Advances in Artificial Neural Networks - Methodological Development and Application

Science.gov (United States)

Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other ne...

Emerging trends in neuro engineering and neural computation

CERN Document Server

Lee, Kendall; Garmestani, Hamid; Lim, Chee

2017-01-01

This book focuses on neuro-engineering and neural computing, a multi-disciplinary field of research attracting considerable attention from engineers, neuroscientists, microbiologists and material scientists. It explores a range of topics concerning the design and development of innovative neural and brain interfacing technologies, as well as novel information acquisition and processing algorithms to make sense of the acquired data. The book also highlights emerging trends and advances regarding the applications of neuro-engineering in real-world scenarios, such as neural prostheses, diagnosis of neural degenerative diseases, deep brain stimulation, biosensors, real neural network-inspired artificial neural networks (ANNs) and the predictive modeling of information flows in neuronal networks. The book is broadly divided into three main sections including: current trends in technological developments, neural computation techniques to make sense of the neural behavioral data, and application of these technologie...

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

Teaching methodology for modeling reference evapotranspiration with artificial neural networks

OpenAIRE

Martí, Pau; Pulido Calvo, Inmaculada; Gutiérrez Estrada, Juan Carlos

2015-01-01

[EN] Artificial neural networks are a robust alternative to conventional models for estimating different targets in irrigation engineering, among others, reference evapotranspiration, a key variable for estimating crop water requirements. This paper presents a didactic methodology for introducing students in the application of artificial neural networks for reference evapotranspiration estimation using MatLab c . Apart from learning a specific application of this software wi...

Fast and Efficient Asynchronous Neural Computation with Adapting Spiking Neural Networks

NARCIS (Netherlands)

D. Zambrano (Davide); S.M. Bohte (Sander)

2016-01-01

textabstractBiological neurons communicate with a sparing exchange of pulses - spikes. It is an open question how real spiking neurons produce the kind of powerful neural computation that is possible with deep artificial neural networks, using only so very few spikes to communicate. Building on

Neural chips, neural computers and application in high and superhigh energy physics experiments

International Nuclear Information System (INIS)

Nikityuk, N.M.; )

2001-01-01

Architecture peculiarity and characteristics of series of neural chips and neural computes used in scientific instruments are considered. Tendency of development and use of them in high energy and superhigh energy physics experiments are described. Comparative data which characterize the efficient use of neural chips for useful event selection, classification elementary particles, reconstruction of tracks of charged particles and for search of hypothesis Higgs particles are given. The characteristics of native neural chips and accelerated neural boards are considered [ru

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan; Naous, Rawan; Neftci, Emre; Cauwenberghs, Gert; Salama, Khaled N.

2015-01-01

. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards

Recurrent Neural Network for Computing the Drazin Inverse.

Science.gov (United States)

Stanimirović, Predrag S; Zivković, Ivan S; Wei, Yimin

2015-11-01

This paper presents a recurrent neural network (RNN) for computing the Drazin inverse of a real matrix in real time. This recurrent neural network (RNN) is composed of n independent parts (subnetworks), where n is the order of the input matrix. These subnetworks can operate concurrently, so parallel and distributed processing can be achieved. In this way, the computational advantages over the existing sequential algorithms can be attained in real-time applications. The RNN defined in this paper is convenient for an implementation in an electronic circuit. The number of neurons in the neural network is the same as the number of elements in the output matrix, which represents the Drazin inverse. The difference between the proposed RNN and the existing ones for the Drazin inverse computation lies in their network architecture and dynamics. The conditions that ensure the stability of the defined RNN as well as its convergence toward the Drazin inverse are considered. In addition, illustrative examples and examples of application to the practical engineering problems are discussed to show the efficacy of the proposed neural network.

Integrated evolutionary computation neural network quality controller for automated systems

Energy Technology Data Exchange (ETDEWEB)

Patro, S.; Kolarik, W.J. [Texas Tech Univ., Lubbock, TX (United States). Dept. of Industrial Engineering

1999-06-01

With increasing competition in the global market, more and more stringent quality standards and specifications are being demands at lower costs. Manufacturing applications of computing power are becoming more common. The application of neural networks to identification and control of dynamic processes has been discussed. The limitations of using neural networks for control purposes has been pointed out and a different technique, evolutionary computation, has been discussed. The results of identifying and controlling an unstable, dynamic process using evolutionary computation methods has been presented. A framework for an integrated system, using both neural networks and evolutionary computation, has been proposed to identify the process and then control the product quality, in a dynamic, multivariable system, in real-time.

A methodology for performing computer security reviews

International Nuclear Information System (INIS)

Hunteman, W.J.

1991-01-01

DOE Order 5637.1, ''Classified Computer Security,'' requires regular reviews of the computer security activities for an ADP system and for a site. Based on experiences gained in the Los Alamos computer security program through interactions with DOE facilities, we have developed a methodology to aid a site or security officer in performing a comprehensive computer security review. The methodology is designed to aid a reviewer in defining goals of the review (e.g., preparation for inspection), determining security requirements based on DOE policies, determining threats/vulnerabilities based on DOE and local threat guidance, and identifying critical system components to be reviewed. Application of the methodology will result in review procedures and checklists oriented to the review goals, the target system, and DOE policy requirements. The review methodology can be used to prepare for an audit or inspection and as a periodic self-check tool to determine the status of the computer security program for a site or specific ADP system. 1 tab

A methodology for performing computer security reviews

International Nuclear Information System (INIS)

Hunteman, W.J.

1991-01-01

This paper reports on DIE Order 5637.1, Classified Computer Security, which requires regular reviews of the computer security activities for an ADP system and for a site. Based on experiences gained in the Los Alamos computer security program through interactions with DOE facilities, the authors have developed a methodology to aid a site or security officer in performing a comprehensive computer security review. The methodology is designed to aid a reviewer in defining goals of the review (e.g., preparation for inspection), determining security requirements based on DOE policies, determining threats/vulnerabilities based on DOE and local threat guidance, and identifying critical system components to be reviewed. Application of the methodology will result in review procedures and checklists oriented to the review goals, the target system, and DOE policy requirements. The review methodology can be used to prepare for an audit or inspection and as a periodic self-check tool to determine the status of the computer security program for a site or specific ADP system

Computer Network Operations Methodology

Science.gov (United States)

2004-03-01

means of their computer information systems. Disrupt - This type of attack focuses on disrupting as “attackers might surreptitiously reprogram enemy...by reprogramming the computers that control distribution within the power grid. A disruption attack introduces disorder and inhibits the effective...between commanders. The use of methodologies is widespread and done subconsciously to assist individuals in decision making. The processes that

Experimental Demonstrations of Optical Neural Computers

OpenAIRE

Hsu, Ken; Brady, David; Psaltis, Demetri

1988-01-01

We describe two experiments in optical neural computing. In the first a closed optical feedback loop is used to implement auto-associative image recall. In the second a perceptron-like learning algorithm is implemented with photorefractive holography.

Brains--Computers--Machines: Neural Engineering in Science Classrooms

Science.gov (United States)

Chudler, Eric H.; Bergsman, Kristen Clapper

2016-01-01

Neural engineering is an emerging field of high relevance to students, teachers, and the general public. This feature presents online resources that educators and scientists can use to introduce students to neural engineering and to integrate core ideas from the life sciences, physical sciences, social sciences, computer science, and engineering…

Efficient universal computing architectures for decoding neural activity.

Directory of Open Access Journals (Sweden)

Benjamin I Rapoport

Full Text Available The ability to decode neural activity into meaningful control signals for prosthetic devices is critical to the development of clinically useful brain- machine interfaces (BMIs. Such systems require input from tens to hundreds of brain-implanted recording electrodes in order to deliver robust and accurate performance; in serving that primary function they should also minimize power dissipation in order to avoid damaging neural tissue; and they should transmit data wirelessly in order to minimize the risk of infection associated with chronic, transcutaneous implants. Electronic architectures for brain- machine interfaces must therefore minimize size and power consumption, while maximizing the ability to compress data to be transmitted over limited-bandwidth wireless channels. Here we present a system of extremely low computational complexity, designed for real-time decoding of neural signals, and suited for highly scalable implantable systems. Our programmable architecture is an explicit implementation of a universal computing machine emulating the dynamics of a network of integrate-and-fire neurons; it requires no arithmetic operations except for counting, and decodes neural signals using only computationally inexpensive logic operations. The simplicity of this architecture does not compromise its ability to compress raw neural data by factors greater than [Formula: see text]. We describe a set of decoding algorithms based on this computational architecture, one designed to operate within an implanted system, minimizing its power consumption and data transmission bandwidth; and a complementary set of algorithms for learning, programming the decoder, and postprocessing the decoded output, designed to operate in an external, nonimplanted unit. The implementation of the implantable portion is estimated to require fewer than 5000 operations per second. A proof-of-concept, 32-channel field-programmable gate array (FPGA implementation of this portion

Neural networks advances and applications 2

CERN Document Server

Gelenbe, E

1992-01-01

The present volume is a natural follow-up to Neural Networks: Advances and Applications which appeared one year previously. As the title indicates, it combines the presentation of recent methodological results concerning computational models and results inspired by neural networks, and of well-documented applications which illustrate the use of such models in the solution of difficult problems. The volume is balanced with respect to these two orientations: it contains six papers concerning methodological developments and five papers concerning applications and examples illustrating the theoret

The super-Turing computational power of plastic recurrent neural networks.

Science.gov (United States)

Cabessa, Jérémie; Siegelmann, Hava T

2014-12-01

We study the computational capabilities of a biologically inspired neural model where the synaptic weights, the connectivity pattern, and the number of neurons can evolve over time rather than stay static. Our study focuses on the mere concept of plasticity of the model so that the nature of the updates is assumed to be not constrained. In this context, we show that the so-called plastic recurrent neural networks (RNNs) are capable of the precise super-Turing computational power--as the static analog neural networks--irrespective of whether their synaptic weights are modeled by rational or real numbers, and moreover, irrespective of whether their patterns of plasticity are restricted to bi-valued updates or expressed by any other more general form of updating. Consequently, the incorporation of only bi-valued plastic capabilities in a basic model of RNNs suffices to break the Turing barrier and achieve the super-Turing level of computation. The consideration of more general mechanisms of architectural plasticity or of real synaptic weights does not further increase the capabilities of the networks. These results support the claim that the general mechanism of plasticity is crucially involved in the computational and dynamical capabilities of biological neural networks. They further show that the super-Turing level of computation reflects in a suitable way the capabilities of brain-like models of computation.

Computational neural network regression model for Host based Intrusion Detection System

Directory of Open Access Journals (Sweden)

Sunil Kumar Gautam

2016-09-01

Full Text Available The current scenario of information gathering and storing in secure system is a challenging task due to increasing cyber-attacks. There exists computational neural network techniques designed for intrusion detection system, which provide security to single machine and entire network's machine. In this paper, we have used two types of computational neural network models, namely, Generalized Regression Neural Network (GRNN model and Multilayer Perceptron Neural Network (MPNN model for Host based Intrusion Detection System using log files that are generated by a single personal computer. The simulation results show correctly classified percentage of normal and abnormal (intrusion class using confusion matrix. On the basis of results and discussion, we found that the Host based Intrusion Systems Model (HISM significantly improved the detection accuracy while retaining minimum false alarm rate.

Neural network modeling for near wall turbulent flow

International Nuclear Information System (INIS)

Milano, Michele; Koumoutsakos, Petros

2002-01-01

A neural network methodology is developed in order to reconstruct the near wall field in a turbulent flow by exploiting flow fields provided by direct numerical simulations. The results obtained from the neural network methodology are compared with the results obtained from prediction and reconstruction using proper orthogonal decomposition (POD). Using the property that the POD is equivalent to a specific linear neural network, a nonlinear neural network extension is presented. It is shown that for a relatively small additional computational cost nonlinear neural networks provide us with improved reconstruction and prediction capabilities for the near wall velocity fields. Based on these results advantages and drawbacks of both approaches are discussed with an outlook toward the development of near wall models for turbulence modeling and control

Connecting Neural Coding to Number Cognition: A Computational Account

Science.gov (United States)

Prather, Richard W.

2012-01-01

The current study presents a series of computational simulations that demonstrate how the neural coding of numerical magnitude may influence number cognition and development. This includes behavioral phenomena cataloged in cognitive literature such as the development of numerical estimation and operational momentum. Though neural research has…

Inherently stochastic spiking neurons for probabilistic neural computation

KAUST Repository

Al-Shedivat, Maruan

2015-04-01

Neuromorphic engineering aims to design hardware that efficiently mimics neural circuitry and provides the means for emulating and studying neural systems. In this paper, we propose a new memristor-based neuron circuit that uniquely complements the scope of neuron implementations and follows the stochastic spike response model (SRM), which plays a cornerstone role in spike-based probabilistic algorithms. We demonstrate that the switching of the memristor is akin to the stochastic firing of the SRM. Our analysis and simulations show that the proposed neuron circuit satisfies a neural computability condition that enables probabilistic neural sampling and spike-based Bayesian learning and inference. Our findings constitute an important step towards memristive, scalable and efficient stochastic neuromorphic platforms. © 2015 IEEE.

Neural Computations in a Dynamical System with Multiple Time Scales.

Science.gov (United States)

Mi, Yuanyuan; Lin, Xiaohan; Wu, Si

2016-01-01

Neural systems display rich short-term dynamics at various levels, e.g., spike-frequency adaptation (SFA) at the single-neuron level, and short-term facilitation (STF) and depression (STD) at the synapse level. These dynamical features typically cover a broad range of time scales and exhibit large diversity in different brain regions. It remains unclear what is the computational benefit for the brain to have such variability in short-term dynamics. In this study, we propose that the brain can exploit such dynamical features to implement multiple seemingly contradictory computations in a single neural circuit. To demonstrate this idea, we use continuous attractor neural network (CANN) as a working model and include STF, SFA and STD with increasing time constants in its dynamics. Three computational tasks are considered, which are persistent activity, adaptation, and anticipative tracking. These tasks require conflicting neural mechanisms, and hence cannot be implemented by a single dynamical feature or any combination with similar time constants. However, with properly coordinated STF, SFA and STD, we show that the network is able to implement the three computational tasks concurrently. We hope this study will shed light on the understanding of how the brain orchestrates its rich dynamics at various levels to realize diverse cognitive functions.

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

Hybrid computing using a neural network with dynamic external memory.

Science.gov (United States)

Graves, Alex; Wayne, Greg; Reynolds, Malcolm; Harley, Tim; Danihelka, Ivo; Grabska-Barwińska, Agnieszka; Colmenarejo, Sergio Gómez; Grefenstette, Edward; Ramalho, Tiago; Agapiou, John; Badia, Adrià Puigdomènech; Hermann, Karl Moritz; Zwols, Yori; Ostrovski, Georg; Cain, Adam; King, Helen; Summerfield, Christopher; Blunsom, Phil; Kavukcuoglu, Koray; Hassabis, Demis

2016-10-27

Artificial neural networks are remarkably adept at sensory processing, sequence learning and reinforcement learning, but are limited in their ability to represent variables and data structures and to store data over long timescales, owing to the lack of an external memory. Here we introduce a machine learning model called a differentiable neural computer (DNC), which consists of a neural network that can read from and write to an external memory matrix, analogous to the random-access memory in a conventional computer. Like a conventional computer, it can use its memory to represent and manipulate complex data structures, but, like a neural network, it can learn to do so from data. When trained with supervised learning, we demonstrate that a DNC can successfully answer synthetic questions designed to emulate reasoning and inference problems in natural language. We show that it can learn tasks such as finding the shortest path between specified points and inferring the missing links in randomly generated graphs, and then generalize these tasks to specific graphs such as transport networks and family trees. When trained with reinforcement learning, a DNC can complete a moving blocks puzzle in which changing goals are specified by sequences of symbols. Taken together, our results demonstrate that DNCs have the capacity to solve complex, structured tasks that are inaccessible to neural networks without external read-write memory.

Computational modeling of spiking neural network with learning rules from STDP and intrinsic plasticity

Science.gov (United States)

Li, Xiumin; Wang, Wei; Xue, Fangzheng; Song, Yongduan

2018-02-01

Recently there has been continuously increasing interest in building up computational models of spiking neural networks (SNN), such as the Liquid State Machine (LSM). The biologically inspired self-organized neural networks with neural plasticity can enhance the capability of computational performance, with the characteristic features of dynamical memory and recurrent connection cycles which distinguish them from the more widely used feedforward neural networks. Despite a variety of computational models for brain-like learning and information processing have been proposed, the modeling of self-organized neural networks with multi-neural plasticity is still an important open challenge. The main difficulties lie in the interplay among different forms of neural plasticity rules and understanding how structures and dynamics of neural networks shape the computational performance. In this paper, we propose a novel approach to develop the models of LSM with a biologically inspired self-organizing network based on two neural plasticity learning rules. The connectivity among excitatory neurons is adapted by spike-timing-dependent plasticity (STDP) learning; meanwhile, the degrees of neuronal excitability are regulated to maintain a moderate average activity level by another learning rule: intrinsic plasticity (IP). Our study shows that LSM with STDP+IP performs better than LSM with a random SNN or SNN obtained by STDP alone. The noticeable improvement with the proposed method is due to the better reflected competition among different neurons in the developed SNN model, as well as the more effectively encoded and processed relevant dynamic information with its learning and self-organizing mechanism. This result gives insights to the optimization of computational models of spiking neural networks with neural plasticity.

