connections, size and handling. After these advances have been experimentally proven in brain using in vivo preparation, the technological concepts developed during consecutive prototypes are discussed in depth now.

Neural networks for sensor validation and plant monitoring

International Nuclear Information System (INIS)

Upadhyaya, B.R.; Eryurek, E.; Mathai, G.

1990-01-01

Sensor and process monitoring in power plants require the estimation of one or more process variables. Neural network paradigms are suitable for establishing general nonlinear relationships among a set of plant variables. Multiple-input multiple-output autoassociative networks can follow changes in plant-wide behavior. The backpropagation algorithm has been applied for training feedforward networks. A new and enhanced algorithm for training neural networks (BPN) has been developed and implemented in a VAX workstation. Operational data from the Experimental Breeder Reactor-II (EBR-II) have been used to study the performance of BPN. Several results of application to the EBR-II are presented

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms

Science.gov (United States)

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-e; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs’ appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers’ attention from different fields and many studies have validated MMORPGs’ positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers’ overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas. PMID:28303097

Language Learning Enhanced by Massive Multiple Online Role-Playing Games (MMORPGs) and the Underlying Behavioral and Neural Mechanisms.

Science.gov (United States)

Zhang, Yongjun; Song, Hongwen; Liu, Xiaoming; Tang, Dinghong; Chen, Yue-E; Zhang, Xiaochu

2017-01-01

Massive Multiple Online Role-Playing Games (MMORPGs) have increased in popularity among children, juveniles, and adults since MMORPGs' appearance in this digital age. MMORPGs can be applied to enhancing language learning, which is drawing researchers' attention from different fields and many studies have validated MMORPGs' positive effect on language learning. However, there are few studies on the underlying behavioral or neural mechanism of such effect. This paper reviews the educational application of the MMORPGs based on relevant macroscopic and microscopic studies, showing that gamers' overall language proficiency or some specific language skills can be enhanced by real-time online interaction with peers and game narratives or instructions embedded in the MMORPGs. Mechanisms underlying the educational assistant role of MMORPGs in second language learning are discussed from both behavioral and neural perspectives. We suggest that attentional bias makes gamers/learners allocate more cognitive resources toward task-related stimuli in a controlled or an automatic way. Moreover, with a moderating role played by activation of reward circuit, playing the MMORPGs may strengthen or increase functional connectivity from seed regions such as left anterior insular/frontal operculum (AI/FO) and visual word form area to other language-related brain areas.

Human Neural Precursor Cells Promote Neurologic Recovery in a Viral Model of Multiple Sclerosis

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Lu Chen

2014-06-01

Full Text Available Using a viral model of the demyelinating disease multiple sclerosis (MS, we show that intraspinal transplantation of human embryonic stem cell-derived neural precursor cells (hNPCs results in sustained clinical recovery, although hNPCs were not detectable beyond day 8 posttransplantation. Improved motor skills were associated with a reduction in neuroinflammation, decreased demyelination, and enhanced remyelination. Evidence indicates that the reduced neuroinflammation is correlated with an increased number of CD4+CD25+FOXP3+ regulatory T cells (Tregs within the spinal cords. Coculture of hNPCs with activated T cells resulted in reduced T cell proliferation and increased Treg numbers. The hNPCs acted, in part, through secretion of TGF-β1 and TGF-β2. These findings indicate that the transient presence of hNPCs transplanted in an animal model of MS has powerful immunomodulatory effects and mediates recovery. Further investigation of the restorative effects of hNPC transplantation may aid in the development of clinically relevant MS treatments.

Fault diagnosis method for nuclear power plants based on neural networks and voting fusion

International Nuclear Information System (INIS)

Zhou Gang; Ge Shengqi; Yang Li

2010-01-01

A new fault diagnosis method based on multiple neural networks (ANNs) and voting fusion for nuclear power plants (NPPs) was proposed in view of the shortcoming of single neural network fault diagnosis method. In this method, multiple neural networks that the types of neural networks were different were trained for the fault diagnosis of NPP. The operation parameters of NPP, which have important affect on the safety of NPP, were selected as the input variable of neural networks. The output of neural networks is fault patterns of NPP. The last results of diagnosis for NPP were obtained by fusing the diagnosing results of different neural networks by voting fusion. The typical operation patterns of NPP were diagnosed to demonstrate the effect of the proposed method. The results show that employing the proposed diagnosing method can improve the precision and reliability of fault diagnosis results of NPPs. (authors)

Quantitative differentiation of multiple virus in blood using nanoporous silicon oxide immunosensor and artificial neural network.

Science.gov (United States)

Chakraborty, W; Ray, R; Samanta, N; RoyChaudhuri, C

2017-12-15

In spite of the rapid developments in various nanosensor technologies, it still remains challenging to realize a reliable ultrasensitive electrical biosensing platform which will be able to detect multiple viruses in blood simultaneously with a fairly high reproducibility without using secondary labels. In this paper, we have reported quantitative differentiation of Hep-B and Hep-C viruses in blood using nanoporous silicon oxide immunosensor array and artificial neural network (ANN). The peak frequency output (f p ) from the steady state sensitivity characteristics and the first cut off frequency (f c ) from the transient characteristics have been considered as inputs to the multilayer ANN. Implementation of several classifier blocks in the ANN architecture and coupling them with both the sensor chips, functionalized with Hep-B and Hep-C antibodies have enabled the quantification of the viruses with an accuracy of around 95% in the range of 0.04fM-1pM and with an accuracy of around 90% beyond 1pM and within 25nM in blood serum. This is the most sensitive report on multiple virus quantification using label free method. Copyright © 2017 Elsevier B.V. All rights reserved.

Adaptive and energy efficient SMA-based handling systems

Science.gov (United States)

Motzki, P.; Kunze, J.; Holz, B.; York, A.; Seelecke, S.

2015-04-01

Shape Memory Alloys (SMA's) are known as actuators with very high energy density. This fact allows for the construction of very light weight and energy-efficient systems. In the field of material handling and automated assembly process, the avoidance of big moments of inertia in robots and kinematic units is essential. High inertial forces require bigger and stronger robot actuators and thus higher energy consumption and costs. For material handling in assembly processes, many different individual grippers for various work piece geometries are used. If one robot has to handle different work pieces, the gripper has to be exchanged and the assembly process is interrupted, which results in higher costs. In this paper, the advantages of using high energy density Shape Memory Alloy actuators in applications of material-handling and gripping-technology are explored. In particular, light-weight SMA actuated prototypes of an adaptive end-effector and a vacuum-gripper are constructed via rapid-prototyping and evaluated. The adaptive end-effector can change its configuration according to the work piece geometry and allows the handling of multiple different shaped objects without exchanging gripper tooling. SMA wires are used to move four independent arms, each arm adds one degree of freedom to the kinematic unit. At the tips of these end-effector arms, SMA-activated suction cups can be installed. The suction cup prototypes are developed separately. The flexible membranes of these suction cups are pulled up by SMA wires and thus a vacuum is created between the membrane and the work piece surface. The self-sensing ability of the SMA wires are used in both prototypes for monitoring their actuation.

All-trans retinoic acid promotes neural lineage entry by pluripotent embryonic stem cells via multiple pathways

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Fang Bo

2009-07-01

Full Text Available Abstract Background All-trans retinoic acid (RA is one of the most important morphogens with pleiotropic actions. Its embryonic distribution correlates with neural differentiation in the developing central nervous system. To explore the precise effects of RA on neural differentiation of mouse embryonic stem cells (ESCs, we detected expression of RA nuclear receptors and RA-metabolizing enzymes in mouse ESCs and investigated the roles of RA in adherent monolayer culture. Results Upon addition of RA, cell differentiation was directed rapidly and exclusively into the neural lineage. Conversely, pharmacological interference with RA signaling suppressed this neural differentiation. Inhibition of fibroblast growth factor (FGF signaling did not suppress significantly neural differentiation in RA-treated cultures. Pharmacological interference with extracellular signal-regulated kinase (ERK pathway or activation of Wnt pathway effectively blocked the RA-promoted neural specification. ERK phosphorylation was enhanced in RA-treated cultures at the early stage of differentiation. Conclusion RA can promote neural lineage entry by ESCs in adherent monolayer culture systems. This effect depends on RA signaling and its crosstalk with the ERK and Wnt pathways.

QSAR models for prediction study of HIV protease inhibitors using support vector machines, neural networks and multiple linear regression

Directory of Open Access Journals (Sweden)

Rachid Darnag

2017-02-01

Full Text Available Support vector machines (SVM represent one of the most promising Machine Learning (ML tools that can be applied to develop a predictive quantitative structure–activity relationship (QSAR models using molecular descriptors. Multiple linear regression (MLR and artificial neural networks (ANNs were also utilized to construct quantitative linear and non linear models to compare with the results obtained by SVM. The prediction results are in good agreement with the experimental value of HIV activity; also, the results reveal the superiority of the SVM over MLR and ANN model. The contribution of each descriptor to the structure–activity relationships was evaluated.

Robustness of the ATLAS pixel clustering neural network algorithm

CERN Document Server

AUTHOR|(INSPIRE)INSPIRE-00407780; The ATLAS collaboration

2016-01-01

Proton-proton collisions at the energy frontier puts strong constraints on track reconstruction algorithms. In the ATLAS track reconstruction algorithm, an artificial neural network is utilised to identify and split clusters of neighbouring read-out elements in the ATLAS pixel detector created by multiple charged particles. The robustness of the neural network algorithm is presented, probing its sensitivity to uncertainties in the detector conditions. The robustness is studied by evaluating the stability of the algorithm's performance under a range of variations in the inputs to the neural networks. Within reasonable variation magnitudes, the neural networks prove to be robust to most variation types.

Neural decoding of collective wisdom with multi-brain computing.

Science.gov (United States)

Eckstein, Miguel P; Das, Koel; Pham, Binh T; Peterson, Matthew F; Abbey, Craig K; Sy, Jocelyn L; Giesbrecht, Barry

2012-01-02

Group decisions and even aggregation of multiple opinions lead to greater decision accuracy, a phenomenon known as collective wisdom. Little is known about the neural basis of collective wisdom and whether its benefits arise in late decision stages or in early sensory coding. Here, we use electroencephalography and multi-brain computing with twenty humans making perceptual decisions to show that combining neural activity across brains increases decision accuracy paralleling the improvements shown by aggregating the observers' opinions. Although the largest gains result from an optimal linear combination of neural decision variables across brains, a simpler neural majority decision rule, ubiquitous in human behavior, results in substantial benefits. In contrast, an extreme neural response rule, akin to a group following the most extreme opinion, results in the least improvement with group size. Analyses controlling for number of electrodes and time-points while increasing number of brains demonstrate unique benefits arising from integrating neural activity across different brains. The benefits of multi-brain integration are present in neural activity as early as 200 ms after stimulus presentation in lateral occipital sites and no additional benefits arise in decision related neural activity. Sensory-related neural activity can predict collective choices reached by aggregating individual opinions, voting results, and decision confidence as accurately as neural activity related to decision components. Estimation of the potential for the collective to execute fast decisions by combining information across numerous brains, a strategy prevalent in many animals, shows large time-savings. Together, the findings suggest that for perceptual decisions the neural activity supporting collective wisdom and decisions arises in early sensory stages and that many properties of collective cognition are explainable by the neural coding of information across multiple brains. Finally

Process & Quality procedures for transport & handling activities

CERN Document Server

Böttcher, O

2002-01-01

To respect the detailed and complex planning of the LHC installation project it is essential to reduce possible faults in every technical service that can cause delays in the schedule. In order to ensure proper execution of transport and handling activities it is important to get detailed information from the clients as early as possible in order to do the planning and the organisation of the required resources. One procedure that requires greater focus in the future is the preparation of the resources. The goal is to prevent equipment breakdowns and accidents while executing transport and handling activities. In the LEP dismantling project multiple breakdowns of important cranes caused serious problems in the project schedule. For the LHC installation project similar incidents in the reliability of the equipment cannot be accepted because of the high sensitivity of the whole schedule. This paper shall outline the efforts and methods that are put in place in order to meet the LHC installation requirements.

Neural-Network-Based Fuzzy Logic Navigation Control for Intelligent Vehicles

Directory of Open Access Journals (Sweden)

Ahcene Farah

2002-06-01

Full Text Available This paper proposes a Neural-Network-Based Fuzzy logic system for navigation control of intelligent vehicles. First, the use of Neural Networks and Fuzzy Logic to provide intelligent vehicles  with more autonomy and intelligence is discussed. Second, the system  for the obstacle avoidance behavior is developed. Fuzzy Logic improves Neural Networks (NN obstacle avoidance approach by handling imprecision and rule-based approximate reasoning. This system must make the vehicle able, after supervised learning, to achieve two tasks: 1- to make one’s way towards its target by a NN, and 2- to avoid static or dynamic obstacles by a Fuzzy NN capturing the behavior of a human expert. Afterwards, two association phases between each task and the appropriate actions are carried out by Trial and Error learning and their coordination allows to decide the appropriate action. Finally, the simulation results display the generalization and adaptation abilities of the system by testing it in new unexplored environments.

A modular method to handle multiple time-dependent quantities in Monte Carlo simulations

International Nuclear Information System (INIS)

Shin, J; Faddegon, B A; Perl, J; Schümann, J; Paganetti, H

2012-01-01

A general method for handling time-dependent quantities in Monte Carlo simulations was developed to make such simulations more accessible to the medical community for a wide range of applications in radiotherapy, including fluence and dose calculation. To describe time-dependent changes in the most general way, we developed a grammar of functions that we call ‘Time Features’. When a simulation quantity, such as the position of a geometrical object, an angle, a magnetic field, a current, etc, takes its value from a Time Feature, that quantity varies over time. The operation of time-dependent simulation was separated into distinct parts: the Sequence samples time values either sequentially at equal increments or randomly from a uniform distribution (allowing quantities to vary continuously in time), and then each time-dependent quantity is calculated according to its Time Feature. Due to this modular structure, time-dependent simulations, even in the presence of multiple time-dependent quantities, can be efficiently performed in a single simulation with any given time resolution. This approach has been implemented in TOPAS (TOol for PArticle Simulation), designed to make Monte Carlo simulations with Geant4 more accessible to both clinical and research physicists. To demonstrate the method, three clinical situations were simulated: a variable water column used to verify constancy of the Bragg peak of the Crocker Lab eye treatment facility of the University of California, the double-scattering treatment mode of the passive beam scattering system at Massachusetts General Hospital (MGH), where a spinning range modulator wheel accompanied by beam current modulation produces a spread-out Bragg peak, and the scanning mode at MGH, where time-dependent pulse shape, energy distribution and magnetic fields control Bragg peak positions. Results confirm the clinical applicability of the method. (paper)

Recursive Bayesian recurrent neural networks for time-series modeling.

Science.gov (United States)

Mirikitani, Derrick T; Nikolaev, Nikolay

2010-02-01

This paper develops a probabilistic approach to recursive second-order training of recurrent neural networks (RNNs) for improved time-series modeling. A general recursive Bayesian Levenberg-Marquardt algorithm is derived to sequentially update the weights and the covariance (Hessian) matrix. The main strengths of the approach are a principled handling of the regularization hyperparameters that leads to better generalization, and stable numerical performance. The framework involves the adaptation of a noise hyperparameter and local weight prior hyperparameters, which represent the noise in the data and the uncertainties in the model parameters. Experimental investigations using artificial and real-world data sets show that RNNs equipped with the proposed approach outperform standard real-time recurrent learning and extended Kalman training algorithms for recurrent networks, as well as other contemporary nonlinear neural models, on time-series modeling.

Nuclear reactors project optimization based on neural network and genetic algorithm; Otimizacao em projetos de reatores nucleares baseada em rede neural e algoritmo genetico

Energy Technology Data Exchange (ETDEWEB)

Pereira, Claudio M.N.A. [Instituto de Engenharia Nuclear (IEN), Rio de Janeiro, RJ (Brazil); Schirru, Roberto; Martinez, Aquilino S. [Universidade Federal, Rio de Janeiro, RJ (Brazil). Coordenacao dos Programas de Pos-graduacao de Engenharia

1997-12-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs.

Recommendations for cask features for robotic handling from the Advanced Handling Technology Project

International Nuclear Information System (INIS)

Drotning, W.

1991-02-01

This report describes the current status and recent progress in the Advanced Handling Technology Project (AHTP) initiated to explore the use of advanced robotic systems and handling technologies to perform automated cask handling operations at radioactive waste handling facilities, and to provide guidance to cask designers on the impact of robotic handling on cask design. Current AHTP tasks have developed system mock-ups to investigate robotic manipulation of impact limiters and cask tiedowns. In addition, cask uprighting and transport, using computer control of a bridge crane and robot, were performed to demonstrate the high speed cask transport operation possible under computer control. All of the current AHTP tasks involving manipulation of impact limiters and tiedowns require robotic operations using a torque wrench. To perform these operations, a pneumatic torque wrench and control system were integrated into the tool suite and control architecture of the gantry robot. The use of captured fasteners is briefly discussed as an area where alternative cask design preferences have resulted from the influence of guidance for robotic handling vs traditional operations experience. Specific robotic handling experiences with these system mock-ups highlight a number of continually recurring design principles: (1) robotic handling feasibility is improved by mechanical designs which emphasize operation with limited dexterity in constrained workspaces; (2) clearances, tolerances, and chamfers must allow for operations under actual conditions with consideration for misalignment and imprecise fixturing; (3) successful robotic handling is enhanced by including design detail in representations for model-based control; (4) robotic handling and overall quality assurance are improved by designs which eliminate the use of loose, disassembled parts. 8 refs., 15 figs

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

Science.gov (United States)

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Introduction to neural networks

International Nuclear Information System (INIS)

Pavlopoulos, P.

1996-01-01

This lecture is a presentation of today's research in neural computation. Neural computation is inspired by knowledge from neuro-science. It draws its methods in large degree from statistical physics and its potential applications lie mainly in computer science and engineering. Neural networks models are algorithms for cognitive tasks, such as learning and optimization, which are based on concepts derived from research into the nature of the brain. The lecture first gives an historical presentation of neural networks development and interest in performing complex tasks. Then, an exhaustive overview of data management and networks computation methods is given: the supervised learning and the associative memory problem, the capacity of networks, the Perceptron networks, the functional link networks, the Madaline (Multiple Adalines) networks, the back-propagation networks, the reduced coulomb energy (RCE) networks, the unsupervised learning and the competitive learning and vector quantization. An example of application in high energy physics is given with the trigger systems and track recognition system (track parametrization, event selection and particle identification) developed for the CPLEAR experiment detectors from the LEAR at CERN. (J.S.). 56 refs., 20 figs., 1 tab., 1 appendix

Lithofacies identification using multiple adaptive resonance theory neural networks and group decision expert system

Science.gov (United States)

Chang, H.-C.; Kopaska-Merkel, D. C.; Chen, H.-C.; Rocky, Durrans S.

2000-01-01

Lithofacies identification supplies qualitative information about rocks. Lithofacies represent rock textures and are important components of hydrocarbon reservoir description. Traditional techniques of lithofacies identification from core data are costly and different geologists may provide different interpretations. In this paper, we present a low-cost intelligent system consisting of three adaptive resonance theory neural networks and a rule-based expert system to consistently and objectively identify lithofacies from well-log data. The input data are altered into different forms representing different perspectives of observation of lithofacies. Each form of input is processed by a different adaptive resonance theory neural network. Among these three adaptive resonance theory neural networks, one neural network processes the raw continuous data, another processes categorial data, and the third processes fuzzy-set data. Outputs from these three networks are then combined by the expert system using fuzzy inference to determine to which facies the input data should be assigned. Rules are prioritized to emphasize the importance of firing order. This new approach combines the learning ability of neural networks, the adaptability of fuzzy logic, and the expertise of geologists to infer facies of the rocks. This approach is applied to the Appleton Field, an oil field located in Escambia County, Alabama. The hybrid intelligence system predicts lithofacies identity from log data with 87.6% accuracy. This prediction is more accurate than those of single adaptive resonance theory networks, 79.3%, 68.0% and 66.0%, using raw, fuzzy-set, and categorical data, respectively, and by an error-backpropagation neural network, 57.3%. (C) 2000 Published by Elsevier Science Ltd. All rights reserved.

A Pruning Neural Network Model in Credit Classification Analysis

Directory of Open Access Journals (Sweden)

Yajiao Tang

2018-01-01

Full Text Available Nowadays, credit classification models are widely applied because they can help financial decision-makers to handle credit classification issues. Among them, artificial neural networks (ANNs have been widely accepted as the convincing methods in the credit industry. In this paper, we propose a pruning neural network (PNN and apply it to solve credit classification problem by adopting the well-known Australian and Japanese credit datasets. The model is inspired by synaptic nonlinearity of a dendritic tree in a biological neural model. And it is trained by an error back-propagation algorithm. The model is capable of realizing a neuronal pruning function by removing the superfluous synapses and useless dendrites and forms a tidy dendritic morphology at the end of learning. Furthermore, we utilize logic circuits (LCs to simulate the dendritic structures successfully which makes PNN be implemented on the hardware effectively. The statistical results of our experiments have verified that PNN obtains superior performance in comparison with other classical algorithms in terms of accuracy and computational efficiency.

Front Propagation in Stochastic Neural Fields

KAUST Repository

Bressloff, Paul C.; Webber, Matthew A.

2012-01-01

We analyze the effects of extrinsic multiplicative noise on front propagation in a scalar neural field with excitatory connections. Using a separation of time scales, we represent the fluctuating front in terms of a diffusive-like displacement

Application of an artificial neural network in the enumeration of yeasts and bacteria adhering to solid substrata

NARCIS (Netherlands)

Wit, P; Busscher, HJ

Artificial neural networks (ANNs) combined with automated image processing are bring used in a growing number of applications, ranging from car license plate identification to speech recognition. ANN analysis is capable of handling complicated images that cannot be dealt with using conventional

Requirement of mouse BCCIP for neural development and progenitor proliferation.

Directory of Open Access Journals (Sweden)

Yi-Yuan Huang

Full Text Available Multiple DNA repair pathways are involved in the orderly development of neural systems at distinct stages. The homologous recombination (HR pathway is required to resolve stalled replication forks and critical for the proliferation of progenitor cells during neural development. BCCIP is a BRCA2 and CDKN1A interacting protein implicated in HR and inhibition of DNA replication stress. In this study, we determined the role of BCCIP in neural development using a conditional BCCIP knock-down mouse model. BCCIP deficiency impaired embryonic and postnatal neural development, causing severe ataxia, cerebral and cerebellar defects, and microcephaly. These development defects are associated with spontaneous DNA damage and subsequent cell death in the proliferative cell populations of the neural system during embryogenesis. With in vitro neural spheroid cultures, BCCIP deficiency impaired neural progenitor's self-renewal capability, and spontaneously activated p53. These data suggest that BCCIP and its anti-replication stress functions are essential for normal neural development by maintaining an orderly proliferation of neural progenitors.

Global robust stability of bidirectional associative memory neural networks with multiple time delays.

Science.gov (United States)

Senan, Sibel; Arik, Sabri

2007-10-01

This correspondence presents a sufficient condition for the existence, uniqueness, and global robust asymptotic stability of the equilibrium point for bidirectional associative memory neural networks with discrete time delays. The results impose constraint conditions on the network parameters of the neural system independently of the delay parameter, and they are applicable to all bounded continuous nonmonotonic neuron activation functions. Some numerical examples are given to compare our results with the previous robust stability results derived in the literature.

Adaptive Control of Nonlinear Discrete-Time Systems by Using OS-ELM Neural Networks

Directory of Open Access Journals (Sweden)

Xiao-Li Li

2014-01-01

Full Text Available As a kind of novel feedforward neural network with single hidden layer, ELM (extreme learning machine neural networks are studied for the identification and control of nonlinear dynamic systems. The property of simple structure and fast convergence of ELM can be shown clearly. In this paper, we are interested in adaptive control of nonlinear dynamic plants by using OS-ELM (online sequential extreme learning machine neural networks. Based on data scope division, the problem that training process of ELM neural network is sensitive to the initial training data is also solved. According to the output range of the controlled plant, the data corresponding to this range will be used to initialize ELM. Furthermore, due to the drawback of conventional adaptive control, when the OS-ELM neural network is used for adaptive control of the system with jumping parameters, the topological structure of the neural network can be adjusted dynamically by using multiple model switching strategy, and an MMAC (multiple model adaptive control will be used to improve the control performance. Simulation results are included to complement the theoretical results.

Remote handling installation of diagnostics in the JET Tokamak

Energy Technology Data Exchange (ETDEWEB)

Allan, P., E-mail: Peter.Allan@ccfe.ac.uk [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Loving, A.B. [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Omran, H. [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Oxford Technologies Ltd, 7 Nuffield Way, Abingdon OX14 1RJ (United Kingdom); Collins, S.; Thomas, J. [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); CCFE, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Parsloe, A.; Merrigan, I. [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); British Nuclear Services, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Hassall, I. [JET-EFDA, Culham Science Centre, Abingdon OX14 3DB (United Kingdom); Oxford Technologies Ltd, 7 Nuffield Way, Abingdon OX14 1RJ (United Kingdom)

2011-10-15

The requirement for an upgrade of the diagnostics for the JET ITER Like Wall (ILW) while maintaining personnel exposure to contamination as low as reasonably practicable or ALARP, has necessitated the development of a bespoke set of diagnostic components. These components, by virtue of their design and location, require a versatile yet comprehensive suite of remote handling tools to undertake their in-vessel installation. The installation of the various diagnostic components is covered in multiple tasks. Each task requires careful assessment and design of tools that can successfully interface with the components and comply with the handling and installation requirements. With remote maintenance a requirement, the looms/conduits were designed to be modular with connections which are electrically connected when the module is fitted or conversely disconnected when removed. The shape of each complex and often bulky component is verified during the design phase, to ensure that it can be delivered and installed to its specified location in the torus. This is done by matching the kinematic capabilities of the remote handling system and the path of the component through the torus by using a state of the art virtual reality system.

Modular representation of layered neural networks.

Science.gov (United States)

Watanabe, Chihiro; Hiramatsu, Kaoru; Kashino, Kunio

2018-01-01

Layered neural networks have greatly improved the performance of various applications including image processing, speech recognition, natural language processing, and bioinformatics. However, it is still difficult to discover or interpret knowledge from the inference provided by a layered neural network, since its internal representation has many nonlinear and complex parameters embedded in hierarchical layers. Therefore, it becomes important to establish a new methodology by which layered neural networks can be understood. In this paper, we propose a new method for extracting a global and simplified structure from a layered neural network. Based on network analysis, the proposed method detects communities or clusters of units with similar connection patterns. We show its effectiveness by applying it to three use cases. (1) Network decomposition: it can decompose a trained neural network into multiple small independent networks thus dividing the problem and reducing the computation time. (2) Training assessment: the appropriateness of a trained result with a given hyperparameter or randomly chosen initial parameters can be evaluated by using a modularity index. And (3) data analysis: in practical data it reveals the community structure in the input, hidden, and output layers, which serves as a clue for discovering knowledge from a trained neural network. Copyright © 2017 Elsevier Ltd. All rights reserved.

Transport energy demand modeling of South Korea using artificial neural network

International Nuclear Information System (INIS)

Geem, Zong Woo

2011-01-01

Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025. - Highlights: → Transport energy demand of South Korea was forecasted using artificial neural network. → Various variables (GDP, population, oil price, number of registrations, etc.) were considered. → Results of artificial neural network were compared with those of multiple linear regression.

Distributed Adaptive Neural Control for Stochastic Nonlinear Multiagent Systems.

Science.gov (United States)

Wang, Fang; Chen, Bing; Lin, Chong; Li, Xuehua

2016-11-14

In this paper, a consensus tracking problem of nonlinear multiagent systems is investigated under a directed communication topology. All the followers are modeled by stochastic nonlinear systems in nonstrict feedback form, where nonlinearities and stochastic disturbance terms are totally unknown. Based on the structural characteristic of neural networks (in Lemma 4), a novel distributed adaptive neural control scheme is put forward. The raised control method not only effectively handles unknown nonlinearities in nonstrict feedback systems, but also copes with the interactions among agents and coupling terms. Based on the stochastic Lyapunov functional method, it is indicated that all the signals of the closed-loop system are bounded in probability and all followers' outputs are convergent to a neighborhood of the output of leader. At last, the efficiency of the control method is testified by a numerical example.

Inner and Outer Recursive Neural Networks for Chemoinformatics Applications.

Science.gov (United States)

Urban, Gregor; Subrahmanya, Niranjan; Baldi, Pierre

2018-02-26

Deep learning methods applied to problems in chemoinformatics often require the use of recursive neural networks to handle data with graphical structure and variable size. We present a useful classification of recursive neural network approaches into two classes, the inner and outer approach. The inner approach uses recursion inside the underlying graph, to essentially "crawl" the edges of the graph, while the outer approach uses recursion outside the underlying graph, to aggregate information over progressively longer distances in an orthogonal direction. We illustrate the inner and outer approaches on several examples. More importantly, we provide open-source implementations [available at www.github.com/Chemoinformatics/InnerOuterRNN and cdb.ics.uci.edu ] for both approaches in Tensorflow which can be used in combination with training data to produce efficient models for predicting the physical, chemical, and biological properties of small molecules.

Neural crest contributions to the lamprey head

Science.gov (United States)

McCauley, David W.; Bronner-Fraser, Marianne

2003-01-01

The neural crest is a vertebrate-specific cell population that contributes to the facial skeleton and other derivatives. We have performed focal DiI injection into the cranial neural tube of the developing lamprey in order to follow the migratory pathways of discrete groups of cells from origin to destination and to compare neural crest migratory pathways in a basal vertebrate to those of gnathostomes. The results show that the general pathways of cranial neural crest migration are conserved throughout the vertebrates, with cells migrating in streams analogous to the mandibular and hyoid streams. Caudal branchial neural crest cells migrate ventrally as a sheet of cells from the hindbrain and super-pharyngeal region of the neural tube and form a cylinder surrounding a core of mesoderm in each pharyngeal arch, similar to that seen in zebrafish and axolotl. In addition to these similarities, we also uncovered important differences. Migration into the presumptive caudal branchial arches of the lamprey involves both rostral and caudal movements of neural crest cells that have not been described in gnathostomes, suggesting that barriers that constrain rostrocaudal movement of cranial neural crest cells may have arisen after the agnathan/gnathostome split. Accordingly, neural crest cells from a single axial level contributed to multiple arches and there was extensive mixing between populations. There was no apparent filling of neural crest derivatives in a ventral-to-dorsal order, as has been observed in higher vertebrates, nor did we find evidence of a neural crest contribution to cranial sensory ganglia. These results suggest that migratory constraints and additional neural crest derivatives arose later in gnathostome evolution.

Extraction of Fuzzy Logic Rules from Data by Means of Artificial Neural Networks

Czech Academy of Sciences Publication Activity Database

Holeňa, Martin

2005-01-01

Roč. 41, č. 3 (2005), s. 297-314 ISSN 0023-5954 R&D Projects: GA AV ČR IAA1030004 Institutional research plan: CEZ:AV0Z10300504 Keywords : knowledge extraction from data * artificial neural networks * fuzzy logic * Lukasiewicz logic * disjunctive normal form Subject RIV: BA - General Mathematics Impact factor: 0.343, year: 2005 http://dml.cz/handle/10338.dmlcz/135657

Acoustophoretic contactless transport and handling of matter in air.

Science.gov (United States)

Foresti, Daniele; Nabavi, Majid; Klingauf, Mirko; Ferrari, Aldo; Poulikakos, Dimos

2013-07-30

Levitation and controlled motion of matter in air have a wealth of potential applications ranging from materials processing to biochemistry and pharmaceuticals. We present a unique acoustophoretic concept for the contactless transport and handling of matter in air. Spatiotemporal modulation of the levitation acoustic field allows continuous planar transport and processing of multiple objects, from near-spherical (volume of 0.1-10 μL) to wire-like, without being limited by the acoustic wavelength. The independence of the handling principle from special material properties (magnetic, optical, or electrical) is illustrated with a wide palette of application experiments, such as contactless droplet coalescence and mixing, solid-liquid encapsulation, absorption, dissolution, and DNA transfection. More than a century after the pioneering work of Lord Rayleigh on acoustic radiation pressure, a path-breaking concept is proposed to harvest the significant benefits of acoustic levitation in air.

Myelo-meningocele: A multi-disciplinary problem | Ibe | Nigerian ...

African Journals Online (AJOL)

Background: Myelo-meningoceles are part of congenital afflictions of the spinal column. They arise from the failure of the neural tube to fuse properly during early embryonic growth. The causes and sequalae are multiple and, therefore, require multiple disciplines, to handle them. This study assessed the role of ...

Full scale tests on remote handled FFTF fuel assembly waste handling and packaging

International Nuclear Information System (INIS)

Allen, C.R.; Cash, R.J.; Dawson, S.A.; Strode, J.N.

1986-01-01

Handling and packaging of remote handled, high activity solid waste fuel assembly hardware components from spent FFTF reactor fuel assemblies have been evaluated using full scale components. The demonstration was performed using FFTF fuel assembly components and simulated components which were handled remotely using electromechanical manipulators, shielding walls, master slave manipulators, specially designed grapples, and remote TV viewing. The testing and evaluation included handling, packaging for current and conceptual shipping containers, and the effects of volume reduction on packing efficiency and shielding requirements. Effects of waste segregation into transuranic (TRU) and non-transuranic fractions also are discussed

Optimized Neural Network for Fault Diagnosis and Classification

International Nuclear Information System (INIS)

Elaraby, S.M.

2005-01-01

This paper presents a developed and implemented toolbox for optimizing neural network structure of fault diagnosis and classification. Evolutionary algorithm based on hierarchical genetic algorithm structure is used for optimization. The simplest feed-forward neural network architecture is selected. Developed toolbox has friendly user interface. Multiple solutions are generated. The performance and applicability of the proposed toolbox is verified with benchmark data patterns and accident diagnosis of Egyptian Second research reactor (ETRR-2)

QCD coherence in tagging b jets by neural networks

International Nuclear Information System (INIS)

Marchesini, G.; Nardulli, G.; Pasquariello, G.

1993-01-01

We propose a neural network classifier for tagging b jets at the Z 0 peak; we include among the input variables infrared sensitive physical observables, such as the charged hadron multiplicity and the energy-multiplicity correlation. A comparison with traditional statistical approaches shows an improvement in the performance. (orig.)

Local Dynamics in Trained Recurrent Neural Networks.

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-23

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Local Dynamics in Trained Recurrent Neural Networks

Science.gov (United States)

Rivkind, Alexander; Barak, Omri

2017-06-01

Learning a task induces connectivity changes in neural circuits, thereby changing their dynamics. To elucidate task-related neural dynamics, we study trained recurrent neural networks. We develop a mean field theory for reservoir computing networks trained to have multiple fixed point attractors. Our main result is that the dynamics of the network's output in the vicinity of attractors is governed by a low-order linear ordinary differential equation. The stability of the resulting equation can be assessed, predicting training success or failure. As a consequence, networks of rectified linear units and of sigmoidal nonlinearities are shown to have diametrically different properties when it comes to learning attractors. Furthermore, a characteristic time constant, which remains finite at the edge of chaos, offers an explanation of the network's output robustness in the presence of variability of the internal neural dynamics. Finally, the proposed theory predicts state-dependent frequency selectivity in the network response.

Vibration monitoring of EDF rotating machinery using artificial neural networks

International Nuclear Information System (INIS)

Alguindigue, I.E.; Loskiewicz-Buczak, A.; Uhrig, R.E.; Hamon, L.; Lefevre, F.

1991-01-01

Vibration monitoring of components in nuclear power plants has been used for a number of years. This technique involves the analysis of vibration data coming from vital components of the plant to detect features which reflect the operational state of machinery. The analysis leads to the identification of potential failures and their causes, and makes it possible to perform efficient preventive maintenance. Earlydetection is important because it can decrease the probability of catastrophic failures, reduce forced outgage, maximize utilization of available assets, increase the life of the plant, and reduce maintenance costs. This paper documents our work on the design of a vibration monitoring methodology based on neural network technology. This technology provides an attractive complement to traditional vibration analysis because of the potential of neural networks to operate in real-time mode and to handle data which may be distorted or noisy. Our efforts have been concentrated on the analysis and classification of vibration signatures collected by Electricite de France (EDF). Two neural networks algorithms were used in our project: the Recirculation algorithm and the Backpropagation algorithm. Although this project is in the early stages of development it indicates that neural networks may provide a viable methodology for monitoring and diagnostics of vibrating components. Our results are very encouraging

Nuclear reactors project optimization based on neural network and genetic algorithm

International Nuclear Information System (INIS)

Pereira, Claudio M.N.A.; Schirru, Roberto; Martinez, Aquilino S.

1997-01-01

This work presents a prototype of a system for nuclear reactor core design optimization based on genetic algorithms and artificial neural networks. A neural network is modeled and trained in order to predict the flux and the neutron multiplication factor values based in the enrichment, network pitch and cladding thickness, with average error less than 2%. The values predicted by the neural network are used by a genetic algorithm in this heuristic search, guided by an objective function that rewards the high flux values and penalizes multiplication factors far from the required value. Associating the quick prediction - that may substitute the reactor physics calculation code - with the global optimization capacity of the genetic algorithm, it was obtained a quick and effective system for nuclear reactor core design optimization. (author). 11 refs., 8 figs., 3 tabs

Nonlinear signal processing using neural networks: Prediction and system modelling

Energy Technology Data Exchange (ETDEWEB)

Lapedes, A.; Farber, R.

1987-06-01

The backpropagation learning algorithm for neural networks is developed into a formalism for nonlinear signal processing. We illustrate the method by selecting two common topics in signal processing, prediction and system modelling, and show that nonlinear applications can be handled extremely well by using neural networks. The formalism is a natural, nonlinear extension of the linear Least Mean Squares algorithm commonly used in adaptive signal processing. Simulations are presented that document the additional performance achieved by using nonlinear neural networks. First, we demonstrate that the formalism may be used to predict points in a highly chaotic time series with orders of magnitude increase in accuracy over conventional methods including the Linear Predictive Method and the Gabor-Volterra-Weiner Polynomial Method. Deterministic chaos is thought to be involved in many physical situations including the onset of turbulence in fluids, chemical reactions and plasma physics. Secondly, we demonstrate the use of the formalism in nonlinear system modelling by providing a graphic example in which it is clear that the neural network has accurately modelled the nonlinear transfer function. It is interesting to note that the formalism provides explicit, analytic, global, approximations to the nonlinear maps underlying the various time series. Furthermore, the neural net seems to be extremely parsimonious in its requirements for data points from the time series. We show that the neural net is able to perform well because it globally approximates the relevant maps by performing a kind of generalized mode decomposition of the maps. 24 refs., 13 figs.

Serotonin, neural markers and memory

Directory of Open Access Journals (Sweden)

Alfredo eMeneses

2015-07-01

Full Text Available Diverse neuropsychiatric disorders present dysfunctional memory and no effective treatment exits for them; likely as result of the absence of neural markers associated to memory. Neurotransmitter systems and signaling pathways have been implicated in memory and dysfunctional memory; however, their role is poorly understood. Hence, neural markers and cerebral functions and dysfunctions are revised. To our knowledge no previous systematic works have been published addressing these issues. The interactions among behavioral tasks, control groups and molecular changes and/or pharmacological effects are mentioned. Neurotransmitter receptors and signaling pathways, during normal and abnormally functioning memory with an emphasis on the behavioral aspects of memory are revised. With focus on serotonin, since as it is a well characterized neurotransmitter, with multiple pharmacological tools, and well characterized downstream signaling in mammals’ species. 5-HT1A, 5-HT4, 5-HT5, 5-HT6 and 5-HT7 receptors as well as SERT (serotonin transporter seem to be useful neural markers and/or therapeutic targets. Certainly, if the mentioned evidence is replicated, then the translatability from preclinical and clinical studies to neural changes might be confirmed. Hypothesis and theories might provide appropriate limits and perspectives of evidence

Speech comprehension aided by multiple modalities: behavioural and neural interactions

Science.gov (United States)

McGettigan, Carolyn; Faulkner, Andrew; Altarelli, Irene; Obleser, Jonas; Baverstock, Harriet; Scott, Sophie K.

2014-01-01

Speech comprehension is a complex human skill, the performance of which requires the perceiver to combine information from several sources – e.g. voice, face, gesture, linguistic context – to achieve an intelligible and interpretable percept. We describe a functional imaging investigation of how auditory, visual and linguistic information interact to facilitate comprehension. Our specific aims were to investigate the neural responses to these different information sources, alone and in interaction, and further to use behavioural speech comprehension scores to address sites of intelligibility-related activation in multifactorial speech comprehension. In fMRI, participants passively watched videos of spoken sentences, in which we varied Auditory Clarity (with noise-vocoding), Visual Clarity (with Gaussian blurring) and Linguistic Predictability. Main effects of enhanced signal with increased auditory and visual clarity were observed in overlapping regions of posterior STS. Two-way interactions of the factors (auditory × visual, auditory × predictability) in the neural data were observed outside temporal cortex, where positive signal change in response to clearer facial information and greater semantic predictability was greatest at intermediate levels of auditory clarity. Overall changes in stimulus intelligibility by condition (as determined using an independent behavioural experiment) were reflected in the neural data by increased activation predominantly in bilateral dorsolateral temporal cortex, as well as inferior frontal cortex and left fusiform gyrus. Specific investigation of intelligibility changes at intermediate auditory clarity revealed a set of regions, including posterior STS and fusiform gyrus, showing enhanced responses to both visual and linguistic information. Finally, an individual differences analysis showed that greater comprehension performance in the scanning participants (measured in a post-scan behavioural test) were associated with

Event- and Time-Driven Techniques Using Parallel CPU-GPU Co-processing for Spiking Neural Networks.

Science.gov (United States)

Naveros, Francisco; Garrido, Jesus A; Carrillo, Richard R; Ros, Eduardo; Luque, Niceto R

2017-01-01

Modeling and simulating the neural structures which make up our central neural system is instrumental for deciphering the computational neural cues beneath. Higher levels of biological plausibility usually impose higher levels of complexity in mathematical modeling, from neural to behavioral levels. This paper focuses on overcoming the simulation problems (accuracy and performance) derived from using higher levels of mathematical complexity at a neural level. This study proposes different techniques for simulating neural models that hold incremental levels of mathematical complexity: leaky integrate-and-fire (LIF), adaptive exponential integrate-and-fire (AdEx), and Hodgkin-Huxley (HH) neural models (ranged from low to high neural complexity). The studied techniques are classified into two main families depending on how the neural-model dynamic evaluation is computed: the event-driven or the time-driven families. Whilst event-driven techniques pre-compile and store the neural dynamics within look-up tables, time-driven techniques compute the neural dynamics iteratively during the simulation time. We propose two modifications for the event-driven family: a look-up table recombination to better cope with the incremental neural complexity together with a better handling of the synchronous input activity. Regarding the time-driven family, we propose a modification in computing the neural dynamics: the bi-fixed-step integration method. This method automatically adjusts the simulation step size to better cope with the stiffness of the neural model dynamics running in CPU platforms. One version of this method is also implemented for hybrid CPU-GPU platforms. Finally, we analyze how the performance and accuracy of these modifications evolve with increasing levels of neural complexity. We also demonstrate how the proposed modifications which constitute the main contribution of this study systematically outperform the traditional event- and time-driven techniques under

Ergonomic material-handling device

Science.gov (United States)

Barsnick, Lance E.; Zalk, David M.; Perry, Catherine M.; Biggs, Terry; Tageson, Robert E.

2004-08-24

A hand-held ergonomic material-handling device capable of moving heavy objects, such as large waste containers and other large objects requiring mechanical assistance. The ergonomic material-handling device can be used with neutral postures of the back, shoulders, wrists and knees, thereby reducing potential injury to the user. The device involves two key features: 1) gives the user the ability to adjust the height of the handles of the device to ergonomically fit the needs of the user's back, wrists and shoulders; and 2) has a rounded handlebar shape, as well as the size and configuration of the handles which keep the user's wrists in a neutral posture during manipulation of the device.

Review On Applications Of Neural Network To Computer Vision

Science.gov (United States)

Li, Wei; Nasrabadi, Nasser M.

1989-03-01

Neural network models have many potential applications to computer vision due to their parallel structures, learnability, implicit representation of domain knowledge, fault tolerance, and ability of handling statistical data. This paper demonstrates the basic principles, typical models and their applications in this field. Variety of neural models, such as associative memory, multilayer back-propagation perceptron, self-stabilized adaptive resonance network, hierarchical structured neocognitron, high order correlator, network with gating control and other models, can be applied to visual signal recognition, reinforcement, recall, stereo vision, motion, object tracking and other vision processes. Most of the algorithms have been simulated on com-puters. Some have been implemented with special hardware. Some systems use features, such as edges and profiles, of images as the data form for input. Other systems use raw data as input signals to the networks. We will present some novel ideas contained in these approaches and provide a comparison of these methods. Some unsolved problems are mentioned, such as extracting the intrinsic properties of the input information, integrating those low level functions to a high-level cognitive system, achieving invariances and other problems. Perspectives of applications of some human vision models and neural network models are analyzed.

Paraconsistent artificial neural networks and Alzheimer disease: A preliminary study

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Jair Minoro Abe

Full Text Available Abstract EEG visual analysis has proved useful in aiding AD diagnosis, being indicated in some clinical protocols. However, such analysis is subject to the inherent imprecision of equipment, patient movements, electric registers, and individual variability of physician visual analysis. Objectives: To employ the Paraconsistent Artificial Neural Network to ascertain how to determine the degree of certainty of probable dementia diagnosis. Methods: Ten EEG records from patients with probable Alzheimer disease and ten controls were obtained during the awake state at rest. An EEG background between 8 Hz and 12 Hz was considered the normal pattern for patients, allowing a variance of 0.5 Hz. Results: The PANN was capable of accurately recognizing waves belonging to Alpha band with favorable evidence of 0.30 and contrary evidence of 0.19, while for waves not belonging to the Alpha pattern, an average favorable evidence of 0.19 and contrary evidence of 0.32 was obtained, indicating that PANN was efficient in recognizing Alpha waves in 80% of the cases evaluated in this study. Artificial Neural Networks - ANN - are well suited to tackle problems such as prediction and pattern recognition. The aim of this work was to recognize predetermined EEG patterns by using a new class of ANN, namely the Paraconsistent Artificial Neural Network - PANN, which is capable of handling uncertain, inconsistent and paracomplete information. An architecture is presented to serve as an auxiliary method in diagnosing Alzheimer disease. Conclusions: We believe the results show PANN to be a promising tool to handle EEG analysis, bearing in mind two considerations: the growing interest of experts in visual analysis of EEG, and the ability of PANN to deal directly with imprecise, inconsistent, and paracomplete data, thereby providing a valuable quantitative analysis.

Neural Global Pattern Similarity Underlies True and False Memories.

Science.gov (United States)

Ye, Zhifang; Zhu, Bi; Zhuang, Liping; Lu, Zhonglin; Chen, Chuansheng; Xue, Gui

2016-06-22

The neural processes giving rise to human memory strength signals remain poorly understood. Inspired by formal computational models that posit a central role of global matching in memory strength, we tested a novel hypothesis that the strengths of both true and false memories arise from the global similarity of an item's neural activation pattern during retrieval to that of all the studied items during encoding (i.e., the encoding-retrieval neural global pattern similarity [ER-nGPS]). We revealed multiple ER-nGPS signals that carried distinct information and contributed differentially to true and false memories: Whereas the ER-nGPS in the parietal regions reflected semantic similarity and was scaled with the recognition strengths of both true and false memories, ER-nGPS in the visual cortex contributed solely to true memory. Moreover, ER-nGPS differences between the parietal and visual cortices were correlated with frontal monitoring processes. By combining computational and neuroimaging approaches, our results advance a mechanistic understanding of memory strength in recognition. What neural processes give rise to memory strength signals, and lead to our conscious feelings of familiarity? Using fMRI, we found that the memory strength of a given item depends not only on how it was encoded during learning, but also on the similarity of its neural representation with other studied items. The global neural matching signal, mainly in the parietal lobule, could account for the memory strengths of both studied and unstudied items. Interestingly, a different global matching signal, originated from the visual cortex, could distinguish true from false memories. The findings reveal multiple neural mechanisms underlying the memory strengths of events registered in the brain. Copyright © 2016 the authors 0270-6474/16/366792-11$15.00/0.

Analysis of surface ozone using a recurrent neural network.

Science.gov (United States)

Biancofiore, Fabio; Verdecchia, Marco; Di Carlo, Piero; Tomassetti, Barbara; Aruffo, Eleonora; Busilacchio, Marcella; Bianco, Sebastiano; Di Tommaso, Sinibaldo; Colangeli, Carlo

2015-05-01

Hourly concentrations of ozone (O₃) and nitrogen dioxide (NO₂) have been measured for 16 years, from 1998 to 2013, in a seaside town in central Italy. The seasonal trends of O₃ and NO₂ recorded in this period have been studied. Furthermore, we used the data collected during one year (2005), to define the characteristics of a multiple linear regression model and a neural network model. Both models are used to model the hourly O₃ concentration, using, two scenarios: 1) in the first as inputs, only meteorological parameters and 2) in the second adding photochemical parameters at those of the first scenario. In order to evaluate the performance of the model four statistical criteria are used: correlation coefficient, fractional bias, normalized mean squared error and a factor of two. All the criteria show that the neural network gives better results, compared to the regression model, in all the model scenarios. Predictions of O₃ have been carried out by many authors using a feed forward neural architecture. In this paper we show that a recurrent architecture significantly improves the performances of neural predictors. Using only the meteorological parameters as input, the recurrent architecture shows performance better than the multiple linear regression model that uses meteorological and photochemical data as input, making the neural network model with recurrent architecture a more useful tool in areas where only weather measurements are available. Finally, we used the neural network model to forecast the O₃ hourly concentrations 1, 3, 6, 12, 24 and 48 h ahead. The performances of the model in predicting O₃ levels are discussed. Emphasis is given to the possibility of using the neural network model in operational ways in areas where only meteorological data are available, in order to predict O₃ also in sites where it has not been measured yet. Copyright © 2015 Elsevier B.V. All rights reserved.

Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain.

Science.gov (United States)

Rousche, Patrick; Schneeweis, David M; Perreault, Eric J; Jensen, Winnie

2008-03-01

A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

Different propagation speeds of recalled sequences in plastic spiking neural networks

Science.gov (United States)

Huang, Xuhui; Zheng, Zhigang; Hu, Gang; Wu, Si; Rasch, Malte J.

2015-03-01

Neural networks can generate spatiotemporal patterns of spike activity. Sequential activity learning and retrieval have been observed in many brain areas, and e.g. is crucial for coding of episodic memory in the hippocampus or generating temporal patterns during song production in birds. In a recent study, a sequential activity pattern was directly entrained onto the neural activity of the primary visual cortex (V1) of rats and subsequently successfully recalled by a local and transient trigger. It was observed that the speed of activity propagation in coordinates of the retinotopically organized neural tissue was constant during retrieval regardless how the speed of light stimulation sweeping across the visual field during training was varied. It is well known that spike-timing dependent plasticity (STDP) is a potential mechanism for embedding temporal sequences into neural network activity. How training and retrieval speeds relate to each other and how network and learning parameters influence retrieval speeds, however, is not well described. We here theoretically analyze sequential activity learning and retrieval in a recurrent neural network with realistic synaptic short-term dynamics and STDP. Testing multiple STDP rules, we confirm that sequence learning can be achieved by STDP. However, we found that a multiplicative nearest-neighbor (NN) weight update rule generated weight distributions and recall activities that best matched the experiments in V1. Using network simulations and mean-field analysis, we further investigated the learning mechanisms and the influence of network parameters on recall speeds. Our analysis suggests that a multiplicative STDP rule with dominant NN spike interaction might be implemented in V1 since recall speed was almost constant in an NMDA-dominant regime. Interestingly, in an AMPA-dominant regime, neural circuits might exhibit recall speeds that instead follow the change in stimulus speeds. This prediction could be tested in

Ear Detection under Uncontrolled Conditions with Multiple Scale Faster Region-Based Convolutional Neural Networks

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Yi Zhang

2017-04-01

Full Text Available Ear detection is an important step in ear recognition approaches. Most existing ear detection techniques are based on manually designing features or shallow learning algorithms. However, researchers found that the pose variation, occlusion, and imaging conditions provide a great challenge to the traditional ear detection methods under uncontrolled conditions. This paper proposes an efficient technique involving Multiple Scale Faster Region-based Convolutional Neural Networks (Faster R-CNN to detect ears from 2D profile images in natural images automatically. Firstly, three regions of different scales are detected to infer the information about the ear location context within the image. Then an ear region filtering approach is proposed to extract the correct ear region and eliminate the false positives automatically. In an experiment with a test set of 200 web images (with variable photographic conditions, 98% of ears were accurately detected. Experiments were likewise conducted on the Collection J2 of University of Notre Dame Biometrics Database (UND-J2 and University of Beira Interior Ear dataset (UBEAR, which contain large occlusion, scale, and pose variations. Detection rates of 100% and 98.22%, respectively, demonstrate the effectiveness of the proposed approach.

QSAR Study of Insecticides of Phthalamide Derivatives Using Multiple Linear Regression and Artificial Neural Network Methods

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Adi Syahputra

2014-03-01

Full Text Available Quantitative structure activity relationship (QSAR for 21 insecticides of phthalamides containing hydrazone (PCH was studied using multiple linear regression (MLR, principle component regression (PCR and artificial neural network (ANN. Five descriptors were included in the model for MLR and ANN analysis, and five latent variables obtained from principle component analysis (PCA were used in PCR analysis. Calculation of descriptors was performed using semi-empirical PM6 method. ANN analysis was found to be superior statistical technique compared to the other methods and gave a good correlation between descriptors and activity (r2 = 0.84. Based on the obtained model, we have successfully designed some new insecticides with higher predicted activity than those of previously synthesized compounds, e.g.2-(decalinecarbamoyl-5-chloro-N’-((5-methylthiophen-2-ylmethylene benzohydrazide, 2-(decalinecarbamoyl-5-chloro-N’-((thiophen-2-yl-methylene benzohydrazide and 2-(decaline carbamoyl-N’-(4-fluorobenzylidene-5-chlorobenzohydrazide with predicted log LC50 of 1.640, 1.672, and 1.769 respectively.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

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Yan Liu

Full Text Available Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS treatment planning. In this work, we developed a deep learning convolutional neural network (CNN algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Mass detection in digital breast tomosynthesis data using convolutional neural networks and multiple instance learning.

Science.gov (United States)

Yousefi, Mina; Krzyżak, Adam; Suen, Ching Y

2018-05-01

Digital breast tomosynthesis (DBT) was developed in the field of breast cancer screening as a new tomographic technique to minimize the limitations of conventional digital mammography breast screening methods. A computer-aided detection (CAD) framework for mass detection in DBT has been developed and is described in this paper. The proposed framework operates on a set of two-dimensional (2D) slices. With plane-to-plane analysis on corresponding 2D slices from each DBT, it automatically learns complex patterns of 2D slices through a deep convolutional neural network (DCNN). It then applies multiple instance learning (MIL) with a randomized trees approach to classify DBT images based on extracted information from 2D slices. This CAD framework was developed and evaluated using 5040 2D image slices derived from 87 DBT volumes. The empirical results demonstrate that this proposed CAD framework achieves much better performance than CAD systems that use hand-crafted features and deep cardinality-restricted Bolzmann machines to detect masses in DBTs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Estimation of perceptible water vapor of atmosphere using artificial neural network, support vector machine and multiple linear regression algorithm and their comparative study

Science.gov (United States)

Shastri, Niket; Pathak, Kamlesh

2018-05-01

The water vapor content in atmosphere plays very important role in climate. In this paper the application of GPS signal in meteorology is discussed, which is useful technique that is used to estimate the perceptible water vapor of atmosphere. In this paper various algorithms like artificial neural network, support vector machine and multiple linear regression are use to predict perceptible water vapor. The comparative studies in terms of root mean square error and mean absolute errors are also carried out for all the algorithms.

Neural controller for adaptive movements with unforeseen payloads.

Science.gov (United States)

Kuperstein, M; Wang, J

1990-01-01

A theory and computer simulation of a neural controller that learns to move and position a link carrying an unforeseen payload accurately are presented. The neural controller learns adaptive dynamic control from its own experience. It does not use information about link mass, link length, or direction of gravity, and it uses only indirect uncalibrated information about payload and actuator limits. Its average positioning accuracy across a large range of payloads after learning is 3% of the positioning range. This neural controller can be used as a basis for coordinating any number of sensory inputs with limbs of any number of joints. The feedforward nature of control allows parallel implementation in real time across multiple joints.

30o inclination in handles of plastic boxes can reduce postural and muscular workload during handling

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Luciana C. C. B. Silva

2013-06-01

Full Text Available BACKGROUND: The handling of materials, which occurs in the industrial sector, is associated with lesions on the lumbar spine and in the upper limbs. Inserting handles in industrial boxes is a way to reduce work-related risks. Although the position and angle of the handles are significant factors in comfort and safety during handling, these factors have rarely been studied objectively. OBJECTIVE: To compare the handling of a commercial box and prototypes with handles and to evaluate the effects on upper limb posture, muscle electrical activity, and perceived acceptability using different grips while handling materials from different heights. METHOD: Thirty-seven healthy volunteers evaluated the handles of prototypes that allowed for changes in position (top and bottom and angle (0°, 15°, and 30°. Wrist, elbow, and shoulder movements were evaluated using electrogoniometry and inclinometry. The muscle electrical activity in the wrist extensors, biceps brachii, and the upper portion of the trapezius was measured using a portable electromyographer. The recorded data on muscle movements and electrical activity were synchronized. Subjective evaluations of acceptability were evaluated using a visual analog scale. RESULTS AND CONCLUSIONS: The prototypes with handles at a 30° angle produced the highest acceptability ratings, more neutral wrist positions, lower levels of electromyographic activity for the upper trapezius, and lower elevation angles for the arms. The different measurement methods were complementary in evaluating the upper limbs during handling.

New transport and handling contract

CERN Multimedia

SC Department

2008-01-01

A new transport and handling contract entered into force on 1.10.2008. As with the previous contract, the user interface is the internal transport/handling request form on EDH: https://edh.cern.ch/Document/TransportRequest/ To ensure that you receive the best possible service, we invite you to complete the various fields as accurately as possible and to include a mobile telephone number on which we can reach you. You can follow the progress of your request (schedule, completion) in the EDH request routing information. We remind you that the following deadlines apply: 48 hours for the transport of heavy goods (up to 8 tonnes) or simple handling operations 5 working days for crane operations, transport of extra-heavy goods, complex handling operations and combined transport and handling operations in the tunnel. For all enquiries, the number to contact remains unchanged: 72202. Heavy Handling Section TS-HE-HH 72672 - 160319

Optimization of multi-response dynamic systems integrating multiple ...

African Journals Online (AJOL)

It also results in better optimization performance than back-propagation neural network-based approach and data mining-based approach reported by the past researchers. Keywords: multiple responses, multiple regression, weighted dynamic signal-to-noise ratio, performance measure modelling, response function ...

Selected Flight Test Results for Online Learning Neural Network-Based Flight Control System

Science.gov (United States)

Williams-Hayes, Peggy S.

2004-01-01

The NASA F-15 Intelligent Flight Control System project team developed a series of flight control concepts designed to demonstrate neural network-based adaptive controller benefits, with the objective to develop and flight-test control systems using neural network technology to optimize aircraft performance under nominal conditions and stabilize the aircraft under failure conditions. This report presents flight-test results for an adaptive controller using stability and control derivative values from an online learning neural network. A dynamic cell structure neural network is used in conjunction with a real-time parameter identification algorithm to estimate aerodynamic stability and control derivative increments to baseline aerodynamic derivatives in flight. This open-loop flight test set was performed in preparation for a future phase in which the learning neural network and parameter identification algorithm output would provide the flight controller with aerodynamic stability and control derivative updates in near real time. Two flight maneuvers are analyzed - pitch frequency sweep and automated flight-test maneuver designed to optimally excite the parameter identification algorithm in all axes. Frequency responses generated from flight data are compared to those obtained from nonlinear simulation runs. Flight data examination shows that addition of flight-identified aerodynamic derivative increments into the simulation improved aircraft pitch handling qualities.

Implementing Signature Neural Networks with Spiking Neurons.

Science.gov (United States)

Carrillo-Medina, José Luis; Latorre, Roberto

2016-01-01

Spiking Neural Networks constitute the most promising approach to develop realistic Artificial Neural Networks (ANNs). Unlike traditional firing rate-based paradigms, information coding in spiking models is based on the precise timing of individual spikes. It has been demonstrated that spiking ANNs can be successfully and efficiently applied to multiple realistic problems solvable with traditional strategies (e.g., data classification or pattern recognition). In recent years, major breakthroughs in neuroscience research have discovered new relevant computational principles in different living neural systems. Could ANNs benefit from some of these recent findings providing novel elements of inspiration? This is an intriguing question for the research community and the development of spiking ANNs including novel bio-inspired information coding and processing strategies is gaining attention. From this perspective, in this work, we adapt the core concepts of the recently proposed Signature Neural Network paradigm-i.e., neural signatures to identify each unit in the network, local information contextualization during the processing, and multicoding strategies for information propagation regarding the origin and the content of the data-to be employed in a spiking neural network. To the best of our knowledge, none of these mechanisms have been used yet in the context of ANNs of spiking neurons. This paper provides a proof-of-concept for their applicability in such networks. Computer simulations show that a simple network model like the discussed here exhibits complex self-organizing properties. The combination of multiple simultaneous encoding schemes allows the network to generate coexisting spatio-temporal patterns of activity encoding information in different spatio-temporal spaces. As a function of the network and/or intra-unit parameters shaping the corresponding encoding modality, different forms of competition among the evoked patterns can emerge even in the absence

Sample handling for kinetics and molecular assembly in flow cytometry

Energy Technology Data Exchange (ETDEWEB)

Sklar, L.A. [Los Alamos National Lab., NM (United States). National Flow Cytometry Resource]|[Univ. of New Mexico, Albuquerque, NM (United States). School of Medicine; Seamer, L.C.; Kuckuck, F.; Prossnitz, E.; Edwards, B. [Univ. of New Mexico, Albuquerque, NM (United States). School of Medicine; Posner, G. [Northern Arizona Univ., Flagstaff, AZ (United States). Dept. of Chemistry

1998-07-01

Flow cytometry discriminates particle associated fluorescence from the fluorescence of the surrounding medium. It permits assemblies of macromolecular complexes on beads or cells to be detected in real-time with precision and specificity. The authors have investigated two types of robust sample handling systems which provide sub-second resolution and high throughput: (1) mixers which use stepper-motor driven syringes to initiate chemical reactions in msec time frames; and (2) flow injection controllers with valves and automated syringes used in chemical process control. In the former system, the authors used fast valves to overcome the disparity between mixing 100 {micro}ls of sample in 100 msecs and delivering sample to a flow cytometer at 1 {micro}l/sec. Particles were detected within 100 msec after mixing, but turbulence was created which lasted for 1 sec after injection of the sample into the flow cytometer. They used optical criteria to discriminate particles which were out of alignment due to the turbulent flow. Complex sample handling protocols involving multiple mixing steps and sample dilution have also been achieved. With the latter system they were able to automate sample handling and delivery with intervals of a few seconds. The authors used a fluidic approach to defeat turbulence caused by sample introduction. By controlling both sheath and sample with individual syringes, the period of turbulence was reduced to {approximately} 200 msecs. Automated sample handling and sub-second resolution should permit broad analytical and diagnostic applications of flow cytometry.

Trends in Modern Exception Handling

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Marcin Kuta

2003-01-01

Full Text Available Exception handling is nowadays a necessary component of error proof information systems. The paper presents overview of techniques and models of exception handling, problems connected with them and potential solutions. The aspects of implementation of propagation mechanisms and exception handling, their effect on semantics and general program efficiency are also taken into account. Presented mechanisms were adopted to modern programming languages. Considering design area, formal methods and formal verification of program properties we can notice exception handling mechanisms are weakly present what makes a field for future research.

Normalization as a canonical neural computation

Science.gov (United States)

Carandini, Matteo; Heeger, David J.

2012-01-01

There is increasing evidence that the brain relies on a set of canonical neural computations, repeating them across brain regions and modalities to apply similar operations to different problems. A promising candidate for such a computation is normalization, in which the responses of neurons are divided by a common factor that typically includes the summed activity of a pool of neurons. Normalization was developed to explain responses in the primary visual cortex and is now thought to operate throughout the visual system, and in many other sensory modalities and brain regions. Normalization may underlie operations such as the representation of odours, the modulatory effects of visual attention, the encoding of value and the integration of multisensory information. Its presence in such a diversity of neural systems in multiple species, from invertebrates to mammals, suggests that it serves as a canonical neural computation. PMID:22108672

Recent Advances in Neural Recording Microsystems

Directory of Open Access Journals (Sweden)

Benoit Gosselin

2011-04-01

Full Text Available The accelerating pace of research in neuroscience has created a considerable demand for neural interfacing microsystems capable of monitoring the activity of large groups of neurons. These emerging tools have revealed a tremendous potential for the advancement of knowledge in brain research and for the development of useful clinical applications. They can extract the relevant control signals directly from the brain enabling individuals with severe disabilities to communicate their intentions to other devices, like computers or various prostheses. Such microsystems are self-contained devices composed of a neural probe attached with an integrated circuit for extracting neural signals from multiple channels, and transferring the data outside the body. The greatest challenge facing development of such emerging devices into viable clinical systems involves addressing their small form factor and low-power consumption constraints, while providing superior resolution. In this paper, we survey the recent progress in the design and the implementation of multi-channel neural recording Microsystems, with particular emphasis on the design of recording and telemetry electronics. An overview of the numerous neural signal modalities is given and the existing microsystem topologies are covered. We present energy-efficient sensory circuits to retrieve weak signals from neural probes and we compare them. We cover data management and smart power scheduling approaches, and we review advances in low-power telemetry. Finally, we conclude by summarizing the remaining challenges and by highlighting the emerging trends in the field.

Using Artificial Neural Networks in Educational Research: Some Comparisons with Linear Statistical Models.

Science.gov (United States)

Everson, Howard T.; And Others

This paper explores the feasibility of neural computing methods such as artificial neural networks (ANNs) and abductory induction mechanisms (AIM) for use in educational measurement. ANNs and AIMS methods are contrasted with more traditional statistical techniques, such as multiple regression and discriminant function analyses, for making…

Xenopus reduced folate carrier regulates neural crest development epigenetically.

Directory of Open Access Journals (Sweden)

Jiejing Li

Full Text Available Folic acid deficiency during pregnancy causes birth neurocristopathic malformations resulting from aberrant development of neural crest cells. The Reduced folate carrier (RFC is a membrane-bound receptor for facilitating transfer of reduced folate into the cells. RFC knockout mice are embryonic lethal and develop multiple malformations, including neurocristopathies. Here we show that XRFC is specifically expressed in neural crest tissues in Xenopus embryos and knockdown of XRFC by specific morpholino results in severe neurocristopathies. Inhibition of RFC blocked the expression of a series of neural crest marker genes while overexpression of RFC or injection of 5-methyltetrahydrofolate expanded the neural crest territories. In animal cap assays, knockdown of RFC dramatically reduced the mono- and trimethyl-Histone3-K4 levels and co-injection of the lysine methyltransferase hMLL1 largely rescued the XRFC morpholino phenotype. Our data revealed that the RFC mediated folate metabolic pathway likely potentiates neural crest gene expression through epigenetic modifications.

Remote handling machines

International Nuclear Information System (INIS)

Sato, Shinri

1985-01-01

In nuclear power facilities, the management of radioactive wastes is made with its technology plus the automatic techniques. Under the radiation field, the maintenance or aid of such systems is important. To cope with this situation, MF-2 system, MF-3 system and a manipulator system as remote handling machines are described. MF-2 system consists of an MF-2 carrier truck, a control unit and a command trailer. It is capable of handling heavy-weight objects. The system is not by hydraulic but by electrical means. MF-3 system consists of a four-crawler truck and a manipulator. The truck is versatile in its posture by means of the four independent crawlers. The manipulator system is bilateral in operation, so that the delicate handling is made possible. (Mori, K.)

The neural subjective frame: from bodily signals to perceptual consciousness.

Science.gov (United States)

Park, Hyeong-Dong; Tallon-Baudry, Catherine

2014-05-05

The report 'I saw the stimulus' operationally defines visual consciousness, but where does the 'I' come from? To account for the subjective dimension of perceptual experience, we introduce the concept of the neural subjective frame. The neural subjective frame would be based on the constantly updated neural maps of the internal state of the body and constitute a neural referential from which first person experience can be created. We propose to root the neural subjective frame in the neural representation of visceral information which is transmitted through multiple anatomical pathways to a number of target sites, including posterior insula, ventral anterior cingulate cortex, amygdala and somatosensory cortex. We review existing experimental evidence showing that the processing of external stimuli can interact with visceral function. The neural subjective frame is a low-level building block of subjective experience which is not explicitly experienced by itself which is necessary but not sufficient for perceptual experience. It could also underlie other types of subjective experiences such as self-consciousness and emotional feelings. Because the neural subjective frame is tightly linked to homeostatic regulations involved in vigilance, it could also make a link between state and content consciousness.

Modeling of asphalt-rubber rotational viscosity by statistical analysis and neural networks

Directory of Open Access Journals (Sweden)

Luciano Pivoto Specht

2007-03-01

Full Text Available It is of a great importance to know binders' viscosity in order to perform handling, mixing, application processes and asphalt mixes compaction in highway surfacing. This paper presents the results of viscosity measurement in asphalt-rubber binders prepared in laboratory. The binders were prepared varying the rubber content, rubber particle size, duration and temperature of mixture, all following a statistical design plan. The statistical analysis and artificial neural networks were used to create mathematical models for prediction of the binders viscosity. The comparison between experimental data and simulated results with the generated models showed best performance of the neural networks analysis in contrast to the statistic models. The results indicated that the rubber content and duration of mixture have major influence on the observed viscosity for the considered interval of parameters variation.

Neural Networks in Control Applications

DEFF Research Database (Denmark)

Sørensen, O.

are examined. The models are separated into three groups representing input/output descriptions as well as state space descriptions: - Models, where all in- and outputs are measurable (static networks). - Models, where some inputs are non-measurable (recurrent networks). - Models, where some in- and some...... outputs are non-measurable (recurrent networks with incomplete state information). The three groups are ordered in increasing complexity, and for each group it is shown how to solve the problems concerning training and application of the specific model type. Of particular interest are the model types...... Kalmann filter) representing state space description. The potentials of neural networks for control of non-linear processes are also examined, focusing on three different groups of control concepts, all considered as generalizations of known linear control concepts to handle also non-linear processes...

Hybrid Neural Network Approach Based Tool for the Modelling of Photovoltaic Panels

Directory of Open Access Journals (Sweden)

Antonino Laudani

2015-01-01

Full Text Available A hybrid neural network approach based tool for identifying the photovoltaic one-diode model is presented. The generalization capabilities of neural networks are used together with the robustness of the reduced form of one-diode model. Indeed, from the studies performed by the authors and the works present in the literature, it was found that a direct computation of the five parameters via multiple inputs and multiple outputs neural network is a very difficult task. The reduced form consists in a series of explicit formulae for the support to the neural network that, in our case, is aimed at predicting just two parameters among the five ones identifying the model: the other three parameters are computed by reduced form. The present hybrid approach is efficient from the computational cost point of view and accurate in the estimation of the five parameters. It constitutes a complete and extremely easy tool suitable to be implemented in a microcontroller based architecture. Validations are made on about 10000 PV panels belonging to the California Energy Commission database.

Safety of Cargo Aircraft Handling Procedure

Directory of Open Access Journals (Sweden)

Daniel Hlavatý

2017-07-01

Full Text Available The aim of this paper is to get acquainted with the ways how to improve the safety management system during cargo aircraft handling. The first chapter is dedicated to general information about air cargo transportation. This includes the history or types of cargo aircraft handling, but also the means of handling. The second part is focused on detailed description of cargo aircraft handling, including a description of activities that are performed before and after handling. The following part of this paper covers a theoretical interpretation of safety, safety indicators and legislative provisions related to the safety of cargo aircraft handling. The fourth part of this paper analyzes the fault trees of events which might occur during handling. The factors found by this analysis are compared with safety reports of FedEx. Based on the comparison, there is a proposal on how to improve the safety management in this transportation company.

Neural Monkey: An Open-source Tool for Sequence Learning

Directory of Open Access Journals (Sweden)

Helcl Jindřich

2017-04-01

Full Text Available In this paper, we announce the development of Neural Monkey – an open-source neural machine translation (NMT and general sequence-to-sequence learning system built over the TensorFlow machine learning library. The system provides a high-level API tailored for fast prototyping of complex architectures with multiple sequence encoders and decoders. Models’ overall architecture is specified in easy-to-read configuration files. The long-term goal of the Neural Monkey project is to create and maintain a growing collection of implementations of recently proposed components or methods, and therefore it is designed to be easily extensible. Trained models can be deployed either for batch data processing or as a web service. In the presented paper, we describe the design of the system and introduce the reader to running experiments using Neural Monkey.

Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain.

Science.gov (United States)

Wolff, J Gerard

2016-01-01

The SP theory of intelligence , with its realization in the SP computer model , aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realized in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory- SP-neural -is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory-outlined in the paper-provides indirect support for SP-neural. In the abstract part of the SP theory (SP-abstract), all kinds of knowledge are represented with patterns , where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a "pattern" is realized as an array of neurons called a pattern assembly , similar to Hebb's concept of a "cell assembly" but with important differences. Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP) and, more specifically, information compression via the powerful concept of multiple alignment , borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another. It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from sensory information and from the

Information Compression, Multiple Alignment, and the Representation and Processing of Knowledge in the Brain

Directory of Open Access Journals (Sweden)

James Gerard Wolff

2016-11-01

Full Text Available The SP theory of intelligence, with its realisation in the SP computer model, aims to simplify and integrate observations and concepts across artificial intelligence, mainstream computing, mathematics, and human perception and cognition, with information compression as a unifying theme. This paper describes how abstract structures and processes in the theory may be realised in terms of neurons, their interconnections, and the transmission of signals between neurons. This part of the SP theory -- SP-neural -- is a tentative and partial model for the representation and processing of knowledge in the brain. Empirical support for the SP theory -- outlined in the paper -- provides indirect support for SP-neural.In the abstract part of the SP theory (SP-abstract, all kinds of knowledge are represented with patterns, where a pattern is an array of atomic symbols in one or two dimensions. In SP-neural, the concept of a ‘pattern’ is realised as an array of neurons called a pattern assembly, similar to Hebb's concept of a ‘cell assembly’ but with important differences.Central to the processing of information in SP-abstract is information compression via the matching and unification of patterns (ICMUP and, more specifically, information compression via the powerful concept of multiple alignment, borrowed and adapted from bioinformatics. Processes such as pattern recognition, reasoning and problem solving are achieved via the building of multiple alignments, while unsupervised learning is achieved by creating patterns from sensory information and also by creating patterns from multiple alignments in which there is a partial match between one pattern and another.It is envisaged that, in SP-neural, short-lived neural structures equivalent to multiple alignments will be created via an inter-play of excitatory and inhibitory neural signals. It is also envisaged that unsupervised learning will be achieved by the creation of pattern assemblies from

Neuron's eye view: Inferring features of complex stimuli from neural responses.

Directory of Open Access Journals (Sweden)

Xin Chen

2017-08-01

Full Text Available Experiments that study neural encoding of stimuli at the level of individual neurons typically choose a small set of features present in the world-contrast and luminance for vision, pitch and intensity for sound-and assemble a stimulus set that systematically varies along these dimensions. Subsequent analysis of neural responses to these stimuli typically focuses on regression models, with experimenter-controlled features as predictors and spike counts or firing rates as responses. Unfortunately, this approach requires knowledge in advance about the relevant features coded by a given population of neurons. For domains as complex as social interaction or natural movement, however, the relevant feature space is poorly understood, and an arbitrary a priori choice of features may give rise to confirmation bias. Here, we present a Bayesian model for exploratory data analysis that is capable of automatically identifying the features present in unstructured stimuli based solely on neuronal responses. Our approach is unique within the class of latent state space models of neural activity in that it assumes that firing rates of neurons are sensitive to multiple discrete time-varying features tied to the stimulus, each of which has Markov (or semi-Markov dynamics. That is, we are modeling neural activity as driven by multiple simultaneous stimulus features rather than intrinsic neural dynamics. We derive a fast variational Bayesian inference algorithm and show that it correctly recovers hidden features in synthetic data, as well as ground-truth stimulus features in a prototypical neural dataset. To demonstrate the utility of the algorithm, we also apply it to cluster neural responses and demonstrate successful recovery of features corresponding to monkeys and faces in the image set.

Recommendations for Collection and Handling of Specimens From Group Breast Cancer Clinical Trials

Science.gov (United States)

Leyland-Jones, Brian R.; Ambrosone, Christine B.; Bartlett, John; Ellis, Matthew J.C.; Enos, Rebecca A.; Raji, Adekunle; Pins, Michael R.; Zujewski, Jo Anne; Hewitt, Stephen M.; Forbes, John F.; Abramovitz, Mark; Braga, Sofia; Cardoso, Fatima; Harbeck, Nadia; Denkert, Carsten; Jewell, Scott D.

2008-01-01

Recommendations for specimen collection and handling have been developed for adoption across breast cancer clinical trials conducted by the Breast International Group (BIG)-sponsored Groups and the National Cancer Institute (NCI)-sponsored North American Cooperative Groups. These recommendations are meant to promote identifiable standards for specimen collection and handling within and across breast cancer trials, such that the variability in collection/handling practices that currently exists is minimized and specimen condition and quality are enhanced, thereby maximizing results from specimen-based diagnostic testing and research. Three working groups were formed from the Cooperative Group Banking Committee, BIG groups, and North American breast cancer cooperative groups to identify standards for collection and handling of (1) formalin-fixed, paraffin-embedded (FFPE) tissue; (2) blood and its components; and (3) fresh/frozen tissue from breast cancer trials. The working groups collected standard operating procedures from multiple group specimen banks, administered a survey on banking practices to those banks, and engaged in a series of discussions from 2005 to 2007. Their contributions were synthesized into this document, which focuses primarily on collection and handling of specimens to the point of shipment to the central bank, although also offers some guidance to central banks. Major recommendations include submission of an FFPE block, whole blood, and serial serum or plasma from breast cancer clinical trials, and use of one fixative and buffer type (10% neutral phosphate-buffered formalin, pH 7) for FFPE tissue across trials. Recommendations for proper handling and shipping were developed for blood, serum, plasma, FFPE, and fresh/frozen tissue. PMID:18955459

Deep Neural Network-Based Chinese Semantic Role Labeling

Institute of Scientific and Technical Information of China (English)

ZHENG Xiaoqing; CHEN Jun; SHANG Guoqiang

2017-01-01

A recent trend in machine learning is to use deep architec-tures to discover multiple levels of features from data, which has achieved impressive results on various natural language processing (NLP) tasks. We propose a deep neural network-based solution to Chinese semantic role labeling (SRL) with its application on message analysis. The solution adopts a six-step strategy: text normalization, named entity recognition (NER), Chinese word segmentation and part-of-speech (POS) tagging, theme classification, SRL, and slot filling. For each step, a novel deep neural network - based model is designed and optimized, particularly for smart phone applications. Ex-periment results on all the NLP sub - tasks of the solution show that the proposed neural networks achieve state-of-the-art performance with the minimal computational cost. The speed advantage of deep neural networks makes them more competitive for large-scale applications or applications requir-ing real-time response, highlighting the potential of the pro-posed solution for practical NLP systems.

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

Directory of Open Access Journals (Sweden)

Petkov T.

2016-03-01

Full Text Available A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC and Gas Chromatography - mass spectrometry (GC-MS were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, “unknown” were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3 and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety “unknown” samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

Upon the opportunity to apply ART2 Neural Network for clusterization of biodiesel fuels

Science.gov (United States)

Petkov, T.; Mustafa, Z.; Sotirov, S.; Milina, R.; Moskovkina, M.

2016-03-01

A chemometric approach using artificial neural network for clusterization of biodiesels was developed. It is based on artificial ART2 neural network. Gas chromatography (GC) and Gas Chromatography - mass spectrometry (GC-MS) were used for quantitative and qualitative analysis of biodiesels, produced from different feedstocks, and FAME (fatty acid methyl esters) profiles were determined. Totally 96 analytical results for 7 different classes of biofuel plants: sunflower, rapeseed, corn, soybean, palm, peanut, "unknown" were used as objects. The analysis of biodiesels showed the content of five major FAME (C16:0, C18:0, C18:1, C18:2, C18:3) and those components were used like inputs in the model. After training with 6 samples, for which the origin was known, ANN was verified and tested with ninety "unknown" samples. The present research demonstrated the successful application of neural network for recognition of biodiesels according to their feedstock which give information upon their properties and handling.

Simulation Study on the Application of the Generalized Entropy Concept in Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Krzysztof Gajowniczek

2018-04-01

Full Text Available Artificial neural networks are currently one of the most commonly used classifiers and over the recent years they have been successfully used in many practical applications, including banking and finance, health and medicine, engineering and manufacturing. A large number of error functions have been proposed in the literature to achieve a better predictive power. However, only a few works employ Tsallis statistics, although the method itself has been successfully applied in other machine learning techniques. This paper undertakes the effort to examine the q -generalized function based on Tsallis statistics as an alternative error measure in neural networks. In order to validate different performance aspects of the proposed function and to enable identification of its strengths and weaknesses the extensive simulation was prepared based on the artificial benchmarking dataset. The results indicate that Tsallis entropy error function can be successfully introduced in the neural networks yielding satisfactory results and handling with class imbalance, noise in data or use of non-informative predictors.

Oscillatory neural representations in the sensory thalamus predict neuropathic pain relief by deep brain stimulation.

Science.gov (United States)

Huang, Yongzhi; Green, Alexander L; Hyam, Jonathan; Fitzgerald, James; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

Understanding the function of sensory thalamic neural activity is essential for developing and improving interventions for neuropathic pain. However, there is a lack of investigation of the relationship between sensory thalamic oscillations and pain relief in patients with neuropathic pain. This study aims to identify the oscillatory neural characteristics correlated with pain relief induced by deep brain stimulation (DBS), and develop a quantitative model to predict pain relief by integrating characteristic measures of the neural oscillations. Measures of sensory thalamic local field potentials (LFPs) in thirteen patients with neuropathic pain were screened in three dimensional feature space according to the rhythm, balancing, and coupling neural behaviours, and correlated with pain relief. An integrated approach based on principal component analysis (PCA) and multiple regression analysis is proposed to integrate the multiple measures and provide a predictive model. This study reveals distinct thalamic rhythms of theta, alpha, high beta and high gamma oscillations correlating with pain relief. The balancing and coupling measures between these neural oscillations were also significantly correlated with pain relief. The study enriches the series research on the function of thalamic neural oscillations in neuropathic pain and relief, and provides a quantitative approach for predicting pain relief by DBS using thalamic neural oscillations. Copyright © 2017 Elsevier Inc. All rights reserved.

Dynamic Neural State Identification in Deep Brain Local Field Potentials of Neuropathic Pain.

Science.gov (United States)

Luo, Huichun; Huang, Yongzhi; Du, Xueying; Zhang, Yunpeng; Green, Alexander L; Aziz, Tipu Z; Wang, Shouyan

2018-01-01

In neuropathic pain, the neurophysiological and neuropathological function of the ventro-posterolateral nucleus of the thalamus (VPL) and the periventricular gray/periaqueductal gray area (PVAG) involves multiple frequency oscillations. Moreover, oscillations related to pain perception and modulation change dynamically over time. Fluctuations in these neural oscillations reflect the dynamic neural states of the nucleus. In this study, an approach to classifying the synchronization level was developed to dynamically identify the neural states. An oscillation extraction model based on windowed wavelet packet transform was designed to characterize the activity level of oscillations. The wavelet packet coefficients sparsely represented the activity level of theta and alpha oscillations in local field potentials (LFPs). Then, a state discrimination model was designed to calculate an adaptive threshold to determine the activity level of oscillations. Finally, the neural state was represented by the activity levels of both theta and alpha oscillations. The relationship between neural states and pain relief was further evaluated. The performance of the state identification approach achieved sensitivity and specificity beyond 80% in simulation signals. Neural states of the PVAG and VPL were dynamically identified from LFPs of neuropathic pain patients. The occurrence of neural states based on theta and alpha oscillations were correlated to the degree of pain relief by deep brain stimulation. In the PVAG LFPs, the occurrence of the state with high activity levels of theta oscillations independent of alpha and the state with low-level alpha and high-level theta oscillations were significantly correlated with pain relief by deep brain stimulation. This study provides a reliable approach to identifying the dynamic neural states in LFPs with a low signal-to-noise ratio by using sparse representation based on wavelet packet transform. Furthermore, it may advance closed-loop deep

Ergonomics and patient handling.

Science.gov (United States)

McCoskey, Kelsey L

2007-11-01

This study aimed to describe patient-handling demands in inpatient units during a 24-hour period at a military health care facility. A 1-day total population survey described the diverse nature and impact of patient-handling tasks relative to a variety of nursing care units, patient characteristics, and transfer equipment. Productivity baselines were established based on patient dependency, physical exertion, type of transfer, and time spent performing the transfer. Descriptions of the physiological effect of transfers on staff based on patient, transfer, and staff characteristics were developed. Nursing staff response to surveys demonstrated how patient-handling demands are impacted by the staff's physical exertion and level of patient dependency. The findings of this study describe the types of transfers occurring in these inpatient units and the physical exertion and time requirements for these transfers. This description may guide selection of the most appropriate and cost-effective patient-handling equipment required for specific units and patients.

Prediction of Currency Volume Issued in Taiwan Using a Hybrid Artificial Neural Network and Multiple Regression Approach

Directory of Open Access Journals (Sweden)

Yuehjen E. Shao

2013-01-01

Full Text Available Because the volume of currency issued by a country always affects its interest rate, price index, income levels, and many other important macroeconomic variables, the prediction of currency volume issued has attracted considerable attention in recent years. In contrast to the typical single-stage forecast model, this study proposes a hybrid forecasting approach to predict the volume of currency issued in Taiwan. The proposed hybrid models consist of artificial neural network (ANN and multiple regression (MR components. The MR component of the hybrid models is established for a selection of fewer explanatory variables, wherein the selected variables are of higher importance. The ANN component is then designed to generate forecasts based on those important explanatory variables. Subsequently, the model is used to analyze a real dataset of Taiwan's currency from 1996 to 2011 and twenty associated explanatory variables. The prediction results reveal that the proposed hybrid scheme exhibits superior forecasting performance for predicting the volume of currency issued in Taiwan.

Equipment for the handling of thorium materials

International Nuclear Information System (INIS)

Heisler, S.W. Jr.; Mihalovich, G.S.

1988-01-01

The Feed Materials Production Center (FMPC) is the United States Department of Energy's storage facility for thorium. FMPC thorium handling and overpacking projects ensure the continued safe handling and storage of the thorium inventory until final disposition of the materials is determined and implemented. The handling and overpacking of the thorium materials requires the design of a system that utilizes remote handling and overpacking equipment not currently utilized at the FMPC in the handling of uranium materials. The use of remote equipment significantly reduces radiation exposure to personnel during the handling and overpacking efforts. The design system combines existing technologies from the nuclear industry, the materials processing and handling industry and the mining industry. The designed system consists of a modified fork lift truck for the transport of thorium containers, automated equipment for material identification and inventory control, and remote handling and overpacking equipment for material identification and inventory control, and remote handling and overpacking equipment for repackaging of the thorium materials

Predictive Control of Hydronic Floor Heating Systems using Neural Networks and Genetic Algorithms

DEFF Research Database (Denmark)

Vinther, Kasper; Green, Torben; Østergaard, Søren

2017-01-01

This paper presents the use a neural network and a micro genetic algorithm to optimize future set-points in existing hydronic floor heating systems for improved energy efficiency. The neural network can be trained to predict the impact of changes in set-points on future room temperatures. Additio...... space is not guaranteed. Evaluation of the performance of multiple neural networks is performed, using different levels of information, and optimization results are presented on a detailed house simulation model....

EEG in the classroom: Synchronised neural recordings during video presentation

DEFF Research Database (Denmark)

Poulsen, Andreas Trier; Kamronn, Simon Due; Dmochowski, Jacek

2017-01-01

We performed simultaneous recordings of electroencephalography (EEG) from multiple students in a classroom, and measured the inter-subject correlation (ISC) of activity evoked by a common video stimulus. The neural reliability, as quantified by ISC, has been linked to engagement and attentional......-evoked neural responses, known to be modulated by attention, can be tracked for groups of students with synchronized EEG acquisition. This is a step towards real-time inference of engagement in the classroom....

Applications of neural networks in environmental and energy sciences and engineering. Proceedings of the 1995 workshop on environmental and energy applications of neural networks

Energy Technology Data Exchange (ETDEWEB)

Hashem, S.; Keller, P.E.; Kouzes, R.T.; Kangas, L.J.

1995-12-31

These proceedings contain edited versions of the technical presentations of the Workshop on Environmental and Energy Applications of Neural Networks, held on March 30--31, 1995, in Richland, Washington. The purpose of the workshop was to provide a forum for discussing environmental, energy, and biomedical applications of neural networks. Panels were held to discuss various research and development issues relating to real-world applications in each of the three areas. The applications covered in the workshop were: Environmental applications -- modeling and predicting soil, air and water pollution, environmental sensing, spectroscopy, hazardous waste handling and cleanup; Energy applications -- process monitoring and optimization of power systems, modeling and control of power plants, environmental monitoring for power systems, power load forecasting, fault location and diagnosis of power systems; and Biomedical applications -- medical image and signal analysis, medical diagnosis, analysis of environmental health effects, and modeling biological systems. Selected papers are indexed separately for inclusion in the Energy Science and Technology Database.

How to Handle Abuse

Science.gov (United States)

... Handle Abuse KidsHealth / For Kids / How to Handle Abuse What's in this article? Tell Right Away How Do You Know Something Is Abuse? ... babysitter, teacher, coach, or a bigger kid. Child abuse can happen anywhere — at ... building. Tell Right Away A kid who is being seriously hurt ...

Waste Handling Building Conceptual Study

International Nuclear Information System (INIS)

G.W. Rowe

2000-01-01

The objective of the ''Waste Handling Building Conceptual Study'' is to develop proposed design requirements for the repository Waste Handling System in sufficient detail to allow the surface facility design to proceed to the License Application effort if the proposed requirements are approved by DOE. Proposed requirements were developed to further refine waste handling facility performance characteristics and design constraints with an emphasis on supporting modular construction, minimizing fuel inventory, and optimizing facility maintainability and dry handling operations. To meet this objective, this study attempts to provide an alternative design to the Site Recommendation design that is flexible, simple, reliable, and can be constructed in phases. The design concept will be input to the ''Modular Design/Construction and Operation Options Report'', which will address the overall program objectives and direction, including options and issues associated with transportation, the subsurface facility, and Total System Life Cycle Cost. This study (herein) is limited to the Waste Handling System and associated fuel staging system

Practices of Handling

DEFF Research Database (Denmark)

Ræbild, Ulla

to touch, pick up, carry, or feel with the hands. Figuratively it is to manage, deal with, direct, train, or control. Additionally, as a noun, a handle is something by which we grasp or open up something. Lastly, handle also has a Nordic root, here meaning to trade, bargain or deal. Together all four...... meanings seem to merge in the fashion design process, thus opening up for an embodied engagement with matter that entails direction giving, organizational management and negotiation. By seeing processes of handling as a key fashion methodological practice, it is possible to divert the discourse away from...... introduces four ways whereby fashion designers apply their own bodies as tools for design; a) re-activating past garment-design experiences, b) testing present garment-design experiences c) probing for new garment-design experiences and d) design of future garment experiences by body proxy. The paper...

Novel paths towards neural cellular products for neurological disorders.

Science.gov (United States)

Daadi, Marcel M

2011-11-01

The prospect of using neural cells derived from stem cells or from reprogrammed adult somatic cells provides a unique opportunity in cell therapy and drug discovery for developing novel strategies for brain repair. Cell-based therapeutic approaches for treating CNS afflictions caused by disease or injury aim to promote structural repair of the injured or diseased neural tissue, an outcome currently not achieved by drug therapy. Preclinical research in animal models of various diseases or injuries report that grafts of neural cells enhance endogenous repair, provide neurotrophic support to neurons undergoing degeneration and replace lost neural cells. In recent years, the sources of neural cells for treating neurological disorders have been rapidly expanding and in addition to offering therapeutic potential, neural cell products hold promise for disease modeling and drug discovery use. Specific neural cell types have been derived from adult or fetal brain, from human embryonic stem cells, from induced pluripotent stem cells and directly transdifferentiated from adult somatic cells, such as skin cells. It is yet to be determined if the latter approach will evolve into a paradigm shift in the fields of stem cell research and regenerative medicine. These multiple sources of neural cells cover a wide spectrum of safety that needs to be balanced with efficacy to determine the viability of the cellular product. In this article, we will review novel sources of neural cells and discuss current obstacles to developing them into viable cellular products for treating neurological disorders.

PERSPECTIVE: Translational neural engineering: multiple perspectives on bringing benchtop research into the clinical domain

Science.gov (United States)

Rousche, Patrick; Schneeweis, David M.; Perreault, Eric J.; Jensen, Winnie

2008-03-01

A half-day forum to address a wide range of issues related to translational neural engineering was conducted at the annual meeting of the Biomedical Engineering Society. Successful practitioners of translational neural engineering from academics, clinical medicine and industry were invited to share a diversity of perspectives and experiences on the translational process. The forum was targeted towards traditional academic researchers who may be interested in the expanded funding opportunities available for translational research that emphasizes product commercialization and clinical implementation. The seminar was funded by the NIH with support from the Rehabilitation Institute of Chicago. We report here a summary of the speaker viewpoints with particular focus on extracting successful strategies for engaging in or conducting translational neural engineering research. Daryl Kipke, PhD, (Department of Biomedical Engineering at the University of Michigan) and Molly Shoichet, PhD, (Department of Chemical Engineering at the University of Toronto) gave details of their extensive experience with product commercialization while holding primary appointments in academic departments. They both encouraged strong clinical input at very early stages of research. Neurosurgeon Fady Charbel, MD, (Department of Neurosurgery at the University of Illinois at Chicago) discussed his role in product commercialization as a clinician. Todd Kuiken, MD, PhD, (Director of the Neural Engineering for Artificial Limbs at the Rehabilitation Institute of Chicago, affiliated with Northwestern University) also a clinician, described a model of translational engineering that emphasized the development of clinically relevant technology, without a strong commercialization imperative. The clinicians emphasized the importance of communicating effectively with engineers. Representing commercial neural engineering was Doug Sheffield, PhD, (Director of New Technology at Vertis Neuroscience, Inc.) who

Development and Flight Testing of a Neural Network Based Flight Control System on the NF-15B Aircraft

Science.gov (United States)

Bomben, Craig R.; Smolka, James W.; Bosworth, John T.; Silliams-Hayes, Peggy S.; Burken, John J.; Larson, Richard R.; Buschbacher, Mark J.; Maliska, Heather A.

2006-01-01

The Intelligent Flight Control System (IFCS) project at the NASA Dryden Flight Research Center, Edwards AFB, CA, has been investigating the use of neural network based adaptive control on a unique NF-15B test aircraft. The IFCS neural network is a software processor that stores measured aircraft response information to dynamically alter flight control gains. In 2006, the neural network was engaged and allowed to learn in real time to dynamically alter the aircraft handling qualities characteristics in the presence of actual aerodynamic failure conditions injected into the aircraft through the flight control system. The use of neural network and similar adaptive technologies in the design of highly fault and damage tolerant flight control systems shows promise in making future aircraft far more survivable than current technology allows. This paper will present the results of the IFCS flight test program conducted at the NASA Dryden Flight Research Center in 2006, with emphasis on challenges encountered and lessons learned.

Calixarene-mediated liquid membrane transport of choline conjugates 3: The effect of handle variation on neurotransmitter transport.

Science.gov (United States)

Collins, James L; Fujii, Ayu; Roshandel, Sahar; To, Cuong-Alexander; Schramm, Michael P

2017-07-01

Upper rim phosphonic acid functionalized calix[4]arene affects selective transport of multiple molecular payloads through a liquid membrane. The secret is in the attachment of a receptor-complementary handle to the payload. We find that the trimethylammonium ethylene group present in choline is one of several general handles for the transport of drug and drug-like species. Herein we compare the effect of handle variation against the transport of serotonin and dopamine. We find that several ionizable amine termini handles are sufficient for transport and identify two ideal candidates. Their performance is significantly enhanced in HEPES buffered solutions. This inquiry completes a series of 3 studies aimed at optimization of this strategy. In completion a new approach towards synthetic receptor mediated selective small molecule transport has emerged; future work in vesicular and cellular systems will follow. Copyright © 2017 Elsevier Ltd. All rights reserved.

Single-site neural tube closure in human embryos revisited.

Science.gov (United States)

de Bakker, Bernadette S; Driessen, Stan; Boukens, Bastiaan J D; van den Hoff, Maurice J B; Oostra, Roelof-Jan

2017-10-01

Since the multi-site closure theory was first proposed in 1991 as explanation for the preferential localizations of neural tube defects, the closure of the neural tube has been debated. Although the multi-site closure theory is much cited in clinical literature, single-site closure is most apparent in literature concerning embryology. Inspired by Victor Hamburgers (1900-2001) statement that "our real teacher has been and still is the embryo, who is, incidentally, the only teacher who is always right", we decided to critically review both theories of neural tube closure. To verify the theories of closure, we studied serial histological sections of 10 mouse embryos between 8.5 and 9.5 days of gestation and 18 human embryos of the Carnegie collection between Carnegie stage 9 (19-21 days) and 13 (28-32 days). Neural tube closure was histologically defined by the neuroepithelial remodeling of the two adjoining neural fold tips in the midline. We did not observe multiple fusion sites in neither mouse nor human embryos. A meta-analysis of case reports on neural tube defects showed that defects can occur at any level of the neural axis. Our data indicate that the human neural tube fuses at a single site and, therefore, we propose to reinstate the single-site closure theory for neural tube closure. We showed that neural tube defects are not restricted to a specific location, thereby refuting the reasoning underlying the multi-site closure theory. Clin. Anat. 30:988-999, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Reducing Wind Tunnel Data Requirements Using Neural Networks

Science.gov (United States)

Ross, James C.; Jorgenson, Charles C.; Norgaard, Magnus

1997-01-01

The use of neural networks to minimize the amount of data required to completely define the aerodynamic performance of a wind tunnel model is examined. The accuracy requirements for commercial wind tunnel test data are very severe and are difficult to reproduce using neural networks. For the current work, multiple input, single output networks were trained using a Levenberg-Marquardt algorithm for each of the aerodynamic coefficients. When applied to the aerodynamics of a 55% scale model of a U.S. Air Force/ NASA generic fighter configuration, this scheme provided accurate models of the lift, drag, and pitching-moment coefficients. Using only 50% of the data acquired during, the wind tunnel test, the trained neural network had a predictive accuracy equal to or better than the accuracy of the experimental measurements.

SRV-automatic handling device

International Nuclear Information System (INIS)

Yamada, Koji

1987-01-01

Automatic handling device for the steam relief valves (SRV's) is developed in order to achieve a decrease in exposure of workers, increase in availability factor, improvement in reliability, improvement in safety of operation, and labor saving. A survey is made during a periodical inspection to examine the actual SVR handling operation. An SRV automatic handling device consists of four components: conveyor, armed conveyor, lifting machine, and control/monitoring system. The conveyor is so designed that the existing I-rail installed in the containment vessel can be used without any modification. This is employed for conveying an SRV along the rail. The armed conveyor, designed for a box rail, is used for an SRV installed away from the rail. By using the lifting machine, an SRV installed away from the I-rail is brought to a spot just below the rail so that the SRV can be transferred by the conveyor. The control/monitoring system consists of a control computer, operation panel, TV monitor and annunciator. The SRV handling device is operated by remote control from a control room. A trial equipment is constructed and performance/function testing is carried out using actual SRV's. As a result, is it shown that the SRV handling device requires only two operators to serve satisfactorily. The required time for removal and replacement of one SRV is about 10 minutes. (Nogami, K.)