Advances in Artificial Neural Networks – Methodological Development and Application

Directory of Open Access Journals (Sweden)

Yanbo Huang

2009-08-01

Full Text Available Artificial neural networks as a major soft-computing technology have been extensively studied and applied during the last three decades. Research on backpropagation training algorithms for multilayer perceptron networks has spurred development of other neural network training algorithms for other networks such as radial basis function, recurrent network, feedback network, and unsupervised Kohonen self-organizing network. These networks, especially the multilayer perceptron network with a backpropagation training algorithm, have gained recognition in research and applications in various scientific and engineering areas. In order to accelerate the training process and overcome data over-fitting, research has been conducted to improve the backpropagation algorithm. Further, artificial neural networks have been integrated with other advanced methods such as fuzzy logic and wavelet analysis, to enhance the ability of data interpretation and modeling and to avoid subjectivity in the operation of the training algorithm. In recent years, support vector machines have emerged as a set of high-performance supervised generalized linear classifiers in parallel with artificial neural networks. A review on development history of artificial neural networks is presented and the standard architectures and algorithms of artificial neural networks are described. Furthermore, advanced artificial neural networks will be introduced with support vector machines, and limitations of ANNs will be identified. The future of artificial neural network development in tandem with support vector machines will be discussed in conjunction with further applications to food science and engineering, soil and water relationship for crop management, and decision support for precision agriculture. Along with the network structures and training algorithms, the applications of artificial neural networks will be reviewed as well, especially in the fields of agricultural and biological

Unification of behavioural, computational and neural accounts of word production errors in post-stroke aphasia

Directory of Open Access Journals (Sweden)

Marija Tochadse

Full Text Available Neuropsychological assessment, brain imaging and computational modelling have augmented our understanding of the multifaceted functional deficits in people with language disorders after stroke. Despite the volume of research using each technique, no studies have attempted to assimilate all three approaches in order to generate a unified behavioural-computational-neural model of post-stroke aphasia.The present study included data from 53 participants with chronic post-stroke aphasia and merged: aphasiological profiles based on a detailed neuropsychological assessment battery which was analysed with principal component and correlational analyses; measures of the impairment taken from Dell's computational model of word production; and the neural correlates of both behavioural and computational accounts analysed by voxel-based correlational methodology.As a result, all three strands coincide with the separation of semantic and phonological stages of aphasic naming, revealing the prominence of these dimensions for the explanation of aphasic performance. Over and above three previously described principal components (phonological ability, semantic ability, executive-demand, we observed auditory working memory as a novel factor. While the phonological Dell parameter was uniquely related to phonological errors/factor, the semantic parameter was less clear-cut, being related to both semantic errors and omissions, and loading heavily with semantic ability and auditory working memory factors. The close relationship between the semantic Dell parameter and omission errors recurred in their high lesion-correlate overlap in the anterior middle temporal gyrus. In addition, the simultaneous overlap of the lesion correlate of omission errors with more dorsal temporal regions, associated with the phonological parameter, highlights the multiple drivers that underpin this error type. The novel auditory working memory factor was located along left superior

Modeling of Throughput in Production Lines Using Response Surface Methodology and Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Federico Nuñez-Piña

2018-01-01

Full Text Available The problem of assigning buffers in a production line to obtain an optimum production rate is a combinatorial problem of type NP-Hard and it is known as Buffer Allocation Problem. It is of great importance for designers of production systems due to the costs involved in terms of space requirements. In this work, the relationship among the number of buffer slots, the number of work stations, and the production rate is studied. Response surface methodology and artificial neural network were used to develop predictive models to find optimal throughput values. 360 production rate values for different number of buffer slots and workstations were used to obtain a fourth-order mathematical model and four hidden layers’ artificial neural network. Both models have a good performance in predicting the throughput, although the artificial neural network model shows a better fit (R=1.0000 against the response surface methodology (R=0.9996. Moreover, the artificial neural network produces better predictions for data not utilized in the models construction. Finally, this study can be used as a guide to forecast the maximum or near maximum throughput of production lines taking into account the buffer size and the number of machines in the line.

Current trends in Bayesian methodology with applications

CERN Document Server

Upadhyay, Satyanshu K; Dey, Dipak K; Loganathan, Appaia

2015-01-01

Collecting Bayesian material scattered throughout the literature, Current Trends in Bayesian Methodology with Applications examines the latest methodological and applied aspects of Bayesian statistics. The book covers biostatistics, econometrics, reliability and risk analysis, spatial statistics, image analysis, shape analysis, Bayesian computation, clustering, uncertainty assessment, high-energy astrophysics, neural networking, fuzzy information, objective Bayesian methodologies, empirical Bayes methods, small area estimation, and many more topics.Each chapter is self-contained and focuses on

Seismic activity prediction using computational intelligence techniques in northern Pakistan

Science.gov (United States)

Asim, Khawaja M.; Awais, Muhammad; Martínez-Álvarez, F.; Iqbal, Talat

2017-10-01

Earthquake prediction study is carried out for the region of northern Pakistan. The prediction methodology includes interdisciplinary interaction of seismology and computational intelligence. Eight seismic parameters are computed based upon the past earthquakes. Predictive ability of these eight seismic parameters is evaluated in terms of information gain, which leads to the selection of six parameters to be used in prediction. Multiple computationally intelligent models have been developed for earthquake prediction using selected seismic parameters. These models include feed-forward neural network, recurrent neural network, random forest, multi layer perceptron, radial basis neural network, and support vector machine. The performance of every prediction model is evaluated and McNemar's statistical test is applied to observe the statistical significance of computational methodologies. Feed-forward neural network shows statistically significant predictions along with accuracy of 75% and positive predictive value of 78% in context of northern Pakistan.

Integrated Markov-neural reliability computation method: A case for multiple automated guided vehicle system

International Nuclear Information System (INIS)

Fazlollahtabar, Hamed; Saidi-Mehrabad, Mohammad; Balakrishnan, Jaydeep

2015-01-01

This paper proposes an integrated Markovian and back propagation neural network approaches to compute reliability of a system. While states of failure occurrences are significant elements for accurate reliability computation, Markovian based reliability assessment method is designed. Due to drawbacks shown by Markovian model for steady state reliability computations and neural network for initial training pattern, integration being called Markov-neural is developed and evaluated. To show efficiency of the proposed approach comparative analyses are performed. Also, for managerial implication purpose an application case for multiple automated guided vehicles (AGVs) in manufacturing networks is conducted. - Highlights: • Integrated Markovian and back propagation neural network approach to compute reliability. • Markovian based reliability assessment method. • Managerial implication is shown in an application case for multiple automated guided vehicles (AGVs) in manufacturing networks

Learning from large scale neural simulations

DEFF Research Database (Denmark)

Serban, Maria

2017-01-01

Large-scale neural simulations have the marks of a distinct methodology which can be fruitfully deployed to advance scientific understanding of the human brain. Computer simulation studies can be used to produce surrogate observational data for better conceptual models and new how...

Methodology of Implementation of Computer Forensics

OpenAIRE

Gelev, Saso; Golubovski, Roman; Hristov, Risto; Nikolov, Elenior

2013-01-01

Compared to other sciences, computer forensics (digital forensics) is a relatively young discipline. It was established in 1999 and it has been an irreplaceable tool in sanctioning cybercrime ever since. Good knowledge of computer forensics can be really helpful in uncovering a committed crime. Not adhering to the methodology of computer forensics, however, makes the obtained evidence invalid/irrelevant and as such it cannot be used in legal proceedings. This paper is to explain the methodolo...

The neural correlates of problem states: testing FMRI predictions of a computational model of multitasking.

Directory of Open Access Journals (Sweden)

Jelmer P Borst

Full Text Available BACKGROUND: It has been shown that people can only maintain one problem state, or intermediate mental representation, at a time. When more than one problem state is required, for example in multitasking, performance decreases considerably. This effect has been explained in terms of a problem state bottleneck. METHODOLOGY: In the current study we use the complimentary methodologies of computational cognitive modeling and neuroimaging to investigate the neural correlates of this problem state bottleneck. In particular, an existing computational cognitive model was used to generate a priori fMRI predictions for a multitasking experiment in which the problem state bottleneck plays a major role. Hemodynamic responses were predicted for five brain regions, corresponding to five cognitive resources in the model. Most importantly, we predicted the intraparietal sulcus to show a strong effect of the problem state manipulations. CONCLUSIONS: Some of the predictions were confirmed by a subsequent fMRI experiment, while others were not matched by the data. The experiment supported the hypothesis that the problem state bottleneck is a plausible cause of the interference in the experiment and that it could be located in the intraparietal sulcus.

Advanced neural network-based computational schemes for robust fault diagnosis

CERN Document Server

Mrugalski, Marcin

2014-01-01

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural networks-based modelling and estimation techniques used for designing robust fault diagnosis schemes for non-linear dynamic systems. A part of the book focuses on fundamental issues such as architectures of dynamic neural networks, methods for designing of neural networks and fault diagnosis schemes as well as the importance of robustness. The book is of a tutorial value and can be perceived as a good starting point for the new-comers to this field. The book is also devoted to advanced schemes of description of neural model uncertainty. In particular, the methods of computation of neural networks uncertainty with robust parameter estimation are presented. Moreover, a novel approach for system identification with the state-space GMDH neural network is delivered. All the concepts described in this book are illustrated by both simple academic illustrative examples and practica...

Computationally efficient model predictive control algorithms a neural network approach

CERN Document Server

Ławryńczuk, Maciej

2014-01-01

This book thoroughly discusses computationally efficient (suboptimal) Model Predictive Control (MPC) techniques based on neural models. The subjects treated include: ·         A few types of suboptimal MPC algorithms in which a linear approximation of the model or of the predicted trajectory is successively calculated on-line and used for prediction. ·         Implementation details of the MPC algorithms for feedforward perceptron neural models, neural Hammerstein models, neural Wiener models and state-space neural models. ·         The MPC algorithms based on neural multi-models (inspired by the idea of predictive control). ·         The MPC algorithms with neural approximation with no on-line linearization. ·         The MPC algorithms with guaranteed stability and robustness. ·         Cooperation between the MPC algorithms and set-point optimization. Thanks to linearization (or neural approximation), the presented suboptimal algorithms do not require d...

Advances in neural networks computational intelligence for ICT

CERN Document Server

Esposito, Anna; Morabito, Francesco; Pasero, Eros

2016-01-01

This carefully edited book is putting emphasis on computational and artificial intelligent methods for learning and their relative applications in robotics, embedded systems, and ICT interfaces for psychological and neurological diseases. The book is a follow-up of the scientific workshop on Neural Networks (WIRN 2015) held in Vietri sul Mare, Italy, from the 20th to the 22nd of May 2015. The workshop, at its 27th edition became a traditional scientific event that brought together scientists from many countries, and several scientific disciplines. Each chapter is an extended version of the original contribution presented at the workshop, and together with the reviewers’ peer revisions it also benefits from the live discussion during the presentation. The content of book is organized in the following sections. 1. Introduction, 2. Machine Learning, 3. Artificial Neural Networks: Algorithms and models, 4. Intelligent Cyberphysical and Embedded System, 5. Computational Intelligence Methods for Biomedical ICT in...

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-01

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

Spatiotemporal Dynamics and Reliable Computations in Recurrent Spiking Neural Networks.

Science.gov (United States)

Pyle, Ryan; Rosenbaum, Robert

2017-01-06

Randomly connected networks of excitatory and inhibitory spiking neurons provide a parsimonious model of neural variability, but are notoriously unreliable for performing computations. We show that this difficulty is overcome by incorporating the well-documented dependence of connection probability on distance. Spatially extended spiking networks exhibit symmetry-breaking bifurcations and generate spatiotemporal patterns that can be trained to perform dynamical computations under a reservoir computing framework.

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.

Fusion of neural computing and PLS techniques for load estimation

Energy Technology Data Exchange (ETDEWEB)

Lu, M.; Xue, H.; Cheng, X. [Northwestern Polytechnical Univ., Xi' an (China); Zhang, W. [Xi' an Inst. of Post and Telecommunication, Xi' an (China)

2007-07-01

A method to predict the electric load of a power system in real time was presented. The method is based on neurocomputing and partial least squares (PLS). Short-term load forecasts for power systems are generally determined by conventional statistical methods and Computational Intelligence (CI) techniques such as neural computing. However, statistical modeling methods often require the input of questionable distributional assumptions, and neural computing is weak, particularly in determining topology. In order to overcome the problems associated with conventional techniques, the authors developed a CI hybrid model based on neural computation and PLS techniques. The theoretical foundation for the designed CI hybrid model was presented along with its application in a power system. The hybrid model is suitable for nonlinear modeling and latent structure extracting. It can automatically determine the optimal topology to maximize the generalization. The CI hybrid model provides faster convergence and better prediction results compared to the abductive networks model because it incorporates a load conversion technique as well as new transfer functions. In order to demonstrate the effectiveness of the hybrid model, load forecasting was performed on a data set obtained from the Puget Sound Power and Light Company. Compared with the abductive networks model, the CI hybrid model reduced the forecast error by 32.37 per cent on workday, and by an average of 27.18 per cent on the weekend. It was concluded that the CI hybrid model has a more powerful predictive ability. 7 refs., 1 tab., 3 figs.

TEACHING AND LEARNING METHODOLOGIES SUPPORTED BY ICT APPLIED IN COMPUTER SCIENCE

Directory of Open Access Journals (Sweden)

Jose CAPACHO

2016-04-01

Full Text Available The main objective of this paper is to show a set of new methodologies applied in the teaching of Computer Science using ICT. The methodologies are framed in the conceptual basis of the following sciences: Psychology, Education and Computer Science. The theoretical framework of the research is supported by Behavioral Theory, Gestalt Theory. Genetic-Cognitive Psychology Theory and Dialectics Psychology. Based on the theoretical framework the following methodologies were developed: Game Theory, Constructivist Approach, Personalized Teaching, Problem Solving, Cooperative Collaborative learning, Learning projects using ICT. These methodologies were applied to the teaching learning process during the Algorithms and Complexity – A&C course, which belongs to the area of ​​Computer Science. The course develops the concepts of Computers, Complexity and Intractability, Recurrence Equations, Divide and Conquer, Greedy Algorithms, Dynamic Programming, Shortest Path Problem and Graph Theory. The main value of the research is the theoretical support of the methodologies and their application supported by ICT using learning objects. The course aforementioned was built on the Blackboard platform evaluating the operation of methodologies. The results of the evaluation are presented for each of them, showing the learning outcomes achieved by students, which verifies that methodologies are functional.

A Simulation-Based Soft Error Estimation Methodology for Computer Systems

OpenAIRE

Sugihara, Makoto; Ishihara, Tohru; Hashimoto, Koji; Muroyama, Masanori

2006-01-01

This paper proposes a simulation-based soft error estimation methodology for computer systems. Accumulating soft error rates (SERs) of all memories in a computer system results in pessimistic soft error estimation. This is because memory cells are used spatially and temporally and not all soft errors in them make the computer system faulty. Our soft-error estimation methodology considers the locations and the timings of soft errors occurring at every level of memory hierarchy and estimates th...

Application of two neural network paradigms to the study of voluntary employee turnover.

Science.gov (United States)

Somers, M J

1999-04-01

Two neural network paradigms--multilayer perceptron and learning vector quantization--were used to study voluntary employee turnover with a sample of 577 hospital employees. The objectives of the study were twofold. The 1st was to assess whether neural computing techniques offered greater predictive accuracy than did conventional turnover methodologies. The 2nd was to explore whether computer models of turnover based on neural network technologies offered new insights into turnover processes. When compared with logistic regression analysis, both neural network paradigms provided considerably more accurate predictions of turnover behavior, particularly with respect to the correct classification of leavers. In addition, these neural network paradigms captured nonlinear relationships that are relevant for theory development. Results are discussed in terms of their implications for future research.

Biological neural networks as model systems for designing future parallel processing computers

Science.gov (United States)

Ross, Muriel D.

1991-01-01

One of the more interesting debates of the present day centers on whether human intelligence can be simulated by computer. The author works under the premise that neurons individually are not smart at all. Rather, they are physical units which are impinged upon continuously by other matter that influences the direction of voltage shifts across the units membranes. It is only the action of a great many neurons, billions in the case of the human nervous system, that intelligent behavior emerges. What is required to understand even the simplest neural system is painstaking analysis, bit by bit, of the architecture and the physiological functioning of its various parts. The biological neural network studied, the vestibular utricular and saccular maculas of the inner ear, are among the most simple of the mammalian neural networks to understand and model. While there is still a long way to go to understand even this most simple neural network in sufficient detail for extrapolation to computers and robots, a start was made. Moreover, the insights obtained and the technologies developed help advance the understanding of the more complex neural networks that underlie human intelligence.

Automated sleep stage detection with a classical and a neural learning algorithm--methodological aspects.

Science.gov (United States)

Schwaibold, M; Schöchlin, J; Bolz, A

2002-01-01

For classification tasks in biosignal processing, several strategies and algorithms can be used. Knowledge-based systems allow prior knowledge about the decision process to be integrated, both by the developer and by self-learning capabilities. For the classification stages in a sleep stage detection framework, three inference strategies were compared regarding their specific strengths: a classical signal processing approach, artificial neural networks and neuro-fuzzy systems. Methodological aspects were assessed to attain optimum performance and maximum transparency for the user. Due to their effective and robust learning behavior, artificial neural networks could be recommended for pattern recognition, while neuro-fuzzy systems performed best for the processing of contextual information.

Instrumentation for Scientific Computing in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics.

Science.gov (United States)

1987-10-01

include Security Classification) Instrumentation for scientific computing in neural networks, information science, artificial intelligence, and...instrumentation grant to purchase equipment for support of research in neural networks, information science, artificail intellignece , and applied mathematics...in Neural Networks, Information Science, Artificial Intelligence, and Applied Mathematics Contract AFOSR 86-0282 Principal Investigator: Stephen

THE COMPUTATIONAL INTELLIGENCE TECHNIQUES FOR PREDICTIONS - ARTIFICIAL NEURAL NETWORKS

OpenAIRE

Mary Violeta Bar

2014-01-01

The computational intelligence techniques are used in problems which can not be solved by traditional techniques when there is insufficient data to develop a model problem or when they have errors.Computational intelligence, as he called Bezdek (Bezdek, 1992) aims at modeling of biological intelligence. Artificial Neural Networks( ANNs) have been applied to an increasing number of real world problems of considerable complexity. Their most important advantage is solving problems that are too c...

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

A Design Methodology for Computer Security Testing

OpenAIRE

Ramilli, Marco

2013-01-01

The field of "computer security" is often considered something in between Art and Science. This is partly due to the lack of widely agreed and standardized methodologies to evaluate the degree of the security of a system. This dissertation intends to contribute to this area by investigating the most common security testing strategies applied nowadays and by proposing an enhanced methodology that may be effectively applied to different threat scenarios with the same degree of effectiveness. ...

Container-code recognition system based on computer vision and deep neural networks

Science.gov (United States)

Liu, Yi; Li, Tianjian; Jiang, Li; Liang, Xiaoyao

2018-04-01

Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.

Computational neuroanatomy: ontology-based representation of neural components and connectivity.

Science.gov (United States)

Rubin, Daniel L; Talos, Ion-Florin; Halle, Michael; Musen, Mark A; Kikinis, Ron

2009-02-05

A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future.

From biological neural networks to thinking machines: Transitioning biological organizational principles to computer technology

Science.gov (United States)

Ross, Muriel D.

1991-01-01

The three-dimensional organization of the vestibular macula is under study by computer assisted reconstruction and simulation methods as a model for more complex neural systems. One goal of this research is to transition knowledge of biological neural network architecture and functioning to computer technology, to contribute to the development of thinking computers. Maculas are organized as weighted neural networks for parallel distributed processing of information. The network is characterized by non-linearity of its terminal/receptive fields. Wiring appears to develop through constrained randomness. A further property is the presence of two main circuits, highly channeled and distributed modifying, that are connected through feedforward-feedback collaterals and biasing subcircuit. Computer simulations demonstrate that differences in geometry of the feedback (afferent) collaterals affects the timing and the magnitude of voltage changes delivered to the spike initiation zone. Feedforward (efferent) collaterals act as voltage followers and likely inhibit neurons of the distributed modifying circuit. These results illustrate the importance of feedforward-feedback loops, of timing, and of inhibition in refining neural network output. They also suggest that it is the distributed modifying network that is most involved in adaptation, memory, and learning. Tests of macular adaptation, through hyper- and microgravitational studies, support this hypothesis since synapses in the distributed modifying circuit, but not the channeled circuit, are altered. Transitioning knowledge of biological systems to computer technology, however, remains problematical.

Human Inspired Self-developmental Model of Neural Network (HIM): Introducing Content/Form Computing

Science.gov (United States)

Krajíček, Jiří

This paper presents cross-disciplinary research between medical/psychological evidence on human abilities and informatics needs to update current models in computer science to support alternative methods for computation and communication. In [10] we have already proposed hypothesis introducing concept of human information model (HIM) as cooperative system. Here we continue on HIM design in detail. In our design, first we introduce Content/Form computing system which is new principle of present methods in evolutionary computing (genetic algorithms, genetic programming). Then we apply this system on HIM (type of artificial neural network) model as basic network self-developmental paradigm. Main inspiration of our natural/human design comes from well known concept of artificial neural networks, medical/psychological evidence and Sheldrake theory of "Nature as Alive" [22].

Neural Computations in a Dynamical System with Multiple Time Scales

Directory of Open Access Journals (Sweden)

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.

Reversible logic synthesis methodologies with application to quantum computing

CERN Document Server

Taha, Saleem Mohammed Ridha

2016-01-01

This book opens the door to a new interesting and ambitious world of reversible and quantum computing research. It presents the state of the art required to travel around that world safely. Top world universities, companies and government institutions  are in a race of developing new methodologies, algorithms and circuits on reversible logic, quantum logic, reversible and quantum computing and nano-technologies. In this book, twelve reversible logic synthesis methodologies are presented for the first time in a single literature with some new proposals. Also, the sequential reversible logic circuitries are discussed for the first time in a book. Reversible logic plays an important role in quantum computing. Any progress in the domain of reversible logic can be directly applied to quantum logic. One of the goals of this book is to show the application of reversible logic in quantum computing. A new implementation of wavelet and multiwavelet transforms using quantum computing is performed for this purpose. Rese...

Computing single step operators of logic programming in radial basis function neural networks

Science.gov (United States)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-07-01

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (Tp:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

Computing single step operators of logic programming in radial basis function neural networks

Energy Technology Data Exchange (ETDEWEB)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong [School of Mathematical Sciences, Universiti Sains Malaysia, 11800 USM, Penang (Malaysia)

2014-07-10

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T{sub p}:I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks.

Computing single step operators of logic programming in radial basis function neural networks

International Nuclear Information System (INIS)

Hamadneh, Nawaf; Sathasivam, Saratha; Choon, Ong Hong

2014-01-01

Logic programming is the process that leads from an original formulation of a computing problem to executable programs. A normal logic program consists of a finite set of clauses. A valuation I of logic programming is a mapping from ground atoms to false or true. The single step operator of any logic programming is defined as a function (T p :I→I). Logic programming is well-suited to building the artificial intelligence systems. In this study, we established a new technique to compute the single step operators of logic programming in the radial basis function neural networks. To do that, we proposed a new technique to generate the training data sets of single step operators. The training data sets are used to build the neural networks. We used the recurrent radial basis function neural networks to get to the steady state (the fixed point of the operators). To improve the performance of the neural networks, we used the particle swarm optimization algorithm to train the networks

Optics in neural computation

Science.gov (United States)

Levene, Michael John

In all attempts to emulate the considerable powers of the brain, one is struck by both its immense size, parallelism, and complexity. While the fields of neural networks, artificial intelligence, and neuromorphic engineering have all attempted oversimplifications on the considerable complexity, all three can benefit from the inherent scalability and parallelism of optics. This thesis looks at specific aspects of three modes in which optics, and particularly volume holography, can play a part in neural computation. First, holography serves as the basis of highly-parallel correlators, which are the foundation of optical neural networks. The huge input capability of optical neural networks make them most useful for image processing and image recognition and tracking. These tasks benefit from the shift invariance of optical correlators. In this thesis, I analyze the capacity of correlators, and then present several techniques for controlling the amount of shift invariance. Of particular interest is the Fresnel correlator, in which the hologram is displaced from the Fourier plane. In this case, the amount of shift invariance is limited not just by the thickness of the hologram, but by the distance of the hologram from the Fourier plane. Second, volume holography can provide the huge storage capacity and high speed, parallel read-out necessary to support large artificial intelligence systems. However, previous methods for storing data in volume holograms have relied on awkward beam-steering or on as-yet non- existent cheap, wide-bandwidth, tunable laser sources. This thesis presents a new technique, shift multiplexing, which is capable of very high densities, but which has the advantage of a very simple implementation. In shift multiplexing, the reference wave consists of a focused spot a few millimeters in front of the hologram. Multiplexing is achieved by simply translating the hologram a few tens of microns or less. This thesis describes the theory for how shift

Teaching and Learning Methodologies Supported by ICT Applied in Computer Science

Science.gov (United States)

Capacho, Jose

2016-01-01

The main objective of this paper is to show a set of new methodologies applied in the teaching of Computer Science using ICT. The methodologies are framed in the conceptual basis of the following sciences: Psychology, Education and Computer Science. The theoretical framework of the research is supported by Behavioral Theory, Gestalt Theory.…

Computational modeling of neural activities for statistical inference

CERN Document Server

Kolossa, Antonio

2016-01-01

This authored monograph supplies empirical evidence for the Bayesian brain hypothesis by modeling event-related potentials (ERP) of the human electroencephalogram (EEG) during successive trials in cognitive tasks. The employed observer models are useful to compute probability distributions over observable events and hidden states, depending on which are present in the respective tasks. Bayesian model selection is then used to choose the model which best explains the ERP amplitude fluctuations. Thus, this book constitutes a decisive step towards a better understanding of the neural coding and computing of probabilities following Bayesian rules. The target audience primarily comprises research experts in the field of computational neurosciences, but the book may also be beneficial for graduate students who want to specialize in this field. .

Optimization of blanking process using neural network simulation

International Nuclear Information System (INIS)

Hambli, R.

2005-01-01

The present work describes a methodology using the finite element method and neural network simulation in order to predict the optimum punch-die clearance during sheet metal blanking processes. A damage model is used in order to describe crack initiation and propagation into the sheet. The proposed approach combines predictive finite element and neural network modeling of the leading blanking parameters. Numerical results obtained by finite element computation including damage and fracture modeling were utilized to train the developed simulation environment based on back propagation neural network modeling. The comparative study between the numerical results and the experimental ones shows the good agreement. (author)

Intelligent computing systems emerging application areas

CERN Document Server

Virvou, Maria; Jain, Lakhmi

2016-01-01

This book at hand explores emerging scientific and technological areas in which Intelligent Computing Systems provide efficient solutions and, thus, may play a role in the years to come. It demonstrates how Intelligent Computing Systems make use of computational methodologies that mimic nature-inspired processes to address real world problems of high complexity for which exact mathematical solutions, based on physical and statistical modelling, are intractable. Common intelligent computational methodologies are presented including artificial neural networks, evolutionary computation, genetic algorithms, artificial immune systems, fuzzy logic, swarm intelligence, artificial life, virtual worlds and hybrid methodologies based on combinations of the previous. The book will be useful to researchers, practitioners and graduate students dealing with mathematically-intractable problems. It is intended for both the expert/researcher in the field of Intelligent Computing Systems, as well as for the general reader in t...

Development of Fast-Running Simulation Methodology Using Neural Networks for Load Follow Operation

International Nuclear Information System (INIS)

Seong, Seung-Hwan; Park, Heui-Youn; Kim, Dong-Hoon; Suh, Yong-Suk; Hur, Seop; Koo, In-Soo; Lee, Un-Chul; Jang, Jin-Wook; Shin, Yong-Chul

2002-01-01

A new fast-running analytic model has been developed for analyzing the load follow operation. The new model was based on the neural network theory, which has the capability of modeling the input/output relationships of a nonlinear system. The new model is made up of two error back-propagation neural networks and procedures to calculate core parameters, such as the distributions and density of xenon in a quasi-steady-state core like load follow operation. One neural network is designed to retrieve the axial offset of power distribution, and the other is for reactivity corresponding to a given core condition. The training data sets for learning the neural networks in the new model are generated with a three-dimensional nodal code and, also, the measured data of the first-day test of load follow operation. Using the new model, the simulation results of the 5-day load follow test in a pressurized water reactor show a good agreement between the simulation data and the actual measured data. Required computing time for simulating a load follow operation is comparable to that of a fast-running lumped model. Moreover, the new model does not require additional engineering factors to compensate for the difference between the actual measurements and analysis results because the neural network has the inherent learning capability of neural networks to new situations

A neural algorithm for a fundamental computing problem.

Science.gov (United States)

Dasgupta, Sanjoy; Stevens, Charles F; Navlakha, Saket

2017-11-10

Similarity search-for example, identifying similar images in a database or similar documents on the web-is a fundamental computing problem faced by large-scale information retrieval systems. We discovered that the fruit fly olfactory circuit solves this problem with a variant of a computer science algorithm (called locality-sensitive hashing). The fly circuit assigns similar neural activity patterns to similar odors, so that behaviors learned from one odor can be applied when a similar odor is experienced. The fly algorithm, however, uses three computational strategies that depart from traditional approaches. These strategies can be translated to improve the performance of computational similarity searches. This perspective helps illuminate the logic supporting an important sensory function and provides a conceptually new algorithm for solving a fundamental computational problem. Copyright © 2017 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.

Neural computation of visual imaging based on Kronecker product in the primary visual cortex

Directory of Open Access Journals (Sweden)

Guozheng Yao

2010-03-01

Full Text Available Abstract Background What kind of neural computation is actually performed by the primary visual cortex and how is this represented mathematically at the system level? It is an important problem in the visual information processing, but has not been well answered. In this paper, according to our understanding of retinal organization and parallel multi-channel topographical mapping between retina and primary visual cortex V1, we divide an image into orthogonal and orderly array of image primitives (or patches, in which each patch will evoke activities of simple cells in V1. From viewpoint of information processing, this activated process, essentially, involves optimal detection and optimal matching of receptive fields of simple cells with features contained in image patches. For the reconstruction of the visual image in the visual cortex V1 based on the principle of minimum mean squares error, it is natural to use the inner product expression in neural computation, which then is transformed into matrix form. Results The inner product is carried out by using Kronecker product between patches and function architecture (or functional column in localized and oriented neural computing. Compared with Fourier Transform, the mathematical description of Kronecker product is simple and intuitive, so is the algorithm more suitable for neural computation of visual cortex V1. Results of computer simulation based on two-dimensional Gabor pyramid wavelets show that the theoretical analysis and the proposed model are reasonable. Conclusions Our results are: 1. The neural computation of the retinal image in cortex V1 can be expressed to Kronecker product operation and its matrix form, this algorithm is implemented by the inner operation between retinal image primitives and primary visual cortex's column. It has simple, efficient and robust features, which is, therefore, such a neural algorithm, which can be completed by biological vision. 2. It is more suitable

SHIPBUILDING PRODUCTION PROCESS DESIGN METHODOLOGY USING COMPUTER SIMULATION

OpenAIRE

Marko Hadjina; Nikša Fafandjel; Tin Matulja

2015-01-01

In this research a shipbuilding production process design methodology, using computer simulation, is suggested. It is expected from suggested methodology to give better and more efficient tool for complex shipbuilding production processes design procedure. Within the first part of this research existing practice for production process design in shipbuilding was discussed, its shortcomings and problem were emphasized. In continuing, discrete event simulation modelling method, as basis of sugge...

Depth perception in frogs and toads a study in neural computing

CERN Document Server

House, Donald

1989-01-01

Depth Perception in Frogs and Toads provides a comprehensive exploration of the phenomenon of depth perception in frogs and toads, as seen from a neuro-computational point of view. Perhaps the most important feature of the book is the development and presentation of two neurally realizable depth perception algorithms that utilize both monocular and binocular depth cues in a cooperative fashion. One of these algorithms is specialized for computation of depth maps for navigation, and the other for the selection and localization of a single prey for prey catching. The book is also unique in that it thoroughly reviews the known neuroanatomical, neurophysiological and behavioral data, and then synthesizes, organizes and interprets that information to explain a complex sensory-motor task. The book will be of special interest to that segment of the neural computing community interested in understanding natural neurocomputational structures, particularly to those working in perception and sensory-motor coordination. ...

Quantum perceptron over a field and neural network architecture selection in a quantum computer.

Science.gov (United States)

da Silva, Adenilton José; Ludermir, Teresa Bernarda; de Oliveira, Wilson Rosa

2016-04-01

In this work, we propose a quantum neural network named quantum perceptron over a field (QPF). Quantum computers are not yet a reality and the models and algorithms proposed in this work cannot be simulated in actual (or classical) computers. QPF is a direct generalization of a classical perceptron and solves some drawbacks found in previous models of quantum perceptrons. We also present a learning algorithm named Superposition based Architecture Learning algorithm (SAL) that optimizes the neural network weights and architectures. SAL searches for the best architecture in a finite set of neural network architectures with linear time over the number of patterns in the training set. SAL is the first learning algorithm to determine neural network architectures in polynomial time. This speedup is obtained by the use of quantum parallelism and a non-linear quantum operator. Copyright © 2016 Elsevier Ltd. All rights reserved.

Evolutionary Computation and Its Applications in Neural and Fuzzy Systems

Directory of Open Access Journals (Sweden)

Biaobiao Zhang

2011-01-01

Full Text Available Neural networks and fuzzy systems are two soft-computing paradigms for system modelling. Adapting a neural or fuzzy system requires to solve two optimization problems: structural optimization and parametric optimization. Structural optimization is a discrete optimization problem which is very hard to solve using conventional optimization techniques. Parametric optimization can be solved using conventional optimization techniques, but the solution may be easily trapped at a bad local optimum. Evolutionary computation is a general-purpose stochastic global optimization approach under the universally accepted neo-Darwinian paradigm, which is a combination of the classical Darwinian evolutionary theory, the selectionism of Weismann, and the genetics of Mendel. Evolutionary algorithms are a major approach to adaptation and optimization. In this paper, we first introduce evolutionary algorithms with emphasis on genetic algorithms and evolutionary strategies. Other evolutionary algorithms such as genetic programming, evolutionary programming, particle swarm optimization, immune algorithm, and ant colony optimization are also described. Some topics pertaining to evolutionary algorithms are also discussed, and a comparison between evolutionary algorithms and simulated annealing is made. Finally, the application of EAs to the learning of neural networks as well as to the structural and parametric adaptations of fuzzy systems is also detailed.