Rotation Invariance Neural Network

OpenAIRE

Li, Shiyuan

2017-01-01

Rotation invariance and translation invariance have great values in image recognition tasks. In this paper, we bring a new architecture in convolutional neural network (CNN) named cyclic convolutional layer to achieve rotation invariance in 2-D symbol recognition. We can also get the position and orientation of the 2-D symbol by the network to achieve detection purpose for multiple non-overlap target. Last but not least, this architecture can achieve one-shot learning in some cases using thos...

Handling missing data in cluster randomized trials: A demonstration of multiple imputation with PAN through SAS

Directory of Open Access Journals (Sweden)

Jiangxiu Zhou

2014-09-01

Full Text Available The purpose of this study is to demonstrate a way of dealing with missing data in clustered randomized trials by doing multiple imputation (MI with the PAN package in R through SAS. The procedure for doing MI with PAN through SAS is demonstrated in detail in order for researchers to be able to use this procedure with their own data. An illustration of the technique with empirical data was also included. In this illustration thePAN results were compared with pairwise deletion and three types of MI: (1 Normal Model (NM-MI ignoring the cluster structure; (2 NM-MI with dummy-coded cluster variables (fixed cluster structure; and (3 a hybrid NM-MI which imputes half the time ignoring the cluster structure, and the other half including the dummy-coded cluster variables. The empirical analysis showed that using PAN and the other strategies produced comparable parameter estimates. However, the dummy-coded MI overestimated the intraclass correlation, whereas MI ignoring the cluster structure and the hybrid MI underestimated the intraclass correlation. When compared with PAN, the p-value and standard error for the treatment effect were higher with dummy-coded MI, and lower with MI ignoring the clusterstructure, the hybrid MI approach, and pairwise deletion. Previous studies have shown that NM-MI is not appropriate for handling missing data in clustered randomized trials. This approach, in addition to the pairwise deletion approach, leads to a biased intraclass correlation and faultystatistical conclusions. Imputation in clustered randomized trials should be performed with PAN. We have demonstrated an easy way for using PAN through SAS.

Convolutional Neural Networks for Human Activity Recognition Using Body-Worn Sensors

Directory of Open Access Journals (Sweden)

Fernando Moya Rueda

2018-05-01

Full Text Available Human activity recognition (HAR is a classification task for recognizing human movements. Methods of HAR are of great interest as they have become tools for measuring occurrences and durations of human actions, which are the basis of smart assistive technologies and manual processes analysis. Recently, deep neural networks have been deployed for HAR in the context of activities of daily living using multichannel time-series. These time-series are acquired from body-worn devices, which are composed of different types of sensors. The deep architectures process these measurements for finding basic and complex features in human corporal movements, and for classifying them into a set of human actions. As the devices are worn at different parts of the human body, we propose a novel deep neural network for HAR. This network handles sequence measurements from different body-worn devices separately. An evaluation of the architecture is performed on three datasets, the Oportunity, Pamap2, and an industrial dataset, outperforming the state-of-the-art. In addition, different network configurations will also be evaluated. We find that applying convolutions per sensor channel and per body-worn device improves the capabilities of convolutional neural network (CNNs.

Grain Handling and Storage.

Science.gov (United States)

Harris, Troy G.; Minor, John

This text for a secondary- or postecondary-level course in grain handling and storage contains ten chapters. Chapter titles are (1) Introduction to Grain Handling and Storage, (2) Elevator Safety, (3) Grain Grading and Seed Identification, (4) Moisture Control, (5) Insect and Rodent Control, (6) Grain Inventory Control, (7) Elevator Maintenance,…

Insulin Regulates Astrocytic Glucose Handling Through Cooperation With IGF-I.

Science.gov (United States)

Fernandez, Ana M; Hernandez-Garzón, Edwin; Perez-Domper, Paloma; Perez-Alvarez, Alberto; Mederos, Sara; Matsui, Takashi; Santi, Andrea; Trueba-Saiz, Angel; García-Guerra, Lucía; Pose-Utrilla, Julia; Fielitz, Jens; Olson, Eric N; Fernandez de la Rosa, Ruben; Garcia Garcia, Luis; Pozo, Miguel Angel; Iglesias, Teresa; Araque, Alfonso; Soya, Hideaki; Perea, Gertrudis; Martin, Eduardo D; Torres Aleman, Ignacio

2017-01-01

Brain activity requires a flux of glucose to active regions to sustain increased metabolic demands. Insulin, the main regulator of glucose handling in the body, has been traditionally considered not to intervene in this process. However, we now report that insulin modulates brain glucose metabolism by acting on astrocytes in concert with IGF-I. The cooperation of insulin and IGF-I is needed to recover neuronal activity after hypoglycemia. Analysis of underlying mechanisms show that the combined action of IGF-I and insulin synergistically stimulates a mitogen-activated protein kinase/protein kinase D pathway resulting in translocation of GLUT1 to the cell membrane through multiple protein-protein interactions involving the scaffolding protein GAIP-interacting protein C terminus and the GTPase RAC1. Our observations identify insulin-like peptides as physiological modulators of brain glucose handling, providing further support to consider the brain as a target organ in diabetes. © 2017 by the American Diabetes Association.

Mockup of an automated material transport system for remote handling

International Nuclear Information System (INIS)

Porter, M.L.

1992-01-01

The automated material transport system (AMTS) was conceived for the transport of samples within the material and process control laboratory (MPCL), located in the plutonium processing building of the special isotope separation (SIS) facility. The MPCL was designed with a dry sample handling laboratory and a wet chemistry analysis laboratory. Each laboratory contained several processing glove boxes. The function of the AMTS was to automate the handling of materials, multiple process samples, and bulky items between process stations with a minimum of operator intervention and with a minimum of waiting periods and nonproductive activities. The AMTS design requirements, design verification mockup plan, and AMTS mockup procurement specification were established prior to cancellation of the SIS project. Due to the AMTS's flexibility, the need for technology development, and applicability to other US Department of Energy facilities, mockup of the AMTS continued. This paper discusses the system design features, capabilities, and results of initial testing

TRANSPORT/HANDLING REQUESTS

CERN Multimedia

Groupe ST/HM

2002-01-01

A new EDH document entitled 'Transport/Handling Request' will be in operation as of Monday, 11th February 2002, when the corresponding icon will be accessible from the EDH desktop, together with the application instructions. This EDH form will replace the paper-format transport/handling request form for all activities involving the transport of equipment and materials. However, the paper form will still be used for all vehicle-hire requests. The introduction of the EDH transport/handling request form is accompanied by the establishment of the following time limits for the various services concerned: 24 hours for the removal of office items, 48 hours for the transport of heavy items (of up to 6 metric tons and of standard road width), 5 working days for a crane operation, extra-heavy transport operation or complete removal, 5 working days for all transport operations relating to LHC installation. ST/HM Group, Logistics Section Tel: 72672 - 72202

Mechanisms underlying metabolic and neural defects in zebrafish and human multiple acyl-CoA dehydrogenase deficiency (MADD.

Directory of Open Access Journals (Sweden)

Yuanquan Song

2009-12-01

Full Text Available In humans, mutations in electron transfer flavoprotein (ETF or electron transfer flavoprotein dehydrogenase (ETFDH lead to MADD/glutaric aciduria type II, an autosomal recessively inherited disorder characterized by a broad spectrum of devastating neurological, systemic and metabolic symptoms. We show that a zebrafish mutant in ETFDH, xavier, and fibroblast cells from MADD patients demonstrate similar mitochondrial and metabolic abnormalities, including reduced oxidative phosphorylation, increased aerobic glycolysis, and upregulation of the PPARG-ERK pathway. This metabolic dysfunction is associated with aberrant neural proliferation in xav, in addition to other neural phenotypes and paralysis. Strikingly, a PPARG antagonist attenuates aberrant neural proliferation and alleviates paralysis in xav, while PPARG agonists increase neural proliferation in wild type embryos. These results show that mitochondrial dysfunction, leading to an increase in aerobic glycolysis, affects neurogenesis through the PPARG-ERK pathway, a potential target for therapeutic intervention.

A Constrained Multi-Objective Learning Algorithm for Feed-Forward Neural Network Classifiers

Directory of Open Access Journals (Sweden)

M. Njah

2017-06-01

Full Text Available This paper proposes a new approach to address the optimal design of a Feed-forward Neural Network (FNN based classifier. The originality of the proposed methodology, called CMOA, lie in the use of a new constraint handling technique based on a self-adaptive penalty procedure in order to direct the entire search effort towards finding only Pareto optimal solutions that are acceptable. Neurons and connections of the FNN Classifier are dynamically built during the learning process. The approach includes differential evolution to create new individuals and then keeps only the non-dominated ones as the basis for the next generation. The designed FNN Classifier is applied to six binary classification benchmark problems, obtained from the UCI repository, and results indicated the advantages of the proposed approach over other existing multi-objective evolutionary neural networks classifiers reported recently in the literature.

vDNN: Virtualized Deep Neural Networks for Scalable, Memory-Efficient Neural Network Design

OpenAIRE

Rhu, Minsoo; Gimelshein, Natalia; Clemons, Jason; Zulfiqar, Arslan; Keckler, Stephen W.

2016-01-01

The most widely used machine learning frameworks require users to carefully tune their memory usage so that the deep neural network (DNN) fits into the DRAM capacity of a GPU. This restriction hampers a researcher's flexibility to study different machine learning algorithms, forcing them to either use a less desirable network architecture or parallelize the processing across multiple GPUs. We propose a runtime memory manager that virtualizes the memory usage of DNNs such that both GPU and CPU...

Bilingualism, dementia, cognitive and neural reserve.

Science.gov (United States)

Perani, Daniela; Abutalebi, Jubin

2015-12-01

We discuss the role of bilingualism as a source of cognitive reserve and we propose the putative neural mechanisms through which lifelong bilingualism leads to a neural reserve that delays the onset of dementia. Recent findings highlight that the use of more than one language affects the human brain in terms of anatomo-structural changes. It is noteworthy that recent evidence from different places and cultures throughout the world points to a significant delay of dementia onset in bilingual/multilingual individuals. This delay has been reported not only for Alzheimer's dementia and its prodromal mild cognitive impairment phase, but also for other dementias such as vascular and fronto-temporal dementia, and was found to be independent of literacy, education and immigrant status. Lifelong bilingualism represents a powerful cognitive reserve delaying the onset of dementia by approximately 4 years. As to the causal mechanism, because speaking more than one language heavily relies upon executive control and attention, brain systems handling these functions are more developed in bilinguals resulting in increases of gray and white matter densities that may help protect from dementia onset. These neurocognitive benefits are even more prominent when second language proficiency and exposure are kept high throughout life.

Neural plasticity and its initiating conditions in tinnitus.

Science.gov (United States)

Roberts, L E

2018-03-01

Deafferentation caused by cochlear pathology (which can be hidden from the audiogram) activates forms of neural plasticity in auditory pathways, generating tinnitus and its associated conditions including hyperacusis. This article discusses tinnitus mechanisms and suggests how these mechanisms may relate to those involved in normal auditory information processing. Research findings from animal models of tinnitus and from electromagnetic imaging of tinnitus patients are reviewed which pertain to the role of deafferentation and neural plasticity in tinnitus and hyperacusis. Auditory neurons compensate for deafferentation by increasing their input/output functions (gain) at multiple levels of the auditory system. Forms of homeostatic plasticity are believed to be responsible for this neural change, which increases the spontaneous and driven activity of neurons in central auditory structures in animals expressing behavioral evidence of tinnitus. Another tinnitus correlate, increased neural synchrony among the affected neurons, is forged by spike-timing-dependent neural plasticity in auditory pathways. Slow oscillations generated by bursting thalamic neurons verified in tinnitus animals appear to modulate neural plasticity in the cortex, integrating tinnitus neural activity with information in brain regions supporting memory, emotion, and consciousness which exhibit increased metabolic activity in tinnitus patients. The latter process may be induced by transient auditory events in normal processing but it persists in tinnitus, driven by phantom signals from the auditory pathway. Several tinnitus therapies attempt to suppress tinnitus through plasticity, but repeated sessions will likely be needed to prevent tinnitus activity from returning owing to deafferentation as its initiating condition.

Recognition of sign language gestures using neural networks

OpenAIRE

Simon Vamplew

2007-01-01

This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan) hand gestures.

Adaptive Backstepping-Based Neural Tracking Control for MIMO Nonlinear Switched Systems Subject to Input Delays.

Science.gov (United States)

Niu, Ben; Li, Lu

2018-06-01

This brief proposes a new neural-network (NN)-based adaptive output tracking control scheme for a class of disturbed multiple-input multiple-output uncertain nonlinear switched systems with input delays. By combining the universal approximation ability of radial basis function NNs and adaptive backstepping recursive design with an improved multiple Lyapunov function (MLF) scheme, a novel adaptive neural output tracking controller design method is presented for the switched system. The feature of the developed design is that different coordinate transformations are adopted to overcome the conservativeness caused by adopting a common coordinate transformation for all subsystems. It is shown that all the variables of the resulting closed-loop system are semiglobally uniformly ultimately bounded under a class of switching signals in the presence of MLF and that the system output can follow the desired reference signal. To demonstrate the practicability of the obtained result, an adaptive neural output tracking controller is designed for a mass-spring-damper system.

Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

International Nuclear Information System (INIS)

Han, Seong Ik; Jeong, Chan Se; Yang, Soon Yong

2012-01-01

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme

Robust sliding mode control for uncertain servo system using friction observer and recurrent fuzzy neural networks

Energy Technology Data Exchange (ETDEWEB)

Han, Seong Ik [Pusan National University, Busan (Korea, Republic of); Jeong, Chan Se; Yang, Soon Yong [University of Ulsan, Ulsan (Korea, Republic of)

2012-04-15

A robust positioning control scheme has been developed using friction parameter observer and recurrent fuzzy neural networks based on the sliding mode control. As a dynamic friction model, the LuGre model is adopted for handling friction compensation because it has been known to capture sufficiently the properties of a nonlinear dynamic friction. A developed friction parameter observer has a simple structure and also well estimates friction parameters of the LuGre friction model. In addition, an approximation method for the system uncertainty is developed using recurrent fuzzy neural networks technology to improve the precision positioning degree. Some simulation and experiment provide the verification on the performance of a proposed robust control scheme.

Reynolds averaged turbulence modelling using deep neural networks with embedded invariance

International Nuclear Information System (INIS)

Ling, Julia; Kurzawski, Andrew; Templeton, Jeremy

2016-01-01

There exists significant demand for improved Reynolds-averaged Navier–Stokes (RANS) turbulence models that are informed by and can represent a richer set of turbulence physics. This paper presents a method of using deep neural networks to learn a model for the Reynolds stress anisotropy tensor from high-fidelity simulation data. A novel neural network architecture is proposed which uses a multiplicative layer with an invariant tensor basis to embed Galilean invariance into the predicted anisotropy tensor. It is demonstrated that this neural network architecture provides improved prediction accuracy compared with a generic neural network architecture that does not embed this invariance property. Furthermore, the Reynolds stress anisotropy predictions of this invariant neural network are propagated through to the velocity field for two test cases. For both test cases, significant improvement versus baseline RANS linear eddy viscosity and nonlinear eddy viscosity models is demonstrated.

Cask system design guidance for robotic handling

International Nuclear Information System (INIS)

Griesmeyer, J.M.; Drotning, W.D.; Morimoto, A.K.; Bennett, P.C.

1990-10-01

Remote automated cask handling has the potential to reduce both the occupational exposure and the time required to process a nuclear waste transport cask at a handling facility. The ongoing Advanced Handling Technologies Project (AHTP) at Sandia National Laboratories is described. AHTP was initiated to explore the use of advanced robotic systems to perform cask handling operations at handling facilities for radioactive waste, and to provide guidance to cask designers regarding the impact of robotic handling on cask design. The proof-of-concept robotic systems developed in AHTP are intended to extrapolate from currently available commercial systems to the systems that will be available by the time that a repository would be open for operation. The project investigates those cask handling operations that would be performed at a nuclear waste repository facility during cask receiving and handling. The ongoing AHTP indicates that design guidance, rather than design specification, is appropriate, since the requirements for robotic handling do not place severe restrictions on cask design but rather focus on attention to detail and design for limited dexterity. The cask system design features that facilitate robotic handling operations are discussed, and results obtained from AHTP design and operation experience are summarized. The application of these design considerations is illustrated by discussion of the robot systems and their operation on cask feature mock-ups used in the AHTP project. 11 refs., 11 figs

Production management of window handles

Directory of Open Access Journals (Sweden)

Manuela Ingaldi

2014-12-01

Full Text Available In the chapter a company involved in the production of aluminum window and door handles was presented. The main customers of the company are primarily companies which produce PCV joinery and wholesalers supplying these companies. One chosen product from the research company - a single-arm pin-lift window handle - was described and its production process depicted technologically. The chapter also includes SWOT analysis conducted in the research company and the value stream of the single-arm pin-lift window handle.

7 CFR 58.443 - Whey handling.

Science.gov (United States)

2010-01-01

... 7 Agriculture 3 2010-01-01 2010-01-01 false Whey handling. 58.443 Section 58.443 Agriculture... Procedures § 58.443 Whey handling. (a) Adequate sanitary facilities shall be provided for the handling of whey. If outside, necessary precautions shall be taken to minimize flies, insects and development of...

Evolutionary fuzzy ARTMAP neural networks for classification of semiconductor defects.

Science.gov (United States)

Tan, Shing Chiang; Watada, Junzo; Ibrahim, Zuwairie; Khalid, Marzuki

2015-05-01

Wafer defect detection using an intelligent system is an approach of quality improvement in semiconductor manufacturing that aims to enhance its process stability, increase production capacity, and improve yields. Occasionally, only few records that indicate defective units are available and they are classified as a minority group in a large database. Such a situation leads to an imbalanced data set problem, wherein it engenders a great challenge to deal with by applying machine-learning techniques for obtaining effective solution. In addition, the database may comprise overlapping samples of different classes. This paper introduces two models of evolutionary fuzzy ARTMAP (FAM) neural networks to deal with the imbalanced data set problems in a semiconductor manufacturing operations. In particular, both the FAM models and hybrid genetic algorithms are integrated in the proposed evolutionary artificial neural networks (EANNs) to classify an imbalanced data set. In addition, one of the proposed EANNs incorporates a facility to learn overlapping samples of different classes from the imbalanced data environment. The classification results of the proposed evolutionary FAM neural networks are presented, compared, and analyzed using several classification metrics. The outcomes positively indicate the effectiveness of the proposed networks in handling classification problems with imbalanced data sets.

Track finding with neural networks in ALICE ITS

International Nuclear Information System (INIS)

Batyunya, B.V.; Belikov, Yu.A.; Fedunov, A.G.; Zinchenko, A.I.

1996-01-01

A program based on Neural Networks technique was developed for track recognition in ALICE ITS. The efficiency of this program was estimated on Monte Carlo simulated events for particles with p T ≤ 100 MeV/c. The advantages of this program are the high performance, the capability of track finding in conditions of the high particle multiplicity and the ability to find tracks distorted by the multiple Coulomb scattering and the energy losses. 7 refs., 1 tab

AKT signaling displays multifaceted functions in neural crest development.

Science.gov (United States)

Sittewelle, Méghane; Monsoro-Burq, Anne H

2018-05-31

AKT signaling is an essential intracellular pathway controlling cell homeostasis, cell proliferation and survival, as well as cell migration and differentiation in adults. Alterations impacting the AKT pathway are involved in many pathological conditions in human disease. Similarly, during development, multiple transmembrane molecules, such as FGF receptors, PDGF receptors or integrins, activate AKT to control embryonic cell proliferation, migration, differentiation, and also cell fate decisions. While many studies in mouse embryos have clearly implicated AKT signaling in the differentiation of several neural crest derivatives, information on AKT functions during the earliest steps of neural crest development had remained relatively scarce until recently. However, recent studies on known and novel regulators of AKT signaling demonstrate that this pathway plays critical roles throughout the development of neural crest progenitors. Non-mammalian models such as fish and frog embryos have been instrumental to our understanding of AKT functions in neural crest development, both in neural crest progenitors and in the neighboring tissues. This review combines current knowledge acquired from all these different vertebrate animal models to describe the various roles of AKT signaling related to neural crest development in vivo. We first describe the importance of AKT signaling in patterning the tissues involved in neural crest induction, namely the dorsal mesoderm and the ectoderm. We then focus on AKT signaling functions in neural crest migration and differentiation. Copyright © 2018 Elsevier Inc. All rights reserved.

Pax7 lineage contributions to the mammalian neural crest.

Directory of Open Access Journals (Sweden)

Barbara Murdoch

Full Text Available Neural crest cells are vertebrate-specific multipotent cells that contribute to a variety of tissues including the peripheral nervous system, melanocytes, and craniofacial bones and cartilage. Abnormal development of the neural crest is associated with several human maladies including cleft/lip palate, aggressive cancers such as melanoma and neuroblastoma, and rare syndromes, like Waardenburg syndrome, a complex disorder involving hearing loss and pigment defects. We previously identified the transcription factor Pax7 as an early marker, and required component for neural crest development in chick embryos. In mammals, Pax7 is also thought to play a role in neural crest development, yet the precise contribution of Pax7 progenitors to the neural crest lineage has not been determined.Here we use Cre/loxP technology in double transgenic mice to fate map the Pax7 lineage in neural crest derivates. We find that Pax7 descendants contribute to multiple tissues including the cranial, cardiac and trunk neural crest, which in the cranial cartilage form a distinct regional pattern. The Pax7 lineage, like the Pax3 lineage, is additionally detected in some non-neural crest tissues, including a subset of the epithelial cells in specific organs.These results demonstrate a previously unappreciated widespread distribution of Pax7 descendants within and beyond the neural crest. They shed light regarding the regionally distinct phenotypes observed in Pax3 and Pax7 mutants, and provide a unique perspective into the potential roles of Pax7 during disease and development.

Neural stem cells show bidirectional experience-dependent plasticity in the perinatal mammalian brain.

Science.gov (United States)

Kippin, Tod E; Cain, Sean W; Masum, Zahra; Ralph, Martin R

2004-03-17

Many of the effects of prenatal stress on the endocrine function, brain morphology, and behavior in mammals can be reversed by brief sessions of postnatal separation and handling. We have tested the hypothesis that the effects of both the prenatal and postnatal experiences are mediated by negative and positive regulation of neural stem cell (NSC) number during critical stages in neurodevelopment. We used the in vitro clonal neurosphere assay to quantify NSCs in hamsters that had experienced prenatal stress (maternal restraint stress for 2 hr per day, for the last 7 d of gestation), postnatal handling (maternal-offspring separation for 15 min per day during postnatal days 1-21), orboth. Prenatal stress reduced the number of NSCs derived from the subependyma of the lateral ventricle. The effect was already present at postnatal day 1 and persisted into adulthood (at least 14 months of age). Similarly, prenatal stress reduced in vivo proliferation in the adult subependyma of the lateral ventricle. Conversely, postnatal handling increased NSC number and reversed the effect of prenatal stress. The effects of prenatal stress on NSCs and proliferation and the effect of postnatal handling on NSCs did not differ between male and females. The findings demonstrate that environmental factors can produce changes in NSC number that are present at birth and endure into late adulthood. These changes may underlie some of the behavioral effects produced by prenatal stress and postnatal handling.

SCYNet. Testing supersymmetric models at the LHC with neural networks

Energy Technology Data Exchange (ETDEWEB)

Bechtle, Philip; Belkner, Sebastian; Hamer, Matthias [Universitaet Bonn, Bonn (Germany); Dercks, Daniel [Universitaet Hamburg, Hamburg (Germany); Keller, Tim; Kraemer, Michael; Sarrazin, Bjoern; Schuette-Engel, Jan; Tattersall, Jamie [RWTH Aachen University, Institute for Theoretical Particle Physics and Cosmology, Aachen (Germany)

2017-10-15

SCYNet (SUSY Calculating Yield Net) is a tool for testing supersymmetric models against LHC data. It uses neural network regression for a fast evaluation of the profile likelihood ratio. Two neural network approaches have been developed: one network has been trained using the parameters of the 11-dimensional phenomenological Minimal Supersymmetric Standard Model (pMSSM-11) as an input and evaluates the corresponding profile likelihood ratio within milliseconds. It can thus be used in global pMSSM-11 fits without time penalty. In the second approach, the neural network has been trained using model-independent signature-related objects, such as energies and particle multiplicities, which were estimated from the parameters of a given new physics model. (orig.)

Development of commercial robots for radwaste handling

International Nuclear Information System (INIS)

Colborn, K.A.

1988-01-01

The cost and dose burden associated with low level radwaste handling activities is a matter of increasing concern to the commercial nuclear power industry. This concern is evidenced by the fact that many utilities have begun to revaluate waste generation, handling, and disposal activities at their plants in an effort to improve their overall radwaste handling operations. This paper reports on the project Robots for Radwaste Handling, to identify the potential of robots to improve radwaste handling operations. The project has focussed on the potential of remote or automated technology to improve well defined, recognizable radwaste operations. The project focussed on repetitive, low skill level radwaste handling and decontamination tasks which involve significant radiation exposure

Neural mechanisms of social dominance

Science.gov (United States)

Watanabe, Noriya; Yamamoto, Miyuki

2015-01-01

In a group setting, individuals' perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems' level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation. PMID:26136644

Neural mechanisms of social dominance

Directory of Open Access Journals (Sweden)

Noriya eWatanabe

2015-06-01

Full Text Available In a group setting, individuals’ perceptions of their own level of dominance or of the dominance level of others, and the ability to adequately control their behavior based on these perceptions are crucial for living within a social environment. Recent advances in neural imaging and molecular technology have enabled researchers to investigate the neural substrates that support the perception of social dominance and the formation of a social hierarchy in humans. At the systems’ level, recent studies showed that dominance perception is represented in broad brain regions which include the amygdala, hippocampus, striatum, and various cortical networks such as the prefrontal, and parietal cortices. Additionally, neurotransmitter systems such as the dopaminergic and serotonergic systems, modulate and are modulated by the formation of the social hierarchy in a group. While these monoamine systems have a wide distribution and multiple functions, it was recently found that the Neuropeptide B/W contributes to the perception of dominance and is present in neurons that have a limited projection primarily to the amygdala. The present review discusses the specific roles of these neural regions and neurotransmitter systems in the perception of dominance and in hierarchy formation.

Preoperational checkout of the remote-handled transuranic waste handling at the Waste Isolation Pilot Plant

International Nuclear Information System (INIS)

1987-09-01

This plan describes the preoperational checkout for handling Remote-Handled Transuranic (RH-TRU) Wastes from their receipt at the Waste Isolation Pilot Plant (WIPP) to their emplacement underground. This plan identifies the handling operations to be performed, personnel groups responsible for executing these operations, and required equipment items. In addition, this plan describes the quality assurance that will be exercised throughout the checkout, and finally, it establishes criteria by which to measure the success of the checkout. 7 refs., 5 figs

Frequency-difference-dependent stochastic resonance in neural systems

Science.gov (United States)

Guo, Daqing; Perc, Matjaž; Zhang, Yangsong; Xu, Peng; Yao, Dezhong

2017-08-01

Biological neurons receive multiple noisy oscillatory signals, and their dynamical response to the superposition of these signals is of fundamental importance for information processing in the brain. Here we study the response of neural systems to the weak envelope modulation signal, which is superimposed by two periodic signals with different frequencies. We show that stochastic resonance occurs at the beat frequency in neural systems at the single-neuron as well as the population level. The performance of this frequency-difference-dependent stochastic resonance is influenced by both the beat frequency and the two forcing frequencies. Compared to a single neuron, a population of neurons is more efficient in detecting the information carried by the weak envelope modulation signal at the beat frequency. Furthermore, an appropriate fine-tuning of the excitation-inhibition balance can further optimize the response of a neural ensemble to the superimposed signal. Our results thus introduce and provide insights into the generation and modulation mechanism of the frequency-difference-dependent stochastic resonance in neural systems.

Handling Missing Data in Structural Equation Models in R: A Replication Study for Applied Researchers

Science.gov (United States)

Wolgast, Anett; Schwinger, Malte; Hahnel, Carolin; Stiensmeier-Pelster, Joachim

2017-01-01

Introduction: Multiple imputation (MI) is one of the most highly recommended methods for replacing missing values in research data. The scope of this paper is to demonstrate missing data handling in SEM by analyzing two modified data examples from educational psychology, and to give practical recommendations for applied researchers. Method: We…

Preference Handling for Artificial Intelligence

OpenAIRE

Goldsmith, Judy; University of Kentucky; Junker, Ulrich; ILOG

2009-01-01

This article explains the benefits of preferences for AI systems and draws a picture of current AI research on preference handling. It thus provides an introduction to the topics covered by this special issue on preference handling.

Religious Serpent Handling and Community Relations.

Science.gov (United States)

Williamson, W Paul; Hood, Ralph W

2015-01-01

Christian serpent handling sects of Appalachia comprise a community that has long been mischaracterized and marginalized by the larger communities surrounding them. To explore this dynamic, this article traces the emergence of serpent handling in Appalachia and the emergence of anti-serpent-handling state laws, which eventually failed to curb the practice, as local communities gave serpent handling groups support. We present two studies to consider for improving community relations with serpent handling sects. In study 1, we present data relating the incidence of reported serpent-bite deaths with the rise of anti-serpent-handling laws and their eventual abatement, based on increasing acceptance of serpent handlers by the larger community. Study 2 presents interview data on serpent bites and death that provide explanations for these events from the cultural and religious perspective. We conclude that first-hand knowledge about serpent handlers, and other marginalized groups, helps to lessen suspicion and allows them to be seen as not much different, which are tendencies that are important for promoting inter-community harmony.

Fatigue in multiple sclerosis: neural correlates and the role of non-invasive brain stimulation

Directory of Open Access Journals (Sweden)

Moussa A. Chalah

2015-11-01

Full Text Available Multiple sclerosis (MS is a chronic progressive inflammatory disease of the central nervous system and the major cause of non-traumatic disability in young adults. Fatigue is a frequent symptom reported by the majority of MS patients during their disease course and drastically af-fects their quality of life. Despite its significant prevalence and impact, the underlying patho-physiological mechanisms are not well elucidated. MS fatigue is still considered the result of multifactorial and complex constellations, and is commonly classified into primary fatigue related to the pathological changes of the disease itself, and secondary fatigue attributed to mimicking symptoms, comorbid sleep and mood disorders, and medications side effects. Data from neuroimaging, neurophysiology, neuroendocrine and neuroimmune studies have raised hypotheses regarding the origin of this symptom, some of which have succeeded in identifying an association between MS fatigue and structural or functional abnormalities within various brain networks. Hence, the aim of this work is to reappraise the neural correlates of MS fatigue and to discuss the rationale for the emergent use of noninvasive brain stimulation (NIBS techniques as potential treatments. This will include a presentation of the various NIBS modalities and a proposition of their potential mechanisms of action in this context. Specific issues related to the value of transcranial direct current stimulation will be addressed.

Safe handling of tritium

International Nuclear Information System (INIS)

1991-01-01

The main objective of this publication is to provide practical guidance and recommendations on operational radiation protection aspects related to the safe handling of tritium in laboratories, industrial-scale nuclear facilities such as heavy-water reactors, tritium removal plants and fission fuel reprocessing plants, and facilities for manufacturing commercial tritium-containing devices and radiochemicals. The requirements of nuclear fusion reactors are not addressed specifically, since there is as yet no tritium handling experience with them. However, much of the material covered is expected to be relevant to them as well. Annex III briefly addresses problems in the comparatively small-scale use of tritium at universities, medical research centres and similar establishments. However, the main subject of this publication is the handling of larger quantities of tritium. Operational aspects include designing for tritium safety, safe handling practice, the selection of tritium-compatible materials and equipment, exposure assessment, monitoring, contamination control and the design and use of personal protective equipment. This publication does not address the technologies involved in tritium control and cleanup of effluents, tritium removal, or immobilization and disposal of tritium wastes, nor does it address the environmental behaviour of tritium. Refs, figs and tabs

Novel perspectives of neural stem cell differentiation: from neurotransmitters to therapeutics.

Science.gov (United States)

Trujillo, Cleber A; Schwindt, Telma T; Martins, Antonio H; Alves, Janaína M; Mello, Luiz Eugênio; Ulrich, Henning

2009-01-01

In the past years, many reports have described the existence of neural progenitor and stem cells in the adult central nervous system capable of generating new neurons, astrocytes, and oligodendrocytes. This discovery has overturned the central assumption in the neuroscience field, of no new neurons being originated in the brain after birth and provided the fundaments to understand the molecular basis of neural differentiation and to develop new therapies for neural tissue repair. Although the mechanisms underlying cell fate during neural development are not yet understood, the importance of intrinsic and extrinsic factors and of an appropriate microenvironment is well known. In this context, emerging evidence strongly suggests that glial cells play a key role in controlling multiple steps of neurogenesis. Those cells, of particular radial glia, are important for migration, cell specification, and integration of neurons into a functional neural network. This review aims to present an update in the neurogenesis area and highlight the modulation of neural stem cell differentiation by neurotransmitters, growth factors, and their receptors, with possible applications for cell therapy strategies of neurological disorders.

Requirement for Foxd3 in the maintenance of neural crest progenitors.

Science.gov (United States)

Teng, Lu; Mundell, Nathan A; Frist, Audrey Y; Wang, Qiaohong; Labosky, Patricia A

2008-05-01

Understanding the molecular mechanisms of stem cell maintenance is crucial for the ultimate goal of manipulating stem cells for the treatment of disease. Foxd3 is required early in mouse embryogenesis; Foxd3(-/-) embryos fail around the time of implantation, cells of the inner cell mass cannot be maintained in vitro, and blastocyst-derived stem cell lines cannot be established. Here, we report that Foxd3 is required for maintenance of the multipotent mammalian neural crest. Using tissue-specific deletion of Foxd3 in the neural crest, we show that Foxd3(flox/-); Wnt1-Cre mice die perinatally with a catastrophic loss of neural crest-derived structures. Cranial neural crest tissues are either missing or severely reduced in size, the peripheral nervous system consists of reduced dorsal root ganglia and cranial nerves, and the entire gastrointestinal tract is devoid of neural crest derivatives. These results demonstrate a global role for this transcriptional repressor in all aspects of neural crest maintenance along the anterior-posterior axis, and establish an unprecedented molecular link between multiple divergent progenitor lineages of the mammalian embryo.

Using a multi-state recurrent neural network to optimize loading patterns in BWRs

International Nuclear Information System (INIS)

Ortiz, Juan Jose; Requena, Ignacio

2004-01-01

A Multi-State Recurrent Neural Network is used to optimize Loading Patterns (LP) in BWRs. We have proposed an energy function that depends on fuel assembly positions and their nuclear cross sections to carry out optimisation. Multi-State Recurrent Neural Networks creates LPs that satisfy the Radial Power Peaking Factor and maximize the effective multiplication factor at the Beginning of the Cycle, and also satisfy the Minimum Critical Power Ratio and Maximum Linear Heat Generation Rate at the End of the Cycle, thereby maximizing the effective multiplication factor. In order to evaluate the LPs, we have used a trained back-propagation neural network to predict the parameter values, instead of using a reactor core simulator, which saved considerable computation time in the search process. We applied this method to find optimal LPs for five cycles of Laguna Verde Nuclear Power Plant (LVNPP) in Mexico

How Retailers Handle Complaint Management

DEFF Research Database (Denmark)

Hansen, Torben; Wilke, Ricky; Zaichkowsky, Judy

2009-01-01

This article fills a gap in the literature by providing insight about the handling of complaint management (CM) across a large cross section of retailers in the grocery, furniture, electronic and auto sectors. Determinants of retailers’ CM handling are investigated and insight is gained as to the......This article fills a gap in the literature by providing insight about the handling of complaint management (CM) across a large cross section of retailers in the grocery, furniture, electronic and auto sectors. Determinants of retailers’ CM handling are investigated and insight is gained...... as to the links between CM and redress of consumers’ complaints. The results suggest that retailers who attach large negative consequences to consumer dissatisfaction are more likely than other retailers to develop a positive strategic view on customer complaining, but at the same time an increase in perceived...

7 CFR 926.9 - Handle.

Science.gov (United States)

2010-01-01

... the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements and Orders; Fruits, Vegetables, Nuts), DEPARTMENT OF AGRICULTURE DATA COLLECTION, REPORTING AND RECORDKEEPING REQUIREMENTS APPLICABLE TO CRANBERRIES NOT SUBJECT TO THE CRANBERRY MARKETING ORDER § 926.9 Handle. Handle...

Recognition of sign language gestures using neural networks

Directory of Open Access Journals (Sweden)

Simon Vamplew

2007-04-01

Full Text Available This paper describes the structure and performance of the SLARTI sign language recognition system developed at the University of Tasmania. SLARTI uses a modular architecture consisting of multiple feature-recognition neural networks and a nearest-neighbour classifier to recognise Australian sign language (Auslan hand gestures.

Toward automatic time-series forecasting using neural networks.

Science.gov (United States)

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Prediction of beta-turns and beta-turn types by a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN).

Science.gov (United States)

Kirschner, Andreas; Frishman, Dmitrij

2008-10-01

Prediction of beta-turns from amino acid sequences has long been recognized as an important problem in structural bioinformatics due to their frequent occurrence as well as their structural and functional significance. Because various structural features of proteins are intercorrelated, secondary structure information has been often employed as an additional input for machine learning algorithms while predicting beta-turns. Here we present a novel bidirectional Elman-type recurrent neural network with multiple output layers (MOLEBRNN) capable of predicting multiple mutually dependent structural motifs and demonstrate its efficiency in recognizing three aspects of protein structure: beta-turns, beta-turn types, and secondary structure. The advantage of our method compared to other predictors is that it does not require any external input except for sequence profiles because interdependencies between different structural features are taken into account implicitly during the learning process. In a sevenfold cross-validation experiment on a standard test dataset our method exhibits the total prediction accuracy of 77.9% and the Mathew's Correlation Coefficient of 0.45, the highest performance reported so far. It also outperforms other known methods in delineating individual turn types. We demonstrate how simultaneous prediction of multiple targets influences prediction performance on single targets. The MOLEBRNN presented here is a generic method applicable in a variety of research fields where multiple mutually depending target classes need to be predicted. http://webclu.bio.wzw.tum.de/predator-web/.

Remote handling at LAMPF

International Nuclear Information System (INIS)

Grisham, D.L.; Lambert, J.E.

1983-01-01

Experimental area A at the Clinton P. Anderson Meson Physics Facility (LAMPF) encompasses a large area. Presently there are four experimental target cells along the main proton beam line that have become highly radioactive, thus dictating that all maintenance be performed remotely. The Monitor remote handling system was developed to perform in situ maintenance at any location within area A. Due to the complexity of experimental systems and confined space, conventional remote handling methods based upon hot cell and/or hot bay concepts are not workable. Contrary to conventional remote handling which require special tooling for each specifically planned operation, the Monitor concept is aimed at providing a totally flexible system capable of remotely performing general mechanical and electrical maintenance operations using standard tools. The Monitor system is described

Elements of an algorithm for optimizing a parameter-structural neural network

Science.gov (United States)

Mrówczyńska, Maria

2016-06-01

The field of processing information provided by measurement results is one of the most important components of geodetic technologies. The dynamic development of this field improves classic algorithms for numerical calculations in the aspect of analytical solutions that are difficult to achieve. Algorithms based on artificial intelligence in the form of artificial neural networks, including the topology of connections between neurons have become an important instrument connected to the problem of processing and modelling processes. This concept results from the integration of neural networks and parameter optimization methods and makes it possible to avoid the necessity to arbitrarily define the structure of a network. This kind of extension of the training process is exemplified by the algorithm called the Group Method of Data Handling (GMDH), which belongs to the class of evolutionary algorithms. The article presents a GMDH type network, used for modelling deformations of the geometrical axis of a steel chimney during its operation.

Nonlinear Dynamics and Chaos in Fractional-Order Hopfield Neural Networks with Delay

Directory of Open Access Journals (Sweden)

Xia Huang

2013-01-01

Full Text Available A fractional-order two-neuron Hopfield neural network with delay is proposed based on the classic well-known Hopfield neural networks, and further, the complex dynamical behaviors of such a network are investigated. A great variety of interesting dynamical phenomena, including single-periodic, multiple-periodic, and chaotic motions, are found to exist. The existence of chaotic attractors is verified by the bifurcation diagram and phase portraits as well.

MRI of meniscal bucket-handle tears

Energy Technology Data Exchange (ETDEWEB)

Magee, T.H.; Hinson, G.W. [Menorah Medical Center, Overland Park, KS (United States). Dept. of Radiology

1998-09-01

A meniscal bucket-handle tear is a tear with an attached fragment displaced from the meniscus of the knee joint. Low sensitivity of MRI for detection of bucket-handle tears (64% as compared with arthroscopy) has been reported previously. We report increased sensitivity for detecting bucket-handle tears with the use of coronal short tau inversion recovery (STIR) images. Results. By using four criteria for diagnosis of meniscal bucket-handle tears, our overall sensitivity compared with arthroscopy was 93% (28 of 30 meniscal bucket-handle tears seen at arthroscopy were detected by MRI). The meniscal fragment was well visualized in all 28 cases on coronal STIR images. The double posterior cruciate ligament sign was seen in 8 of 30 cases, the flipped meniscus was seen in 10 of 30 cases and a fragment in the intercondylar notch was seen in 18 of 30 cases. (orig.)

Time Series Analysis of Soil Radon Data Using Multiple Linear Regression and Artificial Neural Network in Seismic Precursory Studies

Science.gov (United States)

Singh, S.; Jaishi, H. P.; Tiwari, R. P.; Tiwari, R. C.

2017-07-01

This paper reports the analysis of soil radon data recorded in the seismic zone-V, located in the northeastern part of India (latitude 23.73N, longitude 92.73E). Continuous measurements of soil-gas emission along Chite fault in Mizoram (India) were carried out with the replacement of solid-state nuclear track detectors at weekly interval. The present study was done for the period from March 2013 to May 2015 using LR-115 Type II detectors, manufactured by Kodak Pathe, France. In order to reduce the influence of meteorological parameters, statistical analysis tools such as multiple linear regression and artificial neural network have been used. Decrease in radon concentration was recorded prior to some earthquakes that occurred during the observation period. Some false anomalies were also recorded which may be attributed to the ongoing crustal deformation which was not major enough to produce an earthquake.

Computational Models and Emergent Properties of Respiratory Neural Networks

Science.gov (United States)

Lindsey, Bruce G.; Rybak, Ilya A.; Smith, Jeffrey C.

2012-01-01

Computational models of the neural control system for breathing in mammals provide a theoretical and computational framework bringing together experimental data obtained from different animal preparations under various experimental conditions. Many of these models were developed in parallel and iteratively with experimental studies and provided predictions guiding new experiments. This data-driven modeling approach has advanced our understanding of respiratory network architecture and neural mechanisms underlying generation of the respiratory rhythm and pattern, including their functional reorganization under different physiological conditions. Models reviewed here vary in neurobiological details and computational complexity and span multiple spatiotemporal scales of respiratory control mechanisms. Recent models describe interacting populations of respiratory neurons spatially distributed within the Bötzinger and pre-Bötzinger complexes and rostral ventrolateral medulla that contain core circuits of the respiratory central pattern generator (CPG). Network interactions within these circuits along with intrinsic rhythmogenic properties of neurons form a hierarchy of multiple rhythm generation mechanisms. The functional expression of these mechanisms is controlled by input drives from other brainstem components, including the retrotrapezoid nucleus and pons, which regulate the dynamic behavior of the core circuitry. The emerging view is that the brainstem respiratory network has rhythmogenic capabilities at multiple levels of circuit organization. This allows flexible, state-dependent expression of different neural pattern-generation mechanisms under various physiological conditions, enabling a wide repertoire of respiratory behaviors. Some models consider control of the respiratory CPG by pulmonary feedback and network reconfiguration during defensive behaviors such as cough. Future directions in modeling of the respiratory CPG are considered. PMID:23687564

Improvement in separation of isolated muons and pions at low pT in ATLAS hadron calorimeter using artificial neural networks technique

International Nuclear Information System (INIS)

Astvatsaturov, A.; Budagov, Yu.; Chirikov-Zorin, I.; Shigaev, V.; Paplevka, A.; Sushkov, S.; Bosman, M.; Nessi, M.

1995-01-01

Advantages of artificial neural networks techniques in handling data from highly granulated ATLAS hadron calorimeter (HC) are shown in application to isolated π/μ separation task in the range 3 T T muons have a significant probability to be absorbed in the calorimeter and therefore they cannot be reliably registered by the muon detector. The comparative analysis of main characteristics is presented for several neural net discriminators and a linear threshold discriminator operating on energy deposition in the last depth of HC. The analysis is based on MC data obtained with ATLAS simulation programs. 9 refs., 12 figs

Sophisticated fuel handling system evolved

International Nuclear Information System (INIS)

Ross, D.A.

1988-01-01

The control systems at Sellafield fuel handling plant are described. The requirements called for built-in diagnostic features as well as the ability to handle a large sequencing application. Speed was also important; responses better than 50ms were required. The control systems are used to automate operations within each of the three main process caves - two Magnox fuel decanners and an advanced gas-cooled reactor fuel dismantler. The fuel route within the fuel handling plant is illustrated and described. ASPIC (Automated Sequence Package for Industrial Control) which was developed as a controller for the plant processes is described. (U.K.)

Crack identification by artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Hwu, C.B.; Liang, Y.C. [National Cheng Kung Univ., Tainan (Taiwan, Province of China). Inst. of Aeronaut. and Astronaut.

1998-04-01

In this paper, a most popular artificial neural network called the back propagation neural network (BPN) is employed to achieve an ideal on-line identification of the crack embedded in a composite plate. Different from the usual dynamic estimate, the parameters used for the present crack identification are the strains of static deformation. It is known that the crack effects are localized which may not be clearly reflected from the boundary information especially when the data is from static deformation only. To remedy this, we use data from multiple-loading modes in which the loading modes may include the opening, shearing and tearing modes. The results show that our method for crack identification is always stable and accurate no matter how far-away of the test data from its training set. (orig.) 8 refs.

The natural angle between the hand and handle and the effect of handle orientation on wrist radial/ulnar deviation during maximal push exertions.

Science.gov (United States)

Young, Justin G; Lin, Jia-Hua; Chang, Chien-Chi; McGorry, Raymond W

2013-01-01

The purpose of this experiment was to quantify the natural angle between the hand and a handle, and to investigate three design factors: handle rotation, handle tilt and between-handle width on the natural angle as well as resultant wrist radial/ulnar deviation ('RUD') for pushing tasks. Photographs taken of the right upper limb of 31 participants (14 women and 17 men) performing maximal seated push exertions on different handles were analysed. Natural hand/handle angle and RUD were assessed. It was found that all of the three design factors significantly affected natural handle angle and wrist RUD, but participant gender did not. The natural angle between the hand and the cylindrical handle was 65 ± 7°. Wrist deviation was reduced for handles that were rotated 0° (horizontal) and at the narrow width (31 cm). Handles that were tilted forward 15° reduced radial deviation consistently (12-13°) across handle conditions. Manual materials handling (MMH) tasks involving pushing have been related to increased risk of musculoskeletal injury. This study shows that handle orientation influences hand and wrist posture during pushing, and suggests that the design of push handles on carts and other MMH aids can be improved by adjusting their orientation to fit the natural interface between the hand and handle.

9 CFR 3.118 - Handling.

Science.gov (United States)

2010-01-01

... 9 Animals and Animal Products 1 2010-01-01 2010-01-01 false Handling. 3.118 Section 3.118 Animals and Animal Products ANIMAL AND PLANT HEALTH INSPECTION SERVICE, DEPARTMENT OF AGRICULTURE ANIMAL WELFARE STANDARDS Specifications for the Humane Handling, Care, Treatment, and Transportation of Marine...

Safety measuring for sodium handling

Energy Technology Data Exchange (ETDEWEB)

Jeong, Ji Young; Jeong, K C; Kim, T J; Kim, B H; Choi, J H

2001-09-01

This is the report for the safety measures of sodium handling. These contents are prerequisites for the development of sodium technology and thus the workers participate in sodium handling and experiments have to know them perfectly. As an appendix, the relating parts of the laws are presented.

A Perspective on Remote Handling Operations and Human Machine Interface for Remote Handling in Fusion

International Nuclear Information System (INIS)

Haist, B.; Hamilton, D.; Sanders, St.

2006-01-01

A large-scale fusion device presents many challenges to the remote handling operations team. This paper is based on unique operational experience at JET and gives a perspective on remote handling task development, logistics and resource management, as well as command, control and human-machine interface systems. Remote operations require an accurate perception of a dynamic environment, ideally providing the operators with the same unrestricted knowledge of the task scene as would be available if they were actually at the remote work location. Traditional camera based systems suffer from a limited number of viewpoints and also degrade quickly when exposed to high radiation. Virtual Reality and Augmented Reality software offer great assistance. The remote handling system required to maintain a tokamak requires a large number of different and complex pieces of equipment coordinating to perform a large array of tasks. The demands on the operator's skill in performing the tasks can escalate to a point where the efficiency and safety of operations are compromised. An operations guidance system designed to facilitate the planning, development, validation and execution of remote handling procedures is essential. Automatic planning of motion trajectories of remote handling equipment and the remote transfer of heavy loads will be routine and need to be reliable. This paper discusses the solutions developed at JET in these areas and also the trends in management and presentation of operational data as well as command, control and HMI technology development offering the potential to greatly assist remote handling in future fusion machines. (author)

Analysis of ITER upper port plug remote handling maintenance scenarios

International Nuclear Information System (INIS)

Koning, J.F.; Baar, M.R. de; Elzendoorn, B.S.Q.; Heemskerk, C.J.M.; Ronden, D.M.S.; Schuth, W.J.