New design methods for computer aided architecturald design methodology teaching

NARCIS (Netherlands)

Achten, H.H.

2003-01-01

Architects and architectural students are exploring new ways of design using Computer Aided Architectural Design software. This exploration is seldom backed up from a design methodological viewpoint. In this paper, a design methodological framework for reflection on innovate design processes by

A Neural Information Field Approach to Computational Cognition

Science.gov (United States)

2016-11-18

effects of distraction during list memory . These distractions include short and long delays before recall, and continuous distraction (forced rehearsal... memory encoding and replay in hippocampus. Computational Neuroscience Society (CNS), p. 166, 2014. D. A. Pinotsis, Neural Field Coding of Short Term ...performance of children learning to count in a SPA model; proposed a new SPA model of cognitive load using the N-back task; developed a new model of the

Methodology of modeling and measuring computer architectures for plasma simulations

Science.gov (United States)

Wang, L. P. T.

1977-01-01

A brief introduction to plasma simulation using computers and the difficulties on currently available computers is given. Through the use of an analyzing and measuring methodology - SARA, the control flow and data flow of a particle simulation model REM2-1/2D are exemplified. After recursive refinements the total execution time may be greatly shortened and a fully parallel data flow can be obtained. From this data flow, a matched computer architecture or organization could be configured to achieve the computation bound of an application problem. A sequential type simulation model, an array/pipeline type simulation model, and a fully parallel simulation model of a code REM2-1/2D are proposed and analyzed. This methodology can be applied to other application problems which have implicitly parallel nature.

Artificial intelligence. Application of the Statistical Neural Networks computer program in nuclear medicine

International Nuclear Information System (INIS)

Stefaniak, B.; Cholewinski, W.; Tarkowska, A.

2005-01-01

Artificial Neural Networks (ANN) may be a tool alternative and complementary to typical statistical analysis. However, in spite of many computer application of various ANN algorithms ready for use, artificial intelligence is relatively rarely applied to data processing. In this paper practical aspects of scientific application of ANN in medicine using the Statistical Neural Networks Computer program, were presented. Several steps of data analysis with the above ANN software package were discussed shortly, from material selection and its dividing into groups to the types of obtained results. The typical problems connected with assessing scintigrams by ANN were also described. (author)

From systems biology to dynamical neuropharmacology: proposal for a new methodology.

Science.gov (United States)

Erdi, P; Kiss, T; Tóth, J; Ujfalussy, B; Zalányi, L

2006-07-01

The concepts and methods of systems biology are extended to neuropharmacology in order to test and design drugs for the treatment of neurological and psychiatric disorders. Computational modelling by integrating compartmental neural modelling techniques and detailed kinetic descriptions of pharmacological modulation of transmitter-receptor interaction is offered as a method to test the electrophysiological and behavioural effects of putative drugs. Even more, an inverse method is suggested as a method for controlling a neural system to realise a prescribed temporal pattern. In particular, as an application of the proposed new methodology, a computational platform is offered to analyse the generation and pharmacological modulation of theta rhythm related to anxiety.

Artificial intelligence in pharmaceutical product formulation: neural computing

Directory of Open Access Journals (Sweden)

Svetlana Ibrić

2009-10-01

Full Text Available The properties of a formulation are determined not only by the ratios in which the ingredients are combined but also by the processing conditions. Although the relationships between the ingredient levels, processing conditions, and product performance may be known anecdotally, they can rarely be quantified. In the past, formulators tended to use statistical techniques to model their formulations, relying on response surfaces to provide a mechanism for optimazation. However, the optimization by such a method can be misleading, especially if the formulation is complex. More recently, advances in mathematics and computer science have led to the development of alternative modeling and data mining techniques which work with a wider range of data sources: neural networks (an attempt to mimic the processing of the human brain; genetic algorithms (an attempt to mimic the evolutionary process by which biological systems self-organize and adapt, and fuzzy logic (an attempt to mimic the ability of the human brain to draw conclusions and generate responses based on incomplete or imprecise information. In this review the current technology will be examined, as well as its application in pharmaceutical formulation and processing. The challenges, benefits and future possibilities of neural computing will be discussed.

New Computer Simulations of Macular Neural Functioning

Science.gov (United States)

Ross, Muriel D.; Doshay, D.; Linton, S.; Parnas, B.; Montgomery, K.; Chimento, T.

1994-01-01

We use high performance graphics workstations and supercomputers to study the functional significance of the three-dimensional (3-D) organization of gravity sensors. These sensors have a prototypic architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scaled-up, 3-D versions run on a Cray Y-MP supercomputer. A semi-automated method of reconstruction of neural tissue from serial sections studied in a transmission electron microscope has been developed to eliminate tedious conventional photography. The reconstructions use a mesh as a step in generating a neural surface for visualization. Two meshes are required to model calyx surfaces. The meshes are connected and the resulting prisms represent the cytoplasm and the bounding membranes. A finite volume analysis method is employed to simulate voltage changes along the calyx in response to synapse activation on the calyx or on calyceal processes. The finite volume method insures that charge is conserved at the calyx-process junction. These and other models indicate that efferent processes act as voltage followers, and that the morphology of some afferent processes affects their functioning. In a final application, morphological information is symbolically represented in three dimensions in a computer. The possible functioning of the connectivities is tested using mathematical interpretations of physiological parameters taken from the literature. Symbolic, 3-D simulations are in progress to probe the functional significance of the connectivities. This research is expected to advance computer-based studies of macular functioning and of synaptic plasticity.

Spent fuel management fee methodology and computer code user's manual

International Nuclear Information System (INIS)

Engel, R.L.; White, M.K.

1982-01-01

The methodology and computer model described here were developed to analyze the cash flows for the federal government taking title to and managing spent nuclear fuel. The methodology has been used by the US Department of Energy (DOE) to estimate the spent fuel disposal fee that will provide full cost recovery. Although the methodology was designed to analyze interim storage followed by spent fuel disposal, it could be used to calculate a fee for reprocessing spent fuel and disposing of the waste. The methodology consists of two phases. The first phase estimates government expenditures for spent fuel management. The second phase determines the fees that will result in revenues such that the government attains full cost recovery assuming various revenue collection philosophies. These two phases are discussed in detail in subsequent sections of this report. Each of the two phases constitute a computer module, called SPADE (SPent fuel Analysis and Disposal Economics) and FEAN (FEe ANalysis), respectively

Problems and Issues in Using Computer- Based Support Tools to Enhance 'Soft' Systems Methodologies

Directory of Open Access Journals (Sweden)

Mark Stansfield

2001-11-01

Full Text Available This paper explores the issue of whether computer-based support tools can enhance the use of 'soft' systems methodologies as applied to real-world problem situations. Although work has been carried out by a number of researchers in applying computer-based technology to concepts and methodologies relating to 'soft' systems thinking such as Soft Systems Methodology (SSM, such attempts appear to be still in their infancy and have not been applied widely to real-world problem situations. This paper will highlight some of the problems that may be encountered in attempting to develop computer-based support tools for 'soft' systems methodologies. Particular attention will be paid to an attempt by the author to develop a computer-based support tool for a particular 'soft' systems method of inquiry known as the Appreciative Inquiry Method that is based upon Vickers' notion of 'appreciation' (Vickers, 196S and Checkland's SSM (Checkland, 1981. The final part of the paper will explore some of the lessons learnt from developing and applying the computer-based support tool to a real world problem situation, as well as considering the feasibility of developing computer-based support tools for 'soft' systems methodologies. This paper will put forward the point that a mixture of manual and computer-based tools should be employed to allow a methodology to be used in an unconstrained manner, but the benefits provided by computer-based technology should be utilised in supporting and enhancing the more mundane and structured tasks.

Computer simulation system of neural PID control on nuclear reactor

International Nuclear Information System (INIS)

Chen Yuzhong; Yang Kaijun; Shen Yongping

2001-01-01

Neural network proportional integral differential (PID) controller on nuclear reactor is designed, and the control process is simulated by computer. The simulation result show that neutral network PID controller can automatically adjust its parameter to ideal state, and good control result can be gotten in reactor control process

Selection of window sizes for optimizing occupational comfort and hygiene based on computational fluid dynamics and neural networks

Energy Technology Data Exchange (ETDEWEB)

Stavrakakis, G.M.; Karadimou, D.P.; Zervas, P.L.; Markatos, N.C. [Computational Fluid Dynamics Unit, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, GR-15780 Athens (Greece); Sarimveis, H. [Unit of Process Control and Informatics, School of Chemical Engineering, National Technical University of Athens, Heroon Polytechniou 9, GR-15780 Athens (Greece)

2011-02-15

The present paper presents a novel computational method to optimize window sizes for thermal comfort and indoor air quality in naturally ventilated buildings. The methodology is demonstrated by means of a prototype case, which corresponds to a single-sided naturally ventilated apartment. Initially, the airflow in and around the building is simulated using a Computational Fluid Dynamics model. Local prevailing weather conditions are imposed in the CFD model as inlet boundary conditions. The produced airflow patterns are utilized to predict thermal comfort indices, i.e. the PMV and its modifications for non-air-conditioned buildings, as well as indoor air quality indices, such as ventilation effectiveness based on carbon dioxide and volatile organic compounds removal. Mean values of these indices (output/objective variables) within the occupied zone are calculated for different window sizes (input/design variables), to generate a database of input-output data pairs. The database is then used to train and validate Radial Basis Function Artificial Neural Network input-output ''meta-models''. The produced meta-models are used to formulate an optimization problem, which takes into account special constraints recommended by design guidelines. It is concluded that the proposed methodology determines appropriate windows architectural designs for pleasant and healthy indoor environments. (author)

Large-Scale Recurrent Neural Network Based Modelling of Gene Regulatory Network Using Cuckoo Search-Flower Pollination Algorithm.

Science.gov (United States)

Mandal, Sudip; Khan, Abhinandan; Saha, Goutam; Pal, Rajat K

2016-01-01

The accurate prediction of genetic networks using computational tools is one of the greatest challenges in the postgenomic era. Recurrent Neural Network is one of the most popular but simple approaches to model the network dynamics from time-series microarray data. To date, it has been successfully applied to computationally derive small-scale artificial and real-world genetic networks with high accuracy. However, they underperformed for large-scale genetic networks. Here, a new methodology has been proposed where a hybrid Cuckoo Search-Flower Pollination Algorithm has been implemented with Recurrent Neural Network. Cuckoo Search is used to search the best combination of regulators. Moreover, Flower Pollination Algorithm is applied to optimize the model parameters of the Recurrent Neural Network formalism. Initially, the proposed method is tested on a benchmark large-scale artificial network for both noiseless and noisy data. The results obtained show that the proposed methodology is capable of increasing the inference of correct regulations and decreasing false regulations to a high degree. Secondly, the proposed methodology has been validated against the real-world dataset of the DNA SOS repair network of Escherichia coli. However, the proposed method sacrifices computational time complexity in both cases due to the hybrid optimization process.

Design and analysis of sustainable computer mouse using design for disassembly methodology

Science.gov (United States)

Roni Sahroni, Taufik; Fitri Sukarman, Ahmad; Agung Mahardini, Karunia

2017-12-01

This paper presents the design and analysis of computer mouse using Design for Disassembly methodology. Basically, the existing computer mouse model consist a number of unnecessary part that cause the assembly and disassembly time in production. The objective of this project is to design a new computer mouse based on Design for Disassembly (DFD) methodology. The main methodology of this paper was proposed from sketch generation, concept selection, and concept scoring. Based on the design screening, design concept B was selected for further analysis. New design of computer mouse is proposed using fastening system. Furthermore, three materials of ABS, Polycarbonate, and PE high density were prepared to determine the environmental impact category. Sustainable analysis was conducted using software SolidWorks. As a result, PE High Density gives the lowers amount in the environmental category with great maximum stress value.

Neural-network-based depth computation for blind navigation

Science.gov (United States)

Wong, Farrah; Nagarajan, Ramachandran R.; Yaacob, Sazali

2004-12-01

A research undertaken to help blind people to navigate autonomously or with minimum assistance is termed as "Blind Navigation". In this research, an aid that could help blind people in their navigation is proposed. Distance serves as an important clue during our navigation. A stereovision navigation aid implemented with two digital video cameras that are spaced apart and fixed on a headgear to obtain the distance information is presented. In this paper, a neural network methodology is used to obtain the required parameters of the camera which is known as camera calibration. These parameters are not known but obtained by adjusting the weights in the network. The inputs to the network consist of the matching features in the stereo pair images. A back propagation network with 16-input neurons, 3 hidden neurons and 1 output neuron, which gives depth, is created. The distance information is incorporated into the final processed image as four gray levels such as white, light gray, dark gray and black. Preliminary results have shown that the percentage errors fall below 10%. It is envisaged that the distance provided by neural network shall enable blind individuals to go near and pick up an object of interest.

Neural and Computational Mechanisms of Action Processing: Interaction between Visual and Motor Representations.

Science.gov (United States)

Giese, Martin A; Rizzolatti, Giacomo

2015-10-07

Action recognition has received enormous interest in the field of neuroscience over the last two decades. In spite of this interest, the knowledge in terms of fundamental neural mechanisms that provide constraints for underlying computations remains rather limited. This fact stands in contrast with a wide variety of speculative theories about how action recognition might work. This review focuses on new fundamental electrophysiological results in monkeys, which provide constraints for the detailed underlying computations. In addition, we review models for action recognition and processing that have concrete mathematical implementations, as opposed to conceptual models. We think that only such implemented models can be meaningfully linked quantitatively to physiological data and have a potential to narrow down the many possible computational explanations for action recognition. In addition, only concrete implementations allow judging whether postulated computational concepts have a feasible implementation in terms of realistic neural circuits. Copyright © 2015 Elsevier Inc. All rights reserved.

Intelligent neural network diagnostic system

International Nuclear Information System (INIS)

Mohamed, A.H.

2010-01-01

Recently, artificial neural network (ANN) has made a significant mark in the domain of diagnostic applications. Neural networks are used to implement complex non-linear mappings (functions) using simple elementary units interrelated through connections with adaptive weights. The performance of the ANN is mainly depending on their topology structure and weights. Some systems have been developed using genetic algorithm (GA) to optimize the topology of the ANN. But, they suffer from some limitations. They are : (1) The computation time requires for training the ANN several time reaching for the average weight required, (2) Slowness of GA for optimization process and (3) Fitness noise appeared in the optimization of ANN. This research suggests new issues to overcome these limitations for finding optimal neural network architectures to learn particular problems. This proposed methodology is used to develop a diagnostic neural network system. It has been applied for a 600 MW turbo-generator as a case of real complex systems. The proposed system has proved its significant performance compared to two common methods used in the diagnostic applications.

Low-cost autonomous perceptron neural network inspired by quantum computation

Science.gov (United States)

Zidan, Mohammed; Abdel-Aty, Abdel-Haleem; El-Sadek, Alaa; Zanaty, E. A.; Abdel-Aty, Mahmoud

2017-11-01

Achieving low cost learning with reliable accuracy is one of the important goals to achieve intelligent machines to save time, energy and perform learning process over limited computational resources machines. In this paper, we propose an efficient algorithm for a perceptron neural network inspired by quantum computing composite from a single neuron to classify inspirable linear applications after a single training iteration O(1). The algorithm is applied over a real world data set and the results are outer performs the other state-of-the art algorithms.

A New Methodology for Fuel Mass Computation of an operating Aircraft

Directory of Open Access Journals (Sweden)

M Souli

2016-03-01

Full Text Available The paper performs a new computational methodology for an accurate computation of fuel mass inside an aircraft wing during the flight. The computation is carried out using hydrodynamic equations, classically known as Navier-Stokes equations by the CFD community. For this purpose, a computational software is developed, the software computes the fuel mass inside the tank based on experimental data of pressure gages that are inserted in the fuel tank. Actually and for safety reasons, Optical fiber sensor for fluid level sensor detection is used. The optical system consists to an optically controlled acoustic transceiver system which measures the fuel level inside the each compartment of the fuel tank. The system computes fuel volume inside the tank and needs density to compute the total fuel mass. Using optical sensor technique, density measurement inside the tank is required. The method developed in the paper, requires pressure measurements in each tank compartment, the density is then computed based on pressure measurements and hydrostatic assumptions. The methodology is tested using a fuel tank provided by Airbus for time history refueling process.

A Case Study on Neural Inspired Dynamic Memory Management Strategies for High Performance Computing.

Energy Technology Data Exchange (ETDEWEB)

Vineyard, Craig Michael [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States); Verzi, Stephen Joseph [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-09-01

As high performance computing architectures pursue more computational power there is a need for increased memory capacity and bandwidth as well. A multi-level memory (MLM) architecture addresses this need by combining multiple memory types with different characteristics as varying levels of the same architecture. How to efficiently utilize this memory infrastructure is an unknown challenge, and in this research we sought to investigate whether neural inspired approaches can meaningfully help with memory management. In particular we explored neurogenesis inspired re- source allocation, and were able to show a neural inspired mixed controller policy can beneficially impact how MLM architectures utilize memory.

A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator

International Nuclear Information System (INIS)

Almonacid, F.; Pérez-Higueras, P.J.; Fernández, Eduardo F.; Hontoria, L.