2012-01-01

Highlights: ► Remote Handling Study Centre: providing RH compatibility analysis. ► Simulation: virtual reality including kinematics and realtime physics simulator. ► Applied on analysis of RH compatibility of Upper Launcher component replacement. ► Resulting in lowered maintenance procedure time and lessons learned. - Abstract: The ITER tokamak has a modular design, with port plugs, blanket modules and divertor cassettes. This set-up allows for maintenance of diagnostics, heating systems and first wall elements. The maintenance can be done in situ, or in the Hot Cell. Safe and effective remote handling (RH) will be ensured by the RH requirements and standards. Compliance is verified through remote handling compatibility assessments at the ITER Design Review milestones. The Remote Handling Study Centre at FOM Institute DIFFER is created to study ITER RH maintenance processes at different levels of complexity, from relatively simple situational awareness checks using snap-shots in the CAD system, time studies using virtual reality (VR) animations, to extensive operational sequence validation with multiple operators in real-time. The multi-operator facility mimics an RH work-cell as presently foreseen in the ITER RH control room. Novel VR technology is used to create a realistic setting in which a team of RH operators can interact with virtual ITER environments. A physics engine is used to emulate real-time contact interaction as to provide realistic haptic feed-back. Complex interactions between the RH operators and the control room system software are tested. RH task performance is quantified and operational resource usage estimated. The article provides a description and lessons learned from a recent study on replacement of the Steering Mirror Assembly on the ECRH (Electron Cyclotron Resonance Heating) Upper Launcher port plug.

Analysis of ITER upper port plug remote handling maintenance scenarios

Energy Technology Data Exchange (ETDEWEB)

Koning, J.F., E-mail: j.f.koning@heemskerk-innovative.nl [FOM Institute DIFFER - Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Partner in the Trilateral Euregio Cluster and ITER-NL, PO Box 1207, 3430 BE Nieuwegein (Netherlands); Baar, M.R. de; Elzendoorn, B.S.Q. [FOM Institute DIFFER - Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Partner in the Trilateral Euregio Cluster and ITER-NL, PO Box 1207, 3430 BE Nieuwegein (Netherlands); Heemskerk, C.J.M. [Heemskerk Innovative Technology, Noordwijk (Netherlands); Ronden, D.M.S.; Schuth, W.J. [FOM Institute DIFFER - Dutch Institute for Fundamental Energy Research, Association EURATOM-FOM, Partner in the Trilateral Euregio Cluster and ITER-NL, PO Box 1207, 3430 BE Nieuwegein (Netherlands)

2012-08-15

Highlights: Black-Right-Pointing-Pointer Remote Handling Study Centre: providing RH compatibility analysis. Black-Right-Pointing-Pointer Simulation: virtual reality including kinematics and realtime physics simulator. Black-Right-Pointing-Pointer Applied on analysis of RH compatibility of Upper Launcher component replacement. Black-Right-Pointing-Pointer Resulting in lowered maintenance procedure time and lessons learned. - Abstract: The ITER tokamak has a modular design, with port plugs, blanket modules and divertor cassettes. This set-up allows for maintenance of diagnostics, heating systems and first wall elements. The maintenance can be done in situ, or in the Hot Cell. Safe and effective remote handling (RH) will be ensured by the RH requirements and standards. Compliance is verified through remote handling compatibility assessments at the ITER Design Review milestones. The Remote Handling Study Centre at FOM Institute DIFFER is created to study ITER RH maintenance processes at different levels of complexity, from relatively simple situational awareness checks using snap-shots in the CAD system, time studies using virtual reality (VR) animations, to extensive operational sequence validation with multiple operators in real-time. The multi-operator facility mimics an RH work-cell as presently foreseen in the ITER RH control room. Novel VR technology is used to create a realistic setting in which a team of RH operators can interact with virtual ITER environments. A physics engine is used to emulate real-time contact interaction as to provide realistic haptic feed-back. Complex interactions between the RH operators and the control room system software are tested. RH task performance is quantified and operational resource usage estimated. The article provides a description and lessons learned from a recent study on replacement of the Steering Mirror Assembly on the ECRH (Electron Cyclotron Resonance Heating) Upper Launcher port plug.

A Novel Fractional-Order PID Controller for Integrated Pressurized Water Reactor Based on Wavelet Kernel Neural Network Algorithm

Directory of Open Access Journals (Sweden)

Yu-xin Zhao

2014-01-01

Full Text Available This paper presents a novel wavelet kernel neural network (WKNN with wavelet kernel function. It is applicable in online learning with adaptive parameters and is applied on parameters tuning of fractional-order PID (FOPID controller, which could handle time delay problem of the complex control system. Combining the wavelet function and the kernel function, the wavelet kernel function is adopted and validated the availability for neural network. Compared to the conservative wavelet neural network, the most innovative character of the WKNN is its rapid convergence and high precision in parameters updating process. Furthermore, the integrated pressurized water reactor (IPWR system is established by RELAP5, and a novel control strategy combining WKNN and fuzzy logic rule is proposed for shortening controlling time and utilizing the experiential knowledge sufficiently. Finally, experiment results verify that the control strategy and controller proposed have the practicability and reliability in actual complicated system.

Conserved gene regulatory module specifies lateral neural borders across bilaterians.

Science.gov (United States)

Li, Yongbin; Zhao, Di; Horie, Takeo; Chen, Geng; Bao, Hongcun; Chen, Siyu; Liu, Weihong; Horie, Ryoko; Liang, Tao; Dong, Biyu; Feng, Qianqian; Tao, Qinghua; Liu, Xiao

2017-08-01

The lateral neural plate border (NPB), the neural part of the vertebrate neural border, is composed of central nervous system (CNS) progenitors and peripheral nervous system (PNS) progenitors. In invertebrates, PNS progenitors are also juxtaposed to the lateral boundary of the CNS. Whether there are conserved molecular mechanisms determining vertebrate and invertebrate lateral neural borders remains unclear. Using single-cell-resolution gene-expression profiling and genetic analysis, we present evidence that orthologs of the NPB specification module specify the invertebrate lateral neural border, which is composed of CNS and PNS progenitors. First, like in vertebrates, the conserved neuroectoderm lateral border specifier Msx/vab-15 specifies lateral neuroblasts in Caenorhabditis elegans Second, orthologs of the vertebrate NPB specification module ( Msx/vab-15 , Pax3/7/pax-3 , and Zic/ref-2 ) are significantly enriched in worm lateral neuroblasts. In addition, like in other bilaterians, the expression domain of Msx/vab-15 is more lateral than those of Pax3/7/pax-3 and Zic/ref- 2 in C. elegans Third, we show that Msx/vab-15 regulates the development of mechanosensory neurons derived from lateral neural progenitors in multiple invertebrate species, including C. elegans , Drosophila melanogaster , and Ciona intestinalis We also identify a novel lateral neural border specifier, ZNF703/tlp-1 , which functions synergistically with Msx/vab- 15 in both C. elegans and Xenopus laevis These data suggest a common origin of the molecular mechanism specifying lateral neural borders across bilaterians.

Speaking in Multiple Languages: Neural Correlates of Language Proficiency in Multilingual Word Production

Science.gov (United States)

Videsott, Gerda; Herrnberger, Barbel; Hoenig, Klaus; Schilly, Edgar; Grothe, Jo; Wiater, Werner; Spitzer, Manfred; Kiefer, Markus

2010-01-01

The human brain has the fascinating ability to represent and to process several languages. Although the first and further languages activate partially different brain networks, the linguistic factors underlying these differences in language processing have to be further specified. We investigated the neural correlates of language proficiency in a…

Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica.

Science.gov (United States)

Prasad, Archana; Prakash, Om; Mehrotra, Shakti; Khan, Feroz; Mathur, Ajay Kumar; Mathur, Archana

2017-01-01

An artificial neural network (ANN)-based modelling approach is used to determine the synergistic effect of five major components of growth medium (Mg, Cu, Zn, nitrate and sucrose) on improved in vitro biomass yield in multiple shoot cultures of Centella asiatica. The back propagation neural network (BPNN) was employed to predict optimal biomass accumulation in terms of growth index over a defined culture duration of 35 days. The four variable concentrations of five media components, i.e. MgSO 4 (0, 0.75, 1.5, 3.0 mM), ZnSO 4 (0, 15, 30, 60 μM), CuSO 4 (0, 0.05, 0.1, 0.2 μM), NO 3 (20, 30, 40, 60 mM) and sucrose (1, 3, 5, 7 %, w/v) were taken as inputs for the ANN model. The designed model was evaluated by performing three different sets of validation experiments that indicated a greater similarity between the target and predicted dataset. The results of the modelling experiment suggested that 1.5 mM Mg, 30 μM Zn, 0.1 μM Cu, 40 mM NO 3 and 6 % (w/v) sucrose were the respective optimal concentrations of the tested medium components for achieving maximum growth index of 1654.46 with high centelloside yield (62.37 mg DW/culture) in the cultured multiple shoots. This study can facilitate the generation of higher biomass of uniform, clean, good quality C. asiatica herb that can efficiently be utilized by pharmaceutical industries.

The Neural Mechanisms of Re-Experiencing Mental Fatigue Sensation: A Magnetoencephalography Study

OpenAIRE

Ishii, Akira; Karasuyama, Takuma; Kikuchi, Taiki; Tanaka, Masaaki; Yamano, Emi; Watanabe, Yasuyoshi

2015-01-01

There have been several studies which have tried to clarify the neural mechanisms of fatigue sensation; however fatigue sensation has multiple aspects. We hypothesized that past experience related to fatigue sensation is an important factor which contributes to future formation of fatigue sensation through the transfer to memories that are located within specific brain structures. Therefore, we aimed to investigate the neural mechanisms of fatigue sensation related to memory. In the present s...

Neural network application to the neutral meson recognition

International Nuclear Information System (INIS)

Lefevre, F.; Delagrange, H.; Merrouch, R.; Ostendorf, R.; Schutz, Y.; Matulewicz, T.

1991-01-01

The combinatorial background produced by high photon multiplicities expected in TAPS experiments causes problems in precise meson recognition. We use neural networks to reduce this background. First we give a description of this technique, hereafter the first results obtained by applying this method to simulated events and future perspective will be discussed [fr

Asthma, guides for diagnostic and handling

International Nuclear Information System (INIS)

Salgado, Carlos E; Caballero A, Andres S; Garcia G, Elizabeth

1999-01-01

The paper defines the asthma, includes topics as diagnostic, handling of the asthma, special situations as asthma and pregnancy, handling of the asthmatic patient's perioperatory and occupational asthma

Remote-handled transuranic system assessment appendices. Volume 2

Energy Technology Data Exchange (ETDEWEB)

NONE

1995-11-01

Volume 2 of this report contains six appendices to the report: Inventory and generation of remote-handled transuranic waste; Remote-handled transuranic waste site storage; Characterization of remote-handled transuranic waste; RH-TRU waste treatment alternatives system analysis; Packaging and transportation study; and Remote-handled transuranic waste disposal alternatives.

Remote-handled transuranic system assessment appendices. Volume 2

International Nuclear Information System (INIS)

1995-11-01

Volume 2 of this report contains six appendices to the report: Inventory and generation of remote-handled transuranic waste; Remote-handled transuranic waste site storage; Characterization of remote-handled transuranic waste; RH-TRU waste treatment alternatives system analysis; Packaging and transportation study; and Remote-handled transuranic waste disposal alternatives

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

OpenAIRE

Airola, Rasmus; Hager, Kristoffer

2017-01-01

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

Application of Artificial Neural Networks for estimating index floods

Science.gov (United States)

Šimor, Viliam; Hlavčová, Kamila; Kohnová, Silvia; Szolgay, Ján

2012-12-01

This article presents an application of Artificial Neural Networks (ANNs) and multiple regression models for estimating mean annual maximum discharge (index flood) at ungauged sites. Both approaches were tested for 145 small basins in Slovakia in areas ranging from 20 to 300 km2. Using the objective clustering method, the catchments were divided into ten homogeneous pooling groups; for each pooling group, mutually independent predictors (catchment characteristics) were selected for both models. The neural network was applied as a simple multilayer perceptron with one hidden layer and with a back propagation learning algorithm. Hyperbolic tangents were used as an activation function in the hidden layer. Estimating index floods by the multiple regression models were based on deriving relationships between the index floods and catchment predictors. The efficiencies of both approaches were tested by the Nash-Sutcliffe and a correlation coefficients. The results showed the comparative applicability of both models with slightly better results for the index floods achieved using the ANNs methodology.

Multiple Linear Regression and Artificial Neural Network to Predict Blood Glucose in Overweight Patients.

Science.gov (United States)

Wang, J; Wang, F; Liu, Y; Xu, J; Lin, H; Jia, B; Zuo, W; Jiang, Y; Hu, L; Lin, F

2016-01-01

Overweight individuals are at higher risk for developing type II diabetes than the general population. We conducted this study to analyze the correlation between blood glucose and biochemical parameters, and developed a blood glucose prediction model tailored to overweight patients. A total of 346 overweight Chinese people patients ages 18-81 years were involved in this study. Their levels of fasting glucose (fs-GLU), blood lipids, and hepatic and renal functions were measured and analyzed by multiple linear regression (MLR). Based the MLR results, we developed a back propagation artificial neural network (BP-ANN) model by selecting tansig as the transfer function of the hidden layers nodes, and purelin for the output layer nodes, with training goal of 0.5×10(-5). There was significant correlation between fs-GLU with age, BMI, and blood biochemical indexes (P<0.05). The results of MLR analysis indicated that age, fasting alanine transaminase (fs-ALT), blood urea nitrogen (fs-BUN), total protein (fs-TP), uric acid (fs-BUN), and BMI are 6 independent variables related to fs-GLU. Based on these parameters, the BP-ANN model was performed well and reached high prediction accuracy when training 1 000 epoch (R=0.9987). The level of fs-GLU was predictable using the proposed BP-ANN model based on 6 related parameters (age, fs-ALT, fs-BUN, fs-TP, fs-UA and BMI) in overweight patients. © Georg Thieme Verlag KG Stuttgart · New York.

Gas Classification Using Deep Convolutional Neural Networks

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-01

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP). PMID:29316723

Gas Classification Using Deep Convolutional Neural Networks.

Science.gov (United States)

Peng, Pai; Zhao, Xiaojin; Pan, Xiaofang; Ye, Wenbin

2018-01-08

In this work, we propose a novel Deep Convolutional Neural Network (DCNN) tailored for gas classification. Inspired by the great success of DCNN in the field of computer vision, we designed a DCNN with up to 38 layers. In general, the proposed gas neural network, named GasNet, consists of: six convolutional blocks, each block consist of six layers; a pooling layer; and a fully-connected layer. Together, these various layers make up a powerful deep model for gas classification. Experimental results show that the proposed DCNN method is an effective technique for classifying electronic nose data. We also demonstrate that the DCNN method can provide higher classification accuracy than comparable Support Vector Machine (SVM) methods and Multiple Layer Perceptron (MLP).

Study of input variables in group method of data handling methodology

International Nuclear Information System (INIS)

Pereira, Iraci Martinez; Bueno, Elaine Inacio

2013-01-01

The Group Method of Data Handling - GMDH is a combinatorial multi-layer algorithm in which a network of layers and nodes is generated using a number of inputs from the data stream being evaluated. The GMDH network topology has been traditionally determined using a layer by layer pruning process based on a pre-selected criterion of what constitutes the best nodes at each level. The traditional GMDH method is based on an underlying assumption that the data can be modeled by using an approximation of the Volterra Series or Kolmorgorov-Gabor polynomial. A Monitoring and Diagnosis System was developed based on GMDH and ANN methodologies, and applied to the IPEN research Reactor IEA-1. The system performs the monitoring by comparing the GMDH and ANN calculated values with measured ones. As the GMDH is a self-organizing methodology, the input variables choice is made automatically. On the other hand, the results of ANN methodology are strongly dependent on which variables are used as neural network input. (author)

DDOS ATTACK DETECTION SIMULATION AND HANDLING MECHANISM

Directory of Open Access Journals (Sweden)

Ahmad Sanmorino

2013-11-01

Full Text Available In this study we discuss how to handle DDoS attack that coming from the attacker by using detection method and handling mechanism. Detection perform by comparing number of packets and number of flow. Whereas handling mechanism perform by limiting or drop the packets that detected as a DDoS attack. The study begins with simulation on real network, which aims to get the real traffic data. Then, dump traffic data obtained from the simulation used for detection method on our prototype system called DASHM (DDoS Attack Simulation and Handling Mechanism. From the result of experiment that has been conducted, the proposed method successfully detect DDoS attack and handle the incoming packet sent by attacker.

Application of neural networks to connectional expert system for identification of transients in nuclear power plants

International Nuclear Information System (INIS)

Cheon, Se Woo; Kim, Wan Joo; Chang, Soon Heung; Roh, Myung Sub

1991-01-01

The Back-propagation Neural Network (BPN) algorithm is applied to connectionist expert system for the identification of BWR transients. Several powerful features of neural network-based expert systems over traditional rule-based expert systems are described. The general mapping capability of the neural networks enables to identify transients easily. A number of case studies were performed with emphasis on the applicability of the neural networks to the diagnostic domain. It is revealed that the BPN algorithm can identify transients properly, even when incomplete or untrained symptoms are given. It is also shown that multiple transients are easily identified

Comparison of Ordinary Kriging and Multiple Indicator Kriging ...

African Journals Online (AJOL)

Michael O. Mensah

Multiple Indicator Kriging (MIK) is one of the popular non-linear methods that can handle skewed distribution such as that for gold ... historical deposits: Nkran, Adubia, Abore, and a ... information from the mine on the geology of the deposit.

The neural network z-vertex trigger for the Belle II detector

Energy Technology Data Exchange (ETDEWEB)

Skambraks, Sebastian; Neuhaus, Sara [Technische Universitaet Muenchen (Germany); Chen, Yang; Kiesling, Christian [Max-Planck-Institut fuer Physik, Muenchen (Germany); Collaboration: Belle II-Collaboration

2016-07-01

We present a neural network based first level track trigger for the upcoming Belle II detector at the high luminosity SuperKEKB flavor factory. Using hit and drift time information from the Central Drift Chamber (CDC), neural networks estimate the z-coordinates of single track vertex positions. Especially beam induced background, with vertices outside of the interaction region, can clearly be rejected. This allows to relax the track trigger conditions and thus enhances the efficiency for events with a low track multiplicity. In the CDC trigger pipeline, the preceding 2D pattern recognition enables a unique per track input representation and a sectorization of the track parameter phase space. The precise z-vertices are then estimated by an ensemble of sector-specific local expert neural networks. After an introduction to the neural trigger system, the benefits of an improved 3D pattern recognition are discussed.

Calculations of dose distributions using a neural network model

International Nuclear Information System (INIS)

Mathieu, R; Martin, E; Gschwind, R; Makovicka, L; Contassot-Vivier, S; Bahi, J

2005-01-01

The main goal of external beam radiotherapy is the treatment of tumours, while sparing, as much as possible, surrounding healthy tissues. In order to master and optimize the dose distribution within the patient, dosimetric planning has to be carried out. Thus, for determining the most accurate dose distribution during treatment planning, a compromise must be found between the precision and the speed of calculation. Current techniques, using analytic methods, models and databases, are rapid but lack precision. Enhanced precision can be achieved by using calculation codes based, for example, on Monte Carlo methods. However, in spite of all efforts to optimize speed (methods and computer improvements), Monte Carlo based methods remain painfully slow. A newer way to handle all of these problems is to use a new approach in dosimetric calculation by employing neural networks. Neural networks (Wu and Zhu 2000 Phys. Med. Biol. 45 913-22) provide the advantages of those various approaches while avoiding their main inconveniences, i.e., time-consumption calculations. This permits us to obtain quick and accurate results during clinical treatment planning. Currently, results obtained for a single depth-dose calculation using a Monte Carlo based code (such as BEAM (Rogers et al 2003 NRCC Report PIRS-0509(A) rev G)) require hours of computing. By contrast, the practical use of neural networks (Mathieu et al 2003 Proceedings Journees Scientifiques Francophones, SFRP) provides almost instant results and quite low errors (less than 2%) for a two-dimensional dosimetric map

Ergonomics of disposable handles for minimally invasive surgery.

Science.gov (United States)

Büchel, D; Mårvik, R; Hallabrin, B; Matern, U

2010-05-01

The ergonomic deficiencies of currently available minimally invasive surgery (MIS) instrument handles have been addressed in many studies. In this study, a new ergonomic pistol handle concept, realized as a prototype, and two disposable ring handles were investigated according to ergonomic properties set by new European standards. In this study, 25 volunteers performed four practical tasks to evaluate the ergonomics of the handles used in standard operating procedures (e.g., measuring a suture and cutting to length, precise maneuvering and targeting, and dissection of a gallbladder). Moreover, 20 participants underwent electromyography (EMG) tests to measure the muscle strain they experienced while carrying out the basic functions (grasp, rotate, and maneuver) in the x, y, and z axes. The data measured included the number of errors, the time required for task completion, perception of pressure areas, and EMG data. The values for usability in the test were effectiveness, efficiency, and user satisfaction. Surveys relating to the subjective rating were completed after each task for each of the three handles tested. Each handle except the new prototype caused pressure areas and pain. Extreme differences in muscle strain could not be observed for any of the three handles. Experienced surgeons worked more quickly with the prototype when measuring and cutting a suture (approximately 20%) and during precise maneuvering and targeting (approximately 20%). On the other hand, they completed the dissection task faster with the handle manufactured by Ethicon. Fewer errors were made with the prototype in dissection of the gallbladder. In contrast to the handles available on the market, the prototype was always rated as positive by the volunteers in the subjective surveys. None of the handles could fulfil all of the requirements with top scores. Each handle had its advantages and disadvantages. In contrast to the ring handles, the volunteers could fulfil most of the tasks more

Radiological safety aspects of handling plutonium

International Nuclear Information System (INIS)

Sundararajan, A.R.

2016-01-01

Department of Atomic Energy in its scheme of harnessing the nuclear energy for electrical power generation and strategic applications has given a huge role to utilization of plutonium. In the power production programme, fast reactors with plutonium as fuel are expected to play a major role. This would require establishing fuel reprocessing plants to handle both thermal and fast reactor fuels. So in the nuclear fuel cycle facilities variety of chemical, metallurgical, mechanical operations have to be carried out involving significant inventories of "2"3"9 Pu and associated radionuclides. Plutonium is the most radiotoxic radionuclide and therefore any facility handling it has to be designed and operated with utmost care. Two problems of major concern in the protection of persons working in plutonium handling facilities are the internal exposure to the operating personnel from uptake of plutonium and transplutonic nuclides as they are highly radiotoxic and the radiation exposure of hands and eye lens during fuel fabrication operations especially while handling recycled high burn up plutonium. In view of the fact that annual limit for intake is very small for "2"3"9Pu and its radiation emission characteristics are such that it is a huge challenge for the health physicists to detect Pu in air and in workers. This paper discusses the principles and practices followed in providing radiological surveillance to workers in plutonium handling areas. The challenges in protecting the workers from receiving exposures to hands and eye lens in handling high burn up plutonium are also discussed. The sites having Pu fuel cycle facilities should have trained medical staff to handle cases involving excessive intake of plutonium. (author)

Enclosure for handling high activity materials

International Nuclear Information System (INIS)

Jimeno de Osso, F.

1977-01-01

One of the most important problems that are met at the laboratories producing and handling radioisotopes is that of designing, building and operating enclosures suitable for the safe handling of active substances. With this purpose in mind, an enclosure has been designed and built for handling moderately high activities under a shielding made of 150 mm thick lead. In this report a description is given of those aspects that may be of interest to people working in this field. (Author)

Enclosure for handling high activity materials

Energy Technology Data Exchange (ETDEWEB)

Jimeno de Osso, F

1977-07-01

One of the most important problems that are met at the laboratories producing and handling radioisotopes is that of designing, building and operating enclosures suitable for the safe handling of active substances. With this purpose in mind, an enclosure has been designed and built for handling moderately high activities under a shielding made of 150 mm thick lead. In this report a description is given of those aspects that may be of interest to people working in this field. (Author)

Polarity-specific high-level information propagation in neural networks.

Science.gov (United States)

Lin, Yen-Nan; Chang, Po-Yen; Hsiao, Pao-Yueh; Lo, Chung-Chuan

2014-01-01

Analyzing the connectome of a nervous system provides valuable information about the functions of its subsystems. Although much has been learned about the architectures of neural networks in various organisms by applying analytical tools developed for general networks, two distinct and functionally important properties of neural networks are often overlooked. First, neural networks are endowed with polarity at the circuit level: Information enters a neural network at input neurons, propagates through interneurons, and leaves via output neurons. Second, many functions of nervous systems are implemented by signal propagation through high-level pathways involving multiple and often recurrent connections rather than by the shortest paths between nodes. In the present study, we analyzed two neural networks: the somatic nervous system of Caenorhabditis elegans (C. elegans) and the partial central complex network of Drosophila, in light of these properties. Specifically, we quantified high-level propagation in the vertical and horizontal directions: the former characterizes how signals propagate from specific input nodes to specific output nodes and the latter characterizes how a signal from a specific input node is shared by all output nodes. We found that the two neural networks are characterized by very efficient vertical and horizontal propagation. In comparison, classic small-world networks show a trade-off between vertical and horizontal propagation; increasing the rewiring probability improves the efficiency of horizontal propagation but worsens the efficiency of vertical propagation. Our result provides insights into how the complex functions of natural neural networks may arise from a design that allows them to efficiently transform and combine input signals.

Bidirectional Joint Representation Learning with Symmetrical Deep Neural Networks for Multimodal and Crossmodal Applications

OpenAIRE

Vukotic , Vedran; Raymond , Christian; Gravier , Guillaume

2016-01-01

International audience; Common approaches to problems involving multiple modalities (classification, retrieval, hyperlinking, etc.) are early fusion of the initial modalities and crossmodal translation from one modality to the other. Recently, deep neural networks, especially deep autoencoders, have proven promising both for crossmodal translation and for early fusion via multimodal embedding. In this work, we propose a flexible cross-modal deep neural network architecture for multimodal and ...

Encoding Time in Feedforward Trajectories of a Recurrent Neural Network Model.

Science.gov (United States)

Hardy, N F; Buonomano, Dean V

2018-02-01

Brain activity evolves through time, creating trajectories of activity that underlie sensorimotor processing, behavior, and learning and memory. Therefore, understanding the temporal nature of neural dynamics is essential to understanding brain function and behavior. In vivo studies have demonstrated that sequential transient activation of neurons can encode time. However, it remains unclear whether these patterns emerge from feedforward network architectures or from recurrent networks and, furthermore, what role network structure plays in timing. We address these issues using a recurrent neural network (RNN) model with distinct populations of excitatory and inhibitory units. Consistent with experimental data, a single RNN could autonomously produce multiple functionally feedforward trajectories, thus potentially encoding multiple timed motor patterns lasting up to several seconds. Importantly, the model accounted for Weber's law, a hallmark of timing behavior. Analysis of network connectivity revealed that efficiency-a measure of network interconnectedness-decreased as the number of stored trajectories increased. Additionally, the balance of excitation (E) and inhibition (I) shifted toward excitation during each unit's activation time, generating the prediction that observed sequential activity relies on dynamic control of the E/I balance. Our results establish for the first time that the same RNN can generate multiple functionally feedforward patterns of activity as a result of dynamic shifts in the E/I balance imposed by the connectome of the RNN. We conclude that recurrent network architectures account for sequential neural activity, as well as for a fundamental signature of timing behavior: Weber's law.

Specialization and Flexibility in Port Cargo Handling

Directory of Open Access Journals (Sweden)

Hakkı KİŞİ

2016-11-01

Full Text Available Cargo handling appears to be the fundamental function of ports. In this context, the question of type of equipment and capacity rate need to be tackled with respect to cargo handling principles. The purpose of this study is to discuss the types of equipment to be used in ports, relating the matter to costs and capacity. The question is studied with a basic economic theoretical approach. Various conditions like port location, size, resources, cargo traffic, ships, etc. are given parameters to dictate the type and specification of the cargo handling equipment. Besides, a simple approach in the context of cost capacity relation can be useful in deciding whether to use specialized or flexible equipment. Port equipment is sometimes expected to be flexible to handle various types of cargo as many as possible and sometimes to be specialized to handle one specific type of cargo. The cases that might be suitable for those alternatives are discussed from an economic point of view in this article. Consequently, effectiveness and efficiency criteria play important roles in determining the handling equipment in ports.

Neuromorphic neural interfaces: from neurophysiological inspiration to biohybrid coupling with nervous systems

Science.gov (United States)

Broccard, Frédéric D.; Joshi, Siddharth; Wang, Jun; Cauwenberghs, Gert

2017-08-01

Objective. Computation in nervous systems operates with different computational primitives, and on different hardware, than traditional digital computation and is thus subjected to different constraints from its digital counterpart regarding the use of physical resources such as time, space and energy. In an effort to better understand neural computation on a physical medium with similar spatiotemporal and energetic constraints, the field of neuromorphic engineering aims to design and implement electronic systems that emulate in very large-scale integration (VLSI) hardware the organization and functions of neural systems at multiple levels of biological organization, from individual neurons up to large circuits and networks. Mixed analog/digital neuromorphic VLSI systems are compact, consume little power and operate in real time independently of the size and complexity of the model. Approach. This article highlights the current efforts to interface neuromorphic systems with neural systems at multiple levels of biological organization, from the synaptic to the system level, and discusses the prospects for future biohybrid systems with neuromorphic circuits of greater complexity. Main results. Single silicon neurons have been interfaced successfully with invertebrate and vertebrate neural networks. This approach allowed the investigation of neural properties that are inaccessible with traditional techniques while providing a realistic biological context not achievable with traditional numerical modeling methods. At the network level, populations of neurons are envisioned to communicate bidirectionally with neuromorphic processors of hundreds or thousands of silicon neurons. Recent work on brain-machine interfaces suggests that this is feasible with current neuromorphic technology. Significance. Biohybrid interfaces between biological neurons and VLSI neuromorphic systems of varying complexity have started to emerge in the literature. Primarily intended as a

Decoupling control of vehicle chassis system based on neural network inverse system

Science.gov (United States)

Wang, Chunyan; Zhao, Wanzhong; Luan, Zhongkai; Gao, Qi; Deng, Ke

2018-06-01

Steering and suspension are two important subsystems affecting the handling stability and riding comfort of the chassis system. In order to avoid the interference and coupling of the control channels between active front steering (AFS) and active suspension subsystems (ASS), this paper presents a composite decoupling control method, which consists of a neural network inverse system and a robust controller. The neural network inverse system is composed of a static neural network with several integrators and state feedback of the original chassis system to approach the inverse system of the nonlinear systems. The existence of the inverse system for the chassis system is proved by the reversibility derivation of Interactor algorithm. The robust controller is based on the internal model control (IMC), which is designed to improve the robustness and anti-interference of the decoupled system by adding a pre-compensation controller to the pseudo linear system. The results of the simulation and vehicle test show that the proposed decoupling controller has excellent decoupling performance, which can transform the multivariable system into a number of single input and single output systems, and eliminate the mutual influence and interference. Furthermore, it has satisfactory tracking capability and robust performance, which can improve the comprehensive performance of the chassis system.

HMSRP Hawaiian Monk Seal Handling Data

Data.gov (United States)

National Oceanic and Atmospheric Administration, Department of Commerce — This data set contains records for all handling and measurement of Hawaiian monk seals since 1981. Live seals are handled and measured during a variety of events...

Information-geometric measures estimate neural interactions during oscillatory brain states

Directory of Open Access Journals (Sweden)

Yimin eNie

2014-02-01

Full Text Available The characterization of functional network structures among multiple neurons is essential to understanding neural information processing. Information geometry (IG, a theory developed for investigating a space of probability distributions has recently been applied to spike-train analysis and has provided robust estimations of neural interactions. Although neural firing in the equilibrium state is often assumed in these studies, in reality, neural activity is non-stationary. The brain exhibits various oscillations depending on cognitive demands or when an animal is asleep. Therefore, the investigation of the IG measures during oscillatory network states is important for testing how the IG method can be applied to real neural data. Using model networks of binary neurons or more realistic spiking neurons, we studied how the single- and pairwise-IG measures were influenced by oscillatory neural activity. Two general oscillatory mechanisms, externally driven oscillations and internally induced oscillations, were considered. In both mechanisms, we found that the single-IG measure was linearly related to the magnitude of the external input, and that the pairwise-IG measure was linearly related to the sum of connection strengths between two neurons. We also observed that the pairwise-IG measure was not dependent on the oscillation frequency. These results are consistent with the previous findings that were obtained under the equilibrium conditions. Therefore, we demonstrate that the IG method provides useful insights into neural interactions under the oscillatory condition that can often be observed in the real brain.

Multiple linear regression and artificial neural networks for delta-endotoxin and protease yields modelling of Bacillus thuringiensis.

Science.gov (United States)

Ennouri, Karim; Ben Ayed, Rayda; Triki, Mohamed Ali; Ottaviani, Ennio; Mazzarello, Maura; Hertelli, Fathi; Zouari, Nabil

2017-07-01

The aim of the present work was to develop a model that supplies accurate predictions of the yields of delta-endotoxins and proteases produced by B. thuringiensis var. kurstaki HD-1. Using available medium ingredients as variables, a mathematical method, based on Plackett-Burman design (PB), was employed to analyze and compare data generated by the Bootstrap method and processed by multiple linear regressions (MLR) and artificial neural networks (ANN) including multilayer perceptron (MLP) and radial basis function (RBF) models. The predictive ability of these models was evaluated by comparison of output data through the determination of coefficient (R 2 ) and mean square error (MSE) values. The results demonstrate that the prediction of the yields of delta-endotoxin and protease was more accurate by ANN technique (87 and 89% for delta-endotoxin and protease determination coefficients, respectively) when compared with MLR method (73.1 and 77.2% for delta-endotoxin and protease determination coefficients, respectively), suggesting that the proposed ANNs, especially MLP, is a suitable new approach for determining yields of bacterial products that allow us to make more appropriate predictions in a shorter time and with less engineering effort.

Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD

Science.gov (United States)

2016-10-01

1 Award Number: W81XWH-11-1-0796 TITLE: Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD PRINCIPAL...30Sept2015 - 29Sept2016 4. TITLE AND SUBTITLE: Neural Markers and Rehabilitation of Executive Functioning in Veterans with TBI and PTSD 5a. CONTRACT... met criteria for TBI during military service, 48.8% of whom reported multiple head injuries. The most common mechanisms of injury included blast

PERFORMANCE EVALUATION OF VARIANCES IN BACKPROPAGATION NEURAL NETWORK USED FOR HANDWRITTEN CHARACTER RECOGNITION

OpenAIRE

Vairaprakash Gurusamy *1 & K.Nandhini2

2017-01-01

A Neural Network is a powerful data modeling tool that is able to capture and represent complex input/output relationships. The motivation for the development of neural network technology stemmed from the desire to develop an artificial system that could perform "intelligent" tasks similar to those performed by the human brain.Back propagation was created by generalizing the Widrow-Hoff learning rule to multiple-layer networks and nonlinear differentiable transfer functions. The term back pro...

Segmentation of Drosophila Heart in Optical Coherence Microscopy Images Using Convolutional Neural Networks

OpenAIRE

Duan, Lian; Qin, Xi; He, Yuanhao; Sang, Xialin; Pan, Jinda; Xu, Tao; Men, Jing; Tanzi, Rudolph E.; Li, Airong; Ma, Yutao; Zhou, Chao

2018-01-01

Convolutional neural networks are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained convolutional neural network model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union (IOU) of ~86%. Various morphological and dyn...

Multistability of delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions

International Nuclear Information System (INIS)

Huang Yu-Jiao; Hu Hai-Gen

2015-01-01

In this paper, the multistability issue is discussed for delayed complex-valued recurrent neural networks with discontinuous real-imaginary-type activation functions. Based on a fixed theorem and stability definition, sufficient criteria are established for the existence and stability of multiple equilibria of complex-valued recurrent neural networks. The number of stable equilibria is larger than that of real-valued recurrent neural networks, which can be used to achieve high-capacity associative memories. One numerical example is provided to show the effectiveness and superiority of the presented results. (paper)

A Multiple Sensor Machine Vision System for Automatic Hardwood Feature Detection

Science.gov (United States)

D. Earl Kline; Richard W. Conners; Daniel L. Schmoldt; Philip A. Araman; Robert L. Brisbin

1993-01-01

A multiple sensor machine vision prototype is being developed to scan full size hardwood lumber at industrial speeds for automatically detecting features such as knots holes, wane, stain, splits, checks, and color. The prototype integrates a multiple sensor imaging system, a materials handling system, a computer system, and application software. The prototype provides...

Management of transport and handling contracts

CERN Document Server

Rühl, I

2004-01-01

This paper shall outline the content, application and management strategies for the various contracts related to transport and handling activities. In total, the two sections Logistics and Handling Maintenance are in charge of 27 (!) contracts ranging from small supply contracts to big industrial support contracts. The activities as well as the contracts can generally be divided into four main topics "Vehicle Fleet Management"; "Supply, Installation and Commissioning of Lifting and Hoisting Equipment"; "Equipment Maintenance" and "Industrial Support for Transport and Handling". Each activity and contract requires different approaches and permanent adaptation to the often changing CERN's requirements. In particular, the management and the difficulties experienced with the contracts E072 "Maintenance of lifting and hoisting equipment", F420 "Supply of seven overhead traveling cranes for LHC" and S090/S103 "Industrial support for transport and handling" will be explained in detail.

System identification via sparse multiple kernel-based regularization using sequential convex optimization techniques

DEFF Research Database (Denmark)

Chen, Tianshi; Andersen, Martin Skovgaard; Ljung, Lennart

2014-01-01

Model estimation and structure detection with short data records are two issues that receive increasing interests in System Identification. In this paper, a multiple kernel-based regularization method is proposed to handle those issues. Multiple kernels are conic combinations of fixed kernels...

Ergonomics: safe patient handling and mobility.

Science.gov (United States)

Hallmark, Beth; Mechan, Patricia; Shores, Lynne

2015-03-01

This article reviews and investigates the issues surrounding ergonomics, with a specific focus on safe patient handling and mobility. The health care worker of today faces many challenges, one of which is related to the safety of patients. Safe patient handling and mobility is on the forefront of the movement to improve patient safety. This article reviews the risks associated with patient handling and mobility, and informs the reader of current evidence-based practice relevant to this area of care. Copyright © 2015 Elsevier Inc. All rights reserved.

High content screening of defined chemical libraries using normal and glioma-derived neural stem cell lines.

Science.gov (United States)

Danovi, Davide; Folarin, Amos A; Baranowski, Bart; Pollard, Steven M

2012-01-01

Small molecules with potent biological effects on the fate of normal and cancer-derived stem cells represent both useful research tools and new drug leads for regenerative medicine and oncology. Long-term expansion of mouse and human neural stem cells is possible using adherent monolayer culture. These cultures represent a useful cellular resource to carry out image-based high content screening of small chemical libraries. Improvements in automated microscopy, desktop computational power, and freely available image processing tools, now means that such chemical screens are realistic to undertake in individual academic laboratories. Here we outline a cost effective and versatile time lapse imaging strategy suitable for chemical screening. Protocols are described for the handling and screening of human fetal Neural Stem (NS) cell lines and their malignant counterparts, Glioblastoma-derived neural stem cells (GNS). We focus on identification of cytostatic and cytotoxic "hits" and discuss future possibilities and challenges for extending this approach to assay lineage commitment and differentiation. Copyright © 2012 Elsevier Inc. All rights reserved.

A New Neural Network Approach to Short Term Load Forecasting of Electrical Power Systems

Directory of Open Access Journals (Sweden)

Farshid Keynia

2011-03-01

Full Text Available Short-term load forecast (STLF is an important operational function in both regulated power systems and deregulated open electricity markets. However, STLF is not easy to handle due to the nonlinear and random-like behaviors of system loads, weather conditions, and social and economic environment variations. Despite the research work performed in the area, more accurate and robust STLF methods are still needed due to the importance and complexity of STLF. In this paper, a new neural network approach for STLF is proposed. The proposed neural network has a novel learning algorithm based on a new modified harmony search technique. This learning algorithm can widely search the solution space in various directions, and it can also avoid the overfitting problem, trapping in local minima and dead bands. Based on this learning algorithm, the suggested neural network can efficiently extract the input/output mapping function of the forecast process leading to high STLF accuracy. The proposed approach is tested on two practical power systems and the results obtained are compared with the results of several other recently published STLF methods. These comparisons confirm the validity of the developed approach.

Simultaneous multichannel signal transfers via chaos in a recurrent neural network.

Science.gov (United States)

Soma, Ken-ichiro; Mori, Ryota; Sato, Ryuichi; Furumai, Noriyuki; Nara, Shigetoshi

2015-05-01

We propose neural network model that demonstrates the phenomenon of signal transfer between separated neuron groups via other chaotic neurons that show no apparent correlations with the input signal. The model is a recurrent neural network in which it is supposed that synchronous behavior between small groups of input and output neurons has been learned as fragments of high-dimensional memory patterns, and depletion of neural connections results in chaotic wandering dynamics. Computer experiments show that when a strong oscillatory signal is applied to an input group in the chaotic regime, the signal is successfully transferred to the corresponding output group, although no correlation is observed between the input signal and the intermediary neurons. Signal transfer is also observed when multiple signals are applied simultaneously to separate input groups belonging to different memory attractors. In this sense simultaneous multichannel communications are realized, and the chaotic neural dynamics acts as a signal transfer medium in which the signal appears to be hidden.

Remote handling equipment

International Nuclear Information System (INIS)

Clement, G.

1984-01-01

After a definition of intervention, problems encountered for working in an adverse environment are briefly analyzed for development of various remote handling equipments. Some examples of existing equipments are given [fr

Welding method by remote handling

International Nuclear Information System (INIS)

Hashinokuchi, Minoru.

1994-01-01

Water is charged into a pit (or a water reservoir) and an article to be welded is placed on a support in the pit by remote handling. A steel plate is disposed so as to cover the article to be welded by remote handling. The welding device is positioned to the portion to be welded and fixed in a state where the article to be welded is shielded from radiation by water and the steel plate. Water in the pit is drained till the portion to be welded is exposed to the atmosphere. Then, welding is conducted. After completion of the welding, water is charged again to the pit and the welding device and fixing jigs are decomposed in a state where the article to be welded is shielded again from radiation by water and the steel plate. Subsequently, the steel plate is removed by remote handling. Then, the article to be welded is returned from the pit to a temporary placing pool by remote handling. This can reduce operator's exposure. Further, since the amount of the shielding materials can be minimized, the amount of radioactive wastes can be decreased. (I.N.)

Delay-range-dependent exponential H∞ synchronization of a class of delayed neural networks

International Nuclear Information System (INIS)

Karimi, Hamid Reza; Maass, Peter

2009-01-01

This article aims to present a multiple delayed state-feedback control design for exponential H ∞ synchronization problem of a class of delayed neural networks with multiple time-varying discrete delays. On the basis of the drive-response concept and by introducing a descriptor technique and using Lyapunov-Krasovskii functional, new delay-range-dependent sufficient conditions for exponential H ∞ synchronization of the drive-response structure of neural networks are driven in terms of linear matrix inequalities (LMIs). The explicit expression of the controller gain matrices are parameterized based on the solvability conditions such that the drive system and the response system can be exponentially synchronized. A numerical example is included to illustrate the applicability of the proposed design method.

Augmented lagrange hopfield network for economic dispatch with multiple fuel options

International Nuclear Information System (INIS)

Dieu, Vo Ngoc; Ongsakul, Weerakorn; Polprasert, Jirawadee

2011-01-01

This paper proposes an augmented Lagrange Hopfield network (ALHN) for solving economic dispatch (ED) problem with multiple fuel options. The proposed ALHN method is a continuous Hopfield neural network with its energy function based on augmented Lagrangian function. The advantages of ALHN over the conventional Hopfield neural network are easier use, more general applications, faster convergence, better optimal solution, and larger scale of problem implementation. The method solves the problem by directly searching the most suitable fuel among the available fuels of each unit and finding the optimal solution for the problem based on minimization of the energy function of the continuous Hopfield neural network. The proposed method is tested on systems up to 100 units and the obtained results are compared to those from other methods in the literature. The results have shown that the proposed method is efficient for solving the ED problem with multiple fuel options and favorable for implementation in large scale problems.

Efficacy of peripheral lidocaine application (neural therapy) in the treatment of neurogenic detrusor overactivity in multiple sclerosis patients.

Science.gov (United States)

Tamam, Yusuf; Özdemir, Hasan Hüseyin; Gedik, Abdullah; Tamam, Cüneyt; Nazlıkul, Hüseyin

2017-09-01

Many agents and treatments are used in the treatment of neurogenic detrusor overactivity (NDO) in MS patients, but no study has been conducted on the use of peripheric lidocaine (neural therapy-NT) on MS patients. We evaluated the effects of local administration of lidocaine on NDO in Multiple Sclerosis (MS) patients. For each patient local anesthetic lidocaine was injected at each session. Sessions were held once a week for 5 weeks. At each session, Th 10-L1, urogenital segment intradermal injections, Frankenhauser, and sacral epidural injections were given. The patients had clinical and urodynamic assessment 1 month before and 3, 9, and 12 months after NT. In addition, multiple sclerosis quality of life inventory (MSQL-54) and bladder control scale (BLCS) was performed for patients. Twenty-eight patients were included in the study (8 males, 20 females). The patients' average age was 31.7 ± 8.1 years. The injection therapy significantly improved volume at first involuntary bladder contraction (FCV), maximal detrusor pression during filling (P det. max.), maximal cystometric bladder capacity (MCC) after 3 months. Also, the MSQL-54 and BLCS scores were improved with treatment. However, these improvements reached a maximum 3 months after treatment, but from the 9 month a regression was seen in the parameters, and after 12 months the findings were seen to be slightly above their basal levels. These results suggest that NDO treatment in MS patients could be an effective treatment which is easy and has very few side effects, and is cost effective. © 2017 Wiley Periodicals, Inc.

Encoding sensory and motor patterns as time-invariant trajectories in recurrent neural networks.

Science.gov (United States)

Goudar, Vishwa; Buonomano, Dean V

2018-03-14

Much of the information the brain processes and stores is temporal in nature-a spoken word or a handwritten signature, for example, is defined by how it unfolds in time. However, it remains unclear how neural circuits encode complex time-varying patterns. We show that by tuning the weights of a recurrent neural network (RNN), it can recognize and then transcribe spoken digits. The model elucidates how neural dynamics in cortical networks may resolve three fundamental challenges: first, encode multiple time-varying sensory and motor patterns as stable neural trajectories; second, generalize across relevant spatial features; third, identify the same stimuli played at different speeds-we show that this temporal invariance emerges because the recurrent dynamics generate neural trajectories with appropriately modulated angular velocities. Together our results generate testable predictions as to how recurrent networks may use different mechanisms to generalize across the relevant spatial and temporal features of complex time-varying stimuli. © 2018, Goudar et al.

Raingauge-Based Rainfall Nowcasting with Artificial Neural Network

Science.gov (United States)

Liong, Shie-Yui; He, Shan

2010-05-01

Rainfall forecasting and nowcasting are of great importance, for instance, in real-time flood early warning systems. Long term rainfall forecasting demands global climate, land, and sea data, thus, large computing power and storage capacity are required. Rainfall nowcasting's computing requirement, on the other hand, is much less. Rainfall nowcasting may use data captured by radar and/or weather stations. This paper presents the application of Artificial Neural Network (ANN) on rainfall nowcasting using data observed at weather and/or rainfall stations. The study focuses on the North-East monsoon period (December, January and February) in Singapore. Rainfall and weather data from ten stations, between 2000 and 2006, were selected and divided into three groups for training, over-fitting test and validation of the ANN. Several neural network architectures were tried in the study. Two architectures, Backpropagation ANN and Group Method of Data Handling ANN, yielded better rainfall nowcasting, up to two hours, than the other architectures. The obtained rainfall nowcasts were then used by a catchment model to forecast catchment runoff. The results of runoff forecast are encouraging and promising.With ANN's high computational speed, the proposed approach may be deliverable for creating the real-time flood early warning system.

Detection of bars in galaxies using a deep convolutional neural network

Science.gov (United States)

Abraham, Sheelu; Aniyan, A. K.; Kembhavi, Ajit K.; Philip, N. S.; Vaghmare, Kaustubh

2018-06-01

We present an automated method for the detection of bar structure in optical images of galaxies using a deep convolutional neural network that is easy to use and provides good accuracy. In our study, we use a sample of 9346 galaxies in the redshift range of 0.009-0.2 from the Sloan Digital Sky Survey (SDSS), which has 3864 barred galaxies, the rest being unbarred. We reach a top precision of 94 per cent in identifying bars in galaxies using the trained network. This accuracy matches the accuracy reached by human experts on the same data without additional information about the images. Since deep convolutional neural networks can be scaled to handle large volumes of data, the method is expected to have great relevance in an era where astronomy data is rapidly increasing in terms of volume, variety, volatility, and velocity along with other V's that characterize big data. With the trained model, we have constructed a catalogue of barred galaxies from SDSS and made it available online.

Civilsamfundets ABC: H for Handling

DEFF Research Database (Denmark)

Lund, Anker Brink; Meyer, Gitte

2015-01-01

Hvad er civilsamfundet? Anker Brink Lund og Gitte Meyer fra CBS Center for Civil Society Studies gennemgår civilsamfundet bogstav for bogstav. Vi er nået til H for Handling.......Hvad er civilsamfundet? Anker Brink Lund og Gitte Meyer fra CBS Center for Civil Society Studies gennemgår civilsamfundet bogstav for bogstav. Vi er nået til H for Handling....

The technique on handling radiation

International Nuclear Information System (INIS)

1997-11-01

This book describes measurement of radiation and handling radiation. The first part deals with measurement of radiation. The contents of this part are characteristic on measurement technique of radiation, radiation detector, measurement of energy spectrum, measurement of radioactivity, measurement for a level of radiation and county's statistics on radiation. The second parts explains handling radiation with treating of sealed radioisotope, treating unsealed source and radiation shield.

Uranium hexafluoride handling

International Nuclear Information System (INIS)

1991-01-01

The United States Department of Energy, Oak Ridge Field Office, and Martin Marietta Energy Systems, Inc., are co-sponsoring this Second International Conference on Uranium Hexafluoride Handling. The conference is offered as a forum for the exchange of information and concepts regarding the technical and regulatory issues and the safety aspects which relate to the handling of uranium hexafluoride. Through the papers presented here, we attempt not only to share technological advances and lessons learned, but also to demonstrate that we are concerned about the health and safety of our workers and the public, and are good stewards of the environment in which we all work and live. These proceedings are a compilation of the work of many experts in that phase of world-wide industry which comprises the nuclear fuel cycle. Their experience spans the entire range over which uranium hexafluoride is involved in the fuel cycle, from the production of UF 6 from the naturally-occurring oxide to its re-conversion to oxide for reactor fuels. The papers furnish insights into the chemical, physical, and nuclear properties of uranium hexafluoride as they influence its transport, storage, and the design and operation of plant-scale facilities for production, processing, and conversion to oxide. The papers demonstrate, in an industry often cited for its excellent safety record, continuing efforts to further improve safety in all areas of handling uranium hexafluoride

Evolvable synthetic neural system

Science.gov (United States)

Curtis, Steven A. (Inventor)

2009-01-01

An evolvable synthetic neural system includes an evolvable neural interface operably coupled to at least one neural basis function. Each neural basis function includes an evolvable neural interface operably coupled to a heuristic neural system to perform high-level functions and an autonomic neural system to perform low-level functions. In some embodiments, the evolvable synthetic neural system is operably coupled to one or more evolvable synthetic neural systems in a hierarchy.

Neural electrical activity and neural network growth.

Science.gov (United States)

Gafarov, F M

2018-05-01

The development of central and peripheral neural system depends in part on the emergence of the correct functional connectivity in its input and output pathways. Now it is generally accepted that molecular factors guide neurons to establish a primary scaffold that undergoes activity-dependent refinement for building a fully functional circuit. However, a number of experimental results obtained recently shows that the neuronal electrical activity plays an important role in the establishing of initial interneuronal connections. Nevertheless, these processes are rather difficult to study experimentally, due to the absence of theoretical description and quantitative parameters for estimation of the neuronal activity influence on growth in neural networks. In this work we propose a general framework for a theoretical description of the activity-dependent neural network growth. The theoretical description incorporates a closed-loop growth model in which the neural activity can affect neurite outgrowth, which in turn can affect neural activity. We carried out the detailed quantitative analysis of spatiotemporal activity patterns and studied the relationship between individual cells and the network as a whole to explore the relationship between developing connectivity and activity patterns. The model, developed in this work will allow us to develop new experimental techniques for studying and quantifying the influence of the neuronal activity on growth processes in neural networks and may lead to a novel techniques for constructing large-scale neural networks by self-organization. Copyright © 2018 Elsevier Ltd. All rights reserved.

Multiple neural states of representation in short-term memory? It's a matter of attention.

Science.gov (United States)

Larocque, Joshua J; Lewis-Peacock, Jarrod A; Postle, Bradley R

2014-01-01

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM-the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

Multiple neural states of representation in short-term memory? It's a matter of attention

Directory of Open Access Journals (Sweden)

Joshua J Larocque

2014-01-01

Full Text Available Short-term memory (STM refers to the capacity-limited retention of information over a brief period of time, and working memory (WM refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing and long-term memory (LTM – the encoding and retention of an effectively unlimited amount of information for a much longer period of time. Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson & Shiffrin, 1968; Baddeley & Hitch, 1974; and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002. In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and EEG over conventional activation-based analysis approaches (Postle, in press. We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays.

Neural fraud detection in credit card operations.

Science.gov (United States)

Dorronsoro, J R; Ginel, F; Sgnchez, C; Cruz, C S

1997-01-01

This paper presents an online system for fraud detection of credit card operations based on a neural classifier. Since it is installed in a transactional hub for operation distribution, and not on a card-issuing institution, it acts solely on the information of the operation to be rated and of its immediate previous history, and not on historic databases of past cardholder activities. Among the main characteristics of credit card traffic are the great imbalance between proper and fraudulent operations, and a great degree of mixing between both. To ensure proper model construction, a nonlinear version of Fisher's discriminant analysis, which adequately separates a good proportion of fraudulent operations away from other closer to normal traffic, has been used. The system is fully operational and currently handles more than 12 million operations per year with very satisfactory results.

Explaining Consumer Safe Food Handling Through Behavior-Change Theories: A Systematic Review.

Science.gov (United States)

Young, Ian; Reimer, Danielle; Greig, Judy; Meldrum, Richard; Turgeon, Patricia; Waddell, Lisa

2017-11-01

Consumers often engage in unsafe food handling behaviors at home. Previous studies have investigated the ability of behavior-change theories to explain and predict these behaviors. The purpose of this review was to determine which theories are most consistently associated with consumers' safe food handling behaviors across the published literature. A standardized systematic review methodology was used, consisting of the following steps: comprehensive search strategy; relevance screening of identified references; confirmation of relevance and characterization of relevant articles; risk-of-bias assessment; data extraction; and descriptive analysis of study results. A total of 20 relevant studies were identified; they were mostly conducted in Australia (40%) and the United States (35%) and used a cross-sectional design (65%). Most studies targeted young adults (65%), and none focused on high-risk consumer groups. The outcomes of 70% of studies received high overall risk-of-bias ratings, largely due to a lack of control for confounding variables. The most commonly applied theory was the Theory of Planned Behavior (45% of studies), which, along with other investigated theories of behavior change, was frequently associated with consumer safe food handling behavioral intentions and behaviors. However, overall, there was wide variation in the specific constructs found to be significantly associated and in the percentage of variance explained in each outcome across studies. The results suggest that multiple theories of behavior change can help to explain consumer safe food handling behaviors and could be adopted to guide the development of future behavior-change interventions. In these contexts, theories should be appropriately selected and adapted to meet the needs of the specific target population and context of interest.

Handling Pyrophoric Reagents

Energy Technology Data Exchange (ETDEWEB)

Alnajjar, Mikhail S.; Haynie, Todd O.

2009-08-14

Pyrophoric reagents are extremely hazardous. Special handling techniques are required to prevent contact with air and the resulting fire. This document provides several methods for working with pyrophoric reagents outside of an inert atmosphere.

Neural mechanisms of sequence generation in songbirds

Science.gov (United States)

Langford, Bruce

Animal models in research are useful for studying more complex behavior. For example, motor sequence generation of actions requiring good muscle coordination such as writing with a pen, playing an instrument, or speaking, may involve the interaction of many areas in the brain, each a complex system in itself; thus it can be difficult to determine causal relationships between neural behavior and the behavior being studied. Birdsong, however, provides an excellent model behavior for motor sequence learning, memory, and generation. The song consists of learned sequences of notes that are spectrographically stereotyped over multiple renditions of the song, similar to syllables in human speech. The main areas of the songbird brain involve in singing are known, however, the mechanisms by which these systems store and produce song are not well understood. We used a custom built, head-mounted, miniature motorized microdrive to chronically record the neural firing patterns of identified neurons in HVC, a pre-motor cortical nucleus which has been shown to be important in song timing. These were done in Bengalese finch which generate a song made up of stereotyped notes but variable note sequences. We observed song related bursting in neurons projecting to Area X, a homologue to basal ganglia, and tonic firing in HVC interneurons. Interneuron had firing rate patterns that were consistent over multiple renditions of the same note sequence. We also designed and built a light-weight, low-powered wireless programmable neural stimulator using Bluetooth Low Energy Protocol. It was able to generate perturbations in the song when current pulses were administered to RA, which projects to the brainstem nucleus responsible for syringeal muscle control.

Cell surface glycan engineering of neural stem cells augments neurotropism and improves recovery in a murine model of multiple sclerosis

KAUST Repository

Merzaban, Jasmeen; Imitola, Jaime; Starossom, Sarah C.; Zhu, Bing; Wang, Yue; Lee, Jack; Ali, Amal J.; Olah, Marta; AbuElela, Ayman; Khoury, Samia J.; Sackstein, Robert

2015-01-01

Neural stem cell (NSC)-based therapies offer potential for neural repair in central nervous system (CNS) inflammatory and degenerative disorders. Typically, these conditions present with multifocal CNS lesions making it impractical to inject NSCs

The Functional Role of Neural Oscillations in Non-Verbal Emotional Communication.

Science.gov (United States)

Symons, Ashley E; El-Deredy, Wael; Schwartze, Michael; Kotz, Sonja A

2016-01-01

Effective interpersonal communication depends on the ability to perceive and interpret nonverbal emotional expressions from multiple sensory modalities. Current theoretical models propose that visual and auditory emotion perception involves a network of brain regions including the primary sensory cortices, the superior temporal sulcus (STS), and orbitofrontal cortex (OFC). However, relatively little is known about how the dynamic interplay between these regions gives rise to the perception of emotions. In recent years, there has been increasing recognition of the importance of neural oscillations in mediating neural communication within and between functional neural networks. Here we review studies investigating changes in oscillatory activity during the perception of visual, auditory, and audiovisual emotional expressions, and aim to characterize the functional role of neural oscillations in nonverbal emotion perception. Findings from the reviewed literature suggest that theta band oscillations most consistently differentiate between emotional and neutral expressions. While early theta synchronization appears to reflect the initial encoding of emotionally salient sensory information, later fronto-central theta synchronization may reflect the further integration of sensory information with internal representations. Additionally, gamma synchronization reflects facilitated sensory binding of emotional expressions within regions such as the OFC, STS, and, potentially, the amygdala. However, the evidence is more ambiguous when it comes to the role of oscillations within the alpha and beta frequencies, which vary as a function of modality (or modalities), presence or absence of predictive information, and attentional or task demands. Thus, the synchronization of neural oscillations within specific frequency bands mediates the rapid detection, integration, and evaluation of emotional expressions. Moreover, the functional coupling of oscillatory activity across multiples

ITER - torus vacuum pumping system remote handling issues

International Nuclear Information System (INIS)

Stringer, J.

1992-11-01

This report describes further design issues concerning remote maintenance of torus vacuum pumping systems options for ITER. The key issues under investigation in this report are flask support systems for valve seal exchange operations for the compound cryopump scheme and remote maintenance of a proposed multiple turbomolecular pump (TMP) system, an alternative ITER torus exhaust pumping option. Previous studies have shown that the overhead support methods for seal exchange flask equipment could malfunction due to valve/flask misalignment. A floor-mounted support system is described in this report. This scheme provides a more rigid support system for seal exchange operations. An alternative torus pumping system, based on the use of multiple TMPs, is studied from a remote maintenance standpoint. In this concept, centre distance spacing for pump/valve assemblies is too restrictive for remote maintenance. Recommendations are made for adequate spacing of these assemblies based on commercially-available 0.8 m and 1.0 m diameter valves. Fewer pumps will fit in this arrangement, which implies a need for larger TMPs. Pumps of this size are not commercially available. Other concerns regarding the servicing and storage of remote handling equipment in cells are also identified. (9 figs.)

ATA diagnostic data handling system: an overview

International Nuclear Information System (INIS)

Chambers, F.W.; Kallman, J.; McDonald, J.; Slominski, M.

1984-01-01

The functions to be performed by the ATA diagnostic data handling system are discussed. The capabilities of the present data acquisition system (System 0) are presented. The goals for the next generation acquisition system (System 1), currently under design, are discussed. Facilities on the Octopus system for data handling are reviewed. Finally, we discuss what has been learned about diagnostics and computer based data handling during the past year

A Streaming PCA VLSI Chip for Neural Data Compression.

Science.gov (United States)

Wu, Tong; Zhao, Wenfeng; Guo, Hongsun; Lim, Hubert H; Yang, Zhi

2017-12-01

Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm. Throughout the experiments, the chip compresses LFPs by 10 at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25 with 8% reconstruction errors and 3.05-W/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.

Periconceptional Folate Deficiency and Implications in Neural Tube Defects

Directory of Open Access Journals (Sweden)

J. Safi

2012-01-01

Full Text Available Nutritional deficiencies are preventable etiological and epigenetic factors causing congenital abnormalities, first cause of infant mortality. Folate deficiency has a well-established teratogenic effect, leading to an increasing risk of neural tube defects. This paper highlights the most recent medical literature about folate deficiency, be it maternal or paternal. It then focuses on associated deficiencies as nutritional deficiencies are multiple and interrelated. Observational and interventional studies have all been consistent with a 50–70% protective effect of adequate women consumption of folates on neural tube defects. Since strategies to modify women’s dietary habits and vitamin use have achieved little progress, scientific as well as political effort is mandatory in order to implement global preventive public health strategies aimed at improving the alimentation of women in reproductive age, especially folic acid supplementation. Even with the recent breakthrough of fetal surgery for myelomeningocele, the emphasis should still be on prevention as the best practice rather than treatment of neural tube defects.

Precipitation Nowcast using Deep Recurrent Neural Network

Science.gov (United States)

Akbari Asanjan, A.; Yang, T.; Gao, X.; Hsu, K. L.; Sorooshian, S.

2016-12-01

An accurate precipitation nowcast (0-6 hours) with a fine temporal and spatial resolution has always been an important prerequisite for flood warning, streamflow prediction and risk management. Most of the popular approaches used for forecasting precipitation can be categorized into two groups. One type of precipitation forecast relies on numerical modeling of the physical dynamics of atmosphere and another is based on empirical and statistical regression models derived by local hydrologists or meteorologists. Given the recent advances in artificial intelligence, in this study a powerful Deep Recurrent Neural Network, termed as Long Short-Term Memory (LSTM) model, is creatively used to extract the patterns and forecast the spatial and temporal variability of Cloud Top Brightness Temperature (CTBT) observed from GOES satellite. Then, a 0-6 hours precipitation nowcast is produced using a Precipitation Estimation from Remote Sensing Information using Artificial Neural Network (PERSIANN) algorithm, in which the CTBT nowcast is used as the PERSIANN algorithm's raw inputs. Two case studies over the continental U.S. have been conducted that demonstrate the improvement of proposed approach as compared to a classical Feed Forward Neural Network and a couple simple regression models. The advantages and disadvantages of the proposed method are summarized with regard to its capability of pattern recognition through time, handling of vanishing gradient during model learning, and working with sparse data. The studies show that the LSTM model performs better than other methods, and it is able to learn the temporal evolution of the precipitation events through over 1000 time lags. The uniqueness of PERSIANN's algorithm enables an alternative precipitation nowcast approach as demonstrated in this study, in which the CTBT prediction is produced and used as the inputs for generating precipitation nowcast.

Automation of 3D micro object handling process

DEFF Research Database (Denmark)

Gegeckaite, Asta; Hansen, Hans Nørgaard

2007-01-01

Most of the micro objects in industrial production are handled with manual labour or in semiautomatic stations. Manual labour usually makes handling and assembly operations highly flexible, but slow, relatively imprecise and expensive. Handling of 3D micro objects poses special challenges due to ...

Optimal planning of multiple distributed generation sources in distribution networks: A new approach

Energy Technology Data Exchange (ETDEWEB)

AlRashidi, M.R., E-mail: malrash2002@yahoo.com [Department of Electrical Engineering, College of Technological Studies, Public Authority for Applied Education and Training (PAAET) (Kuwait); AlHajri, M.F., E-mail: mfalhajri@yahoo.com [Department of Electrical Engineering, College of Technological Studies, Public Authority for Applied Education and Training (PAAET) (Kuwait)

2011-10-15

Highlights: {yields} A new hybrid PSO for optimal DGs placement and sizing. {yields} Statistical analysis to fine tune PSO parameters. {yields} Novel constraint handling mechanism to handle different constraints types. - Abstract: An improved particle swarm optimization algorithm (PSO) is presented for optimal planning of multiple distributed generation sources (DG). This problem can be divided into two sub-problems: the DG optimal size (continuous optimization) and location (discrete optimization) to minimize real power losses. The proposed approach addresses the two sub-problems simultaneously using an enhanced PSO algorithm capable of handling multiple DG planning in a single run. A design of experiment is used to fine tune the proposed approach via proper analysis of PSO parameters interaction. The proposed algorithm treats the problem constraints differently by adopting a radial power flow algorithm to satisfy the equality constraints, i.e. power flows in distribution networks, while the inequality constraints are handled by making use of some of the PSO features. The proposed algorithm was tested on the practical 69-bus power distribution system. Different test cases were considered to validate the proposed approach consistency in detecting optimal or near optimal solution. Results are compared with those of Sequential Quadratic Programming.

Optimal planning of multiple distributed generation sources in distribution networks: A new approach

International Nuclear Information System (INIS)

AlRashidi, M.R.; AlHajri, M.F.

2011-01-01

Highlights: → A new hybrid PSO for optimal DGs placement and sizing. → Statistical analysis to fine tune PSO parameters. → Novel constraint handling mechanism to handle different constraints types. - Abstract: An improved particle swarm optimization algorithm (PSO) is presented for optimal planning of multiple distributed generation sources (DG). This problem can be divided into two sub-problems: the DG optimal size (continuous optimization) and location (discrete optimization) to minimize real power losses. The proposed approach addresses the two sub-problems simultaneously using an enhanced PSO algorithm capable of handling multiple DG planning in a single run. A design of experiment is used to fine tune the proposed approach via proper analysis of PSO parameters interaction. The proposed algorithm treats the problem constraints differently by adopting a radial power flow algorithm to satisfy the equality constraints, i.e. power flows in distribution networks, while the inequality constraints are handled by making use of some of the PSO features. The proposed algorithm was tested on the practical 69-bus power distribution system. Different test cases were considered to validate the proposed approach consistency in detecting optimal or near optimal solution. Results are compared with those of Sequential Quadratic Programming.

Computed tomography of x-ray images using neural networks

Science.gov (United States)

Allred, Lloyd G.; Jones, Martin H.; Sheats, Matthew J.; Davis, Anthony W.

2000-03-01

Traditional CT reconstruction is done using the technique of Filtered Backprojection. While this technique is widely employed in industrial and medical applications, it is not generally understood that FB has a fundamental flaw. Gibbs phenomena states any Fourier reconstruction will produce errors in the vicinity of all discontinuities, and that the error will equal 28 percent of the discontinuity. A number of years back, one of the authors proposed a biological perception model whereby biological neural networks perceive 3D images from stereo vision. The perception model proports an internal hard-wired neural network which emulates the external physical process. A process is repeated whereby erroneous unknown internal values are used to generate an emulated signal with is compared to external sensed data, generating an error signal. Feedback from the error signal is then sued to update the erroneous internal values. The process is repeated until the error signal no longer decrease. It was soon realized that the same method could be used to obtain CT from x-rays without having to do Fourier transforms. Neural networks have the additional potential for handling non-linearities and missing data. The technique has been applied to some coral images, collected at the Los Alamos high-energy x-ray facility. The initial images show considerable promise, in some instances showing more detail than the FB images obtained from the same data. Although routine production using this new method would require a massively parallel computer, the method shows promise, especially where refined detail is required.

A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

Science.gov (United States)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.

SpikingLab: modelling agents controlled by Spiking Neural Networks in Netlogo.

Science.gov (United States)

Jimenez-Romero, Cristian; Johnson, Jeffrey

2017-01-01

The scientific interest attracted by Spiking Neural Networks (SNN) has lead to the development of tools for the simulation and study of neuronal dynamics ranging from phenomenological models to the more sophisticated and biologically accurate Hodgkin-and-Huxley-based and multi-compartmental models. However, despite the multiple features offered by neural modelling tools, their integration with environments for the simulation of robots and agents can be challenging and time consuming. The implementation of artificial neural circuits to control robots generally involves the following tasks: (1) understanding the simulation tools, (2) creating the neural circuit in the neural simulator, (3) linking the simulated neural circuit with the environment of the agent and (4) programming the appropriate interface in the robot or agent to use the neural controller. The accomplishment of the above-mentioned tasks can be challenging, especially for undergraduate students or novice researchers. This paper presents an alternative tool which facilitates the simulation of simple SNN circuits using the multi-agent simulation and the programming environment Netlogo (educational software that simplifies the study and experimentation of complex systems). The engine proposed and implemented in Netlogo for the simulation of a functional model of SNN is a simplification of integrate and fire (I&F) models. The characteristics of the engine (including neuronal dynamics, STDP learning and synaptic delay) are demonstrated through the implementation of an agent representing an artificial insect controlled by a simple neural circuit. The setup of the experiment and its outcomes are described in this work.

Optimal neural networks for protein-structure prediction

International Nuclear Information System (INIS)

Head-Gordon, T.; Stillinger, F.H.

1993-01-01

The successful application of neural-network algorithms for prediction of protein structure is stymied by three problem areas: the sparsity of the database of known protein structures, poorly devised network architectures which make the input-output mapping opaque, and a global optimization problem in the multiple-minima space of the network variables. We present a simplified polypeptide model residing in two dimensions with only two amino-acid types, A and B, which allows the determination of the global energy structure for all possible sequences of pentamer, hexamer, and heptamer lengths. This model simplicity allows us to compile a complete structural database and to devise neural networks that reproduce the tertiary structure of all sequences with absolute accuracy and with the smallest number of network variables. These optimal networks reveal that the three problem areas are convoluted, but that thoughtful network designs can actually deconvolute these detrimental traits to provide network algorithms that genuinely impact on the ability of the network to generalize or learn the desired mappings. Furthermore, the two-dimensional polypeptide model shows sufficient chemical complexity so that transfer of neural-network technology to more realistic three-dimensional proteins is evident

Scheduling of outbound luggage handling at airports

DEFF Research Database (Denmark)

Barth, Torben C.; Pisinger, David

2012-01-01

This article considers the outbound luggage handling problem at airports. The problem is to assign handling facilities to outbound flights and decide about the handling start time. This dynamic, near real-time assignment problem is part of the daily airport operations. Quality, efficiency......). Another solution method is a decomposition approach. The problem is divided into different subproblems and solved in iterative steps. The different solution approaches are tested on real world data from Frankfurt Airport....

Sequence trajectory generation for garment handling systems

OpenAIRE

Liu, Honghai; Lin, Hua

2008-01-01

This paper presents a novel generic approach to the planning strategy of garment handling systems. An assumption is proposed to separate the components of such systems into a component for intelligent gripper techniques and a component for handling planning strategies. Researchers can concentrate on one of the two components first, then merge the two problems together. An algorithm is addressed to generate the trajectory position and a clothes handling sequence of clothes partitions, which ar...

Enhanced wood fuel handling: market and design studies

Energy Technology Data Exchange (ETDEWEB)

Landen, R.; Rippengal, R.; Redman, A.N.

1997-09-01

This report examines the potential for the manufacture and sale of novel wood fuel handling systems as a means of addressing users' concerns regarding current capital costs and potential high labour costs of non-automated systems. The report considers fuel handling technology that is basically appropriate for wood-fired heating systems of between c.100kW and c.1MW maximum continuous rating. This report details work done by the project collaborators in order to: (1) assess the current status of wood fuel handling technology; (2) evaluate the market appetite for improved wood fuel handling technology; (3) derive capital costs which are acceptable to customers; (4) review design options; and (5) select one or more design options worthy of further development. The current status of wood fuel handling technology is determined, and some basic modelling to give guidance on acceptable capital costs of 100-1000kW wood fuel handling systems is undertaken. (author)

Automated target recognition and tracking using an optical pattern recognition neural network

Science.gov (United States)

Chao, Tien-Hsin

1991-01-01

The on-going development of an automatic target recognition and tracking system at the Jet Propulsion Laboratory is presented. This system is an optical pattern recognition neural network (OPRNN) that is an integration of an innovative optical parallel processor and a feature extraction based neural net training algorithm. The parallel optical processor provides high speed and vast parallelism as well as full shift invariance. The neural network algorithm enables simultaneous discrimination of multiple noisy targets in spite of their scales, rotations, perspectives, and various deformations. This fully developed OPRNN system can be effectively utilized for the automated spacecraft recognition and tracking that will lead to success in the Automated Rendezvous and Capture (AR&C) of the unmanned Cargo Transfer Vehicle (CTV). One of the most powerful optical parallel processors for automatic target recognition is the multichannel correlator. With the inherent advantages of parallel processing capability and shift invariance, multiple objects can be simultaneously recognized and tracked using this multichannel correlator. This target tracking capability can be greatly enhanced by utilizing a powerful feature extraction based neural network training algorithm such as the neocognitron. The OPRNN, currently under investigation at JPL, is constructed with an optical multichannel correlator where holographic filters have been prepared using the neocognitron training algorithm. The computation speed of the neocognitron-type OPRNN is up to 10(exp 14) analog connections/sec that enabling the OPRNN to outperform its state-of-the-art electronics counterpart by at least two orders of magnitude.

Non-Viral Generation of Neural Precursor-like Cells from Adult Human Fibroblasts

Directory of Open Access Journals (Sweden)

Maucksch C

2012-01-01

Full Text Available Recent studies have reported direct reprogramming of human fibroblasts to mature neurons by the introduction of defined neural genes. This technology has potential use in the areas of neurological disease modeling and drug development. However, use of induced neurons for large-scale drug screening and cell-based replacement strategies is limited due to their inability to expand once reprogrammed. We propose it would be more desirable to induce expandable neural precursor cells directly from human fibroblasts. To date several pluripotent and neural transcription factors have been shown to be capable of converting mouse fibroblasts to neural stem/precursor-like cells when delivered by viral vectors. Here we extend these findings and demonstrate that transient ectopic insertion of the transcription factors SOX2 and PAX6 to adult human fibroblasts through use of non-viral plasmid transfection or protein transduction allows the generation of induced neural precursor (iNP colonies expressing a range of neural stem and pro-neural genes. Upon differentiation, iNP cells give rise to neurons exhibiting typical neuronal morphologies and expressing multiple neuronal markers including tyrosine hydroxylase and GAD65/67. Importantly, iNP-derived neurons demonstrate electrophysiological properties of functionally mature neurons with the capacity to generate action potentials. In addition, iNP cells are capable of differentiating into glial fibrillary acidic protein (GFAP-expressing astrocytes. This study represents a novel virus-free approach for direct reprogramming of human fibroblasts to a neural precursor fate.

Remote handling for an ISIS target change

International Nuclear Information System (INIS)

Broome, T.A.; Holding, M.

1989-01-01

During 1987 two ISIS targets were changed. This document describes the main features of the remote handling aspects of the work. All the work has to be carried out using remote handling techniques. The radiation level measured on the surface of the reflector when the second target had been removed was about 800 mGy/h demonstrating that hands on operations on any part of the target reflector moderator assembly is not practical. The target changes were the first large scale operations in the Target Station Remote Handling Cell and a great deal was learned about both equipment and working practices. Some general principles emerged which are applicable to other active handling tasks on facilities like ISIS and these are discussed below. 8 figs

Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

Science.gov (United States)

Vázquez-Marrufo, Manuel; Galvao-Carmona, Alejandro; González-Rosa, Javier J; Hidalgo-Muñoz, Antonio R; Borges, Mónica; Ruiz-Peña, Juan Luis; Izquierdo, Guillermo

2014-01-01

A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (pmultiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention impairment, which could help in the assessment and treatment of relapsing-remitting multiple sclerosis patients.

Materials Handling. Module SH-01. Safety and Health.

Science.gov (United States)

Center for Occupational Research and Development, Inc., Waco, TX.

This student module on materials handling is one of 50 modules concerned with job safety and health. It presents the procedures for safe materials handling. Discussed are manual handling methods (lifting and carrying by hand) and mechanical lifting (lifting by powered trucks, cranes or conveyors). Following the introduction, 15 objectives (each…

Incorporating Handling Qualities Analysis into Rotorcraft Conceptual Design

Science.gov (United States)

Lawrence, Ben

2014-01-01

This paper describes the initial development of a framework to incorporate handling qualities analyses into a rotorcraft conceptual design process. In particular, the paper describes how rotorcraft conceptual design level data can be used to generate flight dynamics models for handling qualities analyses. Also, methods are described that couple a basic stability augmentation system to the rotorcraft flight dynamics model to extend analysis to beyond that of the bare airframe. A methodology for calculating the handling qualities characteristics of the flight dynamics models and for comparing the results to ADS-33E criteria is described. Preliminary results from the application of the handling qualities analysis for variations in key rotorcraft design parameters of main rotor radius, blade chord, hub stiffness and flap moment of inertia are shown. Varying relationships, with counteracting trends for different handling qualities criteria and different flight speeds are exhibited, with the action of the control system playing a complex part in the outcomes. Overall, the paper demonstrates how a broad array of technical issues across flight dynamics stability and control, simulation and modeling, control law design and handling qualities testing and evaluation had to be confronted to implement even a moderately comprehensive handling qualities analysis of relatively low fidelity models. A key outstanding issue is to how to 'close the loop' with an overall design process, and options for the exploration of how to feedback handling qualities results to a conceptual design process are proposed for future work.

Enclosure for handling high activity materials abstract

International Nuclear Information System (INIS)

Jimeno de Osso, F.; Dominguez Rodriguez, G.; Cruz Castillo, F. de la; Rodriguez Esteban, A.

1977-01-01

One of the most important problems that are met at the laboratories producing and handling radioisotopes is that of designing, building and operating enclosures suitable for the safe handling of active substances. With that purpose in mind, an enclosure has been designed and built for handling moderately high activities under a shielding made of 150 mm thick lead. A description is given of those aspects that may be of interest to people working in this field. (author) [es

Using neural networks to enhance the Higgs boson signal at hadron colliders

International Nuclear Information System (INIS)

Field, R.D.; Kanev, Y.; Tayebnejad, M.; Griffin, P.A.

1995-01-01

Neural networks are used to help distinguish the ZZ → ell + ell - -jet-jet signal produced by the decay of a 400 GeV Higgs boson at a proton-proton collider energy of 15 TeV from the ''ordinary'' QCD Z + jets background. The ideal case where only one event at a time enters the detector (no pile-up) and the case of multiple interactions per beam crossing (pile-up) are examined. In both cases, when used in conjunction with the standard cuts, neural networks provide an additional signal to background enhancement

A convolutional neural network-based screening tool for X-ray serial crystallography.

Science.gov (United States)

Ke, Tsung Wei; Brewster, Aaron S; Yu, Stella X; Ushizima, Daniela; Yang, Chao; Sauter, Nicholas K

2018-05-01

A new tool is introduced for screening macromolecular X-ray crystallography diffraction images produced at an X-ray free-electron laser light source. Based on a data-driven deep learning approach, the proposed tool executes a convolutional neural network to detect Bragg spots. Automatic image processing algorithms described can enable the classification of large data sets, acquired under realistic conditions consisting of noisy data with experimental artifacts. Outcomes are compared for different data regimes, including samples from multiple instruments and differing amounts of training data for neural network optimization. open access.

A Collaborative Neurodynamic Approach to Multiple-Objective Distributed Optimization.

Science.gov (United States)

Yang, Shaofu; Liu, Qingshan; Wang, Jun

2018-04-01

This paper is concerned with multiple-objective distributed optimization. Based on objective weighting and decision space decomposition, a collaborative neurodynamic approach to multiobjective distributed optimization is presented. In the approach, a system of collaborative neural networks is developed to search for Pareto optimal solutions, where each neural network is associated with one objective function and given constraints. Sufficient conditions are derived for ascertaining the convergence to a Pareto optimal solution of the collaborative neurodynamic system. In addition, it is proved that each connected subsystem can generate a Pareto optimal solution when the communication topology is disconnected. Then, a switching-topology-based method is proposed to compute multiple Pareto optimal solutions for discretized approximation of Pareto front. Finally, simulation results are discussed to substantiate the performance of the collaborative neurodynamic approach. A portfolio selection application is also given.

Global Neural Pattern Similarity as a Common Basis for Categorization and Recognition Memory

Science.gov (United States)

Xue, Gui; Love, Bradley C.; Preston, Alison R.; Poldrack, Russell A.

2014-01-01

Familiarity, or memory strength, is a central construct in models of cognition. In previous categorization and long-term memory research, correlations have been found between psychological measures of memory strength and activation in the medial temporal lobes (MTLs), which suggests a common neural locus for memory strength. However, activation alone is insufficient for determining whether the same mechanisms underlie neural function across domains. Guided by mathematical models of categorization and long-term memory, we develop a theory and a method to test whether memory strength arises from the global similarity among neural representations. In human subjects, we find significant correlations between global similarity among activation patterns in the MTLs and both subsequent memory confidence in a recognition memory task and model-based measures of memory strength in a category learning task. Our work bridges formal cognitive theories and neuroscientific models by illustrating that the same global similarity computations underlie processing in multiple cognitive domains. Moreover, by establishing a link between neural similarity and psychological memory strength, our findings suggest that there may be an isomorphism between psychological and neural representational spaces that can be exploited to test cognitive theories at both the neural and behavioral levels. PMID:24872552

Recognition of power quality events by using multiwavelet-based neural networks

Energy Technology Data Exchange (ETDEWEB)

Kaewarsa, Suriya; Attakitmongcol, Kitti; Kulworawanichpong, Thanatchai [School of Electrical Engineering, Suranaree University of Technology, 111 University Avenue, Muang District, Nakhon Ratchasima 30000 (Thailand)

2008-05-15

Recognition of power quality events by analyzing the voltage and current waveform disturbances is a very important task for the power system monitoring. This paper presents a novel approach for the recognition of power quality disturbances using multiwavelet transform and neural networks. The proposed method employs the multiwavelet transform using multiresolution signal decomposition techniques working together with multiple neural networks using a learning vector quantization network as a powerful classifier. Various transient events are tested, such as voltage sag, swell, interruption, notching, impulsive transient, and harmonic distortion show that the classifier can detect and classify different power quality signal types efficiency. (author)

Novel mathematical neural models for visual attention

DEFF Research Database (Denmark)

Li, Kang

for the visual attention theories and spiking neuron models for single spike trains. Statistical inference and model selection are performed and various numerical methods are explored. The designed methods also give a framework for neural coding under visual attention theories. We conduct both analysis on real......Visual attention has been extensively studied in psychology, but some fundamental questions remain controversial. We focus on two questions in this study. First, we investigate how a neuron in visual cortex responds to multiple stimuli inside the receptive eld, described by either a response...... system, supported by simulation study. Finally, we present the decoding of multiple temporal stimuli under these visual attention theories, also in a realistic biophysical situation with simulations....

CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Smita K Magdum

2017-10-01

Full Text Available Construction cost prediction is important for construction firms to compete and grow in the industry. Accurate construction cost prediction in the early stage of project is important for project feasibility studies and successful completion. There are many factors that affect the cost prediction. This paper presents construction cost prediction as multiple regression model with cost of six materials as independent variables. The objective of this paper is to develop neural networks and multilayer perceptron based model for construction cost prediction. Different models of NN and MLP are developed with varying hidden layer size and hidden nodes. Four artificial neural network models and twelve multilayer perceptron models are compared. MLP and NN give better results than statistical regression method. As compared to NN, MLP works better on training dataset but fails on testing dataset. Five activation functions are tested to identify suitable function for the problem. ‘elu' transfer function gives better results than other transfer function.

Orion Handling Qualities During ISS Rendezvous and Docking

Science.gov (United States)

Hart, Jeremy J.; Stephens, J. P.; Spehar, P.; Bilimoria, K.; Foster, C.; Gonzalex, R.; Sullivan, K.; Jackson, B.; Brazzel, J.; Hart, J.

2011-01-01

The Orion spacecraft was designed to rendezvous with multiple vehicles in low earth orbit (LEO) and beyond. To perform the required rendezvous and docking task, Orion must provide enough control authority to perform coarse translational maneuvers while maintaining precision to perform the delicate docking corrections. While Orion has autonomous docking capabilities, it is expected that final approach and docking operations with the International Space Station (ISS) will initially be performed in a manual mode. A series of evaluations was conducted by NASA and Lockheed Martin at the Johnson Space Center to determine the handling qualities (HQ) of the Orion spacecraft during different docking and rendezvous conditions using the Cooper-Harper scale. This paper will address the specifics of the handling qualities methodology, vehicle configuration, scenarios flown, data collection tools, and subject ratings and comments. The initial Orion HQ assessment examined Orion docking to the ISS. This scenario demonstrates the Translational Hand Controller (THC) handling qualities of Orion. During this initial assessment, two different scenarios were evaluated. The first was a nominal docking approach to a stable ISS, with Orion initializing with relative position dispersions and a closing rate of approximately 0.1 ft/sec. The second docking scenario was identical to the first, except the attitude motion of the ISS was modeled to simulate a stress case ( 1 degree deadband per axis and 0.01 deg/sec rate deadband per axis). For both scenarios, subjects started each run on final approach at a docking port-to-port range of 20 ft. Subjects used the THC in pulse mode with cues from the docking camera image, window views, and range and range rate data displayed on the Orion display units. As in the actual design, the attitude of the Orion vehicle was held by the automated flight control system at 0.5 degree deadband per axis. Several error sources were modeled including Reaction

Development of tritium-handling technique

International Nuclear Information System (INIS)

Ohmura, Hiroshi; Hosaka, Akio; Okamoto, Takahumi

1988-01-01

The overview of developing activities for tritium-handling techniques in IHI are presented. To establish a fusion power plant, tritium handling is one of the key technologies. Recently in JAERI, conceptual design of FER (Fusion Experimental Reactor) has been carried out, and the FER system requires a processing system for a large amount of tritium. IHI concentrate on investigation of fuel gas purification, isotope separation and storage systems under contract with Toshiba Corporation. Design results of the systems and each components are reviewed. IHI has been developing fundamental handling techniques which are the ZrNi bed for hydrogen isotope storage and isotope separation by laser. The ZrNi bed with a tritium storage capacity of 1000 Ci has been constructed and recovery capability of the hydrogen isotope until 10 -4 Torr {0.013 Pa} was confirmed. In laser isotope separation, the optimum laser wave length has been determined. (author)

Handling of waste in ports

International Nuclear Information System (INIS)

Olson, P.H.

1994-01-01

The regulations governing the handling of port-generated waste are often national and/or local legislation, whereas the handling of ship-generated waste is governed by the MARPOL Convention in most parts of the world. The handling of waste consists of two main phases -collection and treatment. Waste has to be collected in every port and on board every ship, whereas generally only some wastes are treated and to a certain degree in ports and on board ships. This paper considers the different kinds of waste generated in both ports and on board ships, where and how it is generated, how it could be collected and treated. The two sources are treated together to show how some ship-generated waste may be treated in port installations primarily constructed for the treatment of the port-generated waste, making integrated use of the available treatment facilities. (author)

Magnetic resonance imaging of meniscal bucket-handle tears

International Nuclear Information System (INIS)

Dfouni, N.; Garcia, J.; Kindynis, Ph.; Bosson, D.

1997-01-01

To define MR signs of meniscal bucket-handle tears and evaluate the diagnostic efficiency of this technique. Retrospective study of 30 patients with a meniscal bucket-handle tear and 30 with a different type of tear, all proven by arthroscopy. The following MR signs of a bucket-handle tear were evaluated: 'separate meniscal fragment, 'double posterior cruciate ligament', 'snake sign' and 'double anterior horn'. A correct diagnosis of a bucket-handle tear was only made in 18/30 of patients. Several of the MR signs were seen in the same patient in 17 cases. A double posterior cruciate ligament was present only in cases of medial meniscus tears. The 12 menisci without these signs, and therefore not diagnosed as bucket-handle tears, were all classified as meniscal tears on the basis of signal extending to the meniscal surface. Nine of these were not displaced into the inter-condylar notch at arthroscopy. The interobserver agreement was excellent: kappa 0.88. The diagnosis of a bucket-handle meniscal tear, if it is displaced, can be made when one or more of the four MR evaluated signs are present. Other forms of meniscal tears are only exceptionally diagnosed as bucket-handle tears. (authors)

FY 1987 report on the result of the R and D of an automatic sewing system. Cloth handling technology; 1987 nendo jido hosei system no kenkyu kaihatsu seika hokokusho. Kiji handling gijutsu

Energy Technology Data Exchange (ETDEWEB)

NONE

1988-03-01

The paper summarized the R and D of a cloth handling technology which is an element technology of an automatic sewing system by the Automatic Sewing System Technology Research Association. The cloth handling technology is divided into the following three: (1) cloth grasp technology; (2) high-functional positioning technology; (3) element technology of soft cloth conveying technology. In (1), development of the following was conducted: vertical grasp-type module, horizontal grasp-type module, cloth handling-type module, and auxiliary device. Prototypes of each were trially manufactured for experiment on the function confirmation. In (2), the following were conducted: development of experimental models of module handler and module controller for commercialization; study of visual/tactile sense composite multiple-arm cooperative movement control. In (3), in the studies having been made so far, specifications for an experimental model for joint sawing and specifications for the conveying system were studied, and experimental models of stock module and junction module were manufactured/tested/evaluated to study confirmation of function/performance. In this fiscal year, in the light of these results obtained, prototypes of each element device were developed. At the same time, the connection of these element devices made up an experimental model for joint sawing, and effectiveness of the element technology was confirmed in the system. (NEDO)

Computerized detection of multiple sclerosis candidate regions based on a level set method using an artificial neural network

International Nuclear Information System (INIS)

Kuwazuru, Junpei; Magome, Taiki; Arimura, Hidetaka; Yamashita, Yasuo; Oki, Masafumi; Toyofuku, Fukai; Kakeda, Shingo; Yamamoto, Daisuke

2010-01-01

Yamamoto et al. developed the system for computer-aided detection of multiple sclerosis (MS) candidate regions. In a level set method in their proposed method, they employed the constant threshold value for the edge indicator function related to a speed function of the level set method. However, it would be appropriate to adjust the threshold value to each MS candidate region, because the edge magnitudes in MS candidates differ from each other. Our purpose of this study was to develop a computerized detection of MS candidate regions in MR images based on a level set method using an artificial neural network (ANN). To adjust the threshold value for the edge indicator function in the level set method to each true positive (TP) and false positive (FP) region, we constructed the ANN. The ANN could provide the suitable threshold value for each candidate region in the proposed level set method so that TP regions can be segmented and FP regions can be removed. Our proposed method detected MS regions at a sensitivity of 82.1% with 0.204 FPs per slice and similarity index of MS candidate regions was 0.717 on average. (author)

Evaluating ITER remote handling middleware concepts

International Nuclear Information System (INIS)

Koning, J.F.; Heemskerk, C.J.M.; Schoen, P.; Smedinga, D.; Boode, A.H.; Hamilton, D.T.

2013-01-01

Highlights: ► Remote Handling Study Centre: middleware system setup and modules built. ► Aligning to ITER RH Control System Layout: prototype of database, VR and simulator. ► OpenSplice DDS, ZeroC ICE messaging and object oriented middlewares reviewed. ► Windows network latency found problematic for semi-realtime control over the network. -- Abstract: Remote maintenance activities in ITER will be performed by a unique set of hardware systems, supported by an extensive software kit. A layer of middleware will manage and control a complex set of interconnections between teams of operators, hardware devices in various operating theatres, and databases managing tool and task logistics. The middleware is driven by constraints on amounts and timing of data like real-time control loops, camera images, and database access. The Remote Handling Study Centre (RHSC), located at FOM institute DIFFER, has a 4-operator work cell in an ITER relevant RH Control Room setup which connects to a virtual hot cell back-end. The centre is developing and testing flexible integration of the Control Room components, resulting in proof-of-concept tests of this middleware layer. SW components studied include generic human-machine interface software, a prototype of a RH operations management system, and a distributed virtual reality system supporting multi-screen, multi-actor, and multiple independent views. Real-time rigid body dynamics and contact interaction simulation software supports simulation of structural deformation, “augmented reality” operations and operator training. The paper presents generic requirements and conceptual design of middleware components and Operations Management System in the context of a RH Control Room work cell. The simulation software is analyzed for real-time performance and it is argued that it is critical for middleware to have complete control over the physical network to be able to guarantee bandwidth and latency to the components

Evaluating ITER remote handling middleware concepts

Energy Technology Data Exchange (ETDEWEB)

Koning, J.F., E-mail: j.f.koning@differ.nl [FOM Institute DIFFER, Association EURATOM-FOM, Partner in the Trilateral Euregio Cluster and ITER-NL, PO Box 1207, 3430 BE Nieuwegein (Netherlands); Heemskerk, C.J.M.; Schoen, P.; Smedinga, D. [Heemskerk Innovative Technology, Noordwijk (Netherlands); Boode, A.H. [University of Applied Sciences InHolland, Alkmaar (Netherlands); Hamilton, D.T. [ITER Organization, Route de Vinon sur Verdon, 13115 Saint Paul Lez Durance (France)

2013-10-15

Highlights: ► Remote Handling Study Centre: middleware system setup and modules built. ► Aligning to ITER RH Control System Layout: prototype of database, VR and simulator. ► OpenSplice DDS, ZeroC ICE messaging and object oriented middlewares reviewed. ► Windows network latency found problematic for semi-realtime control over the network. -- Abstract: Remote maintenance activities in ITER will be performed by a unique set of hardware systems, supported by an extensive software kit. A layer of middleware will manage and control a complex set of interconnections between teams of operators, hardware devices in various operating theatres, and databases managing tool and task logistics. The middleware is driven by constraints on amounts and timing of data like real-time control loops, camera images, and database access. The Remote Handling Study Centre (RHSC), located at FOM institute DIFFER, has a 4-operator work cell in an ITER relevant RH Control Room setup which connects to a virtual hot cell back-end. The centre is developing and testing flexible integration of the Control Room components, resulting in proof-of-concept tests of this middleware layer. SW components studied include generic human-machine interface software, a prototype of a RH operations management system, and a distributed virtual reality system supporting multi-screen, multi-actor, and multiple independent views. Real-time rigid body dynamics and contact interaction simulation software supports simulation of structural deformation, “augmented reality” operations and operator training. The paper presents generic requirements and conceptual design of middleware components and Operations Management System in the context of a RH Control Room work cell. The simulation software is analyzed for real-time performance and it is argued that it is critical for middleware to have complete control over the physical network to be able to guarantee bandwidth and latency to the components.

Adaptive Gain Scheduled Semiactive Vibration Control Using a Neural Network

Directory of Open Access Journals (Sweden)

Kazuhiko Hiramoto

2018-01-01

Full Text Available We propose an adaptive gain scheduled semiactive control method using an artificial neural network for structural systems subject to earthquake disturbance. In order to design a semiactive control system with high control performance against earthquakes with different time and/or frequency properties, multiple semiactive control laws with high performance for each of multiple earthquake disturbances are scheduled with an adaptive manner. Each semiactive control law to be scheduled is designed based on the output emulation approach that has been proposed by the authors. As the adaptive gain scheduling mechanism, we introduce an artificial neural network (ANN. Input signals of the ANN are the measured earthquake disturbance itself, for example, the acceleration, velocity, and displacement. The output of the ANN is the parameter for the scheduling of multiple semiactive control laws each of which has been optimized for a single disturbance. Parameters such as weight and bias in the ANN are optimized by the genetic algorithm (GA. The proposed design method is applied to semiactive control design of a base-isolated building with a semiactive damper. With simulation study, the proposed adaptive gain scheduling method realizes control performance exceeding single semiactive control optimizing the average of the control performance subject to various earthquake disturbances.

The Complexity of Dynamics in Small Neural Circuits.

Directory of Open Access Journals (Sweden)

Diego Fasoli

2016-08-01

Full Text Available Mean-field approximations are a powerful tool for studying large neural networks. However, they do not describe well the behavior of networks composed of a small number of neurons. In this case, major differences between the mean-field approximation and the real behavior of the network can arise. Yet, many interesting problems in neuroscience involve the study of mesoscopic networks composed of a few tens of neurons. Nonetheless, mathematical methods that correctly describe networks of small size are still rare, and this prevents us to make progress in understanding neural dynamics at these intermediate scales. Here we develop a novel systematic analysis of the dynamics of arbitrarily small networks composed of homogeneous populations of excitatory and inhibitory firing-rate neurons. We study the local bifurcations of their neural activity with an approach that is largely analytically tractable, and we numerically determine the global bifurcations. We find that for strong inhibition these networks give rise to very complex dynamics, caused by the formation of multiple branching solutions of the neural dynamics equations that emerge through spontaneous symmetry-breaking. This qualitative change of the neural dynamics is a finite-size effect of the network, that reveals qualitative and previously unexplored differences between mesoscopic cortical circuits and their mean-field approximation. The most important consequence of spontaneous symmetry-breaking is the ability of mesoscopic networks to regulate their degree of functional heterogeneity, which is thought to help reducing the detrimental effect of noise correlations on cortical information processing.

Integrated Payload Data Handling Systems Using Software Partitioning

Science.gov (United States)

Taylor, Alun; Hann, Mark; Wishart, Alex

2015-09-01

An integrated Payload Data Handling System (I-PDHS) is one in which multiple instruments share a central payload processor for their on-board data processing tasks. This offers a number of advantages over the conventional decentralised architecture. Savings in payload mass and power can be realised because the total processing resource is matched to the requirements, as opposed to the decentralised architecture here the processing resource is in effect the sum of all the applications. Overall development cost can be reduced using a common processor. At individual instrument level the potential benefits include a standardised application development environment, and the opportunity to run the instrument data handling application on a fully redundant and more powerful processing platform [1]. This paper describes a joint program by SCISYS UK Limited, Airbus Defence and Space, Imperial College London and RAL Space to implement a realistic demonstration of an I-PDHS using engineering models of flight instruments (a magnetometer and camera) and a laboratory demonstrator of a central payload processor which is functionally representative of a flight design. The objective is to raise the Technology Readiness Level of the centralised data processing technique by address the key areas of task partitioning to prevent fault propagation and the use of a common development process for the instrument applications. The project is supported by a UK Space Agency grant awarded under the National Space Technology Program SpaceCITI scheme. [1].

How to Handle Impasses in Bargaining.