2014-01-01

Highlights: • The output of the majority of renewables energies depends on the variability of the weather conditions. • The short-term forecast is going to be essential for effectively integrating solar energy sources. • A new method based on artificial neural network to predict the power output of a PV generator one hour ahead is proposed. • This new method is based on dynamic artificial neural network to predict global solar irradiance and the air temperature. • The methodology developed can be used to estimate the power output of a PV generator with a satisfactory margin of error. - Abstract: One of the problems of some renewables energies is that the output of these kinds of systems is non-dispatchable depending on variability of weather conditions that cannot be predicted and controlled. From this point of view, the short-term forecast is going to be essential for effectively integrating solar energy sources, being a very useful tool for the reliability and stability of the grid ensuring that an adequate supply is present. In this paper a new methodology for forecasting the output of a PV generator one hour ahead based on dynamic artificial neural network is presented. The results of this study show that the proposed methodology could be used to forecast the power output of PV systems one hour ahead with an acceptable degree of accuracy

Emission computed tomography: methodology and applications

International Nuclear Information System (INIS)

Reivich, M.; Alavi, A.; Greenberg, J.; Fowler, J.; Christman, D.; Rosenquist, A.; Rintelmann, W.; Hand, P.; MacGregor, R.; Wolf, A.

1980-01-01

A technique for the determination of local cerebral glucose metabolism using positron emission computed tomography is described as an example of the development of use of this methodology for the study of these parameters in man. The method for the determination of local cerebral glucose metabolism utilizes 18 F-2-fluoro-2-deoxyglucose ([ 18 F]-FDG). In this method [ 18 F]-FDG is used as a tracer for the exchange of glucose between plasma and brain and its phosphorylation by hexokinase in the tissue. The labelled product of metabolism, [ 18 F]-FDG phosphate, is essentially trapped in the tissue over the time course of the measurement. The studies demonstrate the potential usefulness of emission computed tomography for the measurement of various biochemical and physiological parameters in man. (Auth.)

Neural network algorithm for image reconstruction using the grid friendly projections

International Nuclear Information System (INIS)

Cierniak, R.

2011-01-01

Full text: The presented paper describes a development of original approach to the reconstruction problem using a recurrent neural network. Particularly, the 'grid-friendly' angles of performed projections are selected according to the discrete Radon transform (DRT) concept to decrease the number of projections required. The methodology of our approach is consistent with analytical reconstruction algorithms. Reconstruction problem is reformulated in our approach to optimization problem. This problem is solved in present concept using method based on the maximum likelihood methodology. The reconstruction algorithm proposed in this work is consequently adapted for more practical discrete fan beam projections. Computer simulation results show that the neural network reconstruction algorithm designed to work in this way improves obtained results and outperforms conventional methods in reconstructed image quality. (author)

Fuzzy Clustering based Methodology for Multidimensional Data Analysis in Computational Forensic Domain

OpenAIRE

Kilian Stoffel; Paul Cotofrei; Dong Han

2012-01-01

As interdisciplinary domain requiring advanced and innovative methodologies the computational forensics domain is characterized by data being simultaneously large scaled and uncertain multidimensional and approximate. Forensic domain experts trained to discover hidden pattern from crime data are limited in their analysis without the assistance of a computational intelligence approach. In this paper a methodology and an automatic procedure based on fuzzy set theory and designed to infer precis...

A state space approach for piecewise-linear recurrent neural networks for identifying computational dynamics from neural measurements.

Directory of Open Access Journals (Sweden)

Daniel Durstewitz

2017-06-01

Full Text Available The computational and cognitive properties of neural systems are often thought to be implemented in terms of their (stochastic network dynamics. Hence, recovering the system dynamics from experimentally observed neuronal time series, like multiple single-unit recordings or neuroimaging data, is an important step toward understanding its computations. Ideally, one would not only seek a (lower-dimensional state space representation of the dynamics, but would wish to have access to its statistical properties and their generative equations for in-depth analysis. Recurrent neural networks (RNNs are a computationally powerful and dynamically universal formal framework which has been extensively studied from both the computational and the dynamical systems perspective. Here we develop a semi-analytical maximum-likelihood estimation scheme for piecewise-linear RNNs (PLRNNs within the statistical framework of state space models, which accounts for noise in both the underlying latent dynamics and the observation process. The Expectation-Maximization algorithm is used to infer the latent state distribution, through a global Laplace approximation, and the PLRNN parameters iteratively. After validating the procedure on toy examples, and using inference through particle filters for comparison, the approach is applied to multiple single-unit recordings from the rodent anterior cingulate cortex (ACC obtained during performance of a classical working memory task, delayed alternation. Models estimated from kernel-smoothed spike time data were able to capture the essential computational dynamics underlying task performance, including stimulus-selective delay activity. The estimated models were rarely multi-stable, however, but rather were tuned to exhibit slow dynamics in the vicinity of a bifurcation point. In summary, the present work advances a semi-analytical (thus reasonably fast maximum-likelihood estimation framework for PLRNNs that may enable to recover

A neutron spectrum unfolding computer code based on artificial neural networks

International Nuclear Information System (INIS)

Ortiz-Rodríguez, J.M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J.M.; Vega-Carrillo, H.R.

2014-01-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6 LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding

Memristor-Based Analog Computation and Neural Network Classification with a Dot Product Engine.

Science.gov (United States)

Hu, Miao; Graves, Catherine E; Li, Can; Li, Yunning; Ge, Ning; Montgomery, Eric; Davila, Noraica; Jiang, Hao; Williams, R Stanley; Yang, J Joshua; Xia, Qiangfei; Strachan, John Paul

2018-03-01

Using memristor crossbar arrays to accelerate computations is a promising approach to efficiently implement algorithms in deep neural networks. Early demonstrations, however, are limited to simulations or small-scale problems primarily due to materials and device challenges that limit the size of the memristor crossbar arrays that can be reliably programmed to stable and analog values, which is the focus of the current work. High-precision analog tuning and control of memristor cells across a 128 × 64 array is demonstrated, and the resulting vector matrix multiplication (VMM) computing precision is evaluated. Single-layer neural network inference is performed in these arrays, and the performance compared to a digital approach is assessed. Memristor computing system used here reaches a VMM accuracy equivalent of 6 bits, and an 89.9% recognition accuracy is achieved for the 10k MNIST handwritten digit test set. Forecasts show that with integrated (on chip) and scaled memristors, a computational efficiency greater than 100 trillion operations per second per Watt is possible. © 2018 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Computer Aided Methodology for Simultaneous Synthesis, Design & Analysis of Chemical Products-Processes

DEFF Research Database (Denmark)

d'Anterroches, Loïc; Gani, Rafiqul

2006-01-01

A new combined methodology for computer aided molecular design and process flowsheet design is presented. The methodology is based on the group contribution approach for prediction of molecular properties and design of molecules. Using the same principles, process groups have been developed...... a wide range of problems. In this paper, only the computer aided flowsheet design related features are presented....... together with their corresponding flowsheet property models. To represent the process flowsheets in the same way as molecules, a unique but simple notation system has been developed. The methodology has been converted into a prototype software, which has been tested with several case studies covering...

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.

Soft tissue deformation modelling through neural dynamics-based reaction-diffusion mechanics.

Science.gov (United States)

Zhang, Jinao; Zhong, Yongmin; Gu, Chengfan

2018-05-30

Soft tissue deformation modelling forms the basis of development of surgical simulation, surgical planning and robotic-assisted minimally invasive surgery. This paper presents a new methodology for modelling of soft tissue deformation based on reaction-diffusion mechanics via neural dynamics. The potential energy stored in soft tissues due to a mechanical load to deform tissues away from their rest state is treated as the equivalent transmembrane potential energy, and it is distributed in the tissue masses in the manner of reaction-diffusion propagation of nonlinear electrical waves. The reaction-diffusion propagation of mechanical potential energy and nonrigid mechanics of motion are combined to model soft tissue deformation and its dynamics, both of which are further formulated as the dynamics of cellular neural networks to achieve real-time computational performance. The proposed methodology is implemented with a haptic device for interactive soft tissue deformation with force feedback. Experimental results demonstrate that the proposed methodology exhibits nonlinear force-displacement relationship for nonlinear soft tissue deformation. Homogeneous, anisotropic and heterogeneous soft tissue material properties can be modelled through the inherent physical properties of mass points. Graphical abstract Soft tissue deformation modelling with haptic feedback via neural dynamics-based reaction-diffusion mechanics.

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

Hybrid response surface methodology-artificial neural network optimization of drying process of banana slices in a forced convective dryer.

Science.gov (United States)

Taheri-Garavand, Amin; Karimi, Fatemeh; Karimi, Mahmoud; Lotfi, Valiullah; Khoobbakht, Golmohammad

2018-06-01

The aim of the study is to fit models for predicting surfaces using the response surface methodology and the artificial neural network to optimize for obtaining the maximum acceptability using desirability functions methodology in a hot air drying process of banana slices. The drying air temperature, air velocity, and drying time were chosen as independent factors and moisture content, drying rate, energy efficiency, and exergy efficiency were dependent variables or responses in the mentioned drying process. A rotatable central composite design as an adequate method was used to develop models for the responses in the response surface methodology. Moreover, isoresponse contour plots were useful to predict the results by performing only a limited set of experiments. The optimum operating conditions obtained from the artificial neural network models were moisture content 0.14 g/g, drying rate 1.03 g water/g h, energy efficiency 0.61, and exergy efficiency 0.91, when the air temperature, air velocity, and drying time values were equal to -0.42 (74.2 ℃), 1.00 (1.50 m/s), and -0.17 (2.50 h) in the coded units, respectively.

Fuzzy logic, neural networks, and soft computing

Science.gov (United States)

Zadeh, Lofti A.

1994-01-01

The past few years have witnessed a rapid growth of interest in a cluster of modes of modeling and computation which may be described collectively as soft computing. The distinguishing characteristic of soft computing is that its primary aims are to achieve tractability, robustness, low cost, and high MIQ (machine intelligence quotient) through an exploitation of the tolerance for imprecision and uncertainty. Thus, in soft computing what is usually sought is an approximate solution to a precisely formulated problem or, more typically, an approximate solution to an imprecisely formulated problem. A simple case in point is the problem of parking a car. Generally, humans can park a car rather easily because the final position of the car is not specified exactly. If it were specified to within, say, a few millimeters and a fraction of a degree, it would take hours or days of maneuvering and precise measurements of distance and angular position to solve the problem. What this simple example points to is the fact that, in general, high precision carries a high cost. The challenge, then, is to exploit the tolerance for imprecision by devising methods of computation which lead to an acceptable solution at low cost. By its nature, soft computing is much closer to human reasoning than the traditional modes of computation. At this juncture, the major components of soft computing are fuzzy logic (FL), neural network theory (NN), and probabilistic reasoning techniques (PR), including genetic algorithms, chaos theory, and part of learning theory. Increasingly, these techniques are used in combination to achieve significant improvement in performance and adaptability. Among the important application areas for soft computing are control systems, expert systems, data compression techniques, image processing, and decision support systems. It may be argued that it is soft computing, rather than the traditional hard computing, that should be viewed as the foundation for artificial

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.

A modular architecture for transparent computation in recurrent neural networks.

Science.gov (United States)

Carmantini, Giovanni S; Beim Graben, Peter; Desroches, Mathieu; Rodrigues, Serafim

2017-01-01

Computation is classically studied in terms of automata, formal languages and algorithms; yet, the relation between neural dynamics and symbolic representations and operations is still unclear in traditional eliminative connectionism. Therefore, we suggest a unique perspective on this central issue, to which we would like to refer as transparent connectionism, by proposing accounts of how symbolic computation can be implemented in neural substrates. In this study we first introduce a new model of dynamics on a symbolic space, the versatile shift, showing that it supports the real-time simulation of a range of automata. We then show that the Gödelization of versatile shifts defines nonlinear dynamical automata, dynamical systems evolving on a vectorial space. Finally, we present a mapping between nonlinear dynamical automata and recurrent artificial neural networks. The mapping defines an architecture characterized by its granular modularity, where data, symbolic operations and their control are not only distinguishable in activation space, but also spatially localizable in the network itself, while maintaining a distributed encoding of symbolic representations. The resulting networks simulate automata in real-time and are programmed directly, in the absence of network training. To discuss the unique characteristics of the architecture and their consequences, we present two examples: (i) the design of a Central Pattern Generator from a finite-state locomotive controller, and (ii) the creation of a network simulating a system of interactive automata that supports the parsing of garden-path sentences as investigated in psycholinguistics experiments. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks.

Science.gov (United States)

Chande, Ruchi D; Hargraves, Rosalyn Hobson; Ortiz-Robinson, Norma; Wayne, Jennifer S

2017-01-01

Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task

Science.gov (United States)

Revechkis, Boris; Aflalo, Tyson NS; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A.

2014-12-01

Objective. To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. Approach. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like ‘Face in a Crowd’ task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the ‘Crowd’) using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a ‘Crowd Off’ condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Main results. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Significance. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet

Parietal neural prosthetic control of a computer cursor in a graphical-user-interface task.

Science.gov (United States)

Revechkis, Boris; Aflalo, Tyson N S; Kellis, Spencer; Pouratian, Nader; Andersen, Richard A

2014-12-01

To date, the majority of Brain-Machine Interfaces have been used to perform simple tasks with sequences of individual targets in otherwise blank environments. In this study we developed a more practical and clinically relevant task that approximated modern computers and graphical user interfaces (GUIs). This task could be problematic given the known sensitivity of areas typically used for BMIs to visual stimuli, eye movements, decision-making, and attentional control. Consequently, we sought to assess the effect of a complex, GUI-like task on the quality of neural decoding. A male rhesus macaque monkey was implanted with two 96-channel electrode arrays in area 5d of the superior parietal lobule. The animal was trained to perform a GUI-like 'Face in a Crowd' task on a computer screen that required selecting one cued, icon-like, face image from a group of alternatives (the 'Crowd') using a neurally controlled cursor. We assessed whether the crowd affected decodes of intended cursor movements by comparing it to a 'Crowd Off' condition in which only the matching target appeared without alternatives. We also examined if training a neural decoder with the Crowd On rather than Off had any effect on subsequent decode quality. Despite the additional demands of working with the Crowd On, the animal was able to robustly perform the task under Brain Control. The presence of the crowd did not itself affect decode quality. Training the decoder with the Crowd On relative to Off had no negative influence on subsequent decoding performance. Additionally, the subject was able to gaze around freely without influencing cursor position. Our results demonstrate that area 5d recordings can be used for decoding in a complex, GUI-like task with free gaze. Thus, this area is a promising source of signals for neural prosthetics that utilize computing devices with GUI interfaces, e.g. personal computers, mobile devices, and tablet computers.

Neural dynamics as sampling: a model for stochastic computation in recurrent networks of spiking neurons.

Science.gov (United States)

Buesing, Lars; Bill, Johannes; Nessler, Bernhard; Maass, Wolfgang

2011-11-01

The organization of computations in networks of spiking neurons in the brain is still largely unknown, in particular in view of the inherently stochastic features of their firing activity and the experimentally observed trial-to-trial variability of neural systems in the brain. In principle there exists a powerful computational framework for stochastic computations, probabilistic inference by sampling, which can explain a large number of macroscopic experimental data in neuroscience and cognitive science. But it has turned out to be surprisingly difficult to create a link between these abstract models for stochastic computations and more detailed models of the dynamics of networks of spiking neurons. Here we create such a link and show that under some conditions the stochastic firing activity of networks of spiking neurons can be interpreted as probabilistic inference via Markov chain Monte Carlo (MCMC) sampling. Since common methods for MCMC sampling in distributed systems, such as Gibbs sampling, are inconsistent with the dynamics of spiking neurons, we introduce a different approach based on non-reversible Markov chains that is able to reflect inherent temporal processes of spiking neuronal activity through a suitable choice of random variables. We propose a neural network model and show by a rigorous theoretical analysis that its neural activity implements MCMC sampling of a given distribution, both for the case of discrete and continuous time. This provides a step towards closing the gap between abstract functional models of cortical computation and more detailed models of networks of spiking neurons.

Fast computation with spikes in a recurrent neural network

International Nuclear Information System (INIS)

Jin, Dezhe Z.; Seung, H. Sebastian

2002-01-01

Neural networks with recurrent connections are sometimes regarded as too slow at computation to serve as models of the brain. Here we analytically study a counterexample, a network consisting of N integrate-and-fire neurons with self excitation, all-to-all inhibition, instantaneous synaptic coupling, and constant external driving inputs. When the inhibition and/or excitation are large enough, the network performs a winner-take-all computation for all possible external inputs and initial states of the network. The computation is done very quickly: As soon as the winner spikes once, the computation is completed since no other neurons will spike. For some initial states, the winner is the first neuron to spike, and the computation is done at the first spike of the network. In general, there are M potential winners, corresponding to the top M external inputs. When the external inputs are close in magnitude, M tends to be larger. If M>1, the selection of the actual winner is strongly influenced by the initial states. If a special relation between the excitation and inhibition is satisfied, the network always selects the neuron with the maximum external input as the winner

Predictive Behavior of a Computational Foot/Ankle Model through Artificial Neural Networks

Directory of Open Access Journals (Sweden)

Ruchi D. Chande

2017-01-01

Full Text Available Computational models are useful tools to study the biomechanics of human joints. Their predictive performance is heavily dependent on bony anatomy and soft tissue properties. Imaging data provides anatomical requirements while approximate tissue properties are implemented from literature data, when available. We sought to improve the predictive capability of a computational foot/ankle model by optimizing its ligament stiffness inputs using feedforward and radial basis function neural networks. While the former demonstrated better performance than the latter per mean square error, both networks provided reasonable stiffness predictions for implementation into the computational model.

Optimization of extraction of linarin from Flos chrysanthemi indici by response surface methodology and artificial neural network.

Science.gov (United States)

Pan, Hongye; Zhang, Qing; Cui, Keke; Chen, Guoquan; Liu, Xuesong; Wang, Longhu

2017-05-01

The extraction of linarin from Flos chrysanthemi indici by ethanol was investigated. Two modeling techniques, response surface methodology and artificial neural network, were adopted to optimize the process parameters, such as, ethanol concentration, extraction period, extraction frequency, and solvent to material ratio. We showed that both methods provided good predictions, but artificial neural network provided a better and more accurate result. The optimum process parameters include, ethanol concentration of 74%, extraction period of 2 h, extraction three times, solvent to material ratio of 12 mL/g. The experiment yield of linarin was 90.5% that deviated less than 1.6% from that obtained by predicted result. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Programmable neural processing on a smartdust for brain-computer interfaces.

Science.gov (United States)

Yuwen Sun; Shimeng Huang; Oresko, Joseph J; Cheng, Allen C

2010-10-01

Brain-computer interfaces (BCIs) offer tremendous promise for improving the quality of life for disabled individuals. BCIs use spike sorting to identify the source of each neural firing. To date, spike sorting has been performed by either using off-chip analysis, which requires a wired connection penetrating the skull to a bulky external power/processing unit, or via custom application-specific integrated circuits that lack the programmability to perform different algorithms and upgrades. In this research, we propose and test the feasibility of performing on-chip, real-time spike sorting on a programmable smartdust, including feature extraction, classification, compression, and wireless transmission. A detailed power/performance tradeoff analysis using DVFS is presented. Our experimental results show that the execution time and power density meet the requirements to perform real-time spike sorting and wireless transmission on a single neural channel.

Concise Neural Nonaffine Control of Air-Breathing Hypersonic Vehicles Subject to Parametric Uncertainties

Directory of Open Access Journals (Sweden)

Xiangwei Bu

2017-01-01

Full Text Available In this paper, a novel simplified neural control strategy is proposed for the longitudinal dynamics of an air-breathing hypersonic vehicle (AHV directly using nonaffine models instead of affine ones. For the velocity dynamics, an adaptive neural controller is devised based on a minimal-learning parameter (MLP technique for the sake of decreasing computational loads. The altitude dynamics is rewritten as a pure feedback nonaffine formulation, for which a novel concise neural control approach is achieved without backstepping. The special contributions are that the control architecture is concise and the computational cost is low. Moreover, the exploited controller possesses good practicability since there is no need for affine models. The semiglobally uniformly ultimate boundedness of all the closed-loop system signals is guaranteed via Lyapunov stability theory. Finally, simulation results are presented to validate the effectiveness of the investigated control methodology in the presence of parametric uncertainties.

Prediction of Software Reliability using Bio Inspired Soft Computing Techniques.

Science.gov (United States)

Diwaker, Chander; Tomar, Pradeep; Poonia, Ramesh C; Singh, Vijander

2018-04-10

A lot of models have been made for predicting software reliability. The reliability models are restricted to using particular types of methodologies and restricted number of parameters. There are a number of techniques and methodologies that may be used for reliability prediction. There is need to focus on parameters consideration while estimating reliability. The reliability of a system may increase or decreases depending on the selection of different parameters used. Thus there is need to identify factors that heavily affecting the reliability of the system. In present days, reusability is mostly used in the various area of research. Reusability is the basis of Component-Based System (CBS). The cost, time and human skill can be saved using Component-Based Software Engineering (CBSE) concepts. CBSE metrics may be used to assess those techniques which are more suitable for estimating system reliability. Soft computing is used for small as well as large-scale problems where it is difficult to find accurate results due to uncertainty or randomness. Several possibilities are available to apply soft computing techniques in medicine related problems. Clinical science of medicine using fuzzy-logic, neural network methodology significantly while basic science of medicine using neural-networks-genetic algorithm most frequently and preferably. There is unavoidable interest shown by medical scientists to use the various soft computing methodologies in genetics, physiology, radiology, cardiology and neurology discipline. CBSE boost users to reuse the past and existing software for making new products to provide quality with a saving of time, memory space, and money. This paper focused on assessment of commonly used soft computing technique like Genetic Algorithm (GA), Neural-Network (NN), Fuzzy Logic, Support Vector Machine (SVM), Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and Artificial Bee Colony (ABC). This paper presents working of soft computing

Neural Computation Scheme of Compound Control: Tacit Learning for Bipedal Locomotion

Science.gov (United States)

Shimoda, Shingo; Kimura, Hidenori

The growing need for controlling complex behaviors of versatile robots working in unpredictable environment has revealed the fundamental limitation of model-based control strategy that requires precise models of robots and environments before their operations. This difficulty is fundamental and has the same root with the well-known frame problem in artificial intelligence. It has been a central long standing issue in advanced robotics, as well as machine intelligence, to find a prospective clue to attack this fundamental difficulty. The general consensus shared by many leading researchers in the related field is that the body plays an important role in acquiring intelligence that can conquer unknowns. In particular, purposeful behaviors emerge during body-environment interactions with the help of an appropriately organized neural computational scheme that can exploit what the environment can afford. Along this line, we propose a new scheme of neural computation based on compound control which represents a typical feature of biological controls. This scheme is based on classical neuron models with local rules that can create macroscopic purposeful behaviors. This scheme is applied to a bipedal robot and generates the rhythm of walking without any model of robot dynamics and environments.

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling.

Science.gov (United States)

Dick, Thomas E; Molkov, Yaroslav I; Nieman, Gary; Hsieh, Yee-Hsee; Jacono, Frank J; Doyle, John; Scheff, Jeremy D; Calvano, Steve E; Androulakis, Ioannis P; An, Gary; Vodovotz, Yoram

2012-01-01

Acute inflammation leads to organ failure by engaging catastrophic feedback loops in which stressed tissue evokes an inflammatory response and, in turn, inflammation damages tissue. Manifestations of this maladaptive inflammatory response include cardio-respiratory dysfunction that may be reflected in reduced heart rate and ventilatory pattern variabilities. We have developed signal-processing algorithms that quantify non-linear deterministic characteristics of variability in biologic signals. Now, coalescing under the aegis of the NIH Computational Biology Program and the Society for Complexity in Acute Illness, two research teams performed iterative experiments and computational modeling on inflammation and cardio-pulmonary dysfunction in sepsis as well as on neural control of respiration and ventilatory pattern variability. These teams, with additional collaborators, have recently formed a multi-institutional, interdisciplinary consortium, whose goal is to delineate the fundamental interrelationship between the inflammatory response and physiologic variability. Multi-scale mathematical modeling and complementary physiological experiments will provide insight into autonomic neural mechanisms that may modulate the inflammatory response to sepsis and simultaneously reduce heart rate and ventilatory pattern variabilities associated with sepsis. This approach integrates computational models of neural control of breathing and cardio-respiratory coupling with models that combine inflammation, cardiovascular function, and heart rate variability. The resulting integrated model will provide mechanistic explanations for the phenomena of respiratory sinus-arrhythmia and cardio-ventilatory coupling observed under normal conditions, and the loss of these properties during sepsis. This approach holds the potential of modeling cross-scale physiological interactions to improve both basic knowledge and clinical management of acute inflammatory diseases such as sepsis and trauma.

Computer simulations of neural mechanisms explaining upper and lower limb excitatory neural coupling

Directory of Open Access Journals (Sweden)

Ferris Daniel P

2010-12-01

Full Text Available Abstract Background When humans perform rhythmic upper and lower limb locomotor-like movements, there is an excitatory effect of upper limb exertion on lower limb muscle recruitment. To investigate potential neural mechanisms for this behavioral observation, we developed computer simulations modeling interlimb neural pathways among central pattern generators. We hypothesized that enhancement of muscle recruitment from interlimb spinal mechanisms was not sufficient to explain muscle enhancement levels observed in experimental data. Methods We used Matsuoka oscillators for the central pattern generators (CPG and determined parameters that enhanced amplitudes of rhythmic steady state bursts. Potential mechanisms for output enhancement were excitatory and inhibitory sensory feedback gains, excitatory and inhibitory interlimb coupling gains, and coupling geometry. We first simulated the simplest case, a single CPG, and then expanded the model to have two CPGs and lastly four CPGs. In the two and four CPG models, the lower limb CPGs did not receive supraspinal input such that the only mechanisms available for enhancing output were interlimb coupling gains and sensory feedback gains. Results In a two-CPG model with inhibitory sensory feedback gains, only excitatory gains of ipsilateral flexor-extensor/extensor-flexor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 26%. In a two-CPG model with excitatory sensory feedback gains, excitatory gains of contralateral flexor-flexor/extensor-extensor coupling produced reciprocal upper-lower limb bursts and enhanced output up to 100%. However, within a given excitatory sensory feedback gain, enhancement due to excitatory interlimb gains could only reach levels up to 20%. Interconnecting four CPGs to have ipsilateral flexor-extensor/extensor-flexor coupling, contralateral flexor-flexor/extensor-extensor coupling, and bilateral flexor-extensor/extensor-flexor coupling could enhance

A neutron spectrum unfolding computer code based on artificial neural networks

Science.gov (United States)

Ortiz-Rodríguez, J. M.; Reyes Alfaro, A.; Reyes Haro, A.; Cervantes Viramontes, J. M.; Vega-Carrillo, H. R.

2014-02-01

The Bonner Spheres Spectrometer consists of a thermal neutron sensor placed at the center of a number of moderating polyethylene spheres of different diameters. From the measured readings, information can be derived about the spectrum of the neutron field where measurements were made. Disadvantages of the Bonner system are the weight associated with each sphere and the need to sequentially irradiate the spheres, requiring long exposure periods. Provided a well-established response matrix and adequate irradiation conditions, the most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. The drawbacks associated with traditional unfolding procedures have motivated the need of complementary approaches. Novel methods based on Artificial Intelligence, mainly Artificial Neural Networks, have been widely investigated. In this work, a neutron spectrum unfolding code based on neural nets technology is presented. This code is called Neutron Spectrometry and Dosimetry with Artificial Neural networks unfolding code that was designed in a graphical interface. The core of the code is an embedded neural network architecture previously optimized using the robust design of artificial neural networks methodology. The main features of the code are: easy to use, friendly and intuitive to the user. This code was designed for a Bonner Sphere System based on a 6LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation. The main feature of the code is that as entrance data, for unfolding the neutron spectrum, only seven rate counts measured with seven Bonner spheres are required; simultaneously the code calculates 15 dosimetric quantities as well as the total flux for radiation protection purposes. This code generates a full report with all information of the unfolding in

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY AUTHENTICATION USING ARTIFICIAL NEURAL NETWORKS

Directory of Open Access Journals (Sweden)

Timur Kartbayev

2017-03-01

Full Text Available The aim of the study is to increase the effectiveness of automated face recognition to authenticate identity, considering features of change of the face parameters over time. The improvement of the recognition accuracy, as well as consideration of the features of temporal changes in a human face can be based on the methodology of artificial neural networks. Hybrid neural networks, combining the advantages of classical neural networks and fuzzy logic systems, allow using the network learnability along with the explanation of the findings. The structural scheme of intelligent system for identification based on artificial neural networks is proposed in this work. It realizes the principles of digital information processing and identity recognition taking into account the forecast of key characteristics’ changes over time (e.g., due to aging. The structural scheme has a three-tier architecture and implements preliminary processing, recognition and identification of images obtained as a result of monitoring. On the basis of expert knowledge, the fuzzy base of products is designed. It allows assessing possible changes in key characteristics, used to authenticate identity based on the image. To take this possibility into consideration, a neuro-fuzzy network of ANFIS type was used, which implements the algorithm of Tagaki-Sugeno. The conducted experiments showed high efficiency of the developed neural network and a low value of learning errors, which allows recommending this approach for practical implementation. Application of the developed system of fuzzy production rules that allow predicting changes in individuals over time, will improve the recognition accuracy, reduce the number of authentication failures and improve the efficiency of information processing and decision-making in applications, such as authentication of bank customers, users of mobile applications, or in video monitoring systems of sensitive sites.

Tornado missile simulation and design methodology. Volume 1: simulation methodology, design applications, and TORMIS computer code. Final report

International Nuclear Information System (INIS)

Twisdale, L.A.; Dunn, W.L.

1981-08-01

A probabilistic methodology has been developed to predict the probabilities of tornado-propelled missiles impacting and damaging nuclear power plant structures. Mathematical models of each event in the tornado missile hazard have been developed and sequenced to form an integrated, time-history simulation methodology. The models are data based where feasible. The data include documented records of tornado occurrence, field observations of missile transport, results of wind tunnel experiments, and missile impact tests. Probabilistic Monte Carlo techniques are used to estimate the risk probabilities. The methodology has been encoded in the TORMIS computer code to facilitate numerical analysis and plant-specific tornado missile probability assessments. Sensitivity analyses have been performed on both the individual models and the integrated methodology, and risk has been assessed for a hypothetical nuclear power plant design case study

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 web-based system for neural network based classification in temporomandibular joint osteoarthritis.

Science.gov (United States)

de Dumast, Priscille; Mirabel, Clément; Cevidanes, Lucia; Ruellas, Antonio; Yatabe, Marilia; Ioshida, Marcos; Ribera, Nina Tubau; Michoud, Loic; Gomes, Liliane; Huang, Chao; Zhu, Hongtu; Muniz, Luciana; Shoukri, Brandon; Paniagua, Beatriz; Styner, Martin; Pieper, Steve; Budin, Francois; Vimort, Jean-Baptiste; Pascal, Laura; Prieto, Juan Carlos

2018-07-01

The purpose of this study is to describe the methodological innovations of a web-based system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely compute a deep neural network classifier of temporomandibular joint osteoarthritis (TMJOA). This study imaging dataset consisted of three-dimensional (3D) surface meshes of mandibular condyles constructed from cone beam computed tomography (CBCT) scans. The training dataset consisted of 259 condyles, 105 from control subjects and 154 from patients with diagnosis of TMJ OA. For the image analysis classification, 34 right and left condyles from 17 patients (39.9 ± 11.7 years), who experienced signs and symptoms of the disease for less than 5 years, were included as the testing dataset. For the integrative statistical model of clinical, biological and imaging markers, the sample consisted of the same 17 test OA subjects and 17 age and sex matched control subjects (39.4 ± 15.4 years), who did not show any sign or symptom of OA. For these 34 subjects, a standardized clinical questionnaire, blood and saliva samples were also collected. The technological methodologies in this study include a deep neural network classifier of 3D condylar morphology (ShapeVariationAnalyzer, SVA), and a flexible web-based system for data storage, computation and integration (DSCI) of high dimensional imaging, clinical, and biological data. The DSCI system trained and tested the neural network, indicating 5 stages of structural degenerative changes in condylar morphology in the TMJ with 91% close agreement between the clinician consensus and the SVA classifier. The DSCI remotely ran with a novel application of a statistical analysis, the Multivariate Functional Shape Data Analysis, that computed high dimensional correlations between shape 3D coordinates, clinical pain levels and levels of biological markers, and then graphically displayed the computation results. The findings of this

Topology and computational performance of attractor neural networks

International Nuclear Information System (INIS)

McGraw, Patrick N.; Menzinger, Michael

2003-01-01

To explore the relation between network structure and function, we studied the computational performance of Hopfield-type attractor neural nets with regular lattice, random, small-world, and scale-free topologies. The random configuration is the most efficient for storage and retrieval of patterns by the network as a whole. However, in the scale-free case retrieval errors are not distributed uniformly among the nodes. The portion of a pattern encoded by the subset of highly connected nodes is more robust and efficiently recognized than the rest of the pattern. The scale-free network thus achieves a very strong partial recognition. The implications of these findings for brain function and social dynamics are suggestive

Population coding and decoding in a neural field: a computational study.

Science.gov (United States)

Wu, Si; Amari, Shun-Ichi; Nakahara, Hiroyuki

2002-05-01

This study uses a neural field model to investigate computational aspects of population coding and decoding when the stimulus is a single variable. A general prototype model for the encoding process is proposed, in which neural responses are correlated, with strength specified by a gaussian function of their difference in preferred stimuli. Based on the model, we study the effect of correlation on the Fisher information, compare the performances of three decoding methods that differ in the amount of encoding information being used, and investigate the implementation of the three methods by using a recurrent network. This study not only rediscovers main results in existing literatures in a unified way, but also reveals important new features, especially when the neural correlation is strong. As the neural correlation of firing becomes larger, the Fisher information decreases drastically. We confirm that as the width of correlation increases, the Fisher information saturates and no longer increases in proportion to the number of neurons. However, we prove that as the width increases further--wider than (sqrt)2 times the effective width of the turning function--the Fisher information increases again, and it increases without limit in proportion to the number of neurons. Furthermore, we clarify the asymptotic efficiency of the maximum likelihood inference (MLI) type of decoding methods for correlated neural signals. It shows that when the correlation covers a nonlocal range of population (excepting the uniform correlation and when the noise is extremely small), the MLI type of method, whose decoding error satisfies the Cauchy-type distribution, is not asymptotically efficient. This implies that the variance is no longer adequate to measure decoding accuracy.