Science.gov (United States)

Durrant, Robert E.

Guidelines in an outline format are presented to school board members and administrators on how to handle impasses in bargaining. The following two rules are given: there sometimes may be strikes, but there always will be settlements; and on the way to settlements, there always will be impasses. Suggestions for handling impasses are listed under…

Neural underpinnings of divergent production of rules in numerical analogical reasoning.

Science.gov (United States)

Wu, Xiaofei; Jung, Rex E; Zhang, Hao

2016-05-01

Creativity plays an important role in numerical problem solving. Although the neural underpinnings of creativity have been studied over decades, very little is known about neural mechanisms of the creative process that relates to numerical problem solving. In the present study, we employed a numerical analogical reasoning task with functional Magnetic Resonance Imaging (fMRI) to investigate the neural correlates of divergent production of rules in numerical analogical reasoning. Participants performed two tasks: a multiple solution analogical reasoning task and a single solution analogical reasoning task. Results revealed that divergent production of rules involves significant activations at Brodmann area (BA) 10 in the right middle frontal cortex, BA 40 in the left inferior parietal lobule, and BA 8 in the superior frontal cortex. The results suggest that right BA 10 and left BA 40 are involved in the generation of novel rules, and BA 8 is associated with the inhibition of initial rules in numerical analogical reasoning. The findings shed light on the neural mechanisms of creativity in numerical processing. Copyright © 2016 Elsevier B.V. All rights reserved.

Inactivity-induced respiratory plasticity: Protecting the drive to breathe in disorders that reduce respiratory neural activity☆

Science.gov (United States)

Strey, K.A.; Baertsch, N.A.; Baker-Herman, T.L.

2013-01-01

Multiple forms of plasticity are activated following reduced respiratory neural activity. For example, in ventilated rats, a central neural apnea elicits a rebound increase in phrenic and hypoglossal burst amplitude upon resumption of respiratory neural activity, forms of plasticity called inactivity-induced phrenic and hypoglossal motor facilitation (iPMF and iHMF), respectively. Here, we provide a conceptual framework for plasticity following reduced respiratory neural activity to guide future investigations. We review mechanisms giving rise to iPMF and iHMF, present new data suggesting that inactivity-induced plasticity is observed in inspiratory intercostals (iIMF) and point out gaps in our knowledge. We then survey conditions relevant to human health characterized by reduced respiratory neural activity and discuss evidence that inactivity-induced plasticity is elicited during these conditions. Understanding the physiological impact and circumstances in which inactivity-induced respiratory plasticity is elicited may yield novel insights into the treatment of disorders characterized by reductions in respiratory neural activity. PMID:23816599

H∞ state estimation of generalised neural networks with interval time-varying delays

Science.gov (United States)

Saravanakumar, R.; Syed Ali, M.; Cao, Jinde; Huang, He

2016-12-01

This paper focuses on studying the H∞ state estimation of generalised neural networks with interval time-varying delays. The integral terms in the time derivative of the Lyapunov-Krasovskii functional are handled by the Jensen's inequality, reciprocally convex combination approach and a new Wirtinger-based double integral inequality. A delay-dependent criterion is derived under which the estimation error system is globally asymptotically stable with H∞ performance. The proposed conditions are represented by linear matrix inequalities. Optimal H∞ norm bounds are obtained easily by solving convex problems in terms of linear matrix inequalities. The advantage of employing the proposed inequalities is illustrated by numerical examples.

Architectures and methodologies for future deployment of multi-site Zettabyte-Exascale data handling platforms

CERN Document Server

Acín, V; Boccali, T; Cancio, G; Collier, I P; Corney, D; Delaunay, B; Delfino, M; dell'Agnello, L; Flix, J; Fuhrmann, P; Gasthuber, M; Gülzow, V; Heiss, A; Lamanna, G; Macchi, P E; Maggi, M; Matthews, B; Neissner, C; Nief, J Y; Porto, M C; Sansum, A; Schulz, M; Shiers, J

2015-01-01

Several scientific fields, including Astrophysics, Astroparticle Physics, Cosmology, Nuclear and Particle Physics, and Research with Photons, are estimating that by the 2020 decade they will require data handling systems with data volumes approaching the Zettabyte distributed amongst as many as 10(18) individually addressable data objects (Zettabyte-Exascale systems). It may be convenient or necessary to deploy such systems using multiple physical sites. This paper describes the findings of a working group composed of experts from several

Architectures and methodologies for future deployment of multi-site Zettabyte-Exascale data handling platforms

International Nuclear Information System (INIS)

Acín, V; Neissner, C; Bird, I; Cancio, G; Boccali, T; Dell'Agnello, L; Maggi, M; Collier, I P; Corney, D; Matthews, B; Delaunay, B; Lamanna, G; Macchi, P-E; Nief, J-Y; Delfino, M; Flix, J; Fuhrmann, P; Gasthuber, M; Gülzow, V; Heiss, A

2015-01-01

Several scientific fields, including Astrophysics, Astroparticle Physics, Cosmology, Nuclear and Particle Physics, and Research with Photons, are estimating that by the 2020 decade they will require data handling systems with data volumes approaching the Zettabyte distributed amongst as many as 10 18 individually addressable data objects (Zettabyte-Exascale systems). It may be convenient or necessary to deploy such systems using multiple physical sites. This paper describes the findings of a working group composed of experts from several (paper)

Architectures and methodologies for future deployment of multi-site Zettabyte-Exascale data handling platforms

Science.gov (United States)

Acín, V.; Bird, I.; Boccali, T.; Cancio, G.; Collier, I. P.; Corney, D.; Delaunay, B.; Delfino, M.; dell'Agnello, L.; Flix, J.; Fuhrmann, P.; Gasthuber, M.; Gülzow, V.; Heiss, A.; Lamanna, G.; Macchi, P.-E.; Maggi, M.; Matthews, B.; Neissner, C.; Nief, J.-Y.; Porto, M. C.; Sansum, A.; Schulz, M.; Shiers, J.

2015-12-01

Several scientific fields, including Astrophysics, Astroparticle Physics, Cosmology, Nuclear and Particle Physics, and Research with Photons, are estimating that by the 2020 decade they will require data handling systems with data volumes approaching the Zettabyte distributed amongst as many as 1018 individually addressable data objects (Zettabyte-Exascale systems). It may be convenient or necessary to deploy such systems using multiple physical sites. This paper describes the findings of a working group composed of experts from several

Emulation of Neural Networks on a Nanoscale Architecture

International Nuclear Information System (INIS)

Eshaghian-Wilner, Mary M; Friesz, Aaron; Khitun, Alex; Navab, Shiva; Parker, Alice C; Wang, Kang L; Zhou, Chongwu

2007-01-01

In this paper, we propose using a nanoscale spin-wave-based architecture for implementing neural networks. We show that this architecture can efficiently realize highly interconnected neural network models such as the Hopfield model. In our proposed architecture, no point-to-point interconnection is required, so unlike standard VLSI design, no fan-in/fan-out constraint limits the interconnectivity. Using spin-waves, each neuron could broadcast to all other neurons simultaneously and similarly a neuron could concurrently receive and process multiple data. Therefore in this architecture, the total weighted sum to each neuron can be computed by the sum of the values from all the incoming waves to that neuron. In addition, using the superposition property of waves, this computation can be done in O(1) time, and neurons can update their states quite rapidly

Neural Networks

International Nuclear Information System (INIS)

Smith, Patrick I.

2003-01-01

Physicists use large detectors to measure particles created in high-energy collisions at particle accelerators. These detectors typically produce signals indicating either where ionization occurs along the path of the particle, or where energy is deposited by the particle. The data produced by these signals is fed into pattern recognition programs to try to identify what particles were produced, and to measure the energy and direction of these particles. Ideally, there are many techniques used in this pattern recognition software. One technique, neural networks, is particularly suitable for identifying what type of particle caused by a set of energy deposits. Neural networks can derive meaning from complicated or imprecise data, extract patterns, and detect trends that are too complex to be noticed by either humans or other computer related processes. To assist in the advancement of this technology, Physicists use a tool kit to experiment with several neural network techniques. The goal of this research is interface a neural network tool kit into Java Analysis Studio (JAS3), an application that allows data to be analyzed from any experiment. As the final result, a physicist will have the ability to train, test, and implement a neural network with the desired output while using JAS3 to analyze the results or output. Before an implementation of a neural network can take place, a firm understanding of what a neural network is and how it works is beneficial. A neural network is an artificial representation of the human brain that tries to simulate the learning process [5]. It is also important to think of the word artificial in that definition as computer programs that use calculations during the learning process. In short, a neural network learns by representative examples. Perhaps the easiest way to describe the way neural networks learn is to explain how the human brain functions. The human brain contains billions of neural cells that are responsible for processing

Gender-related differences in functional connectivity in multiple sclerosis

NARCIS (Netherlands)

Schoonheim, M.M.; Hulst, H.E.; Landi, D.; Ciccarelli, O.; Roosendaal, S.D.; Sanz-Arigita, E.J.; Vrenken, H.; Polman, C.H.; Stam, C.J.; Barkhof, F.; Geurts, J.J.G.

2012-01-01

Background: Gender effects are strong in multiple sclerosis (MS), with male patients showing a worse clinical outcome than female patients. Functional reorganization of neural activity may contribute to limit disability, and possible gender differences in this process may have important clinical

Measuring the frailty index of multiple myeloma cancer patients

DEFF Research Database (Denmark)

Corradini, Andrea; Bøgelund Hansen, Martin; Savic, Toma

2016-01-01

We report on a responsive web-based application that we have been developing for the cancer hospital in Vejle, Denmark. The application administers and handles systematic frailty scoring of patients with multiple myeloma and helps doctors make a more efficient and effective treatment choice. The ...

WASTE HANDLING BUILDING FIRE PROTECTION SYSTEM DESCRIPTION DOCUMENT

Energy Technology Data Exchange (ETDEWEB)

J. D. Bigbee

2000-06-21

The Waste Handling Building Fire Protection System provides the capability to detect, control, and extinguish fires and/or mitigate explosions throughout the Waste Handling Building (WHB). Fire protection includes appropriate water-based and non-water-based suppression, as appropriate, and includes the distribution and delivery systems for the fire suppression agents. The Waste Handling Building Fire Protection System includes fire or explosion detection panel(s) controlling various detectors, system actuation, annunciators, equipment controls, and signal outputs. The system interfaces with the Waste Handling Building System for mounting of fire protection equipment and components, location of fire suppression equipment, suppression agent runoff, and locating fire rated barriers. The system interfaces with the Waste Handling Building System for adequate drainage and removal capabilities of liquid runoff resulting from fire protection discharges. The system interfaces with the Waste Handling Building Electrical Distribution System for power to operate, and with the Site Fire Protection System for fire protection water supply to automatic sprinklers, standpipes, and hose stations. The system interfaces with the Site Fire Protection System for fire signal transmission outside the WHB as needed to respond to a fire emergency, and with the Waste Handling Building Ventilation System to detect smoke and fire in specific areas, to protect building high-efficiency particulate air (HEPA) filters, and to control portions of the Waste Handling Building Ventilation System for smoke management and manual override capability. The system interfaces with the Monitored Geologic Repository (MGR) Operations Monitoring and Control System for annunciation, and condition status.

WASTE HANDLING BUILDING FIRE PROTECTION SYSTEM DESCRIPTION DOCUMENT

International Nuclear Information System (INIS)

J. D. Bigbee

2000-01-01

The Waste Handling Building Fire Protection System provides the capability to detect, control, and extinguish fires and/or mitigate explosions throughout the Waste Handling Building (WHB). Fire protection includes appropriate water-based and non-water-based suppression, as appropriate, and includes the distribution and delivery systems for the fire suppression agents. The Waste Handling Building Fire Protection System includes fire or explosion detection panel(s) controlling various detectors, system actuation, annunciators, equipment controls, and signal outputs. The system interfaces with the Waste Handling Building System for mounting of fire protection equipment and components, location of fire suppression equipment, suppression agent runoff, and locating fire rated barriers. The system interfaces with the Waste Handling Building System for adequate drainage and removal capabilities of liquid runoff resulting from fire protection discharges. The system interfaces with the Waste Handling Building Electrical Distribution System for power to operate, and with the Site Fire Protection System for fire protection water supply to automatic sprinklers, standpipes, and hose stations. The system interfaces with the Site Fire Protection System for fire signal transmission outside the WHB as needed to respond to a fire emergency, and with the Waste Handling Building Ventilation System to detect smoke and fire in specific areas, to protect building high-efficiency particulate air (HEPA) filters, and to control portions of the Waste Handling Building Ventilation System for smoke management and manual override capability. The system interfaces with the Monitored Geologic Repository (MGR) Operations Monitoring and Control System for annunciation, and condition status

Quinone-induced protein handling changes: Implications for major protein handling systems in quinone-mediated toxicity

International Nuclear Information System (INIS)

Xiong, Rui; Siegel, David; Ross, David

2014-01-01

Para-quinones such as 1,4-Benzoquinone (BQ) and menadione (MD) and ortho-quinones including the oxidation products of catecholamines, are derived from xenobiotics as well as endogenous molecules. The effects of quinones on major protein handling systems in cells; the 20/26S proteasome, the ER stress response, autophagy, chaperone proteins and aggresome formation, have not been investigated in a systematic manner. Both BQ and aminochrome (AC) inhibited proteasomal activity and activated the ER stress response and autophagy in rat dopaminergic N27 cells. AC also induced aggresome formation while MD had little effect on any protein handling systems in N27 cells. The effect of NQO1 on quinone induced protein handling changes and toxicity was examined using N27 cells stably transfected with NQO1 to generate an isogenic NQO1-overexpressing line. NQO1 protected against BQ–induced apoptosis but led to a potentiation of AC- and MD-induced apoptosis. Modulation of quinone-induced apoptosis in N27 and NQO1-overexpressing cells correlated only with changes in the ER stress response and not with changes in other protein handling systems. These data suggested that NQO1 modulated the ER stress response to potentiate toxicity of AC and MD, but protected against BQ toxicity. We further demonstrated that NQO1 mediated reduction to unstable hydroquinones and subsequent redox cycling was important for the activation of the ER stress response and toxicity for both AC and MD. In summary, our data demonstrate that quinone-specific changes in protein handling are evident in N27 cells and the induction of the ER stress response is associated with quinone-mediated toxicity. - Highlights: • Unstable hydroquinones contributed to quinone-induced ER stress and toxicity

Quinone-induced protein handling changes: Implications for major protein handling systems in quinone-mediated toxicity

Energy Technology Data Exchange (ETDEWEB)

Xiong, Rui; Siegel, David; Ross, David, E-mail: david.ross@ucdenver.edu

2014-10-15

Para-quinones such as 1,4-Benzoquinone (BQ) and menadione (MD) and ortho-quinones including the oxidation products of catecholamines, are derived from xenobiotics as well as endogenous molecules. The effects of quinones on major protein handling systems in cells; the 20/26S proteasome, the ER stress response, autophagy, chaperone proteins and aggresome formation, have not been investigated in a systematic manner. Both BQ and aminochrome (AC) inhibited proteasomal activity and activated the ER stress response and autophagy in rat dopaminergic N27 cells. AC also induced aggresome formation while MD had little effect on any protein handling systems in N27 cells. The effect of NQO1 on quinone induced protein handling changes and toxicity was examined using N27 cells stably transfected with NQO1 to generate an isogenic NQO1-overexpressing line. NQO1 protected against BQ–induced apoptosis but led to a potentiation of AC- and MD-induced apoptosis. Modulation of quinone-induced apoptosis in N27 and NQO1-overexpressing cells correlated only with changes in the ER stress response and not with changes in other protein handling systems. These data suggested that NQO1 modulated the ER stress response to potentiate toxicity of AC and MD, but protected against BQ toxicity. We further demonstrated that NQO1 mediated reduction to unstable hydroquinones and subsequent redox cycling was important for the activation of the ER stress response and toxicity for both AC and MD. In summary, our data demonstrate that quinone-specific changes in protein handling are evident in N27 cells and the induction of the ER stress response is associated with quinone-mediated toxicity. - Highlights: • Unstable hydroquinones contributed to quinone-induced ER stress and toxicity.

Granular neural networks, pattern recognition and bioinformatics

CERN Document Server

Pal, Sankar K; Ganivada, Avatharam

2017-01-01

This book provides a uniform framework describing how fuzzy rough granular neural network technologies can be formulated and used in building efficient pattern recognition and mining models. It also discusses the formation of granules in the notion of both fuzzy and rough sets. Judicious integration in forming fuzzy-rough information granules based on lower approximate regions enables the network to determine the exactness in class shape as well as to handle the uncertainties arising from overlapping regions, resulting in efficient and speedy learning with enhanced performance. Layered network and self-organizing analysis maps, which have a strong potential in big data, are considered as basic modules,. The book is structured according to the major phases of a pattern recognition system (e.g., classification, clustering, and feature selection) with a balanced mixture of theory, algorithm, and application. It covers the latest findings as well as directions for future research, particularly highlighting bioinf...

Handling Kids in Crisis with Care

Science.gov (United States)

Bushinski, Cari

2018-01-01

The Handle with Care program helps schools help students who experience trauma. While at the scene of an event like a domestic violence call, drug raid, or car accident, law enforcement personnel determine the names and school of any children present. They notify that child's school to "handle ___ with care" the next day, and the school…

Survey of tritiated oil sources and handling practices

International Nuclear Information System (INIS)

Miller, J.M.

1994-08-01

Tritium interactions with oil sources (primarily associated with pumps) in tritium-handling facilities can lead to the incorporation of tritium in the oil and the production of tritiated hydrocarbons. This results in a source of radiological hazard and the need for special handling considerations during maintenance, decontamination, decommissioning and waste packaging and storage. The results of a general survey of tritiated-oil sources and their associated characteristics, handling practices, analysis techniques and waste treatment/storage methods are summarized here. Information was obtained from various tritium-handling laboratories, fusion devices, and CANDU plants. 38 refs., 1 fig

Neural Based Orthogonal Data Fitting The EXIN Neural Networks

CERN Document Server

Cirrincione, Giansalvo

2008-01-01

Written by three leaders in the field of neural based algorithms, Neural Based Orthogonal Data Fitting proposes several neural networks, all endowed with a complete theory which not only explains their behavior, but also compares them with the existing neural and traditional algorithms. The algorithms are studied from different points of view, including: as a differential geometry problem, as a dynamic problem, as a stochastic problem, and as a numerical problem. All algorithms have also been analyzed on real time problems (large dimensional data matrices) and have shown accurate solutions. Wh

Effects of handling on fear reactions in young Icelandic horses

DEFF Research Database (Denmark)

Marsbøll, Anna Feldberg; Christensen, Janne Winther

2015-01-01

To investigate the effect of a short-term standardised handling procedure on reactions of young horses in 2 types of fear tests (including and excluding human handling). Study design An experimental study with 3-year-old Icelandic horses (n = 24). Methods Handled horses (n = 12) were trained according...... to a standardised handling procedure whereas controls (n = 12) remained untrained. Behavioural and heart rate responses in a novel object test and 2 handling fear tests (HFTs) were measured. The HFTs were conducted with both an unknown (HFT-unknown) and a known handler (HFT-known). Results There was no effect...... correlated significantly between tests. Conclusions Previous handling may affect the behavioural fear response of horses when handled by their usual handler, whereas this effect did not apply to an unknown handler. Heart rates appeared unaffected by handling and may be a more reliable indicator...

A new approach to self-organizing fuzzy polynomial neural networks guided by genetic optimization

International Nuclear Information System (INIS)

Oh, Sung-Kwun; Pedrycz, Witold

2005-01-01

In this study, we introduce a new topology of Fuzzy Polynomial Neural Networks (FPNN) that is based on a genetically optimized multilayer perceptron with fuzzy polynomial neurons (FPNs) and discuss its comprehensive design methodology. The underlying methodology involves mechanisms of genetic optimization, especially genetic algorithms (GAs). Let us recall that the design of the 'conventional' FPNNs uses an extended Group Method of Data Handling (GMDH) and exploits a fixed fuzzy inference type located at each FPN of the FPNN as well as considers a fixed number of input nodes at FPNs (or nodes) located in each layer. The proposed FPNN gives rise to a structurally optimized structure and comes with a substantial level of flexibility in comparison to the one we encounter in conventional FPNNs. The structural optimization is realized via GAs whereas in the case of the parametric optimization we proceed with a standard least square method based learning. Through the consecutive process of such structural and parametric optimization, an optimized and flexible fuzzy neural network is generated in a dynamic fashion. The performance of the proposed gFPNN is quantified through experimentation that exploits standard data already being used in fuzzy modeling. The results reveal superiority of the proposed networks over the existing fuzzy and neural models

Multiple Object Permanence Tracking: Maintenance, Retrieval and Transformation of Dynamic Object Representations

OpenAIRE

Saiki, Jun

2008-01-01

Multiple object permanence tracking (MOPT) task revealed that our ability of maintaining and transforming multiple representations of complex feature-bound objects is limited to handle only 1-2 objects. Often reported capacity of 3-5 objects likely reflects memory for partial representations of objects and simple cases such as just color and their locations. Also, performance in multiple object tracking (MOT) task is likely mediated by spatiotemporal indices, not by feature-bound object repre...

Neo: an object model for handling electrophysiology data in multiple formats

Directory of Open Access Journals (Sweden)

Samuel eGarcia

2014-02-01

Full Text Available Neuroscientists use many different software tools to acquire, analyse and visualise electrophysiological signals. However, incompatible data models and file formats make it difficult to exchange data between these tools. This reduces scientific productivity, renders potentially useful analysis methods inaccessible and impedes collaboration between labs.A common representation of the core data would improve interoperability and facilitate data-sharing.To that end, we propose here a language-independent object model, named Neo, suitable for representing data acquired from electroencephalographic, intracellular, or extracellular recordings, or generated from simulations. As a concrete instantiation of this object model we have developed an open source implementation in the Python programming language.In addition to representing electrophysiology data in memory for the purposes of analysis and visualisation, the Python implementation provides a set of input/output (IO modules for reading/writing the data from/to a variety of commonly used file formats.Support is included for formats produced by most of the major manufacturers of electrophysiology recording equipment and also for more generic formats such as MATLAB.Data representation and data analysis are conceptually separate: it is easier to write robust analysis code if it is focused on analysis and relies on an underlying package to handle data representation.For that reason, and also to be as lightweight as possible, the Neo object model and the associated Python package are deliberately limited to representation of data, with no functions for data analysis or visualisation.Software for neurophysiology data analysis and visualisation built on top of Neo automatically gains the benefits of interoperability, easier data sharing and automatic format conversion; there is already a burgeoning ecosystem of such tools. We intend that Neo should become the standard basis for Python tools in

Handling wood shavings

Energy Technology Data Exchange (ETDEWEB)

1974-09-18

Details of bulk handling equipment suitable for collection and compressing wood waste from commercial joinery works are discussed. The Redler Bin Discharger ensures free flow of chips from storage silo discharge prior to compression into briquettes for use as fuel or processing into chipboard.

Non-neural androgen receptors affect sexual differentiation of brain and behaviour.

Science.gov (United States)

Monks, D A; Swift-Gallant, A

2018-02-01

Although gonadal testosterone is the principal endocrine factor that promotes masculine traits in mammals, the development of a male phenotype requires local production of both androgenic and oestrogenic signals within target tissues. Much of our knowledge concerning androgenic components of testosterone signalling in sexual differentiation comes from studies of androgen receptor (Ar) loss of function mutants. Here, we review these studies of loss of Ar function and of AR overexpression either globally or selectively in the nervous system of mice. Global and neural mutations affect socio-sexual behaviour and the neuroanatomy of these mice in a sexually differentiated manner. Some masculine traits are affected by both global and neural mutation, indicative of neural mediation, whereas other masculine traits are affected only by global mutation, indicative of an obligatory non-neural androgen target. These results support a model in which multiple sites of androgen action coordinate to produce masculine phenotypes. Furthermore, AR overexpression does not always have a phenotype opposite to that of loss of Ar function mutants, indicative of a nonlinear relationship between androgen dose and masculine phenotype in some cases. Potential mechanisms of Ar gene function in non-neural targets in producing masculine phenotypes are discussed. © 2017 British Society for Neuroendocrinology.

Advances in spectral analysis using artificial neural networks

International Nuclear Information System (INIS)

Martinez, M.; Vigneron, V.

1995-01-01

Artificial Neural networks (ANNs) have a powerful representational capacity and ability to handle with any multi-input multi-output mapping problem, e.g. in clustering, pattern recognition and identification areas, particularly when combined with some a priori knowledge and statistical point of view. They can be useful in spectrometry for the uranium enrichment methods by examples, where numerous approaches like models fitting or experts analysis are limited. These depends on the radiation measured: the methods most widely used developed over the past 20 years were based on the counting of the 185.7-keV peak with a sodium iodide scintillation detector or the 163.4-keV peak of 235 U. But these methods depend critically of the source-detector geometry. A means of improving the above conventional methods is to reduce the region of interest: it is possible by focusing at the region called KαX where the three elementary components are present. The measurement of these components in mixtures leads to the isotope ratio 235 U / ( 235 U + 236 U + 238 U). In this paper we explore statistical orientations and their consequences on 'neural' parameters. We show this decisions are induced by a log-linear model, a special case of a GLIM (Generalized LInear Model) and correspond to a Maximum Likelihood Estimation problem. (authors). 15 refs., 7 figs., 2 tabs

Application of artificial neural networks for response surface modelling in HPLC method development

Directory of Open Access Journals (Sweden)

Mohamed A. Korany

2012-01-01

Full Text Available This paper discusses the usefulness of artificial neural networks (ANNs for response surface modelling in HPLC method development. In this study, the combined effect of pH and mobile phase composition on the reversed-phase liquid chromatographic behaviour of a mixture of salbutamol (SAL and guaiphenesin (GUA, combination I, and a mixture of ascorbic acid (ASC, paracetamol (PAR and guaiphenesin (GUA, combination II, was investigated. The results were compared with those produced using multiple regression (REG analysis. To examine the respective predictive power of the regression model and the neural network model, experimental and predicted response factor values, mean of squares error (MSE, average error percentage (Er%, and coefficients of correlation (r were compared. It was clear that the best networks were able to predict the experimental responses more accurately than the multiple regression analysis.

Handling of bulk solids theory and practice

CERN Document Server

Shamlou, P A

1990-01-01

Handling of Bulk Solids provides a comprehensive discussion of the field of solids flow and handling in the process industries. Presentation of the subject follows classical lines of separate discussions for each topic, so each chapter is self-contained and can be read on its own. Topics discussed include bulk solids flow and handling properties; pressure profiles in bulk solids storage vessels; the design of storage silos for reliable discharge of bulk materials; gravity flow of particulate materials from storage vessels; pneumatic transportation of bulk solids; and the hazards of solid-mater

Torus sector handling system

International Nuclear Information System (INIS)

Grisham, D.L.

1981-01-01

A remote handling system is proposed for moving a torus sector of the accelerator from under the cryostat to a point where it can be handled by a crane and for the reverse process for a new sector. Equipment recommendations are presented, as well as possible alignment schemes. Some general comments about future remote-handling methods and the present capabilities of existing systems will also be included. The specific task to be addressed is the removal and replacement of a 425 to 450 ton torus sector. This requires a horizontal movement of approx. 10 m from a normal operating position to a point where its further transport can be accomplished by more conventional means (crane or floor transporter). The same horizontal movement is required for reinstallation, but a positional tolerance of 2 cm is required to allow reasonable fit-up for the vacuum seal from the radial frames to the torus sector. Since the sectors are not only heavy but rather tall and narrow, the transport system must provide a safe, stable, and repeatable method fo sector movement. This limited study indicates that the LAMPF-based method of transporting torus sectors offers a proven method of moving heavy items. In addition, the present state of the art in remote equipment is adequate for FED maintenance

Robust handling of dynamics and multiple failures in a diagnostic event analyzer

International Nuclear Information System (INIS)

Finch, F.E.; Kramer, M.A.

1991-01-01

MIDAS diagnoses malfunctions in continuous chemical and refinery processes using a plant-independent strategy based on qualitative and quantitative process models. MIDAS specifically addresses problems not treated in past systems, including: process dynamics with control system responses, multiple faults and induced failures, and out-of-order and false alarms. This paper discusses both the structure of the process models and the diagnostic reasoning strategies employed by MIDAS

Predicting musically induced emotions from physiological inputs: linear and neural network models.

Science.gov (United States)

Russo, Frank A; Vempala, Naresh N; Sandstrom, Gillian M

2013-01-01

Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of "felt" emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants-heart rate (HR), respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA) dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a non-linear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The non-linear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the non-linear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Predicting musically induced emotions from physiological inputs: Linear and neural network models

Directory of Open Access Journals (Sweden)

Frank A. Russo

2013-08-01

Full Text Available Listening to music often leads to physiological responses. Do these physiological responses contain sufficient information to infer emotion induced in the listener? The current study explores this question by attempting to predict judgments of 'felt' emotion from physiological responses alone using linear and neural network models. We measured five channels of peripheral physiology from 20 participants – heart rate, respiration, galvanic skin response, and activity in corrugator supercilii and zygomaticus major facial muscles. Using valence and arousal (VA dimensions, participants rated their felt emotion after listening to each of 12 classical music excerpts. After extracting features from the five channels, we examined their correlation with VA ratings, and then performed multiple linear regression to see if a linear relationship between the physiological responses could account for the ratings. Although linear models predicted a significant amount of variance in arousal ratings, they were unable to do so with valence ratings. We then used a neural network to provide a nonlinear account of the ratings. The network was trained on the mean ratings of eight of the 12 excerpts and tested on the remainder. Performance of the neural network confirms that physiological responses alone can be used to predict musically induced emotion. The nonlinear model derived from the neural network was more accurate than linear models derived from multiple linear regression, particularly along the valence dimension. A secondary analysis allowed us to quantify the relative contributions of inputs to the nonlinear model. The study represents a novel approach to understanding the complex relationship between physiological responses and musically induced emotion.

Multiple neural states of representation in short-term memory? It’s a matter of attention

Science.gov (United States)

LaRocque, Joshua J.; Lewis-Peacock, Jarrod A.; Postle, Bradley R.

2014-01-01

Short-term memory (STM) refers to the capacity-limited retention of information over a brief period of time, and working memory (WM) refers to the manipulation and use of that information to guide behavior. In recent years it has become apparent that STM and WM interact and overlap with other cognitive processes, including attention (the selection of a subset of information for further processing) and long-term memory (LTM—the encoding and retention of an effectively unlimited amount of information for a much longer period of time). Broadly speaking, there have been two classes of memory models: systems models, which posit distinct stores for STM and LTM (Atkinson and Shiffrin, 1968; Baddeley and Hitch, 1974); and state-based models, which posit a common store with different activation states corresponding to STM and LTM (Cowan, 1995; McElree, 1996; Oberauer, 2002). In this paper, we will focus on state-based accounts of STM. First, we will consider several theoretical models that postulate, based on considerable behavioral evidence, that information in STM can exist in multiple representational states. We will then consider how neural data from recent studies of STM can inform and constrain these theoretical models. In the process we will highlight the inferential advantage of multivariate, information-based analyses of neuroimaging data (fMRI and electroencephalography (EEG)) over conventional activation-based analysis approaches (Postle, in press). We will conclude by addressing lingering questions regarding the fractionation of STM, highlighting differences between the attention to information vs. the retention of information during brief memory delays. PMID:24478671

Capacity of Human Dental Follicle Cells to Differentiate into Neural Cells In Vitro

Directory of Open Access Journals (Sweden)

Shingo Kanao

2017-01-01

Full Text Available The dental follicle is an ectomesenchymal tissue surrounding the developing tooth germ. Human dental follicle cells (hDFCs have the capacity to commit to differentiation into multiple cell types. Here we investigated the capacity of hDFCs to differentiate into neural cells and the efficiency of a two-step strategy involving floating neurosphere-like bodies for neural differentiation. Undifferentiated hDFCs showed a spindle-like morphology and were positive for neural markers such as nestin, β-III-tubulin, and S100β. The cellular morphology of several cells was neuronal-like including branched dendrite-like processes and neurites. Next, hDFCs were used for neurosphere formation in serum-free medium containing basic fibroblast growth factor, epidermal growth factor, and B27 supplement. The number of cells with neuronal-like morphology and that were strongly positive for neural markers increased with sphere formation. Gene expression of neural markers also increased in hDFCs with sphere formation. Next, gene expression of neural markers was examined in hDFCs during neuronal differentiation after sphere formation. Expression of Musashi-1 and Musashi-2, MAP2, GFAP, MBP, and SOX10 was upregulated in hDFCs undergoing neuronal differentiation via neurospheres, whereas expression of nestin and β-III-tubulin was downregulated. In conclusion, hDFCs may be another optimal source of neural/glial cells for cell-based therapies to treat neurological diseases.

Getting to grips with remote handling and robotics

Energy Technology Data Exchange (ETDEWEB)

Mosey, D [Ontario Hydro, Toronto (Canada)

1984-12-01

A report on the Canadian Nuclear Society Conference on robotics and remote handling in the nuclear industry, September 1984. Remote handling in reactor operations, particularly in the Candu reactors is discussed, and the costs and benefits of use of remote handling equipment are considered. Steam generator inspection and repair is an area in which practical application of robotic technology has made a major advance.

Neural Dynamics Associated with Semantic and Episodic Memory for Faces: Evidence from Multiple Frequency Bands

Science.gov (United States)

Zion-Golumbic, Elana; Kutas, Marta; Bentin, Shlomo

2010-01-01

Prior semantic knowledge facilitates episodic recognition memory for faces. To examine the neural manifestation of the interplay between semantic and episodic memory, we investigated neuroelectric dynamics during the creation (study) and the retrieval (test) of episodic memories for famous and nonfamous faces. Episodic memory effects were evident…

Concurrent heterogeneous neural model simulation on real-time neuromimetic hardware.

Science.gov (United States)

Rast, Alexander; Galluppi, Francesco; Davies, Sergio; Plana, Luis; Patterson, Cameron; Sharp, Thomas; Lester, David; Furber, Steve

2011-11-01

Dedicated hardware is becoming increasingly essential to simulate emerging very-large-scale neural models. Equally, however, it needs to be able to support multiple models of the neural dynamics, possibly operating simultaneously within the same system. This may be necessary either to simulate large models with heterogeneous neural types, or to simplify simulation and analysis of detailed, complex models in a large simulation by isolating the new model to a small subpopulation of a larger overall network. The SpiNNaker neuromimetic chip is a dedicated neural processor able to support such heterogeneous simulations. Implementing these models on-chip uses an integrated library-based tool chain incorporating the emerging PyNN interface that allows a modeller to input a high-level description and use an automated process to generate an on-chip simulation. Simulations using both LIF and Izhikevich models demonstrate the ability of the SpiNNaker system to generate and simulate heterogeneous networks on-chip, while illustrating, through the network-scale effects of wavefront synchronisation and burst gating, methods that can provide effective behavioural abstractions for large-scale hardware modelling. SpiNNaker's asynchronous virtual architecture permits greater scope for model exploration, with scalable levels of functional and temporal abstraction, than conventional (or neuromorphic) computing platforms. The complete system illustrates a potential path to understanding the neural model of computation, by building (and breaking) neural models at various scales, connecting the blocks, then comparing them against the biology: computational cognitive neuroscience. Copyright © 2011 Elsevier Ltd. All rights reserved.

Bitter taste stimuli induce differential neural codes in mouse brain.

Directory of Open Access Journals (Sweden)

David M Wilson

Full Text Available A growing literature suggests taste stimuli commonly classified as "bitter" induce heterogeneous neural and perceptual responses. Here, the central processing of bitter stimuli was studied in mice with genetically controlled bitter taste profiles. Using these mice removed genetic heterogeneity as a factor influencing gustatory neural codes for bitter stimuli. Electrophysiological activity (spikes was recorded from single neurons in the nucleus tractus solitarius during oral delivery of taste solutions (26 total, including concentration series of the bitter tastants quinine, denatonium benzoate, cycloheximide, and sucrose octaacetate (SOA, presented to the whole mouth for 5 s. Seventy-nine neurons were sampled; in many cases multiple cells (2 to 5 were recorded from a mouse. Results showed bitter stimuli induced variable gustatory activity. For example, although some neurons responded robustly to quinine and cycloheximide, others displayed concentration-dependent activity (p<0.05 to quinine but not cycloheximide. Differential activity to bitter stimuli was observed across multiple neurons recorded from one animal in several mice. Across all cells, quinine and denatonium induced correlated spatial responses that differed (p<0.05 from those to cycloheximide and SOA. Modeling spatiotemporal neural ensemble activity revealed responses to quinine/denatonium and cycloheximide/SOA diverged during only an early, at least 1 s wide period of the taste response. Our findings highlight how temporal features of sensory processing contribute differences among bitter taste codes and build on data suggesting heterogeneity among "bitter" stimuli, data that challenge a strict monoguesia model for the bitter quality.

Modeling of methane emissions using artificial neural network approach

Directory of Open Access Journals (Sweden)

Stamenković Lidija J.

2015-01-01

Full Text Available The aim of this study was to develop a model for forecasting CH4 emissions at the national level, using Artificial Neural Networks (ANN with broadly available sustainability, economical and industrial indicators as their inputs. ANN modeling was performed using two different types of architecture; a Backpropagation Neural Network (BPNN and a General Regression Neural Network (GRNN. A conventional multiple linear regression (MLR model was also developed in order to compare model performance and assess which model provides the best results. ANN and MLR models were developed and tested using the same annual data for 20 European countries. The ANN model demonstrated very good performance, significantly better than the MLR model. It was shown that a forecast of CH4 emissions at the national level using the ANN model can be made successfully and accurately for a future period of up to two years, thereby opening the possibility to apply such a modeling technique which can be used to support the implementation of sustainable development strategies and environmental management policies. [Projekat Ministarstva nauke Republike Srbije, br. 172007

Hardware Implementation of Artificial Neural Network for Data Ciphering

Directory of Open Access Journals (Sweden)

Sahar L. Kadoory

2016-10-01

Full Text Available This paper introduces the design and realization of multiple blocks ciphering techniques on the FPGA (Field Programmable Gate Arrays. A back propagation neural networks have been built for substitution, permutation and XOR blocks ciphering using Neural Network Toolbox in MATLAB program. They are trained to encrypt the data, after obtaining the suitable weights, biases, activation function and layout. Afterward, they are described using VHDL and implemented using Xilinx Spartan-3E FPGA using two approaches: serial and parallel versions. The simulation results obtained with Xilinx ISE 9.2i software. The numerical precision is chosen carefully when implementing the Neural Network on FPGA. Obtained results from the hardware designs show accurate numeric values to cipher the data. As expected, the synthesis results indicate that the serial version requires less area resources than the parallel version. As, the data throughput in parallel version is higher than the serial version in rang between (1.13-1.5 times. Also, a slight difference can be observed in the maximum frequency.

Containment control of networked autonomous underwater vehicles: A predictor-based neural DSC design.

Science.gov (United States)

Peng, Zhouhua; Wang, Dan; Wang, Wei; Liu, Lu

2015-11-01

This paper investigates the containment control problem of networked autonomous underwater vehicles in the presence of model uncertainty and unknown ocean disturbances. A predictor-based neural dynamic surface control design method is presented to develop the distributed adaptive containment controllers, under which the trajectories of follower vehicles nearly converge to the dynamic convex hull spanned by multiple reference trajectories over a directed network. Prediction errors, rather than tracking errors, are used to update the neural adaptation laws, which are independent of the tracking error dynamics, resulting in two time-scales to govern the entire system. The stability property of the closed-loop network is established via Lyapunov analysis, and transient property is quantified in terms of L2 norms of the derivatives of neural weights, which are shown to be smaller than the classical neural dynamic surface control approach. Comparative studies are given to show the substantial improvements of the proposed new method. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Multiple Linear Regression Model Based on Neural Network and Its Application in the MBR Simulation

Directory of Open Access Journals (Sweden)

Chunqing Li

2012-01-01

Full Text Available The computer simulation of the membrane bioreactor MBR has become the research focus of the MBR simulation. In order to compensate for the defects, for example, long test period, high cost, invisible equipment seal, and so forth, on the basis of conducting in-depth study of the mathematical model of the MBR, combining with neural network theory, this paper proposed a three-dimensional simulation system for MBR wastewater treatment, with fast speed, high efficiency, and good visualization. The system is researched and developed with the hybrid programming of VC++ programming language and OpenGL, with a multifactor linear regression model of affecting MBR membrane fluxes based on neural network, applying modeling method of integer instead of float and quad tree recursion. The experiments show that the three-dimensional simulation system, using the above models and methods, has the inspiration and reference for the future research and application of the MBR simulation technology.

Level of Processing Modulates the Neural Correlates of Emotional Memory Formation

Science.gov (United States)

Ritchey, Maureen; LaBar, Kevin S.; Cabeza, Roberto

2011-01-01

Emotion is known to influence multiple aspects of memory formation, including the initial encoding of the memory trace and its consolidation over time. However, the neural mechanisms whereby emotion impacts memory encoding remain largely unexplored. The present study used a levels-of-processing manipulation to characterize the impact of emotion on…

Handling knowledge on osteoporosis - a qualitative study

DEFF Research Database (Denmark)

Nielsen, Dorthe; Huniche, Lotte; Brixen, Kim

2013-01-01

Scand J Caring Sci; 2012 Handling knowledge on osteoporosis - a qualitative study The aim of this qualitative study was to increase understanding of the importance of osteoporosis information and knowledge for patients' ways of handling osteoporosis in their everyday lives. Interviews were...

Matrix regulators in neural stem cell functions.

Science.gov (United States)

Wade, Anna; McKinney, Andrew; Phillips, Joanna J

2014-08-01

Neural stem/progenitor cells (NSPCs) reside within a complex and dynamic extracellular microenvironment, or niche. This niche regulates fundamental aspects of their behavior during normal neural development and repair. Precise yet dynamic regulation of NSPC self-renewal, migration, and differentiation is critical and must persist over the life of an organism. In this review, we summarize some of the major components of the NSPC niche and provide examples of how cues from the extracellular matrix regulate NSPC behaviors. We use proteoglycans to illustrate the many diverse roles of the niche in providing temporal and spatial regulation of cellular behavior. The NSPC niche is comprised of multiple components that include; soluble ligands, such as growth factors, morphogens, chemokines, and neurotransmitters, the extracellular matrix, and cellular components. As illustrated by proteoglycans, a major component of the extracellular matrix, the NSPC, niche provides temporal and spatial regulation of NSPC behaviors. The factors that control NSPC behavior are vital to understand as we attempt to modulate normal neural development and repair. Furthermore, an improved understanding of how these factors regulate cell proliferation, migration, and differentiation, crucial for malignancy, may reveal novel anti-tumor strategies. This article is part of a Special Issue entitled Matrix-mediated cell behaviour and properties. Copyright © 2014 Elsevier B.V. All rights reserved.