Neural and computational processes underlying dynamic changes in self-esteem

Science.gov (United States)

Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-01-01

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an ‘interpersonal vulnerability’ dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability. PMID:29061228

Neural and computational processes underlying dynamic changes in self-esteem.

Science.gov (United States)

Will, Geert-Jan; Rutledge, Robb B; Moutoussis, Michael; Dolan, Raymond J

2017-10-24

Self-esteem is shaped by the appraisals we receive from others. Here, we characterize neural and computational mechanisms underlying this form of social influence. We introduce a computational model that captures fluctuations in self-esteem engendered by prediction errors that quantify the difference between expected and received social feedback. Using functional MRI, we show these social prediction errors correlate with activity in ventral striatum/subgenual anterior cingulate cortex, while updates in self-esteem resulting from these errors co-varied with activity in ventromedial prefrontal cortex (vmPFC). We linked computational parameters to psychiatric symptoms using canonical correlation analysis to identify an 'interpersonal vulnerability' dimension. Vulnerability modulated the expression of prediction error responses in anterior insula and insula-vmPFC connectivity during self-esteem updates. Our findings indicate that updating of self-evaluative beliefs relies on learning mechanisms akin to those used in learning about others. Enhanced insula-vmPFC connectivity during updating of those beliefs may represent a marker for psychiatric vulnerability.

Neural networks for genetic epidemiology: past, present, and future

Directory of Open Access Journals (Sweden)

Motsinger-Reif Alison A

2008-07-01

Full Text Available Abstract During the past two decades, the field of human genetics has experienced an information explosion. The completion of the human genome project and the development of high throughput SNP technologies have created a wealth of data; however, the analysis and interpretation of these data have created a research bottleneck. While technology facilitates the measurement of hundreds or thousands of genes, statistical and computational methodologies are lacking for the analysis of these data. New statistical methods and variable selection strategies must be explored for identifying disease susceptibility genes for common, complex diseases. Neural networks (NN are a class of pattern recognition methods that have been successfully implemented for data mining and prediction in a variety of fields. The application of NN for statistical genetics studies is an active area of research. Neural networks have been applied in both linkage and association analysis for the identification of disease susceptibility genes. In the current review, we consider how NN have been used for both linkage and association analyses in genetic epidemiology. We discuss both the successes of these initial NN applications, and the questions that arose during the previous studies. Finally, we introduce evolutionary computing strategies, Genetic Programming Neural Networks (GPNN and Grammatical Evolution Neural Networks (GENN, for using NN in association studies of complex human diseases that address some of the caveats illuminated by previous work.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network.

Science.gov (United States)

Falat, Lukas; Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Neural fields theory and applications

CERN Document Server

Graben, Peter; Potthast, Roland; Wright, James

2014-01-01

With this book, the editors present the first comprehensive collection in neural field studies, authored by leading scientists in the field - among them are two of the founding-fathers of neural field theory. Up to now, research results in the field have been disseminated across a number of distinct journals from mathematics, computational neuroscience, biophysics, cognitive science and others. Starting with a tutorial for novices in neural field studies, the book comprises chapters on emergent patterns, their phase transitions and evolution, on stochastic approaches, cortical development, cognition, robotics and computation, large-scale numerical simulations, the coupling of neural fields to the electroencephalogram and phase transitions in anesthesia. The intended readership are students and scientists in applied mathematics, theoretical physics, theoretical biology, and computational neuroscience. Neural field theory and its applications have a long-standing tradition in the mathematical and computational ...

Quantum neural network-based EEG filtering for a brain-computer interface.

Science.gov (United States)

Gandhi, Vaibhav; Prasad, Girijesh; Coyle, Damien; Behera, Laxmidhar; McGinnity, Thomas Martin

2014-02-01

A novel neural information processing architecture inspired by quantum mechanics and incorporating the well-known Schrodinger wave equation is proposed in this paper. The proposed architecture referred to as recurrent quantum neural network (RQNN) can characterize a nonstationary stochastic signal as time-varying wave packets. A robust unsupervised learning algorithm enables the RQNN to effectively capture the statistical behavior of the input signal and facilitates the estimation of signal embedded in noise with unknown characteristics. The results from a number of benchmark tests show that simple signals such as dc, staircase dc, and sinusoidal signals embedded within high noise can be accurately filtered and particle swarm optimization can be employed to select model parameters. The RQNN filtering procedure is applied in a two-class motor imagery-based brain-computer interface where the objective was to filter electroencephalogram (EEG) signals before feature extraction and classification to increase signal separability. A two-step inner-outer fivefold cross-validation approach is utilized to select the algorithm parameters subject-specifically for nine subjects. It is shown that the subject-specific RQNN EEG filtering significantly improves brain-computer interface performance compared to using only the raw EEG or Savitzky-Golay filtered EEG across multiple sessions.

A Dynamic Connectome Supports the Emergence of Stable Computational Function of Neural Circuits through Reward-Based Learning.

Science.gov (United States)

Kappel, David; Legenstein, Robert; Habenschuss, Stefan; Hsieh, Michael; Maass, Wolfgang

2018-01-01

Synaptic connections between neurons in the brain are dynamic because of continuously ongoing spine dynamics, axonal sprouting, and other processes. In fact, it was recently shown that the spontaneous synapse-autonomous component of spine dynamics is at least as large as the component that depends on the history of pre- and postsynaptic neural activity. These data are inconsistent with common models for network plasticity and raise the following questions: how can neural circuits maintain a stable computational function in spite of these continuously ongoing processes, and what could be functional uses of these ongoing processes? Here, we present a rigorous theoretical framework for these seemingly stochastic spine dynamics and rewiring processes in the context of reward-based learning tasks. We show that spontaneous synapse-autonomous processes, in combination with reward signals such as dopamine, can explain the capability of networks of neurons in the brain to configure themselves for specific computational tasks, and to compensate automatically for later changes in the network or task. Furthermore, we show theoretically and through computer simulations that stable computational performance is compatible with continuously ongoing synapse-autonomous changes. After reaching good computational performance it causes primarily a slow drift of network architecture and dynamics in task-irrelevant dimensions, as observed for neural activity in motor cortex and other areas. On the more abstract level of reinforcement learning the resulting model gives rise to an understanding of reward-driven network plasticity as continuous sampling of network configurations.

Methodology for characterizing modeling and discretization uncertainties in computational simulation

Energy Technology Data Exchange (ETDEWEB)

ALVIN,KENNETH F.; OBERKAMPF,WILLIAM L.; RUTHERFORD,BRIAN M.; DIEGERT,KATHLEEN V.

2000-03-01

This research effort focuses on methodology for quantifying the effects of model uncertainty and discretization error on computational modeling and simulation. The work is directed towards developing methodologies which treat model form assumptions within an overall framework for uncertainty quantification, for the purpose of developing estimates of total prediction uncertainty. The present effort consists of work in three areas: framework development for sources of uncertainty and error in the modeling and simulation process which impact model structure; model uncertainty assessment and propagation through Bayesian inference methods; and discretization error estimation within the context of non-deterministic analysis.

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.

Neural-Fitted TD-Leaf Learning for Playing Othello With Structured Neural Networks

NARCIS (Netherlands)

van den Dries, Sjoerd; Wiering, Marco A.

This paper describes a methodology for quickly learning to play games at a strong level. The methodology consists of a novel combination of three techniques, and a variety of experiments on the game of Othello demonstrates their usefulness. First, structures or topologies in neural network

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

Science.gov (United States)

Dai, Yonghui; Han, Dongmei; Dai, Weihui

2014-01-01

The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP) neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market. PMID:24782659

Modeling and Computing of Stock Index Forecasting Based on Neural Network and Markov Chain

Directory of Open Access Journals (Sweden)

Yonghui Dai

2014-01-01

Full Text Available The stock index reflects the fluctuation of the stock market. For a long time, there have been a lot of researches on the forecast of stock index. However, the traditional method is limited to achieving an ideal precision in the dynamic market due to the influences of many factors such as the economic situation, policy changes, and emergency events. Therefore, the approach based on adaptive modeling and conditional probability transfer causes the new attention of researchers. This paper presents a new forecast method by the combination of improved back-propagation (BP neural network and Markov chain, as well as its modeling and computing technology. This method includes initial forecasting by improved BP neural network, division of Markov state region, computing of the state transition probability matrix, and the prediction adjustment. Results of the empirical study show that this method can achieve high accuracy in the stock index prediction, and it could provide a good reference for the investment in stock market.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Directory of Open Access Journals (Sweden)

Lukas Falat

2016-01-01

Full Text Available This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process.

Intelligent Soft Computing on Forex: Exchange Rates Forecasting with Hybrid Radial Basis Neural Network

Science.gov (United States)

Marcek, Dusan; Durisova, Maria

2016-01-01

This paper deals with application of quantitative soft computing prediction models into financial area as reliable and accurate prediction models can be very helpful in management decision-making process. The authors suggest a new hybrid neural network which is a combination of the standard RBF neural network, a genetic algorithm, and a moving average. The moving average is supposed to enhance the outputs of the network using the error part of the original neural network. Authors test the suggested model on high-frequency time series data of USD/CAD and examine the ability to forecast exchange rate values for the horizon of one day. To determine the forecasting efficiency, they perform a comparative statistical out-of-sample analysis of the tested model with autoregressive models and the standard neural network. They also incorporate genetic algorithm as an optimizing technique for adapting parameters of ANN which is then compared with standard backpropagation and backpropagation combined with K-means clustering algorithm. Finally, the authors find out that their suggested hybrid neural network is able to produce more accurate forecasts than the standard models and can be helpful in eliminating the risk of making the bad decision in decision-making process. PMID:26977450

The impact of natural aging on computational and neural indices of perceptual decision making: A review.

Science.gov (United States)

Dully, Jessica; McGovern, David P; O'Connell, Redmond G

2018-02-10

It is well established that natural aging negatively impacts on a wide variety of cognitive functions and research has sought to identify core neural mechanisms that may account for these disparate changes. A central feature of any cognitive task is the requirement to translate sensory information into an appropriate action - a process commonly known as perceptual decision making. While computational, psychophysical, and neurophysiological research has made substantial progress in establishing the key computations and neural mechanisms underpinning decision making, it is only relatively recently that this knowledge has begun to be applied to research on aging. The purpose of this review is to provide an overview of this work which is beginning to offer new insights into the core psychological processes that mediate age-related cognitive decline in adults aged 65 years and over. Mathematical modelling studies have consistently reported that older adults display longer non-decisional processing times and implement more conservative decision policies than their younger counterparts. However, there are limits on what we can learn from behavioural modeling alone and neurophysiological analyses can play an essential role in empirically validating model predictions and in pinpointing the precise neural mechanisms that are impacted by aging. Although few studies to date have explicitly examined correspondences between computational models and neural data with respect to cognitive aging, neurophysiological studies have already highlighted age-related changes at multiple levels of the sensorimotor hierarchy that are likely to be consequential for decision making behaviour. Here, we provide an overview of this literature and suggest some future directions for the field. Copyright © 2018 The Authors. Published by Elsevier B.V. All rights reserved.

Influence of extracellular oscillations on neural communication: a computational perspective

Directory of Open Access Journals (Sweden)

Zoran eTiganj

2014-02-01

Full Text Available Neural communication generates oscillations of electric potential in the extracellular medium. In feedback, these oscillations affect the electrochemical processes within the neurons, influencing the timing and the number of action potentials. It is unclear whether this influence should be considered only as noise or it has some functional role in neural communication. Through computer simulations we investigated the effect of various sinusoidal extracellular oscillations on the timing and number of action potentials. Each simulation is based on a multicompartment model of a single neuron, which is stimulated through spatially distributed synaptic activations. A thorough analysis is conducted on a large number of simulations with different models of CA3 and CA1 pyramidal neurons which are modeled using realistic morphologies and active ion conductances. We demonstrated that the influence of the weak extracellular oscillations, which are commonly present in the brain, is rather stochastic and modest. We found that the stronger fields, which are spontaneously present in the brain only in some particular cases (e.g. during seizures or that can be induced externally, could significantly modulate spike timings.

A GIS-based multi-criteria seismic vulnerability assessment using the integration of granular computing rule extraction and artificial neural networks

NARCIS (Netherlands)

Sheikhian, Hossein; Delavar, Mahmoud Reza; Stein, Alfred

2017-01-01

This study proposes multi‐criteria group decision‐making to address seismic physical vulnerability assessment. Granular computing rule extraction is combined with a feed forward artificial neural network to form a classifier capable of training a neural network on the basis of the rules provided by

Concepts and Relations in Neurally Inspired In Situ Concept-Based Computing.

Science.gov (United States)

van der Velde, Frank

2016-01-01

In situ concept-based computing is based on the notion that conceptual representations in the human brain are "in situ." In this way, they are grounded in perception and action. Examples are neuronal assemblies, whose connection structures develop over time and are distributed over different brain areas. In situ concepts representations cannot be copied or duplicated because that will disrupt their connection structure, and thus the meaning of these concepts. Higher-level cognitive processes, as found in language and reasoning, can be performed with in situ concepts by embedding them in specialized neurally inspired "blackboards." The interactions between the in situ concepts and the blackboards form the basis for in situ concept computing architectures. In these architectures, memory (concepts) and processing are interwoven, in contrast with the separation between memory and processing found in Von Neumann architectures. Because the further development of Von Neumann computing (more, faster, yet power limited) is questionable, in situ concept computing might be an alternative for concept-based computing. In situ concept computing will be illustrated with a recently developed BABI reasoning task. Neurorobotics can play an important role in the development of in situ concept computing because of the development of in situ concept representations derived in scenarios as needed for reasoning tasks. Neurorobotics would also benefit from power limited and in situ concept computing.

Architecture and biological applications of artificial neural networks: a tuberculosis perspective.

Science.gov (United States)

Darsey, Jerry A; Griffin, William O; Joginipelli, Sravanthi; Melapu, Venkata Kiran

2015-01-01

Advancement of science and technology has prompted researchers to develop new intelligent systems that can solve a variety of problems such as pattern recognition, prediction, and optimization. The ability of the human brain to learn in a fashion that tolerates noise and error has attracted many researchers and provided the starting point for the development of artificial neural networks: the intelligent systems. Intelligent systems can acclimatize to the environment or data and can maximize the chances of success or improve the efficiency of a search. Due to massive parallelism with large numbers of interconnected processers and their ability to learn from the data, neural networks can solve a variety of challenging computational problems. Neural networks have the ability to derive meaning from complicated and imprecise data; they are used in detecting patterns, and trends that are too complex for humans, or other computer systems. Solutions to the toughest problems will not be found through one narrow specialization; therefore we need to combine interdisciplinary approaches to discover the solutions to a variety of problems. Many researchers in different disciplines such as medicine, bioinformatics, molecular biology, and pharmacology have successfully applied artificial neural networks. This chapter helps the reader in understanding the basics of artificial neural networks, their applications, and methodology; it also outlines the network learning process and architecture. We present a brief outline of the application of neural networks to medical diagnosis, drug discovery, gene identification, and protein structure prediction. We conclude with a summary of the results from our study on tuberculosis data using neural networks, in diagnosing active tuberculosis, and predicting chronic vs. infiltrative forms of tuberculosis.

A computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic properties

KAUST Repository

Ahmed, Ahfaz; Goteng, Gokop; Shankar, Vijai; Al-Qurashi, Khalid; Roberts, William L.; Sarathy, Mani

2015-01-01

simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating

A methodology for performing virtual measurements in a nuclear reactor system

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Uhrig, R.E.; Tsoukalas, L.H.

1992-01-01

A novel methodology is presented for monitoring nonphysically measurable variables in an experimental nuclear reactor. It is based on the employment of artificial neural networks to generate fuzzy values. Neural networks map spatiotemporal information (in the form of time series) to algebraically defined membership functions. The entire process can be thought of as a virtual measurement. Through such virtual measurements the values of nondirectly monitored parameters with operational significance, e.g., transient-type, valve-position, or performance, can be determined. Generating membership functions is a crucial step in the development and practical utilization of fuzzy reasoning, a computational approach that offers the advantage of describing the state of the system in a condensed, linguistic form, convenient for monitoring, diagnostics, and control algorithms

Calculation and evaluation methodology of the flawed pipe and the compute program development

International Nuclear Information System (INIS)

Liu Chang; Qian Hao; Yao Weida; Liang Xingyun

2013-01-01

Background: The crack will grow gradually under alternating load for a pressurized pipe, whereas the load is less than the fatigue strength limit. Purpose: Both calculation and evaluation methodology for a flawed pipe that have been detected during in-service inspection is elaborated here base on the Elastic Plastic Fracture Mechanics (EPFM) criteria. Methods: In the compute, the depth and length interaction of a flaw has been considered and a compute program is developed per Visual C++. Results: The fluctuating load of the Reactor Coolant System transients, the initial flaw shape, the initial flaw orientation are all accounted here. Conclusions: The calculation and evaluation methodology here is an important basis for continue working or not. (authors)

MicroComputed Tomography: Methodology and Applications

International Nuclear Information System (INIS)

Stock, Stuart R.

2009-01-01

Due to the availability of commercial laboratory systems and the emergence of user facilities at synchrotron radiation sources, studies of microcomputed tomography or microCT have increased exponentially. MicroComputed Technology provides a complete introduction to the technology, describing how to use it effectively and understand its results. The first part of the book focuses on methodology, covering experimental methods, data analysis, and visualization approaches. The second part addresses various microCT applications, including porous solids, microstructural evolution, soft tissue studies, multimode studies, and indirect analyses. The author presents a sufficient amount of fundamental material so that those new to the field can develop a relative understanding of how to design their own microCT studies. One of the first full-length references dedicated to microCT, this book provides an accessible introduction to field, supplemented with application examples and color images.