Neural networks

International Nuclear Information System (INIS)

Denby, Bruce; Lindsey, Clark; Lyons, Louis

1992-01-01

The 1980s saw a tremendous renewal of interest in 'neural' information processing systems, or 'artificial neural networks', among computer scientists and computational biologists studying cognition. Since then, the growth of interest in neural networks in high energy physics, fueled by the need for new information processing technologies for the next generation of high energy proton colliders, can only be described as explosive

A neural network clustering algorithm for the ATLAS silicon pixel detector

CERN Document Server

Aad, Georges; Abdallah, Jalal; Abdel Khalek, Samah; Abdinov, Ovsat; Aben, Rosemarie; Abi, Babak; Abolins, Maris; AbouZeid, Ossama; Abramowicz, Halina; Abreu, Henso; Abreu, Ricardo; Abulaiti, Yiming; Acharya, Bobby Samir; Adamczyk, Leszek; Adams, David; Adelman, Jahred; Adomeit, Stefanie; Adye, Tim; Agatonovic-Jovin, Tatjana; Aguilar-Saavedra, Juan Antonio; Agustoni, Marco; Ahlen, Steven; Ahmadov, Faig; Aielli, Giulio; Akerstedt, Henrik; Åkesson, Torsten Paul Ake; Akimoto, Ginga; Akimov, Andrei; Alberghi, Gian Luigi; Albert, Justin; Albrand, Solveig; Alconada Verzini, Maria Josefina; Aleksa, Martin; Aleksandrov, Igor; Alexa, Calin; Alexander, Gideon; Alexandre, Gauthier; Alexopoulos, Theodoros; Alhroob, Muhammad; Alimonti, Gianluca; Alio, Lion; Alison, John; Allbrooke, Benedict; Allison, Lee John; Allport, Phillip; Almond, John; Aloisio, Alberto; Alonso, Alejandro; Alonso, Francisco; Alpigiani, Cristiano; Altheimer, Andrew David; Alvarez Gonzalez, Barbara; Alviggi, Mariagrazia; Amako, Katsuya; Amaral Coutinho, Yara; Amelung, Christoph; Amidei, Dante; Amor Dos Santos, Susana Patricia; Amorim, Antonio; Amoroso, Simone; Amram, Nir; Amundsen, Glenn; Anastopoulos, Christos; Ancu, Lucian Stefan; Andari, Nansi; Andeen, Timothy; Anders, Christoph Falk; Anders, Gabriel; Anderson, Kelby; Andreazza, Attilio; Andrei, George Victor; Anduaga, Xabier; Angelidakis, Stylianos; Angelozzi, Ivan; Anger, Philipp; Angerami, Aaron; Anghinolfi, Francis; Anisenkov, Alexey; Anjos, Nuno; Annovi, Alberto; Antonaki, Ariadni; Antonelli, Mario; Antonov, Alexey; Antos, Jaroslav; Anulli, Fabio; Aoki, Masato; Aperio Bella, Ludovica; Apolle, Rudi; Arabidze, Giorgi; Aracena, Ignacio; Arai, Yasuo; Araque, Juan Pedro; Arce, Ayana; Arguin, Jean-Francois; Argyropoulos, Spyridon; Arik, Metin; Armbruster, Aaron James; Arnaez, Olivier; Arnal, Vanessa; Arnold, Hannah; Arratia, Miguel; Arslan, Ozan; Artamonov, Andrei; Artoni, Giacomo; Asai, Shoji; Asbah, Nedaa; Ashkenazi, Adi; Åsman, Barbro; Asquith, Lily; Assamagan, Ketevi; Astalos, Robert; Atkinson, Markus; Atlay, Naim Bora; Auerbach, Benjamin; Augsten, Kamil; Aurousseau, Mathieu; Avolio, Giuseppe; Azuelos, Georges; Azuma, Yuya; Baak, Max; Baas, Alessandra; Bacci, Cesare; Bachacou, Henri; Bachas, Konstantinos; Backes, Moritz; Backhaus, Malte; Backus Mayes, John; Badescu, Elisabeta; Bagiacchi, Paolo; Bagnaia, Paolo; Bai, Yu; Bain, Travis; Baines, John; Baker, Oliver Keith; Balek, Petr; Balli, Fabrice; Banas, Elzbieta; Banerjee, Swagato; Bannoura, Arwa A E; Bansal, Vikas; Bansil, Hardeep Singh; Barak, Liron; Baranov, Sergei; Barberio, Elisabetta Luigia; Barberis, Dario; Barbero, Marlon; Barillari, Teresa; Barisonzi, Marcello; Barklow, Timothy; Barlow, Nick; Barnett, Bruce; Barnett, Michael; Barnovska, Zuzana; Baroncelli, Antonio; Barone, Gaetano; Barr, Alan; Barreiro, Fernando; Barreiro Guimarães da Costa, João; Bartoldus, Rainer; Barton, Adam Edward; Bartos, Pavol; Bartsch, Valeria; Bassalat, Ahmed; Basye, Austin; Bates, Richard; Batkova, Lucia; Batley, Richard; Battaglia, Marco; Battistin, Michele; Bauer, Florian; Bawa, Harinder Singh; Beau, Tristan; Beauchemin, Pierre-Hugues; Beccherle, Roberto; Bechtle, Philip; Beck, Hans Peter; Becker, Anne Kathrin; Becker, Sebastian; Beckingham, Matthew; Becot, Cyril; Beddall, Andrew; Beddall, Ayda; Bedikian, Sourpouhi; Bednyakov, Vadim; Bee, Christopher; Beemster, Lars; Beermann, Thomas; Begel, Michael; Behr, Katharina; Belanger-Champagne, Camille; Bell, Paul; Bell, William; Bella, Gideon; Bellagamba, Lorenzo; Bellerive, Alain; Bellomo, Massimiliano; Belotskiy, Konstantin; Beltramello, Olga; Benary, Odette; Benchekroun, Driss; Bendtz, Katarina; Benekos, Nektarios; Benhammou, Yan; Benhar Noccioli, Eleonora; Benitez Garcia, Jorge-Armando; Benjamin, Douglas; Bensinger, James; Benslama, Kamal; Bentvelsen, Stan; Berge, David; Bergeaas Kuutmann, Elin; Berger, Nicolas; Berghaus, Frank; Beringer, Jürg; Bernard, Clare; Bernat, Pauline; Bernius, Catrin; Bernlochner, Florian Urs; Berry, Tracey; Berta, Peter; Bertella, Claudia; Bertoli, Gabriele; Bertolucci, Federico; Bertsche, David; Besana, Maria Ilaria; Besjes, Geert-Jan; Bessidskaia, Olga; Bessner, Martin Florian; Besson, Nathalie; Betancourt, Christopher; Bethke, Siegfried; Bhimji, Wahid; Bianchi, Riccardo-Maria; Bianchini, Louis; Bianco, Michele; Biebel, Otmar; Bieniek, Stephen Paul; Bierwagen, Katharina; Biesiada, Jed; Biglietti, Michela; Bilbao De Mendizabal, Javier; Bilokon, Halina; Bindi, Marcello; Binet, Sebastien; Bingul, Ahmet; Bini, Cesare; Black, Curtis; Black, James; Black, Kevin; Blackburn, Daniel; Blair, Robert; Blanchard, Jean-Baptiste; Blazek, Tomas; Bloch, Ingo; Blocker, Craig; Blum, Walter; Blumenschein, Ulrike; Bobbink, Gerjan; Bobrovnikov, Victor; Bocchetta, Simona Serena; Bocci, Andrea; Bock, Christopher; Boddy, Christopher Richard; Boehler, Michael; Boek, Thorsten Tobias; Bogaerts, Joannes Andreas; Bogdanchikov, Alexander; Bogouch, Andrei; Bohm, Christian; Bohm, Jan; Boisvert, Veronique; Bold, Tomasz; Boldea, Venera; Boldyrev, Alexey; Bomben, Marco; Bona, Marcella; Boonekamp, Maarten; Borisov, Anatoly; Borissov, Guennadi; Borri, Marcello; Borroni, Sara; Bortfeldt, Jonathan; Bortolotto, Valerio; Bos, Kors; Boscherini, Davide; Bosman, Martine; Boterenbrood, Hendrik; Boudreau, Joseph; Bouffard, Julian; Bouhova-Thacker, Evelina Vassileva; Boumediene, Djamel Eddine; Bourdarios, Claire; Bousson, Nicolas; Boutouil, Sara; Boveia, Antonio; Boyd, James; Boyko, Igor; Bracinik, Juraj; Brandt, Andrew; Brandt, Gerhard; Brandt, Oleg; Bratzler, Uwe; Brau, Benjamin; Brau, James; Braun, Helmut; Brazzale, Simone Federico; Brelier, Bertrand; Brendlinger, Kurt; Brennan, Amelia Jean; Brenner, Richard; Bressler, Shikma; Bristow, Kieran; Bristow, Timothy Michael; Britton, Dave; Brochu, Frederic; Brock, Ian; Brock, Raymond; Bromberg, Carl; Bronner, Johanna; Brooijmans, Gustaaf; Brooks, Timothy; Brooks, William; Brosamer, Jacquelyn; Brost, Elizabeth; Brown, Jonathan; Bruckman de Renstrom, Pawel; Bruncko, Dusan; Bruneliere, Renaud; Brunet, Sylvie; Bruni, Alessia; Bruni, Graziano; Bruschi, Marco; Bryngemark, Lene; Buanes, Trygve; Buat, Quentin; Bucci, Francesca; Buchholz, Peter; Buckingham, Ryan; Buckley, Andrew; Buda, Stelian Ioan; Budagov, Ioulian; Buehrer, Felix; Bugge, Lars; Bugge, Magnar Kopangen; Bulekov, Oleg; Bundock, Aaron Colin; Burckhart, Helfried; Burdin, Sergey; Burghgrave, Blake; Burke, Stephen; Burmeister, Ingo; Busato, Emmanuel; Büscher, Daniel; Büscher, Volker; Bussey, Peter; Buszello, Claus-Peter; Butler, Bart; Butler, John; Butt, Aatif Imtiaz; Buttar, Craig; Butterworth, Jonathan; Butti, Pierfrancesco; Buttinger, William; Buzatu, Adrian; Byszewski, Marcin; Cabrera Urbán, Susana; Caforio, Davide; Cakir, Orhan; Calafiura, Paolo; Calandri, Alessandro; Calderini, Giovanni; Calfayan, Philippe; Calkins, Robert; Caloba, Luiz; Calvet, David; Calvet, Samuel; Camacho Toro, Reina; Camarda, Stefano; Cameron, David; Caminada, Lea Michaela; Caminal Armadans, Roger; Campana, Simone; Campanelli, Mario; Campoverde, Angel; Canale, Vincenzo; Canepa, Anadi; Cano Bret, Marc; Cantero, Josu; Cantrill, Robert; Cao, Tingting; Capeans Garrido, Maria Del Mar; Caprini, Irinel; Caprini, Mihai; Capua, Marcella; Caputo, Regina; Cardarelli, Roberto; Carli, Tancredi; Carlino, Gianpaolo; Carminati, Leonardo; Caron, Sascha; Carquin, Edson; Carrillo-Montoya, German D; Carter, Janet; Carvalho, João; Casadei, Diego; Casado, Maria Pilar; Casolino, Mirkoantonio; Castaneda-Miranda, Elizabeth; Castelli, Angelantonio; Castillo Gimenez, Victoria; Castro, Nuno Filipe; Catastini, Pierluigi; Catinaccio, Andrea; Catmore, James; Cattai, Ariella; Cattani, Giordano; Caughron, Seth; Cavaliere, Viviana; Cavalli, Donatella; Cavalli-Sforza, Matteo; Cavasinni, Vincenzo; Ceradini, Filippo; Cerio, Benjamin; Cerny, Karel; Santiago Cerqueira, Augusto; Cerri, Alessandro; Cerrito, Lucio; Cerutti, Fabio; Cerv, Matevz; Cervelli, Alberto; Cetin, Serkant Ali; Chafaq, Aziz; Chakraborty, Dhiman; Chalupkova, Ina; Chang, Philip; Chapleau, Bertrand; Chapman, John Derek; Charfeddine, Driss; Charlton, Dave; Chau, Chav Chhiv; Chavez Barajas, Carlos Alberto; Cheatham, Susan; Chegwidden, Andrew; Chekanov, Sergei; Chekulaev, Sergey; Chelkov, Gueorgui; Chelstowska, Magda Anna; Chen, Chunhui; Chen, Hucheng; Chen, Karen; Chen, Liming; Chen, Shenjian; Chen, Xin; Chen, Yujiao; Cheng, Hok Chuen; Cheng, Yangyang; Cheplakov, Alexander; Cherkaoui El Moursli, Rajaa; Chernyatin, Valeriy; Cheu, Elliott; Chevalier, Laurent; Chiarella, Vitaliano; Chiefari, Giovanni; Childers, John Taylor; Chilingarov, Alexandre; Chiodini, Gabriele; Chisholm, Andrew; Chislett, Rebecca Thalatta; Chitan, Adrian; Chizhov, Mihail; Chouridou, Sofia; Chow, Bonnie Kar Bo; Chromek-Burckhart, Doris; Chu, Ming-Lee; Chudoba, Jiri; Chwastowski, Janusz; Chytka, Ladislav; Ciapetti, Guido; Ciftci, Abbas Kenan; Ciftci, Rena; Cinca, Diane; Cindro, Vladimir; Ciocio, Alessandra; Cirkovic, Predrag; Citron, Zvi Hirsh; Citterio, Mauro; Ciubancan, Mihai; Clark, Allan G; Clark, Philip James; Clarke, Robert; Cleland, Bill; Clemens, Jean-Claude; Clement, Christophe; Coadou, Yann; Cobal, Marina; Coccaro, Andrea; Cochran, James H; Coffey, Laurel; Cogan, Joshua Godfrey; Coggeshall, James; Cole, Brian; Cole, Stephen; Colijn, Auke-Pieter; Collot, Johann; Colombo, Tommaso; Colon, German; Compostella, Gabriele; Conde Muiño, Patricia; Coniavitis, Elias; Conidi, Maria Chiara; Connell, Simon Henry; Connelly, Ian; Consonni, Sofia Maria; Consorti, Valerio; Constantinescu, Serban; Conta, Claudio; Conti, Geraldine; Conventi, Francesco; Cooke, Mark; Cooper, Ben; Cooper-Sarkar, Amanda; Cooper-Smith, Neil; Copic, Katherine; Cornelissen, Thijs; Corradi, Massimo; Corriveau, Francois; Corso-Radu, Alina; Cortes-Gonzalez, Arely; Cortiana, Giorgio; Costa, Giuseppe; Costa, María José; Costanzo, Davide; Côté, David; Cottin, Giovanna; Cowan, Glen; Cox, Brian; Cranmer, Kyle; Cree, Graham; Crépé-Renaudin, Sabine; Crescioli, Francesco; Cribbs, Wayne Allen; Crispin Ortuzar, Mireia; Cristinziani, Markus; Croft, Vince; Crosetti, Giovanni; Cuciuc, Constantin-Mihai; Cuhadar Donszelmann, Tulay; Cummings, Jane; Curatolo, Maria; Cuthbert, Cameron; Czirr, Hendrik; Czodrowski, Patrick; Czyczula, Zofia; D'Auria, Saverio; D'Onofrio, Monica; Da Cunha Sargedas De Sousa, Mario Jose; Da Via, Cinzia; Dabrowski, Wladyslaw; Dafinca, Alexandru; Dai, Tiesheng; Dale, Orjan; Dallaire, Frederick; Dallapiccola, Carlo; Dam, Mogens; Daniells, Andrew Christopher; Dano Hoffmann, Maria; Dao, Valerio; Darbo, Giovanni; Darmora, Smita; Dassoulas, James; Dattagupta, Aparajita; Davey, Will; David, Claire; Davidek, Tomas; Davies, Eleanor; Davies, Merlin; Davignon, Olivier; Davison, Adam; Davison, Peter; Davygora, Yuriy; Dawe, Edmund; Dawson, Ian; Daya-Ishmukhametova, Rozmin; De, Kaushik; de Asmundis, Riccardo; De Castro, Stefano; De Cecco, Sandro; De Groot, Nicolo; de Jong, Paul; De la Torre, Hector; De Lorenzi, Francesco; De Nooij, Lucie; De Pedis, Daniele; De Salvo, Alessandro; De Sanctis, Umberto; De Santo, Antonella; De Vivie De Regie, Jean-Baptiste; Dearnaley, William James; Debbe, Ramiro; Debenedetti, Chiara; Dechenaux, Benjamin; Dedovich, Dmitri; Deigaard, Ingrid; Del Peso, Jose; Del Prete, Tarcisio; Deliot, Frederic; Delitzsch, Chris Malena; Deliyergiyev, Maksym; Dell'Acqua, Andrea; Dell'Asta, Lidia; Dell'Orso, Mauro; Della Pietra, Massimo; della Volpe, Domenico; Delmastro, Marco; Delsart, Pierre-Antoine; Deluca, Carolina; Demers, Sarah; Demichev, Mikhail; Demilly, Aurelien; Denisov, Sergey; Derendarz, Dominik; Derkaoui, Jamal Eddine; Derue, Frederic; Dervan, Paul; Desch, Klaus Kurt; Deterre, Cecile; Deviveiros, Pier-Olivier; Dewhurst, Alastair; Dhaliwal, Saminder; Di Ciaccio, Anna; Di Ciaccio, Lucia; Di Domenico, Antonio; Di Donato, Camilla; Di Girolamo, Alessandro; Di Girolamo, Beniamino; Di Mattia, Alessandro; Di Micco, Biagio; Di Nardo, Roberto; Di Simone, Andrea; Di Sipio, Riccardo; Di Valentino, David; Dias, Flavia; Diaz, Marco Aurelio; Diehl, Edward; Dietrich, Janet; Dietzsch, Thorsten; Diglio, Sara; Dimitrievska, Aleksandra; Dingfelder, Jochen; Dionisi, Carlo; Dita, Petre; Dita, Sanda; Dittus, Fridolin; Djama, Fares; Djobava, Tamar; Barros do Vale, Maria Aline; Do Valle Wemans, André; Doan, Thi Kieu Oanh; Dobos, Daniel; Doglioni, Caterina; Doherty, Tom; Dohmae, Takeshi; Dolejsi, Jiri; Dolezal, Zdenek; Dolgoshein, Boris; Donadelli, Marisilvia; Donati, Simone; Dondero, Paolo; Donini, Julien; Dopke, Jens; Doria, Alessandra; Dova, Maria-Teresa; Doyle, Tony; Dris, Manolis; Dubbert, Jörg; Dube, Sourabh; Dubreuil, Emmanuelle; Duchovni, Ehud; Duckeck, Guenter; Ducu, Otilia Anamaria; Duda, Dominik; Dudarev, Alexey; Dudziak, Fanny; Duflot, Laurent; Duguid, Liam; Dührssen, Michael; Dunford, Monica; Duran Yildiz, Hatice; Düren, Michael; Durglishvili, Archil; Dwuznik, Michal; Dyndal, Mateusz; Ebke, Johannes; Edson, William; Edwards, Nicholas Charles; Ehrenfeld, Wolfgang; Eifert, Till; Eigen, Gerald; Einsweiler, Kevin; Ekelof, Tord; El Kacimi, Mohamed; Ellert, Mattias; Elles, Sabine; Ellinghaus, Frank; Ellis, Nicolas; Elmsheuser, Johannes; Elsing, Markus; Emeliyanov, Dmitry; Enari, Yuji; Endner, Oliver Chris; Endo, Masaki; Engelmann, Roderich; Erdmann, Johannes; Ereditato, Antonio; Eriksson, Daniel; Ernis, Gunar; Ernst, Jesse; Ernst, Michael; Ernwein, Jean; Errede, Deborah; Errede, Steven; Ertel, Eugen; Escalier, Marc; Esch, Hendrik; Escobar, Carlos; Esposito, Bellisario; Etienvre, Anne-Isabelle; Etzion, Erez; Evans, Hal; Ezhilov, Alexey; Fabbri, Laura; Facini, Gabriel; Fakhrutdinov, Rinat; Falciano, Speranza; Falla, Rebecca Jane; Faltova, Jana; Fang, Yaquan; Fanti, Marcello; Farbin, Amir; Farilla, Addolorata; Farooque, Trisha; Farrell, Steven; Farrington, Sinead; Farthouat, Philippe; Fassi, Farida; Fassnacht, Patrick; Fassouliotis, Dimitrios; Favareto, Andrea; Fayard, Louis; Federic, Pavol; Fedin, Oleg; Fedorko, Wojciech; Fehling-Kaschek, Mirjam; Feigl, Simon; Feligioni, Lorenzo; Feng, Cunfeng; Feng, Eric; Feng, Haolu; Fenyuk, Alexander; Fernandez Perez, Sonia; Ferrag, Samir; Ferrando, James; Ferrari, Arnaud; Ferrari, Pamela; Ferrari, Roberto; Ferreira de Lima, Danilo Enoque; Ferrer, Antonio; Ferrere, Didier; Ferretti, Claudio; Ferretto Parodi, Andrea; Fiascaris, Maria; Fiedler, Frank; Filipčič, Andrej; Filipuzzi, Marco; Filthaut, Frank; Fincke-Keeler, Margret; Finelli, Kevin Daniel; Fiolhais, Miguel; Fiorini, Luca; Firan, Ana; Fischer, Adam; Fischer, Julia; Fisher, Wade Cameron; Fitzgerald, Eric Andrew; Flechl, Martin; Fleck, Ivor; Fleischmann, Philipp; Fleischmann, Sebastian; Fletcher, Gareth Thomas; Fletcher, Gregory; Flick, Tobias; Floderus, Anders; Flores Castillo, Luis; Florez Bustos, Andres Carlos; Flowerdew, Michael; Formica, Andrea; Forti, Alessandra; Fortin, Dominique; Fournier, Daniel; Fox, Harald; Fracchia, Silvia; Francavilla, Paolo; Franchini, Matteo; Franchino, Silvia; Francis, David; Franklin, Melissa; Franz, Sebastien; Fraternali, Marco; French, Sky; Friedrich, Conrad; Friedrich, Felix; Froidevaux, Daniel; Frost, James; Fukunaga, Chikara; Fullana Torregrosa, Esteban; Fulsom, Bryan Gregory; Fuster, Juan; Gabaldon, Carolina; Gabizon, Ofir; Gabrielli, Alessandro; Gabrielli, Andrea; Gadatsch, Stefan; Gadomski, Szymon; Gagliardi, Guido; Gagnon, Pauline; Galea, Cristina; Galhardo, Bruno; Gallas, Elizabeth; Gallo, Valentina Santina; Gallop, Bruce; Gallus, Petr; Galster, Gorm Aske Gram Krohn; Gan, KK; Gandrajula, Reddy Pratap; Gao, Jun; Gao, Yongsheng; Garay Walls, Francisca; Garberson, Ford; García, Carmen; García Navarro, José Enrique; Garcia-Sciveres, Maurice; Gardner, Robert; Garelli, Nicoletta; Garonne, Vincent; Gatti, Claudio; Gaudio, Gabriella; Gaur, Bakul; Gauthier, Lea; Gauzzi, Paolo; Gavrilenko, Igor; Gay, Colin; Gaycken, Goetz; Gazis, Evangelos; Ge, Peng; Gecse, Zoltan; Gee, Norman; Geerts, Daniël Alphonsus Adrianus; Geich-Gimbel, Christoph; Gellerstedt, Karl; Gemme, Claudia; Gemmell, Alistair; Genest, Marie-Hélène; Gentile, Simonetta; George, Matthias; George, Simon; Gerbaudo, Davide; Gershon, Avi; Ghazlane, Hamid; Ghodbane, Nabil; Giacobbe, Benedetto; Giagu, Stefano; Giangiobbe, Vincent; Giannetti, Paola; Gianotti, Fabiola; Gibbard, Bruce; Gibson, Stephen; Gilchriese, Murdock; Gillam, Thomas; Gillberg, Dag; Gilles, Geoffrey; Gingrich, Douglas; Giokaris, Nikos; Giordani, MarioPaolo; Giordano, Raffaele; Giorgi, Filippo Maria; Giorgi, Francesco Michelangelo; Giraud, Pierre-Francois; Giugni, Danilo; Giuliani, Claudia; Giulini, Maddalena; Gjelsten, Børge Kile; Gkaitatzis, Stamatios; Gkialas, Ioannis; Gladilin, Leonid; Glasman, Claudia; Glatzer, Julian; Glaysher, Paul; Glazov, Alexandre; Glonti, George; Goblirsch-Kolb, Maximilian; Goddard, Jack Robert; Godfrey, Jennifer; Godlewski, Jan; Goeringer, Christian; Goldfarb, Steven; Golling, Tobias; Golubkov, Dmitry; Gomes, Agostinho; Gomez Fajardo, Luz Stella; Gonçalo, Ricardo; Goncalves Pinto Firmino Da Costa, Joao; Gonella, Laura; González de la Hoz, Santiago; Gonzalez Parra, Garoe; Gonzalez-Sevilla, Sergio; Goossens, Luc; Gorbounov, Petr Andreevich; Gordon, Howard; Gorelov, Igor; Gorini, Benedetto; Gorini, Edoardo; Gorišek, Andrej; Gornicki, Edward; Goshaw, Alfred; Gössling, Claus; Gostkin, Mikhail Ivanovitch; Gouighri, Mohamed; Goujdami, Driss; Goulette, Marc Phillippe; Goussiou, Anna; Goy, Corinne; Gozpinar, Serdar; Grabas, Herve Marie Xavier; Graber, Lars; Grabowska-Bold, Iwona; Grafström, Per; Grahn, Karl-Johan; Gramling, Johanna; Gramstad, Eirik; Grancagnolo, Sergio; Grassi, Valerio; Gratchev, Vadim; Gray, Heather; Graziani, Enrico; Grebenyuk, Oleg; Greenwood, Zeno Dixon; Gregersen, Kristian; Gregor, Ingrid-Maria; Grenier, Philippe; Griffiths, Justin; Grillo, Alexander; Grimm, Kathryn; Grinstein, Sebastian; Gris, Philippe Luc Yves; Grishkevich, Yaroslav; Grivaz, Jean-Francois; Grohs, Johannes Philipp; Grohsjean, Alexander; Gross, Eilam; Grosse-Knetter, Joern; Grossi, Giulio Cornelio; Groth-Jensen, Jacob; Grout, Zara Jane; Guan, Liang; Guescini, Francesco; Guest, Daniel; Gueta, Orel; Guicheney, Christophe; Guido, Elisa; Guillemin, Thibault; Guindon, Stefan; Gul, Umar; Gumpert, Christian; Gunther, Jaroslav; Guo, Jun; Gupta, Shaun; Gutierrez, Phillip; Gutierrez Ortiz, Nicolas Gilberto; Gutschow, Christian; Guttman, Nir; Guyot, Claude; Gwenlan, Claire; Gwilliam, Carl; Haas, Andy; Haber, Carl; Hadavand, Haleh Khani; Haddad, Nacim; Haefner, Petra; Hageböck, Stephan; Hajduk, Zbigniew; Hakobyan, Hrachya; Haleem, Mahsana; Hall, David; Halladjian, Garabed; Hamacher, Klaus; Hamal, Petr; Hamano, Kenji; Hamer, Matthias; Hamilton, Andrew; Hamilton, Samuel; Hamnett, Phillip George; Han, Liang; Hanagaki, Kazunori; Hanawa, Keita; Hance, Michael; Hanke, Paul; Hanna, Remie; Hansen, Jørgen Beck; Hansen, Jorn Dines; Hansen, Peter Henrik; Hara, Kazuhiko; Hard, Andrew; Harenberg, Torsten; Hariri, Faten; Harkusha, Siarhei; Harper, Devin; Harrington, Robert; Harris, Orin; Harrison, Paul Fraser; Hartjes, Fred; Hasegawa, Satoshi; Hasegawa, Yoji; Hasib, A; Hassani, Samira; Haug, Sigve; Hauschild, Michael; Hauser, Reiner; Havranek, Miroslav; Hawkes, Christopher; Hawkings, Richard John; Hawkins, Anthony David; Hayashi, Takayasu; Hayden, Daniel; Hays, Chris; Hayward, Helen; Haywood, Stephen; Head, Simon; Heck, Tobias; Hedberg, Vincent; Heelan, Louise; Heim, Sarah; Heim, Timon; Heinemann, Beate; Heinrich, Lukas; Hejbal, Jiri; Helary, Louis; Heller, Claudio; Heller, Matthieu; Hellman, Sten; Hellmich, Dennis; Helsens, Clement; Henderson, James; Henderson, Robert; Heng, Yang; Hengler, Christopher; Henrichs, Anna; Henriques Correia, Ana Maria; Henrot-Versille, Sophie; Hensel, Carsten; Herbert, Geoffrey Henry; Hernández Jiménez, Yesenia; Herrberg-Schubert, Ruth; Herten, Gregor; Hertenberger, Ralf; Hervas, Luis; Hesketh, Gavin Grant; Hessey, Nigel; Hickling, Robert; Higón-Rodriguez, Emilio; Hill, Ewan; Hill, John; Hiller, Karl Heinz; Hillert, Sonja; Hillier, Stephen; Hinchliffe, Ian; Hines, Elizabeth; Hirose, Minoru; Hirschbuehl, Dominic; Hobbs, John; Hod, Noam; Hodgkinson, Mark; Hodgson, Paul; Hoecker, Andreas; Hoeferkamp, Martin; Hoffman, Julia; Hoffmann, Dirk; Hofmann, Julia Isabell; Hohlfeld, Marc; Holmes, Tova Ray; Hong, Tae Min; Hooft van Huysduynen, Loek; Hostachy, Jean-Yves; Hou, Suen; Hoummada, Abdeslam; Howard, Jacob; Howarth, James; Hrabovsky, Miroslav; Hristova, Ivana; Hrivnac, Julius; Hryn'ova, Tetiana; Hsu, Catherine; Hsu, Pai-hsien Jennifer; Hsu, Shih-Chieh; Hu, Diedi; Hu, Xueye; Huang, Yanping; Hubacek, Zdenek; Hubaut, Fabrice; Huegging, Fabian; Huffman, Todd Brian; Hughes, Emlyn; Hughes, Gareth; Huhtinen, Mika; Hülsing, Tobias Alexander; Hurwitz, Martina; Huseynov, Nazim; Huston, Joey; Huth, John; Iacobucci, Giuseppe; Iakovidis, Georgios; Ibragimov, Iskander; Iconomidou-Fayard, Lydia; Ideal, Emma; Iengo, Paolo; Igonkina, Olga; Iizawa, Tomoya; Ikegami, Yoichi; Ikematsu, Katsumasa; Ikeno, Masahiro; Ilchenko, Iurii; Iliadis, Dimitrios; Ilic, Nikolina; Inamaru, Yuki; Ince, Tayfun; Ioannou, Pavlos; Iodice, Mauro; Iordanidou, Kalliopi; Ippolito, Valerio; Irles Quiles, Adrian; Isaksson, Charlie; Ishino, Masaya; Ishitsuka, Masaki; Ishmukhametov, Renat; Issever, Cigdem; Istin, Serhat; Iturbe Ponce, Julia Mariana; Iuppa, Roberto; Ivarsson, Jenny; Iwanski, Wieslaw; Iwasaki, Hiroyuki; Izen, Joseph; Izzo, Vincenzo; Jackson, Brett; Jackson, Matthew; Jackson, Paul; Jaekel, Martin; Jain, Vivek; Jakobs, Karl; Jakobsen, Sune; Jakoubek, Tomas; Jakubek, Jan; Jamin, David Olivier; Jana, Dilip; Jansen, Eric; Jansen, Hendrik; Janssen, Jens; Janus, Michel; Jarlskog, Göran; Javadov, Namig; Javůrek, Tomáš; Jeanty, Laura; Jejelava, Juansher; Jeng, Geng-yuan; Jennens, David; Jenni, Peter; Jentzsch, Jennifer; Jeske, Carl; Jézéquel, Stéphane; Ji, Haoshuang; Ji, Weina; Jia, Jiangyong; Jiang, Yi; Jimenez Belenguer, Marcos; Jin, Shan; Jinaru, Adam; Jinnouchi, Osamu; Joergensen, Morten Dam; Johansson, Erik; Johansson, Per; Johns, Kenneth; Jon-And, Kerstin; Jones, Graham; Jones, Roger; Jones, Tim; Jongmanns, Jan; Jorge, Pedro; Joshi, Kiran Daniel; Jovicevic, Jelena; Ju, Xiangyang; Jung, Christian; Jungst, Ralph Markus; Jussel, Patrick; Juste Rozas, Aurelio; Kaci, Mohammed; Kaczmarska, Anna; Kado, Marumi; Kagan, Harris; Kagan, Michael; Kajomovitz, Enrique; Kalderon, Charles William; Kama, Sami; Kamenshchikov, Andrey; Kanaya, Naoko; Kaneda, Michiru; Kaneti, Steven; Kantserov, Vadim; Kanzaki, Junichi; Kaplan, Benjamin; Kapliy, Anton; Kar, Deepak; Karakostas, Konstantinos; Karastathis, Nikolaos; Karnevskiy, Mikhail; Karpov, Sergey; Karpova, Zoya; Karthik, Krishnaiyengar; Kartvelishvili, Vakhtang; Karyukhin, Andrey; Kashif, Lashkar; Kasieczka, Gregor; Kass, Richard; Kastanas, Alex; Kataoka, Yousuke; Katre, Akshay; Katzy, Judith; Kaushik, Venkatesh; Kawagoe, Kiyotomo; Kawamoto, Tatsuo; Kawamura, Gen; Kazama, Shingo; Kazanin, Vassili; Kazarinov, Makhail; Keeler, Richard; Kehoe, Robert; Keil, Markus; Keller, John; Kempster, Jacob Julian; Keoshkerian, Houry; Kepka, Oldrich; Kerševan, Borut Paul; Kersten, Susanne; Kessoku, Kohei; Keung, Justin; Khalil-zada, Farkhad; Khandanyan, Hovhannes; Khanov, Alexander; Khodinov, Alexander; Khomich, Andrei; Khoo, Teng Jian; Khoriauli, Gia; Khoroshilov, Andrey; Khovanskiy, Valery; Khramov, Evgeniy; Khubua, Jemal; Kim, Hee Yeun; Kim, Hyeon Jin; Kim, Shinhong; Kimura, Naoki; Kind, Oliver; King, Barry; King, Matthew; King, Robert Steven Beaufoy; King, Samuel Burton; Kirk, Julie; Kiryunin, Andrey; Kishimoto, Tomoe; Kisielewska, Danuta; Kiss, Florian; Kittelmann, Thomas; Kiuchi, Kenji; Kladiva, Eduard; Klein, Max; Klein, Uta; Kleinknecht, Konrad; Klimek, Pawel; Klimentov, Alexei; Klingenberg, Reiner; Klinger, Joel Alexander; Klioutchnikova, Tatiana; Klok, Peter; Kluge, Eike-Erik; Kluit, Peter; Kluth, Stefan; Kneringer, Emmerich; Knoops, Edith; Knue, Andrea; Kobayashi, Dai; Kobayashi, Tomio; Kobel, Michael; Kocian, Martin; Kodys, Peter; Koevesarki, Peter; Koffas, Thomas; Koffeman, Els; Kogan, Lucy Anne; Kohlmann, Simon; Kohout, Zdenek; Kohriki, Takashi; Koi, Tatsumi; Kolanoski, Hermann; Koletsou, Iro; Koll, James; Komar, Aston; Komori, Yuto; Kondo, Takahiko; Kondrashova, Nataliia; Köneke, Karsten; König, Adriaan; König, Sebastian; Kono, Takanori; Konoplich, Rostislav; Konstantinidis, Nikolaos; Kopeliansky, Revital; Koperny, Stefan; Köpke, Lutz; Kopp, Anna Katharina; Korcyl, Krzysztof; Kordas, Kostantinos; Korn, Andreas; Korol, Aleksandr; Korolkov, Ilya; Korolkova, Elena; Korotkov, Vladislav; Kortner, Oliver; Kortner, Sandra; Kostyukhin, Vadim; Kotov, Vladislav; Kotwal, Ashutosh; Kourkoumelis, Christine; Kouskoura, Vasiliki; Koutsman, Alex; Kowalewski, Robert Victor; Kowalski, Tadeusz; Kozanecki, Witold; Kozhin, Anatoly; Kral, Vlastimil; Kramarenko, Viktor; Kramberger, Gregor; Krasnopevtsev, Dimitriy; Krasny, Mieczyslaw Witold; Krasznahorkay, Attila; Kraus, Jana; Kravchenko, Anton; Kreiss, Sven; Kretz, Moritz; Kretzschmar, Jan; Kreutzfeldt, Kristof; Krieger, Peter; Kroeninger, Kevin; Kroha, Hubert; Kroll, Joe; Kroseberg, Juergen; Krstic, Jelena; Kruchonak, Uladzimir; Krüger, Hans; Kruker, Tobias; Krumnack, Nils; Krumshteyn, Zinovii; Kruse, Amanda; Kruse, Mark; Kruskal, Michael; Kubota, Takashi; Kuday, Sinan; Kuehn, Susanne; Kugel, Andreas; Kuhl, Andrew; Kuhl, Thorsten; Kukhtin, Victor; Kulchitsky, Yuri; Kuleshov, Sergey; Kuna, Marine; Kunkle, Joshua; Kupco, Alexander; Kurashige, Hisaya; Kurochkin, Yurii; Kurumida, Rie; Kus, Vlastimil; Kuwertz, Emma Sian; Kuze, Masahiro; Kvita, Jiri; La Rosa, Alessandro; La Rotonda, Laura; Lacasta, Carlos; Lacava, Francesco; Lacey, James; Lacker, Heiko; Lacour, Didier; Lacuesta, Vicente Ramón; Ladygin, Evgueni; Lafaye, Remi; Laforge, Bertrand; Lagouri, Theodota; Lai, Stanley; Laier, Heiko; Lambourne, Luke; Lammers, Sabine; Lampen, Caleb; Lampl, Walter; Lançon, Eric; Landgraf, Ulrich; Landon, Murrough; Lang, Valerie Susanne; Lankford, Andrew; Lanni, Francesco; Lantzsch, Kerstin; Laplace, Sandrine; Lapoire, Cecile; Laporte, Jean-Francois; Lari, Tommaso; Lassnig, Mario; Laurelli, Paolo; Lavrijsen, Wim; Law, Alexander; Laycock, Paul; Le, Bao Tran; Le Dortz, Olivier; Le Guirriec, Emmanuel; Le Menedeu, Eve; LeCompte, Thomas; Ledroit-Guillon, Fabienne Agnes Marie; Lee, Claire Alexandra; Lee, Hurng-Chun; Lee, Jason; Lee, Shih-Chang; Lee, Lawrence; Lefebvre, Guillaume; Lefebvre, Michel; Legger, Federica; Leggett, Charles; Lehan, Allan; Lehmacher, Marc; Lehmann Miotto, Giovanna; Lei, Xiaowen; Leight, William Axel; Leisos, Antonios; Leister, Andrew Gerard; Leite, Marco Aurelio Lisboa; Leitner, Rupert; Lellouch, Daniel; Lemmer, Boris; Leney, Katharine; Lenz, Tatjana; Lenzen, Georg; Lenzi, Bruno; Leone, Robert; Leone, Sandra; Leonhardt, Kathrin; Leonidopoulos, Christos; Leontsinis, Stefanos; Leroy, Claude; Lester, Christopher; Lester, Christopher Michael; Levchenko, Mikhail; Levêque, Jessica; Levin, Daniel; Levinson, Lorne; Levy, Mark; Lewis, Adrian; Lewis, George; Leyko, Agnieszka; Leyton, Michael; Li, Bing; Li, Bo; Li, Haifeng; Li, Ho Ling; Li, Lei; Li, Liang; Li, Shu; Li, Yichen; Liang, Zhijun; Liao, Hongbo; Liberti, Barbara; Lichard, Peter; Lie, Ki; Liebal, Jessica; Liebig, Wolfgang; Limbach, Christian; Limosani, Antonio; Lin, Simon; Lin, Tai-Hua; Linde, Frank; Lindquist, Brian Edward; Linnemann, James; Lipeles, Elliot; Lipniacka, Anna; Lisovyi, Mykhailo; Liss, Tony; Lissauer, David; Lister, Alison; Litke, Alan; Liu, Bo; Liu, Dong; Liu, Jianbei; Liu, Kun; Liu, Lulu; Liu, Miaoyuan; Liu, Minghui; Liu, Yanwen; Livan, Michele; Livermore, Sarah; Lleres, Annick; Llorente Merino, Javier; Lloyd, Stephen; Lo Sterzo, Francesco; Lobodzinska, Ewelina; Loch, Peter; Lockman, William; Loddenkoetter, Thomas; Loebinger, Fred; Loevschall-Jensen, Ask Emil; Loginov, Andrey; Loh, Chang Wei; Lohse, Thomas; Lohwasser, Kristin; Lokajicek, Milos; Lombardo, Vincenzo Paolo; Long, Brian Alexander; Long, Jonathan; Long, Robin Eamonn; Lopes, Lourenco; Lopez Mateos, David; Lopez Paredes, Brais; Lopez Paz, Ivan; Lorenz, Jeanette; Lorenzo Martinez, Narei; Losada, Marta; Loscutoff, Peter; Lou, XinChou; Lounis, Abdenour; Love, Jeremy; Love, Peter; Lowe, Andrew; Lu, Feng; Lubatti, Henry; Luci, Claudio; Lucotte, Arnaud; Luehring, Frederick; Lukas, Wolfgang; Luminari, Lamberto; Lundberg, Olof; Lund-Jensen, Bengt; Lungwitz, Matthias; Lynn, David; Lysak, Roman; Lytken, Else; Ma, Hong; Ma, Lian Liang; Maccarrone, Giovanni; Macchiolo, Anna; Machado Miguens, Joana; Macina, Daniela; Madaffari, Daniele; Madar, Romain; Maddocks, Harvey Jonathan; Mader, Wolfgang; Madsen, Alexander; Maeno, Mayuko; Maeno, Tadashi; Magradze, Erekle; Mahboubi, Kambiz; Mahlstedt, Joern; Mahmoud, Sara; Maiani, Camilla; Maidantchik, Carmen; Maier, Andreas Alexander; Maio, Amélia; Majewski, Stephanie; Makida, Yasuhiro; Makovec, Nikola; Mal, Prolay; Malaescu, Bogdan; Malecki, Pawel; Maleev, Victor; Malek, Fairouz; Mallik, Usha; Malon, David; Malone, Caitlin; Maltezos, Stavros; Malyshev, Vladimir; Malyukov, Sergei; Mamuzic, Judita; Mandelli, Beatrice; Mandelli, Luciano; Mandić, Igor; Mandrysch, Rocco; Maneira, José; Manfredini, Alessandro; Manhaes de Andrade Filho, Luciano; Manjarres Ramos, Joany Andreina; Mann, Alexander; Manning, Peter; Manousakis-Katsikakis, Arkadios; Mansoulie, Bruno; Mantifel, Rodger; Mapelli, Livio; March, Luis; Marchand, Jean-Francois; Marchiori, Giovanni; Marcisovsky, Michal; Marino, Christopher; Marjanovic, Marija; Marques, Carlos; Marroquim, Fernando; Marsden, Stephen Philip; Marshall, Zach; Marti, Lukas Fritz; Marti-Garcia, Salvador; Martin, Brian; Martin, Brian Thomas; Martin, Tim; Martin, Victoria Jane; Martin dit Latour, Bertrand; Martinez, Homero; Martinez, Mario; Martin-Haugh, Stewart; Martyniuk, Alex; Marx, Marilyn; Marzano, Francesco; Marzin, Antoine; Masetti, Lucia; Mashimo, Tetsuro; Mashinistov, Ruslan; Masik, Jiri; Maslennikov, Alexey; Massa, Ignazio; Massol, Nicolas; Mastrandrea, Paolo; Mastroberardino, Anna; Masubuchi, Tatsuya; Mättig, Peter; Mattmann, Johannes; Maurer, Julien; Maxfield, Stephen; Maximov, Dmitriy; Mazini, Rachid; Mazzaferro, Luca; Mc Goldrick, Garrin; Mc Kee, Shawn Patrick; McCarn, Allison; McCarthy, Robert; McCarthy, Tom; McCubbin, Norman; McFarlane, Kenneth; Mcfayden, Josh; Mchedlidze, Gvantsa; McMahon, Steve; McPherson, Robert; Meade, Andrew; Mechnich, Joerg; Medinnis, Michael; Meehan, Samuel; Mehlhase, Sascha; Mehta, Andrew; Meier, Karlheinz; Meineck, Christian; Meirose, Bernhard; Melachrinos, Constantinos; Mellado Garcia, Bruce Rafael; Meloni, Federico; Mengarelli, Alberto; Menke, Sven; Meoni, Evelin; Mercurio, Kevin Michael; Mergelmeyer, Sebastian; Meric, Nicolas; Mermod, Philippe; Merola, Leonardo; Meroni, Chiara; Merritt, Frank; Merritt, Hayes; Messina, Andrea; Metcalfe, Jessica; Mete, Alaettin Serhan; Meyer, Carsten; Meyer, Christopher; Meyer, Jean-Pierre; Meyer, Jochen; Middleton, Robin; Migas, Sylwia; Mijović, Liza; Mikenberg, Giora; Mikestikova, Marcela; Mikuž, Marko; Milic, Adriana; Miller, David; Mills, Corrinne; Milov, Alexander; Milstead, David; Milstein, Dmitry; Minaenko, Andrey; Minashvili, Irakli; Mincer, Allen; Mindur, Bartosz; Mineev, Mikhail; Ming, Yao; Mir, Lluisa-Maria; Mirabelli, Giovanni; Mitani, Takashi; Mitrevski, Jovan; Mitsou, Vasiliki A; Mitsui, Shingo; Miucci, Antonio; Miyagawa, Paul; Mjörnmark, Jan-Ulf; Moa, Torbjoern; Mochizuki, Kazuya; Mohapatra, Soumya; Mohr, Wolfgang; Molander, Simon; Moles-Valls, Regina; Mönig, Klaus; Monini, Caterina; Monk, James; Monnier, Emmanuel; Montejo Berlingen, Javier; Monticelli, Fernando; Monzani, Simone; Moore, Roger; Moraes, Arthur; Morange, Nicolas; Moreno, Deywis; Moreno Llácer, María; Morettini, Paolo; Morgenstern, Marcus; Morii, Masahiro; Moritz, Sebastian; Morley, Anthony Keith; Mornacchi, Giuseppe; Morris, John; Morvaj, Ljiljana; Moser, Hans-Guenther; Mosidze, Maia; Moss, Josh; Motohashi, Kazuki; Mount, Richard; Mountricha, Eleni; Mouraviev, Sergei; Moyse, Edward; Muanza, Steve; Mudd, Richard; Mueller, Felix; Mueller, James; Mueller, Klemens; Mueller, Thibaut; Mueller, Timo; Muenstermann, Daniel; Munwes, Yonathan; Murillo Quijada, Javier Alberto; Murray, Bill; Musheghyan, Haykuhi; Musto, Elisa; Myagkov, Alexey; Myska, Miroslav; Nackenhorst, Olaf; Nadal, Jordi; Nagai, Koichi; Nagai, Ryo; Nagai, Yoshikazu; Nagano, Kunihiro; Nagarkar, Advait; Nagasaka, Yasushi; Nagel, Martin; Nairz, Armin Michael; Nakahama, Yu; Nakamura, Koji; Nakamura, Tomoaki; Nakano, Itsuo; Namasivayam, Harisankar; Nanava, Gizo; Narayan, Rohin; Nattermann, Till; Naumann, Thomas; Navarro, Gabriela; Nayyar, Ruchika; Neal, Homer; Nechaeva, Polina; Neep, Thomas James; Nef, Pascal Daniel; Negri, Andrea; Negri, Guido; Negrini, Matteo; Nektarijevic, Snezana; Nelson, Andrew; Nelson, Timothy Knight; Nemecek, Stanislav; Nemethy, Peter; Nepomuceno, Andre Asevedo; Nessi, Marzio; Neubauer, Mark; Neumann, Manuel; Neves, Ricardo; Nevski, Pavel; Newman, Paul; Nguyen, Duong Hai; Nickerson, Richard; Nicolaidou, Rosy; Nicquevert, Bertrand; Nielsen, Jason; Nikiforou, Nikiforos; Nikiforov, Andriy; Nikolaenko, Vladimir; Nikolic-Audit, Irena; Nikolics, Katalin; Nikolopoulos, Konstantinos; Nilsson, Paul; Ninomiya, Yoichi; Nisati, Aleandro; Nisius, Richard; Nobe, Takuya; Nodulman, Lawrence; Nomachi, Masaharu; Nomidis, Ioannis; Norberg, Scarlet; Nordberg, Markus; Novgorodova, Olga; Nowak, Sebastian; Nozaki, Mitsuaki; Nozka, Libor; Ntekas, Konstantinos; Nunes Hanninger, Guilherme; Nunnemann, Thomas; Nurse, Emily; Nuti, Francesco; O'Brien, Brendan Joseph; O'grady, Fionnbarr; O'Neil, Dugan; O'Shea, Val; Oakham, Gerald; Oberlack, Horst; Obermann, Theresa; Ocariz, Jose; Ochi, Atsuhiko; Ochoa, Ines; Oda, Susumu; Odaka, Shigeru; Ogren, Harold; Oh, Alexander; Oh, Seog; Ohm, Christian; Ohman, Henrik; Ohshima, Takayoshi; Okamura, Wataru; Okawa, Hideki; Okumura, Yasuyuki; Okuyama, Toyonobu; Olariu, Albert; Olchevski, Alexander; Olivares Pino, Sebastian Andres; Oliveira Damazio, Denis; Oliver Garcia, Elena; Olszewski, Andrzej; Olszowska, Jolanta; Onofre, António; Onyisi, Peter; Oram, Christopher; Oreglia, Mark; Oren, Yona; Orestano, Domizia; Orlando, Nicola; Oropeza Barrera, Cristina; Orr, Robert; Osculati, Bianca; Ospanov, Rustem; Otero y Garzon, Gustavo; Otono, Hidetoshi; Ouchrif, Mohamed; Ouellette, Eric; Ould-Saada, Farid; Ouraou, Ahmimed; Oussoren, Koen Pieter; Ouyang, Qun; Ovcharova, Ana; Owen, Mark; Ozcan, Veysi Erkcan; Ozturk, Nurcan; Pachal, Katherine; Pacheco Pages, Andres; Padilla Aranda, Cristobal; Pagáčová, Martina; Pagan Griso, Simone; Paganis, Efstathios; Pahl, Christoph; Paige, Frank; Pais, Preema; Pajchel, Katarina; Palacino, Gabriel; Palestini, Sandro; Palka, Marek; Pallin, Dominique; Palma, Alberto; Palmer, Jody; Pan, Yibin; Panagiotopoulou, Evgenia; Panduro Vazquez, William; Pani, Priscilla; Panikashvili, Natalia; Panitkin, Sergey; Pantea, Dan; Paolozzi, Lorenzo; Papadopoulou, Theodora; Papageorgiou, Konstantinos; Paramonov, Alexander; Paredes Hernandez, Daniela; Parker, Michael Andrew; Parodi, Fabrizio; Parsons, John; Parzefall, Ulrich; Pasqualucci, Enrico; Passaggio, Stefano; Passeri, Antonio; Pastore, Fernanda; Pastore, Francesca; Pásztor, Gabriella; Pataraia, Sophio; Patel, Nikhul; Pater, Joleen; Patricelli, Sergio; Pauly, Thilo; Pearce, James; Pedersen, Maiken; Pedraza Lopez, Sebastian; Pedro, Rute; Peleganchuk, Sergey; Pelikan, Daniel; Peng, Haiping; Penning, Bjoern; Penwell, John; Perepelitsa, Dennis; Perez Codina, Estel; Pérez García-Estañ, María Teresa; Perez Reale, Valeria; Perini, Laura; Pernegger, Heinz; Perrino, Roberto; Peschke, Richard; Peshekhonov, Vladimir; Peters, Krisztian; Peters, Yvonne; Petersen, Brian; Petersen, Troels; Petit, Elisabeth; Petridis, Andreas; Petridou, Chariclia; Petrolo, Emilio; Petrucci, Fabrizio; Pettersson, Nora Emilia; Pezoa, Raquel; Phillips, Peter William; Piacquadio, Giacinto; Pianori, Elisabetta; Picazio, Attilio; Piccaro, Elisa; Piccinini, Maurizio; Piegaia, Ricardo; Pignotti, David; Pilcher, James; Pilkington, Andrew; Pina, João Antonio; Pinamonti, Michele; Pinder, Alex; Pinfold, James; Pingel, Almut; Pinto, Belmiro; Pires, Sylvestre; Pitt, Michael; Pizio, Caterina; Plazak, Lukas; Pleier, Marc-Andre; Pleskot, Vojtech; Plotnikova, Elena; Plucinski, Pawel; Poddar, Sahill; Podlyski, Fabrice; Poettgen, Ruth; Poggioli, Luc; Pohl, David-leon; Pohl, Martin; Polesello, Giacomo; Policicchio, Antonio; Polifka, Richard; Polini, Alessandro; Pollard, Christopher Samuel; Polychronakos, Venetios; Pommès, Kathy; Pontecorvo, Ludovico; Pope, Bernard; Popeneciu, Gabriel Alexandru; Popovic, Dragan; Poppleton, Alan; Portell Bueso, Xavier; Pospisil, Stanislav; Potamianos, Karolos; Potrap, Igor; Potter, Christina; Potter, Christopher; Poulard, Gilbert; Poveda, Joaquin; Pozdnyakov, Valery; Pralavorio, Pascal; Pranko, Aliaksandr; Prasad, Srivas; Pravahan, Rishiraj; Prell, Soeren; Price, Darren; Price, Joe; Price, Lawrence; Prieur, Damien; Primavera, Margherita; Proissl, Manuel; Prokofiev, Kirill; Prokoshin, Fedor; Protopapadaki, Eftychia-sofia; Protopopescu, Serban; Proudfoot, James; Przybycien, Mariusz; Przysiezniak, Helenka; Ptacek, Elizabeth; Puddu, Daniele; Pueschel, Elisa; Puldon, David; Purohit, Milind; Puzo, Patrick; Qian, Jianming; Qin, Gang; Qin, Yang; Quadt, Arnulf; Quarrie, David; Quayle, William; Queitsch-Maitland, Michaela; Quilty, Donnchadha; Qureshi, Anum; Radeka, Veljko; Radescu, Voica; Radhakrishnan, Sooraj Krishnan; Radloff, Peter; Rados, Pere; Ragusa, Francesco; Rahal, Ghita; Rajagopalan, Srinivasan; Rammensee, Michael; Randle-Conde, Aidan Sean; Rangel-Smith, Camila; Rao, Kanury; Rauscher, Felix; Rave, Tobias Christian; Ravenscroft, Thomas; Raymond, Michel; Read, Alexander Lincoln; Readioff, Nathan Peter; Rebuzzi, Daniela; Redelbach, Andreas; Redlinger, George; Reece, Ryan; Reeves, Kendall; Rehnisch, Laura; Reisin, Hernan; Relich, Matthew; Rembser, Christoph; Ren, Huan; Ren, Zhongliang; Renaud, Adrien; Rescigno, Marco; Resconi, Silvia; Rezanova, Olga; Reznicek, Pavel; Rezvani, Reyhaneh; Richter, Robert; Ridel, Melissa; Rieck, Patrick; Rieger, Julia; Rijssenbeek, Michael; Rimoldi, Adele; Rinaldi, Lorenzo; Ritsch, Elmar; Riu, Imma; Rizatdinova, Flera; Rizvi, Eram; Robertson, Steven; Robichaud-Veronneau, Andree; Robinson, Dave; Robinson, James; Robson, Aidan; Roda, Chiara; Rodrigues, Luis; Roe, Shaun; Røhne, Ole; Rolli, Simona; Romaniouk, Anatoli; Romano, Marino; Romero Adam, Elena; Rompotis, Nikolaos; Roos, Lydia; Ros, Eduardo; Rosati, Stefano; Rosbach, Kilian; Rose, Matthew; Rosendahl, Peter Lundgaard; Rosenthal, Oliver; Rossetti, Valerio; Rossi, Elvira; Rossi, Leonardo Paolo; Rosten, Rachel; Rotaru, Marina; Roth, Itamar; Rothberg, Joseph; Rousseau, David; Royon, Christophe; Rozanov, Alexandre; Rozen, Yoram; Ruan, Xifeng; Rubbo, Francesco; Rubinskiy, Igor; Rud, Viacheslav; Rudolph, Christian; Rudolph, Matthew Scott; Rühr, Frederik; Ruiz-Martinez, Aranzazu; Rurikova, Zuzana; Rusakovich, Nikolai; Ruschke, Alexander; Rutherfoord, John; Ruthmann, Nils; Ryabov, Yury; Rybar, Martin; Rybkin, Grigori; Ryder, Nick; Saavedra, Aldo; Sacerdoti, Sabrina; Saddique, Asif; Sadeh, Iftach; Sadrozinski, Hartmut; Sadykov, Renat; Safai Tehrani, Francesco; Sakamoto, Hiroshi; Sakurai, Yuki; Salamanna, Giuseppe; Salamon, Andrea; Saleem, Muhammad; Salek, David; Sales De Bruin, Pedro Henrique; Salihagic, Denis; Salnikov, Andrei; Salt, José; Salvachua Ferrando, Belén; Salvatore, Daniela; Salvatore, Pasquale Fabrizio; Salvucci, Antonio; Salzburger, Andreas; Sampsonidis, Dimitrios; Sanchez, Arturo; Sánchez, Javier; Sanchez Martinez, Victoria; Sandaker, Heidi; Sandbach, Ruth Laura; Sander, Heinz Georg; Sanders, Michiel; Sandhoff, Marisa; Sandoval, Tanya; Sandoval, Carlos; Sandstroem, Rikard; Sankey, Dave; Sansoni, Andrea; Santoni, Claudio; Santonico, Rinaldo; Santos, Helena; Santoyo Castillo, Itzebelt; Sapp, Kevin; Sapronov, Andrey; Saraiva, João; Sarrazin, Bjorn; Sartisohn, Georg; Sasaki, Osamu; Sasaki, Yuichi; Sauvage, Gilles; Sauvan, Emmanuel; Savard, Pierre; Savu, Dan Octavian; Sawyer, Craig; Sawyer, Lee; Saxon, David; Saxon, James; Sbarra, Carla; Sbrizzi, Antonio; Scanlon, Tim; Scannicchio, Diana; Scarcella, Mark; Scarfone, Valerio; Schaarschmidt, Jana; Schacht, Peter; Schaefer, Douglas; Schaefer, Ralph; Schaepe, Steffen; Schaetzel, Sebastian; Schäfer, Uli; Schaffer, Arthur; Schaile, Dorothee; Schamberger, R. Dean; Scharf, Veit; Schegelsky, Valery; Scheirich, Daniel; Schernau, Michael; Scherzer, Max; Schiavi, Carlo; Schieck, Jochen; Schillo, Christian; Schioppa, Marco; Schlenker, Stefan; Schmidt, Evelyn; Schmieden, Kristof; Schmitt, Christian; Schmitt, Christopher; Schmitt, Sebastian; Schneider, Basil; Schnellbach, Yan Jie; Schnoor, Ulrike; Schoeffel, Laurent; Schoening, Andre; Schoenrock, Bradley Daniel; Schorlemmer, Andre Lukas; Schott, Matthias; Schouten, Doug; Schovancova, Jaroslava; Schramm, Steven; Schreyer, Manuel; Schroeder, Christian; Schuh, Natascha; Schultens, Martin Johannes; Schultz-Coulon, Hans-Christian; Schulz, Holger; Schumacher, Markus; Schumm, Bruce; Schune, Philippe; Schwanenberger, Christian; Schwartzman, Ariel; Schwegler, Philipp; Schwemling, Philippe; Schwienhorst, Reinhard; Schwindling, Jerome; Schwindt, Thomas; Schwoerer, Maud; Sciacca, Gianfranco; Scifo, Estelle; Sciolla, Gabriella; Scott, Bill; Scuri, Fabrizio; Scutti, Federico; Searcy, Jacob; Sedov, George; Sedykh, Evgeny; Seidel, Sally; Seiden, Abraham; Seifert, Frank; Seixas, José; Sekhniaidze, Givi; Sekula, Stephen; Selbach, Karoline Elfriede; Seliverstov, Dmitry; Sellers, Graham; Semprini-Cesari, Nicola; Serfon, Cedric; Serin, Laurent; Serkin, Leonid; Serre, Thomas; Seuster, Rolf; Severini, Horst; Sfiligoj, Tina; Sforza, Federico; Sfyrla, Anna; Shabalina, Elizaveta; Shamim, Mansoora; Shan, Lianyou; Shang, Ruo-yu; Shank, James; Shapiro, Marjorie; Shatalov, Pavel; Shaw, Kate; Shehu, Ciwake Yusufu; Sherwood, Peter; Shi, Liaoshan; Shimizu, Shima; Shimmin, Chase Owen; Shimojima, Makoto; Shiyakova, Mariya; Shmeleva, Alevtina; Shochet, Mel; Short, Daniel; Shrestha, Suyog; Shulga, Evgeny; Shupe, Michael; Shushkevich, Stanislav; Sicho, Petr; Sidiropoulou, Ourania; Sidorov, Dmitri; Sidoti, Antonio; Siegert, Frank; Sijacki, Djordje; Silva, José; Silver, Yiftah; Silverstein, Daniel; Silverstein, Samuel; Simak, Vladislav; Simard, Olivier; Simic, Ljiljana; Simion, Stefan; Simioni, Eduard; Simmons, Brinick; Simoniello, Rosa; Simonyan, Margar; Sinervo, Pekka; Sinev, Nikolai; Sipica, Valentin; Siragusa, Giovanni; Sircar, Anirvan; Sisakyan, Alexei; Sivoklokov, Serguei; Sjölin, Jörgen; Sjursen, Therese; Skottowe, Hugh Philip; Skovpen, Kirill; Skubic, Patrick; Slater, Mark; Slavicek, Tomas; Sliwa, Krzysztof; Smakhtin, Vladimir; Smart, Ben; Smestad, Lillian; Smirnov, Sergei; Smirnov, Yury; Smirnova, Lidia; Smirnova, Oxana; Smith, Kenway; Smizanska, Maria; Smolek, Karel; Snesarev, Andrei; Snidero, Giacomo; Snyder, Scott; Sobie, Randall; Socher, Felix; Soffer, Abner; Soh, Dart-yin; Solans, Carlos; Solar, Michael; Solc, Jaroslav; Soldatov, Evgeny; Soldevila, Urmila; Solfaroli Camillocci, Elena; Solodkov, Alexander; Soloshenko, Alexei; Solovyanov, Oleg; Solovyev, Victor; Sommer, Philip; Song, Hong Ye; Soni, Nitesh; Sood, Alexander; Sopczak, Andre; Sopko, Bruno; Sopko, Vit; Sorin, Veronica; Sosebee, Mark; Soualah, Rachik; Soueid, Paul; Soukharev, Andrey; South, David; Spagnolo, Stefania; Spanò, Francesco; Spearman, William Robert; Spettel, Fabian; Spighi, Roberto; Spigo, Giancarlo; Spousta, Martin; Spreitzer, Teresa; Spurlock, Barry; St Denis, Richard Dante; Staerz, Steffen; Stahlman, Jonathan; Stamen, Rainer; Stanecka, Ewa; Stanek, Robert; Stanescu, Cristian; Stanescu-Bellu, Madalina; Stanitzki, Marcel Michael; Stapnes, Steinar; Starchenko, Evgeny; Stark, Jan; Staroba, Pavel; Starovoitov, Pavel; Staszewski, Rafal; Stavina, Pavel; Steinberg, Peter; Stelzer, Bernd; Stelzer, Harald Joerg; Stelzer-Chilton, Oliver; Stenzel, Hasko; Stern, Sebastian; Stewart, Graeme; Stillings, Jan Andre; Stockton, Mark; Stoebe, Michael; Stoicea, Gabriel; Stolte, Philipp; Stonjek, Stefan; Stradling, Alden; Straessner, Arno; Stramaglia, Maria Elena; Strandberg, Jonas; Strandberg, Sara; Strandlie, Are; Strauss, Emanuel; Strauss, Michael; Strizenec, Pavol; Ströhmer, Raimund; Strom, David; Stroynowski, Ryszard; Stucci, Stefania Antonia; Stugu, Bjarne; Styles, Nicholas Adam; Su, Dong; Su, Jun; Subramania, Halasya Siva; Subramaniam, Rajivalochan; Succurro, Antonella; Sugaya, Yorihito; Suhr, Chad; Suk, Michal; Sulin, Vladimir; Sultansoy, Saleh; Sumida, Toshi; Sun, Xiaohu; Sundermann, Jan Erik; Suruliz, Kerim; Susinno, Giancarlo; Sutton, Mark; Suzuki, Yu; Svatos, Michal; Swedish, Stephen; Swiatlowski, Maximilian; Sykora, Ivan; Sykora, Tomas; Ta, Duc; Taccini, Cecilia; Tackmann, Kerstin; Taenzer, Joe; Taffard, Anyes; Tafirout, Reda; Taiblum, Nimrod; Takahashi, Yuta; Takai, Helio; Takashima, Ryuichi; Takeda, Hiroshi; Takeshita, Tohru; Takubo, Yosuke; Talby, Mossadek; Talyshev, Alexey; Tam, Jason; Tan, Kong Guan; Tanaka, Junichi; Tanaka, Reisaburo; Tanaka, Satoshi; Tanaka, Shuji; Tanasijczuk, Andres Jorge; Tannenwald, Benjamin Bordy; Tannoury, Nancy; Tapprogge, Stefan; Tarem, Shlomit; Tarrade, Fabien; Tartarelli, Giuseppe Francesco; Tas, Petr; Tasevsky, Marek; Tashiro, Takuya; Tassi, Enrico; Tavares Delgado, Ademar; Tayalati, Yahya; Taylor, Frank; Taylor, Geoffrey; Taylor, Wendy; Teischinger, Florian Alfred; Teixeira Dias Castanheira, Matilde; Teixeira-Dias, Pedro; Temming, Kim Katrin; Ten Kate, Herman; Teng, Ping-Kun; Teoh, Jia Jian; Terada, Susumu; Terashi, Koji; Terron, Juan; Terzo, Stefano; Testa, Marianna; Teuscher, Richard; Therhaag, Jan; Theveneaux-Pelzer, Timothée; Thomas, Juergen; Thomas-Wilsker, Joshuha; Thompson, Emily; Thompson, Paul; Thompson, Peter; Thompson, Stan; Thomsen, Lotte Ansgaard; Thomson, Evelyn; Thomson, Mark; Thong, Wai Meng; Thun, Rudolf; Tian, Feng; Tibbetts, Mark James; Tikhomirov, Vladimir; Tikhonov, Yury; Timoshenko, Sergey; Tiouchichine, Elodie; Tipton, Paul; Tisserant, Sylvain; Todorov, Theodore; Todorova-Nova, Sharka; Toggerson, Brokk; Tojo, Junji; Tokár, Stanislav; Tokushuku, Katsuo; Tollefson, Kirsten; Tomlinson, Lee; Tomoto, Makoto; Tompkins, Lauren; Toms, Konstantin; Topilin, Nikolai; Torrence, Eric; Torres, Heberth; Torró Pastor, Emma; Toth, Jozsef; Touchard, Francois; Tovey, Daniel; Tran, Huong Lan; Trefzger, Thomas; Tremblet, Louis; Tricoli, Alessandro; Trigger, Isabel Marian; Trincaz-Duvoid, Sophie; Tripiana, Martin; Triplett, Nathan; Trischuk, William; Trocmé, Benjamin; Troncon, Clara; Trottier-McDonald, Michel; Trovatelli, Monica; True, Patrick; Trzebinski, Maciej; Trzupek, Adam; Tsarouchas, Charilaos; Tseng, Jeffrey; Tsiareshka, Pavel; Tsionou, Dimitra; Tsipolitis, Georgios; Tsirintanis, Nikolaos; Tsiskaridze, Shota; Tsiskaridze, Vakhtang; Tskhadadze, Edisher; Tsukerman, Ilya; Tsulaia, Vakhtang; Tsuno, Soshi; Tsybychev, Dmitri; Tudorache, Alexandra; Tudorache, Valentina; Tuna, Alexander Naip; Tupputi, Salvatore; Turchikhin, Semen; Turecek, Daniel; Turk Cakir, Ilkay; Turra, Ruggero; Tuts, Michael; Tykhonov, Andrii; Tylmad, Maja; Tyndel, Mike; Uchida, Kirika; Ueda, Ikuo; Ueno, Ryuichi; Ughetto, Michael; Ugland, Maren; Uhlenbrock, Mathias; Ukegawa, Fumihiko; Unal, Guillaume; Undrus, Alexander; Unel, Gokhan; Ungaro, Francesca; Unno, Yoshinobu; Urbaniec, Dustin; Urquijo, Phillip; Usai, Giulio; Usanova, Anna; Vacavant, Laurent; Vacek, Vaclav; Vachon, Brigitte; Valencic, Nika; Valentinetti, Sara; Valero, Alberto; Valery, Loic; Valkar, Stefan; Valladolid Gallego, Eva; Vallecorsa, Sofia; Valls Ferrer, Juan Antonio; Van Den Wollenberg, Wouter; Van Der Deijl, Pieter; van der Geer, Rogier; van der Graaf, Harry; Van Der Leeuw, Robin; van der Ster, Daniel; van Eldik, Niels; van Gemmeren, Peter; Van Nieuwkoop, Jacobus; van Vulpen, Ivo; van Woerden, Marius Cornelis; Vanadia, Marco; Vandelli, Wainer; Vanguri, Rami; Vaniachine, Alexandre; Vankov, Peter; Vannucci, Francois; Vardanyan, Gagik; Vari, Riccardo; Varnes, Erich; Varol, Tulin; Varouchas, Dimitris; Vartapetian, Armen; Varvell, Kevin; Vazeille, Francois; Vazquez Schroeder, Tamara; Veatch, Jason; Veloso, Filipe; Veneziano, Stefano; Ventura, Andrea; Ventura, Daniel; Venturi, Manuela; Venturi, Nicola; Venturini, Alessio; Vercesi, Valerio; Verducci, Monica; Verkerke, Wouter; Vermeulen, Jos; Vest, Anja; Vetterli, Michel; Viazlo, Oleksandr; Vichou, Irene; Vickey, Trevor; Vickey Boeriu, Oana Elena; Viehhauser, Georg; Viel, Simon; Vigne, Ralph; Villa, Mauro; Villaplana Perez, Miguel; Vilucchi, Elisabetta; Vincter, Manuella; Vinogradov, Vladimir; Virzi, Joseph; Vivarelli, Iacopo; Vives Vaque, Francesc; Vlachos, Sotirios; Vladoiu, Dan; Vlasak, Michal; Vogel, Adrian; Vogel, Marcelo; Vokac, Petr; Volpi, Guido; Volpi, Matteo; von der Schmitt, Hans; von Radziewski, Holger; von Toerne, Eckhard; Vorobel, Vit; Vorobev, Konstantin; Vos, Marcel; Voss, Rudiger; Vossebeld, Joost; Vranjes, Nenad; Vranjes Milosavljevic, Marija; Vrba, Vaclav; Vreeswijk, Marcel; Vu Anh, Tuan; Vuillermet, Raphael; Vukotic, Ilija; Vykydal, Zdenek; Wagner, Peter; Wagner, Wolfgang; Wahlberg, Hernan; Wahrmund, Sebastian; Wakabayashi, Jun; Walder, James; Walker, Rodney; Walkowiak, Wolfgang; Wall, Richard; Waller, Peter; Walsh, Brian; Wang, Chao; Wang, Chiho; Wang, Fuquan; Wang, Haichen; Wang, Hulin; Wang, Jike; Wang, Jin; Wang, Kuhan; Wang, Rui; Wang, Song-Ming; Wang, Tan; Wang, Xiaoxiao; Wanotayaroj, Chaowaroj; Warburton, Andreas; Ward, Patricia; Wardrope, David Robert; Warsinsky, Markus; Washbrook, Andrew; Wasicki, Christoph; Watkins, Peter; Watson, Alan; Watson, Ian; Watson, Miriam; Watts, Gordon; Watts, Stephen; Waugh, Ben; Webb, Samuel; Weber, Michele; Weber, Stefan Wolf; Webster, Jordan S; Weidberg, Anthony; Weigell, Philipp; Weinert, Benjamin; Weingarten, Jens; Weiser, Christian; Weits, Hartger; Wells, Phillippa; Wenaus, Torre; Wendland, Dennis; Weng, Zhili; Wengler, Thorsten; Wenig, Siegfried; Wermes, Norbert; Werner, Matthias; Werner, Per; Wessels, Martin; Wetter, Jeffrey; Whalen, Kathleen; White, Andrew; White, Martin; White, Ryan; White, Sebastian; Whiteson, Daniel; Wicke, Daniel; Wickens, Fred; Wiedenmann, Werner; Wielers, Monika; Wienemann, Peter; Wiglesworth, Craig; Wiik-Fuchs, Liv Antje Mari; Wijeratne, Peter Alexander; Wildauer, Andreas; Wildt, Martin Andre; Wilkens, Henric George; Will, Jonas Zacharias; Williams, Hugh; Williams, Sarah; Willis, Christopher; Willocq, Stephane; Wilson, Alan; Wilson, John; Wingerter-Seez, Isabelle; Winklmeier, Frank; Winter, Benedict Tobias; Wittgen, Matthias; Wittig, Tobias; Wittkowski, Josephine; Wollstadt, Simon Jakob; Wolter, Marcin Wladyslaw; Wolters, Helmut; Wosiek, Barbara; Wotschack, Jorg; Woudstra, Martin; Wozniak, Krzysztof; Wright, Michael; Wu, Mengqing; Wu, Sau Lan; Wu, Xin; Wu, Yusheng; Wulf, Evan; Wyatt, Terry Richard; Wynne, Benjamin; Xella, Stefania; Xiao, Meng; Xu, Da; Xu, Lailin; Yabsley, Bruce; Yacoob, Sahal; Yamada, Miho; Yamaguchi, Hiroshi; Yamaguchi, Yohei; Yamamoto, Akira; Yamamoto, Kyoko; Yamamoto, Shimpei; Yamamura, Taiki; Yamanaka, Takashi; Yamauchi, Katsuya; Yamazaki, Yuji; Yan, Zhen; Yang, Haijun; Yang, Hongtao; Yang, Un-Ki; Yang, Yi; Yanush, Serguei; Yao, Liwen; Yao, Weiming; Yasu, Yoshiji; Yatsenko, Elena; Yau Wong, Kaven Henry; Ye, Jingbo; Ye, Shuwei; Yen, Andy L; Yildirim, Eda; Yilmaz, Metin; Yoosoofmiya, Reza; Yorita, Kohei; Yoshida, Rikutaro; Yoshihara, Keisuke; Young, Charles; Young, Christopher John; Youssef, Saul; Yu, David Ren-Hwa; Yu, Jaehoon; Yu, Jiaming; Yu, Jie; Yuan, Li; Yurkewicz, Adam; Yusuff, Imran; Zabinski, Bartlomiej; Zaidan, Remi; Zaitsev, Alexander; Zaman, Aungshuman; Zambito, Stefano; Zanello, Lucia; Zanzi, Daniele; Zeitnitz, Christian; Zeman, Martin; Zemla, Andrzej; Zengel, Keith; Zenin, Oleg; Ženiš, Tibor; Zerwas, Dirk; Zevi della Porta, Giovanni; Zhang, Dongliang; Zhang, Fangzhou; Zhang, Huaqiao; Zhang, Jinlong; Zhang, Lei; Zhang, Xueyao; Zhang, Zhiqing; Zhao, Zhengguo; Zhemchugov, Alexey; Zhong, Jiahang; Zhou, Bing; Zhou, Lei; Zhou, Ning; Zhu, Cheng Guang; Zhu, Hongbo; Zhu, Junjie; Zhu, Yingchun; Zhuang, Xuai; Zhukov, Konstantin; Zibell, Andre; Zieminska, Daria; Zimine, Nikolai; Zimmermann, Christoph; Zimmermann, Robert; Zimmermann, Simone; Zimmermann, Stephanie; Zinonos, Zinonas; Ziolkowski, Michael; Zobernig, Georg; Zoccoli, Antonio; zur Nedden, Martin; Zurzolo, Giovanni; Zutshi, Vishnu; Zwalinski, Lukasz

2014-09-15

A novel technique to identify and split clusters created by multiple charged particles in the ATLAS pixel detector using a set of artificial neural networks is presented. Such merged clusters are a common feature of tracks originating from highly energetic objects, such as jets. Neural networks are trained using Monte Carlo samples produced with a detailed detector simulation. This technique replaces the former clustering approach based on a connected component analysis and charge interpolation. The performance of the neural network splitting technique is quantified using data from proton-proton collisions at the LHC collected by the ATLAS detector in 2011 and from Monte Carlo simulations. This technique reduces the number of clusters shared between tracks in highly energetic jets by up to a factor of three. It also provides more precise position and error estimates of the clusters in both the transverse and longitudinal impact parameter resolution.

Redefining NHS complaint handling--the real challenge.

Science.gov (United States)

Seelos, L; Adamson, C

1994-01-01

More and more organizations find that a constructive and open dialogue with their customers can be an effective strategy for building long-term customer relations. In this context, it has been recognized that effective complaint-contact handling can make a significant contribution to organizations' attempts to maximize customer satisfaction and loyalty. Within the NHS, an intellectual awareness exists that effective complaint/contact handling can contribute to making services more efficient and cost-effective by developing customer-oriented improvement initiatives. Recent efforts have focused on redefining NHS complaint-handling procedures to make them more user-friendly and effective for both NHS employees and customers. Discusses the challenges associated with opening up the NHS to customer feedback. Highlights potential weaknesses in the current approach and argues that the real challenge is for NHS managers to facilitate a culture change that moves the NHS away from a long-established defensive complaint handling practice.

Large deep neural networks for MS lesion segmentation

Science.gov (United States)

Prieto, Juan C.; Cavallari, Michele; Palotai, Miklos; Morales Pinzon, Alfredo; Egorova, Svetlana; Styner, Martin; Guttmann, Charles R. G.

2017-02-01

Multiple sclerosis (MS) is a multi-factorial autoimmune disorder, characterized by spatial and temporal dissemination of brain lesions that are visible in T2-weighted and Proton Density (PD) MRI. Assessment of lesion burden and is useful for monitoring the course of the disease, and assessing correlates of clinical outcomes. Although there are established semi-automated methods to measure lesion volume, most of them require human interaction and editing, which are time consuming and limits the ability to analyze large sets of data with high accuracy. The primary objective of this work is to improve existing segmentation algorithms and accelerate the time consuming operation of identifying and validating MS lesions. In this paper, a Deep Neural Network for MS Lesion Segmentation is implemented. The MS lesion samples are extracted from the Partners Comprehensive Longitudinal Investigation of Multiple Sclerosis (CLIMB) study. A set of 900 subjects with T2, PD and a manually corrected label map images were used to train a Deep Neural Network and identify MS lesions. Initial tests using this network achieved a 90% accuracy rate. A secondary goal was to enable this data repository for big data analysis by using this algorithm to segment the remaining cases available in the CLIMB repository.

Neural correlates of continuous causal word generation.

Science.gov (United States)

Wende, Kim C; Straube, Benjamin; Stratmann, Mirjam; Sommer, Jens; Kircher, Tilo; Nagels, Arne

2012-09-01

Causality provides a natural structure for organizing our experience and language. Causal reasoning during speech production is a distinct aspect of verbal communication, whose related brain processes are yet unknown. The aim of the current study was to investigate the neural mechanisms underlying the continuous generation of cause-and-effect coherences during overt word production. During fMRI data acquisition participants performed three verbal fluency tasks on identical cue words: A novel causal verbal fluency task (CVF), requiring the production of multiple reasons to a given cue word (e.g. reasons for heat are fire, sun etc.), a semantic (free association, FA, e.g. associations with heat are sweat, shower etc.) and a phonological control task (phonological verbal fluency, PVF, e.g. rhymes with heat are meat, wheat etc.). We found that, in contrast to PVF, both CVF and FA activated a left lateralized network encompassing inferior frontal, inferior parietal and angular regions, with further bilateral activation in middle and inferior as well as superior temporal gyri and the cerebellum. For CVF contrasted against FA, we found greater bold responses only in the left middle frontal cortex. Large overlaps in the neural activations during free association and causal verbal fluency indicate that the access to causal relationships between verbal concepts is at least partly based on the semantic neural network. The selective activation in the left middle frontal cortex for causal verbal fluency suggests that distinct neural processes related to cause-and-effect-relations are associated with the recruitment of middle frontal brain areas. Copyright © 2012 Elsevier Inc. All rights reserved.

PERAMALAN KONSUMSI LISTRIK JANGKA PENDEK DENGAN ARIMA MUSIMAN GANDA DAN ELMAN-RECURRENT NEURAL NETWORK

Directory of Open Access Journals (Sweden)

Suhartono Suhartono

2009-07-01

Full Text Available Neural network (NN is one of many method used to predict the electricity consumption per hour in many countries. NN method which is used in many previous studies is Feed-Forward Neural Network (FFNN or Autoregressive Neural Network(AR-NN. AR-NN model is not able to capture and explain the effect of moving average (MA order on a time series of data. This research was conducted with the purpose of reviewing the application of other types of NN, that is Elman-Recurrent Neural Network (Elman-RNN which could explain MA order effect and compare the result of prediction accuracy with multiple seasonal ARIMA (Autoregressive Integrated Moving Average models. As a case study, we used data electricity consumption per hour in Mengare Gresik. Result of analysis showed that the best of double seasonal Arima models suited to short-term forecasting in the case study data is ARIMA([1,2,3,4,6,7,9,10,14,21,33],1,8(0,1,124 (1,1,0168. This model produces a white noise residuals, but it does not have a normal distribution due to suspected outlier. Outlier detection in iterative produce 14 innovation outliers. There are 4 inputs of Elman-RNN network that were examined and tested for forecasting the data, the input according to lag Arima, input such as lag Arima plus 14 dummy outlier, inputs are the lag-multiples of 24 up to lag 480, and the inputs are lag 1 and lag multiples of 24+1. All of four network uses one hidden layer with tangent sigmoid activation function and one output with a linear function. The result of comparative forecast accuracy through value of MAPE out-sample showed that the fourth networks, namely Elman-RNN (22, 3, 1, is the best model for forecasting electricity consumption per hour in short term in Mengare Gresik.

MHSS: a material handling system simulator

Energy Technology Data Exchange (ETDEWEB)

Pomernacki, L.; Hollstien, R.B.

1976-04-07

A Material Handling System Simulator (MHSS) program is described that provides specialized functional blocks for modeling and simulation of nuclear material handling systems. Models of nuclear fuel fabrication plants may be built using functional blocks that simulate material receiving, storage, transport, inventory, processing, and shipping operations as well as the control and reporting tasks of operators or on-line computers. Blocks are also provided that allow the user to observe and gather statistical information on the dynamic behavior of simulated plants over single or replicated runs. Although it is currently being developed for the nuclear materials handling application, MHSS can be adapted to other industries in which material accountability is important. In this paper, emphasis is on the simulation methodology of the MHSS program with application to the nuclear material safeguards problem. (auth)

Exploring the connectome: Petascale volume visualization of microscopy data streams

KAUST Repository

Beyer, Johanna; Hadwiger, Markus; Al-Awami, Ali K.; Jeong, Wonki; Kasthuri, Narayanan; Lichtman, Jeff W M D; Pfister, Hanspeter

2013-01-01

Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled. © 1981-2012 IEEE.

Exploring the connectome: Petascale volume visualization of microscopy data streams

KAUST Repository

Beyer, Johanna

2013-07-01

Recent advances in high-resolution microscopy let neuroscientists acquire neural-tissue volume data of extremely large sizes. However, the tremendous resolution and the high complexity of neural structures present big challenges to storage, processing, and visualization at interactive rates. A proposed system provides interactive exploration of petascale (petavoxel) volumes resulting from high-throughput electron microscopy data streams. The system can concurrently handle multiple volumes and can support the simultaneous visualization of high-resolution voxel segmentation data. Its visualization-driven design restricts most computations to a small subset of the data. It employs a multiresolution virtual-memory architecture for better scalability than previous approaches and for handling incomplete data. Researchers have employed it for a 1-teravoxel mouse cortex volume, of which several hundred axons and dendrites as well as synapses have been segmented and labeled. © 1981-2012 IEEE.

Development of Safe Food Handling Guidelines for Korean Consumers.

Science.gov (United States)

Kang, Hee-Jin; Lee, Min-Woo; Hwang, In-Kyeong; Kim, Jeong-Weon

2015-08-01

The purpose of this study was to develop guidelines for Korean consumers with regard to safe food handling practices at home by identifying current food handling issues. Korean consumers' behaviors regarding their safe food handling were identified via survey questionnaires that included items on individual hygiene practices, prepreparation steps when cooking, the cooking process, and the storage of leftover foods. The subjects were 417 Korean parents with elementary school children living in Seoul and Gyeonggi Province in the central area of Korea. The survey results revealed gaps between the knowledge or practices of Korean consumers and scientific evidence pertaining to safe food handling practices. Based on these findings, a leaflet on safe food handling guidelines was developed in accordance with Korean food culture. These guidelines suggest personal hygiene practices as well as fundamental principles and procedures for safe food handling from the stage of food purchase to that of keeping leftover dishes. A pilot application study with 50 consumers revealed that the guidelines effectively improved Korean consumers' safe food handling practices, suggesting that they can serve as practical educational material suitable for Korean consumers.

Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

Directory of Open Access Journals (Sweden)

C. W. Dawson

2002-01-01

Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

Stress, glucocorticoid hormones, and hippocampal neural progenitor cells: implications to mood disorders.

Science.gov (United States)

Kino, Tomoshige

2015-01-01

The hypothalamic-pituitary-adrenal (HPA) axis and its end-effectors glucocorticoid hormones play central roles in the adaptive response to numerous stressors that can be either internal or external. Thus, this system has a strong impact on the brain hippocampus and its major functions, such as cognition, memory as well as behavior, and mood. The hippocampal area of the adult brain contains neural stem cells or more committed neural progenitor cells, which retain throughout the human life the ability of self-renewal and to differentiate into multiple neural cell lineages, such as neurons, astrocytes, and oligodendrocytes. Importantly, these characteristic cells contribute significantly to the above-indicated functions of the hippocampus, while various stressors and glucocorticoids influence proliferation, differentiation, and fate of these cells. This review offers an overview of the current understanding on the interactions between the HPA axis/glucocorticoid stress-responsive system and hippocampal neural progenitor cells by focusing on the actions of glucocorticoids. Also addressed is a further discussion on the implications of such interactions to the pathophysiology of mood disorders.

Environmental and safety aspects. The best of 'powder handling and processing' 1989 - 1997. 'Bulk solids handling' 1992 - 1997 (H/2000)

Energy Technology Data Exchange (ETDEWEB)

Woehlbier, R.H. (ed.)

2000-07-01

The book contains articles published either during 1992-1997 in ''bulk solids handling'' or during 1989-1997 in ''powder handling and processing''. Main topics are aspects of safety and environmental protection in bulk solids handling: dusts, hazardous powders, prevention and mitigation of dust explosions, powdered coal handling, dedusting, filters, electrostatic precipitation, materials recovery, occupational safety.(uke)

Multispectral embedding-based deep neural network for three-dimensional human pose recovery

Science.gov (United States)

Yu, Jialin; Sun, Jifeng

2018-01-01

Monocular image-based three-dimensional (3-D) human pose recovery aims to retrieve 3-D poses using the corresponding two-dimensional image features. Therefore, the pose recovery performance highly depends on the image representations. We propose a multispectral embedding-based deep neural network (MSEDNN) to automatically obtain the most discriminative features from multiple deep convolutional neural networks and then embed their penultimate fully connected layers into a low-dimensional manifold. This compact manifold can explore not only the optimum output from multiple deep networks but also the complementary properties of them. Furthermore, the distribution of each hierarchy discriminative manifold is sufficiently smooth so that the training process of our MSEDNN can be effectively implemented only using few labeled data. Our proposed network contains a body joint detector and a human pose regressor that are jointly trained. Extensive experiments conducted on four databases show that our proposed MSEDNN can achieve the best recovery performance compared with the state-of-the-art methods.

7 CFR 959.126 - Handling of culls.

Science.gov (United States)

2010-01-01

... 7 Agriculture 8 2010-01-01 2010-01-01 false Handling of culls. 959.126 Section 959.126 Agriculture Regulations of the Department of Agriculture (Continued) AGRICULTURAL MARKETING SERVICE (Marketing Agreements...) Handled for canning or freezing. (b) As a safeguard against culls entering fresh market channels each...

Survey of postharvest handling, preservation and processing ...

African Journals Online (AJOL)

Survey of postharvest handling, preservation and processing practices along the camel milk chain in Isiolo district, Kenya. ... Despite the important contribution of camel milk to food security for pastoralists in Kenya, little is known about the postharvest handling, preservation and processing practices. In this study, existing ...

Handling uncertainty through adaptiveness in planning approaches

NARCIS (Netherlands)

Zandvoort, M.; Vlist, van der M.J.; Brink, van den A.

2018-01-01

Planners and water managers seek to be adaptive to handle uncertainty through the use of planning approaches. In this paper, we study what type of adaptiveness is proposed and how this may be operationalized in planning approaches to adequately handle different uncertainties. We took a

PREPD O and VE remote handling system

International Nuclear Information System (INIS)

Theil, T.N.

1985-01-01

The Process Experimental Pilot Plant (PREPP) at the Idaho National Engineering Laboratory is designed for volume reduction and packaging of transuranic (TRU) waste. The PREPP opening and verification enclosure (O and VE) remote handling system, within that facility, is designed to provide examination of the contents of various TRU waste storage containers. This remote handling system will provide the means of performing a hazardous operation that is currently performed manually. The TeleRobot to be used in this system is a concept that will incorporate and develop man in the loop operation (manual mode), standardized automatic sequencing of end effector tools, increased payload and reach over currently available computer-controlled robots, and remote handling of a hazardous waste operation. The system is designed within limited space constraints and an operation that was originally planned, and is currently being manually performed at other plants. The PREPP O and VE remote handling system design incorporates advancing technology to improve the working environment in the nuclear field

Harvesting and handling agricultural residues for energy

Energy Technology Data Exchange (ETDEWEB)

Jenkins, B.M.; Summer, H.R.

1986-05-01

Significant progress in understanding the needs for design of agricultural residue collection and handling systems has been made but additional research is required. Recommendations are made for research to (a) integrate residue collection and handling systems into general agricultural practices through the development of multi-use equipment and total harvest systems; (b) improve methods for routine evaluation of agricultural residue resources, possibly through remote sensing and image processing; (c) analyze biomass properties to obtain detailed data relevant to engineering design and analysis; (d) evaluate long-term environmental, social, and agronomic impacts of residue collection; (e) develop improved equipment with higher capacities to reduce residue collection and handling costs, with emphasis on optimal design of complete systems including collection, transportation, processing, storage, and utilization; and (f) produce standard forms of biomass fuels or products to enhance material handling and expand biomass markets through improved reliability and automatic control of biomass conversion and other utilization systems. 118 references.

An electronic system for simulation of neural networks with a micro-second real time constraint

International Nuclear Information System (INIS)

Chorti, Arsenia; Granado, Bertrand; Denby, Bruce; Garda, Patrick

2001-01-01

Neural networks implemented in hardware can perform pattern recognition very quickly, and as such have been used to advantage in the triggering systems of certain high energy physics experiments. Typically, time constants of the order of a few microseconds are required. In this paper, we present a new system. MAHARADJA, for evaluating MLP and RBF neural network paradigms in real time. The system is tested on a possible ATLAS muon triggering application suggested by the Tel Aviv ATLAS group, consisting of a 4-8-8-4 MLP which must be evaluated in 10 microseconds. The inputs to the net are dx/dz, x(z=0), dy/dz, and y(z=0), whereas the outputs give pt, tan(phi), sin(theta), and q, the charge. With a 10 MHz clock, MAHARADJA calculates the result in 6.8 microseconds; at 20 MHz, which is readily attainable, this would be reduced to only 3.4 microseconds. The system can also handle RBF networks with 3 different distance metrics (Euclidean, Manhattan and Mahalanobis), and can simulate any MLP of 10 hidden layers or less. The electronic implementation is with FPGA's, which can be optimized for a specific neural network because the number of processing elements can be modified

30 CFR 75.817 - Cable handling and support systems.

Science.gov (United States)

2010-07-01

... High-Voltage Longwalls § 75.817 Cable handling and support systems. Longwall mining equipment must be provided with cable-handling and support systems that are constructed, installed and maintained to minimize... 30 Mineral Resources 1 2010-07-01 2010-07-01 false Cable handling and support systems. 75.817...

Cooper-Harper Experience Report for Spacecraft Handling Qualities Applications

Science.gov (United States)

Bailey, Randall E.; Jackson, E. Bruce; Bilimoria, Karl D.; Mueller, Eric R.; Frost, Chad R.; Alderete, Thomas S.

2009-01-01

A synopsis of experience from the fixed-wing and rotary-wing aircraft communities in handling qualities development and the use of the Cooper-Harper pilot rating scale is presented as background for spacecraft handling qualities research, development, test, and evaluation (RDT&E). In addition, handling qualities experiences and lessons-learned from previous United States (US) spacecraft developments are reviewed. This report is intended to provide a central location for references, best practices, and lessons-learned to guide current and future spacecraft handling qualities RDT&E.

Cellular Manufacturing System with Dynamic Lot Size Material Handling

Science.gov (United States)

Khannan, M. S. A.; Maruf, A.; Wangsaputra, R.; Sutrisno, S.; Wibawa, T.

2016-02-01

Material Handling take as important role in Cellular Manufacturing System (CMS) design. In several study at CMS design material handling was assumed per pieces or with constant lot size. In real industrial practice, lot size may change during rolling period to cope with demand changes. This study develops CMS Model with Dynamic Lot Size Material Handling. Integer Linear Programming is used to solve the problem. Objective function of this model is minimizing total expected cost consisting machinery depreciation cost, operating costs, inter-cell material handling cost, intra-cell material handling cost, machine relocation costs, setup costs, and production planning cost. This model determines optimum cell formation and optimum lot size. Numerical examples are elaborated in the paper to ilustrate the characterictic of the model.

Handling of Solid Residues

International Nuclear Information System (INIS)

Medina Bermudez, Clara Ines

1999-01-01

The topic of solid residues is specifically of great interest and concern for the authorities, institutions and community that identify in them a true threat against the human health and the atmosphere in the related with the aesthetic deterioration of the urban centers and of the natural landscape; in the proliferation of vectorial transmitters of illnesses and the effect on the biodiversity. Inside the wide spectrum of topics that they keep relationship with the environmental protection, the inadequate handling of solid residues and residues dangerous squatter an important line in the definition of political and practical environmentally sustainable. The industrial development and the population's growth have originated a continuous increase in the production of solid residues; of equal it forms, their composition day after day is more heterogeneous. The base for the good handling includes the appropriate intervention of the different stages of an integral administration of residues, which include the separation in the source, the gathering, the handling, the use, treatment, final disposition and the institutional organization of the administration. The topic of the dangerous residues generates more expectation. These residues understand from those of pathogen type that are generated in the establishments of health that of hospital attention, until those of combustible, inflammable type, explosive, radio-active, volatile, corrosive, reagent or toxic, associated to numerous industrial processes, common in our countries in development

Dynamics of coupled mode solitons in bursting neural networks

Science.gov (United States)

Nfor, N. Oma; Ghomsi, P. Guemkam; Moukam Kakmeni, F. M.

2018-02-01

Using an electrically coupled chain of Hindmarsh-Rose neural models, we analytically derived the nonlinearly coupled complex Ginzburg-Landau equations. This is realized by superimposing the lower and upper cutoff modes of wave propagation and by employing the multiple scale expansions in the semidiscrete approximation. We explore the modified Hirota method to analytically obtain the bright-bright pulse soliton solutions of our nonlinearly coupled equations. With these bright solitons as initial conditions of our numerical scheme, and knowing that electrical signals are the basis of information transfer in the nervous system, it is found that prior to collisions at the boundaries of the network, neural information is purely conveyed by bisolitons at lower cutoff mode. After collision, the bisolitons are completely annihilated and neural information is now relayed by the upper cutoff mode via the propagation of plane waves. It is also shown that the linear gain of the system is inextricably linked to the complex physiological mechanisms of ion mobility, since the speeds and spatial profiles of the coupled nerve impulses vary with the gain. A linear stability analysis performed on the coupled system mainly confirms the instability of plane waves in the neural network, with a glaring example of the transition of weak plane waves into a dark soliton and then static kinks. Numerical simulations have confirmed the annihilation phenomenon subsequent to collision in neural systems. They equally showed that the symmetry breaking of the pulse solution of the system leaves in the network static internal modes, sometime referred to as Goldstone modes.

Neural correlates of alerting and orienting impairment in multiple sclerosis patients.

Directory of Open Access Journals (Sweden)

Manuel Vázquez-Marrufo

Full Text Available BACKGROUND: A considerable percentage of multiple sclerosis patients have attentional impairment, but understanding its neurophysiological basis remains a challenge. The Attention Network Test allows 3 attentional networks to be studied. Previous behavioural studies using this test have shown that the alerting network is impaired in multiple sclerosis. The aim of this study was to identify neurophysiological indexes of the attention impairment in relapsing-remitting multiple sclerosis patients using this test. RESULTS: After general slowing had been removed in patients group to isolate the effects of each condition, some behavioral differences between them were obtained. About Contingent Negative Variation, a statistically significant decrement were found in the amplitude for Central and Spatial Cue Conditions for patient group (p<0.05. ANOVAs showed for the patient group a significant latency delay for P1 and N1 components (p<0.05 and a decrease of P3 amplitude for congruent and incongruent stimuli (p<0.01. With regard to correlation analysis, PASAT-3s and SDMT showed significant correlations with behavioral measures of the Attention Network Test (p<0.01 and an ERP parameter (CNV amplitude. CONCLUSIONS: Behavioral data are highly correlated with the neuropsychological scores and show that the alerting and orienting mechanisms in the patient group were impaired. Reduced amplitude for the Contingent Negative Variation in the patient group suggests that this component could be a physiological marker related to the alerting and orienting impairment in relapsing-remitting multiple sclerosis. P1 and N1 delayed latencies are evidence of the demyelination process that causes impairment in the first steps of the visual sensory processing. Lastly, P3 amplitude shows a general decrease for the pathological group probably indexing a more central impairment. These results suggest that the Attention Network Test give evidence of multiple levels of attention

Repository waste-handling operations, 1998

International Nuclear Information System (INIS)

Cottam, A.E.; Connell, L.

1986-04-01

The Civilian Radioactive Waste Management Program Mission Plan and the Generic Requirements for a Mined Geologic Disposal System state that beginning in 1998, commercial spent fuel not exceeding 70,000 metric tons of heavy metal, or a quantity of solidified high-level radioactive waste resulting from the reprocessing of such a quantity of spent fuel, will be shipped to a deep geologic repository for permanent storage. The development of a waste-handling system that can process 3000 metric tons of heavy metal annually will require the adoption of a fully automated approach. The safety and minimum exposure of personnel will be the prime goals of the repository waste handling system. A man-out-of-the-loop approach will be used in all operations including the receipt of spent fuel in shipping casks, the inspection and unloading of the spent fuel into automated hot-cell facilities, the disassembly of spent fuel assemblies, the consolidation of fuel rods, and the packaging of fuel rods into heavy-walled site-specific containers. These containers are designed to contain the radionuclides for up to 1000 years. The ability of a repository to handle more than 6000 pressurized water reactor spent-fuel rods per day on a production basis for approximately a 23-year period will require that a systems approach be adopted that combines space-age technology, robotics, and sophisticated automated computerized equipment. New advanced inspection techniques, maintenance by robots, and safety will be key factors in the design, construction, and licensing of a repository waste-handling facility for 1998

Signaling through three chemokine receptors triggers the migration of transplanted neural precursor cells in a model of multiple sclerosis.

Science.gov (United States)

Cohen, Mikhal E; Fainstein, Nina; Lavon, Iris; Ben-Hur, Tamir

2014-09-01

Multiple sclerosis (MS) is a multifocal disease, and precursor cells need to migrate into the multiple lesions in order to exert their therapeutic effects. Therefore, cell migration is a crucial element in regenerative processes in MS, dictating the route of delivery, when cell transplantation is considered. We have previously shown that inflammation triggers migration of multi-potential neural precursor cells (NPCs) into the white matter of experimental autoimmune encephalomyelitis (EAE) rodents, a widely used model of MS. Here we investigated the molecular basis of this attraction. NPCs were grown from E13 embryonic mouse brains and transplanted into the lateral cerebral ventricles of EAE mice. Transplanted NPC migration was directed by three tissue-derived chemokines. Stromal cell-derived factor-1α, monocyte chemo-attractant protein-1 and hepatocyte growth factor were expressed in the EAE brain and specifically in microglia and astrocytes. Their cognate receptors, CXCR4, CCR2 or c-Met were constitutively expressed on NPCs. Selective blockage of CXCR4, CCR2 or c-Met partially inhibited NPC migration in EAE brains. Blocking all three receptors had an additive effect and resulted in profound inhibition of NPC migration, as compared to extensive migration of control NPCs. The inflammation-triggered NPC migration into white matter tracts was dependent on a motile NPC phenotype. Specifically, depriving NPCs from epidermal growth factor (EGF) prevented the induction of glial commitment and a motile phenotype (as indicated by an in vitro motility assay), hampering their response to neuroinflammation. In conclusion, signaling via three chemokine systems accounts for most of the inflammation-induced, tissue-derived attraction of transplanted NPCs into white matter tracts during EAE. Copyright © 2014. Published by Elsevier B.V.

The use of artificial neural networks and multiple linear regression to predict rate of medical waste generation

International Nuclear Information System (INIS)

Jahandideh, Sepideh; Jahandideh, Samad; Asadabadi, Ebrahim Barzegari; Askarian, Mehrdad; Movahedi, Mohammad Mehdi; Hosseini, Somayyeh; Jahandideh, Mina

2009-01-01

Prediction of the amount of hospital waste production will be helpful in the storage, transportation and disposal of hospital waste management. Based on this fact, two predictor models including artificial neural networks (ANNs) and multiple linear regression (MLR) were applied to predict the rate of medical waste generation totally and in different types of sharp, infectious and general. In this study, a 5-fold cross-validation procedure on a database containing total of 50 hospitals of Fars province (Iran) were used to verify the performance of the models. Three performance measures including MAR, RMSE and R 2 were used to evaluate performance of models. The MLR as a conventional model obtained poor prediction performance measure values. However, MLR distinguished hospital capacity and bed occupancy as more significant parameters. On the other hand, ANNs as a more powerful model, which has not been introduced in predicting rate of medical waste generation, showed high performance measure values, especially 0.99 value of R 2 confirming the good fit of the data. Such satisfactory results could be attributed to the non-linear nature of ANNs in problem solving which provides the opportunity for relating independent variables to dependent ones non-linearly. In conclusion, the obtained results showed that our ANN-based model approach is very promising and may play a useful role in developing a better cost-effective strategy for waste management in future.

Probing neural cell behaviors through micro-/nano-patterned chitosan substrates

International Nuclear Information System (INIS)

Sung, Chun-Yen; Yang, Chung-Yao; Yeh, J Andrew; Chen, Wen-Shiang; Wang, Yang-Kao; Cheng, Chao-Min

2015-01-01

In this study, we describe the development of surface-modified chitosan substrates to examine topographically related Neuro-2a cell behaviors. Different functional groups can be modified on chitosan surfaces to probe Neuro-2a cell morphology. To prepare chitosan substrates with micro/nano-scaled features, we demonstrated an easy-to-handle method that combined photolithography, inductively coupled plasma reactive ion etching, Ag nanoparticle-assisted etching, and solution casting. The results show that Neuro-2a cells preferred to adhere to a flat chitosan surface rather than a nanotextured chitosan surface as evidenced by greater immobilization and differentiation, suggesting that surface topography is crucial for neural patterning. In addition, we developed chitosan substrates with different geometric patterns and flat region depth; this allowed us to re-arrange or re-pattern Neuro-2a cell colonies at desired locations. We found that a polarity-induced micropattern provided the most suitable surface pattern for promoting neural network formation on a chitosan substrate. The cellular polarity of single Neuro-2a cell spreading correlated to a diamond-like geometry and neurite outgrowth was induced from the corners toward the grooves of the structures. This study provide greater insight into neurobiology, including neurotransmitter screening, electrophysiological stimulation platforms, and biomedical engineering. (paper)

Robustness of the Artificial Neural Networks Used for Clustering in the ATLAS Pixel Detector

CERN Document Server

The ATLAS collaboration

2015-01-01

A study of the robustness of the ATLAS pixel neural network clustering algorithm is presented. The sensitivity to variations to its input is evaluated. These variations are motivated by potential discrepancies between data and simulation due to uncertainties in the modelling of pixel clusters in simulation, as well as uncertainties from the detector calibration. Within reasonable variation magnitudes, the neural networks prove to be robust to most variations. The neural network used to identify pixel clusters created by multiple charged particles, is most sensitive to variations affecting the total amount of charge collected in the cluster. Modifying the read-out threshold has the biggest effect on the clustering's ability to estimate the position of the particle's intersection with the detector.

20 CFR 658.421 - Handling of JS-related complaints.

Science.gov (United States)

2010-04-01

... 20 Employees' Benefits 3 2010-04-01 2010-04-01 false Handling of JS-related complaints. 658.421... ADMINISTRATIVE PROVISIONS GOVERNING THE JOB SERVICE SYSTEM Job Service Complaint System Federal Js Complaint System § 658.421 Handling of JS-related complaints. (a) No JS-related complaint shall be handled at the...

Convergent dynamics for multistable delayed neural networks

International Nuclear Information System (INIS)

Shih, Chih-Wen; Tseng, Jui-Pin

2008-01-01

This investigation aims at developing a methodology to establish convergence of dynamics for delayed neural network systems with multiple stable equilibria. The present approach is general and can be applied to several network models. We take the Hopfield-type neural networks with both instantaneous and delayed feedbacks to illustrate the idea. We shall construct the complete dynamical scenario which comprises exactly 2 n stable equilibria and exactly (3 n − 2 n ) unstable equilibria for the n-neuron network. In addition, it is shown that every solution of the system converges to one of the equilibria as time tends to infinity. The approach is based on employing the geometrical structure of the network system. Positively invariant sets and componentwise dynamical properties are derived under the geometrical configuration. An iteration scheme is subsequently designed to confirm the convergence of dynamics for the system. Two examples with numerical simulations are arranged to illustrate the present theory

Macrocell path loss prediction using artificial intelligence techniques

Science.gov (United States)

Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

2014-04-01

The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

Incidents Prediction in Road Junctions Using Artificial Neural Networks