A computational relationship between thalamic sensory neural responses and contrast perception.

Science.gov (United States)

Jiang, Yaoguang; Purushothaman, Gopathy; Casagrande, Vivien A

2015-01-01

Uncovering the relationship between sensory neural responses and perceptual decisions remains a fundamental problem in neuroscience. Decades of experimental and modeling work in the sensory cortex have demonstrated that a perceptual decision pool is usually composed of tens to hundreds of neurons, the responses of which are significantly correlated not only with each other, but also with the behavioral choices of an animal. Few studies, however, have measured neural activity in the sensory thalamus of awake, behaving animals. Therefore, it remains unclear how many thalamic neurons are recruited and how the information from these neurons is pooled at subsequent cortical stages to form a perceptual decision. In a previous study we measured neural activity in the macaque lateral geniculate nucleus (LGN) during a two alternative forced choice (2AFC) contrast detection task, and found that single LGN neurons were significantly correlated with the monkeys' behavioral choices, despite their relatively poor contrast sensitivity and a lack of overall interneuronal correlations. We have now computationally tested a number of specific hypotheses relating these measured LGN neural responses to the contrast detection behavior of the animals. We modeled the perceptual decisions with different numbers of neurons and using a variety of pooling/readout strategies, and found that the most successful model consisted of about 50-200 LGN neurons, with individual neurons weighted differentially according to their signal-to-noise ratios (quantified as d-primes). These results supported the hypothesis that in contrast detection the perceptual decision pool consists of multiple thalamic neurons, and that the response fluctuations in these neurons can influence contrast perception, with the more sensitive thalamic neurons likely to exert a greater influence.

Statistical Methodologies to Integrate Experimental and Computational Research

Science.gov (United States)

Parker, P. A.; Johnson, R. T.; Montgomery, D. C.

2008-01-01

Development of advanced algorithms for simulating engine flow paths requires the integration of fundamental experiments with the validation of enhanced mathematical models. In this paper, we provide an overview of statistical methods to strategically and efficiently conduct experiments and computational model refinement. Moreover, the integration of experimental and computational research efforts is emphasized. With a statistical engineering perspective, scientific and engineering expertise is combined with statistical sciences to gain deeper insights into experimental phenomenon and code development performance; supporting the overall research objectives. The particular statistical methods discussed are design of experiments, response surface methodology, and uncertainty analysis and planning. Their application is illustrated with a coaxial free jet experiment and a turbulence model refinement investigation. Our goal is to provide an overview, focusing on concepts rather than practice, to demonstrate the benefits of using statistical methods in research and development, thereby encouraging their broader and more systematic application.

Cat Swarm Optimization Based Functional Link Artificial Neural Network Filter for Gaussian Noise Removal from Computed Tomography Images

Directory of Open Access Journals (Sweden)

M. Kumar

2016-01-01

Full Text Available Gaussian noise is one of the dominant noises, which degrades the quality of acquired Computed Tomography (CT image data. It creates difficulties in pathological identification or diagnosis of any disease. Gaussian noise elimination is desirable to improve the clarity of a CT image for clinical, diagnostic, and postprocessing applications. This paper proposes an evolutionary nonlinear adaptive filter approach, using Cat Swarm Functional Link Artificial Neural Network (CS-FLANN to remove the unwanted noise. The structure of the proposed filter is based on the Functional Link Artificial Neural Network (FLANN and the Cat Swarm Optimization (CSO is utilized for the selection of optimum weight of the neural network filter. The applied filter has been compared with the existing linear filters, like the mean filter and the adaptive Wiener filter. The performance indices, such as peak signal to noise ratio (PSNR, have been computed for the quantitative analysis of the proposed filter. The experimental evaluation established the superiority of the proposed filtering technique over existing methods.

A new paradigm of knowledge engineering by soft computing

CERN Document Server

Ding, Liya

2001-01-01

Soft computing (SC) consists of several computing paradigms, including neural networks, fuzzy set theory, approximate reasoning, and derivative-free optimization methods such as genetic algorithms. The integration of those constituent methodologies forms the core of SC. In addition, the synergy allows SC to incorporate human knowledge effectively, deal with imprecision and uncertainty, and learn to adapt to unknown or changing environments for better performance. Together with other modern technologies, SC and its applications exert unprecedented influence on intelligent systems that mimic hum

Prediction of Optimal Design and Deflection of Space Structures Using Neural Networks

Directory of Open Access Journals (Sweden)

Reza Kamyab Moghadas

2012-01-01

Full Text Available The main aim of the present work is to determine the optimal design and maximum deflection of double layer grids spending low computational cost using neural networks. The design variables of the optimization problem are cross-sectional area of the elements as well as the length of the span and height of the structures. In this paper, a number of double layer grids with various random values of length and height are selected and optimized by simultaneous perturbation stochastic approximation algorithm. Then, radial basis function (RBF and generalized regression (GR neural networks are trained to predict the optimal design and maximum deflection of the structures. The numerical results demonstrate the efficiency of the proposed methodology.

Data mining in soft computing framework: a survey.

Science.gov (United States)

Mitra, S; Pal, S K; Mitra, P

2002-01-01

The present article provides a survey of the available literature on data mining using soft computing. A categorization has been provided based on the different soft computing tools and their hybridizations used, the data mining function implemented, and the preference criterion selected by the model. The utility of the different soft computing methodologies is highlighted. Generally fuzzy sets are suitable for handling the issues related to understandability of patterns, incomplete/noisy data, mixed media information and human interaction, and can provide approximate solutions faster. Neural networks are nonparametric, robust, and exhibit good learning and generalization capabilities in data-rich environments. Genetic algorithms provide efficient search algorithms to select a model, from mixed media data, based on some preference criterion/objective function. Rough sets are suitable for handling different types of uncertainty in data. Some challenges to data mining and the application of soft computing methodologies are indicated. An extensive bibliography is also included.

Evidence for Neural Computations of Temporal Coherence in an Auditory Scene and Their Enhancement during Active Listening.

Science.gov (United States)

O'Sullivan, James A; Shamma, Shihab A; Lalor, Edmund C

2015-05-06

The human brain has evolved to operate effectively in highly complex acoustic environments, segregating multiple sound sources into perceptually distinct auditory objects. A recent theory seeks to explain this ability by arguing that stream segregation occurs primarily due to the temporal coherence of the neural populations that encode the various features of an individual acoustic source. This theory has received support from both psychoacoustic and functional magnetic resonance imaging (fMRI) studies that use stimuli which model complex acoustic environments. Termed stochastic figure-ground (SFG) stimuli, they are composed of a "figure" and background that overlap in spectrotemporal space, such that the only way to segregate the figure is by computing the coherence of its frequency components over time. Here, we extend these psychoacoustic and fMRI findings by using the greater temporal resolution of electroencephalography to investigate the neural computation of temporal coherence. We present subjects with modified SFG stimuli wherein the temporal coherence of the figure is modulated stochastically over time, which allows us to use linear regression methods to extract a signature of the neural processing of this temporal coherence. We do this under both active and passive listening conditions. Our findings show an early effect of coherence during passive listening, lasting from ∼115 to 185 ms post-stimulus. When subjects are actively listening to the stimuli, these responses are larger and last longer, up to ∼265 ms. These findings provide evidence for early and preattentive neural computations of temporal coherence that are enhanced by active analysis of an auditory scene. Copyright © 2015 the authors 0270-6474/15/357256-08$15.00/0.

A Computer-Based System Integrating Instruction and Information Retrieval: A Description of Some Methodological Considerations.

Science.gov (United States)

Selig, Judith A.; And Others

This report, summarizing the activities of the Vision Information Center (VIC) in the field of computer-assisted instruction from December, 1966 to August, 1967, describes the methodology used to load a large body of information--a programed text on basic opthalmology--onto a computer for subsequent information retrieval and computer-assisted…

Neural Cognition and Affective Computing on Cyber Language.

Science.gov (United States)

Huang, Shuang; Zhou, Xuan; Xue, Ke; Wan, Xiqiong; Yang, Zhenyi; Xu, Duo; Ivanović, Mirjana; Yu, Xueer

2015-01-01

Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance) emotional space.

Neural Cognition and Affective Computing on Cyber Language

Directory of Open Access Journals (Sweden)

Shuang Huang

2015-01-01

Full Text Available Characterized by its customary symbol system and simple and vivid expression patterns, cyber language acts as not only a tool for convenient communication but also a carrier of abundant emotions and causes high attention in public opinion analysis, internet marketing, service feedback monitoring, and social emergency management. Based on our multidisciplinary research, this paper presents a classification of the emotional symbols in cyber language, analyzes the cognitive characteristics of different symbols, and puts forward a mechanism model to show the dominant neural activities in that process. Through the comparative study of Chinese, English, and Spanish, which are used by the largest population in the world, this paper discusses the expressive patterns of emotions in international cyber languages and proposes an intelligent method for affective computing on cyber language in a unified PAD (Pleasure-Arousal-Dominance emotional space.

FPGA hardware acceleration for high performance neutron transport computation based on agent methodology - 318

International Nuclear Information System (INIS)

Shanjie, Xiao; Tatjana, Jevremovic

2010-01-01

The accurate, detailed and 3D neutron transport analysis for Gen-IV reactors is still time-consuming regardless of advanced computational hardware available in developed countries. This paper introduces a new concept in addressing the computational time while persevering the detailed and accurate modeling; a specifically designed FPGA co-processor accelerates robust AGENT methodology for complex reactor geometries. For the first time this approach is applied to accelerate the neutronics analysis. The AGENT methodology solves neutron transport equation using the method of characteristics. The AGENT methodology performance was carefully analyzed before the hardware design based on the FPGA co-processor was adopted. The most time-consuming kernel part is then transplanted into the FPGA co-processor. The FPGA co-processor is designed with data flow-driven non von-Neumann architecture and has much higher efficiency than the conventional computer architecture. Details of the FPGA co-processor design are introduced and the design is benchmarked using two different examples. The advanced chip architecture helps the FPGA co-processor obtaining more than 20 times speed up with its working frequency much lower than the CPU frequency. (authors)

Identification of nonlinear dynamics in power plant components using neural networks

International Nuclear Information System (INIS)

Parlos, A.G.; Fernandez, B.; Tsai, W.K.

1990-01-01

Advances in digital computer technology have enabled widespread implementation of closed-loop digital control systems in a variety of industries. In some instances, however, the complexity of the plant and the uncertainty associated with the parameters involved in the mathematical modeling narrow the range of applicability of most systematic control system design methodologies. A multiyear project has been initiated to assess the feasibility of the artificial neural networks (ANNs) technology for computerized enhanced diagnostics and control of nuclear power plant components. At this stage of the project, a new methodology, based on backpropagation learning, has been developed for identifying the nonlinear dynamic systems from a set of input-output data known as the training set

Just-in-Time Compilation-Inspired Methodology for Parallelization of Compute Intensive Java Code

Directory of Open Access Journals (Sweden)

GHULAM MUSTAFA

2017-01-01

Full Text Available Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots and exploit their parallelization potential on multicore systems. Proposed methodology selects and parallelizes each DOALL loop that is either contained in a hotspot method or calls a hotspot method. The methodology could be integrated in front-end of a JIT compiler to parallelize sequential code, just before native translation. However, compilation to native code is out of scope of this work. As a case study, we analyze eighteen JGF (Java Grande Forum benchmarks to determine parallelization potential of hotspots. Eight benchmarks demonstrate a speedup of up to 7.6x on an 8-core system

Just-in-time compilation-inspired methodology for parallelization of compute intensive java code

International Nuclear Information System (INIS)

Mustafa, G.; Ghani, M.U.

2017-01-01

Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time) compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots and exploit their parallelization potential on multicore systems. Proposed methodology selects and parallelizes each DOALL loop that is either contained in a hotspot method or calls a hotspot method. The methodology could be integrated in front-end of a JIT compiler to parallelize sequential code, just before native translation. However, compilation to native code is out of scope of this work. As a case study, we analyze eighteen JGF (Java Grande Forum) benchmarks to determine parallelization potential of hotspots. Eight benchmarks demonstrate a speedup of up to 7.6x on an 8-core system. (author)

Use of neural networks to monitor power plant components

International Nuclear Information System (INIS)

Ikonomopoulos, A.; Tsoukalas, L.H.

1992-01-01

A new methodology is presented for nondestructive evaluation (NDE) of check valve performance and degradation. Artificial neural network (ANN) technology is utilized for processing frequency domain signatures of check valves operating in a nuclear power plant (NPP). Acoustic signatures obtained from different locations on a check valve are transformed from the time domain to the frequency domain and then used as input to a pretrained neural network. The neural network has been trained with data sets corresponding to normal operation, therefore establishing a basis for check valve satisfactory performance. Results obtained from the proposed methodology demonstrate the ability of neural networks to perform accurate and quick evaluations of check valve performance

Detecting and diagnosing SSME faults using an autoassociative neural network topology

Science.gov (United States)

Ali, M.; Dietz, W. E.; Kiech, E. L.

1989-01-01

An effort is underway at the University of Tennessee Space Institute to develop diagnostic expert system methodologies based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. Neural networks are being investigated as a means of storing and retrieving fault scenarios. Neural networks offer several powerful features in fault diagnosis, including (1) general pattern matching capabilities, (2) resistance to noisy input data, (3) the ability to be trained by example, and (4) the potential for implementation on parallel computer architectures. This paper presents (1) an autoassociative neural network topology, i.e. the network input and output is identical when properly trained, and hence learning is unsupervised; (2) the training regimen used; and (3) the response of the system to inputs representing both previously observed and unkown fault scenarios. The effects of noise on the integrity of the diagnosis are also evaluated.

A new methodology for the computer-aided construction of fault trees

International Nuclear Information System (INIS)

Salem, S.L.; Apostolakis, G.E.; Okrent, D.

1977-01-01

A methodology for systematically constructing fault trees for general complex systems is developed. A means of modeling component behaviour via decision tables is presented, and a procedure, and a procedure for constructing and editing fault trees, either manually or by computer, is developed. The techniques employed result in a complete fault tree in standard form. In order to demonstrate the methodology, the computer program CAT was developed and is used to construct trees for a nuclear system. By analyzing and comparing these fault trees, several conclusions are reached. First, such an approach can be used to produce fault trees that accurately describe system behaviour. Second, multiple trees can be rapidly produced by defining various TOP events, including system success. Finally, the accuracy and utility of such trees is shown to depend upon the careful development of the decision table models by the analyst, and of the overall system definition itself. Thus the method is seen to be a tool for assisting in the work of fault tree construction rather than a replacement for the careful work of the fault tree analyst. (author)

Recurrent Neural Network for Computing Outer Inverse.

Science.gov (United States)

Živković, Ivan S; Stanimirović, Predrag S; Wei, Yimin

2016-05-01

Two linear recurrent neural networks for generating outer inverses with prescribed range and null space are defined. Each of the proposed recurrent neural networks is based on the matrix-valued differential equation, a generalization of dynamic equations proposed earlier for the nonsingular matrix inversion, the Moore-Penrose inversion, as well as the Drazin inversion, under the condition of zero initial state. The application of the first approach is conditioned by the properties of the spectrum of a certain matrix; the second approach eliminates this drawback, though at the cost of increasing the number of matrix operations. The cases corresponding to the most common generalized inverses are defined. The conditions that ensure stability of the proposed neural network are presented. Illustrative examples present the results of numerical simulations.

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

New computational methodology for large 3D neutron transport problems

International Nuclear Information System (INIS)

Dahmani, M.; Roy, R.; Koclas, J.

2004-01-01

We present a new computational methodology, based on 3D characteristics method, dedicated to solve very large 3D problems without spatial homogenization. In order to eliminate the input/output problems occurring when solving these large problems, we set up a new computing scheme that requires more CPU resources than the usual one, based on sweeps over large tracking files. The huge capacity of storage needed in some problems and the related I/O queries needed by the characteristics solver are replaced by on-the-fly recalculation of tracks at each iteration step. Using this technique, large 3D problems are no longer I/O-bound, and distributed CPU resources can be efficiently used. (authors)

Fuzzy logic and neural networks in artificial intelligence and pattern recognition

Science.gov (United States)

Sanchez, Elie

1991-10-01

With the use of fuzzy logic techniques, neural computing can be integrated in symbolic reasoning to solve complex real world problems. In fact, artificial neural networks, expert systems, and fuzzy logic systems, in the context of approximate reasoning, share common features and techniques. A model of Fuzzy Connectionist Expert System is introduced, in which an artificial neural network is designed to construct the knowledge base of an expert system from, training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the synaptic connections in an AND-OR structure: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through min-max fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feedforward network is described and first illustrated in a biomedical application (medical diagnosis assistance from inflammatory-syndromes/proteins profiles). Then, it is shown how this methodology can be utilized for handwritten pattern recognition (characters play the role of diagnoses): in a fuzzy neuron describing a number for example, the linguistic weights represent fuzzy sets on cross-detecting lines and the numerical weights reflect the importance (or weakness) of connections between cross-detecting lines and characters.

Application of artificial neural network with extreme learning machine for economic growth estimation

Science.gov (United States)

Milačić, Ljubiša; Jović, Srđan; Vujović, Tanja; Miljković, Jovica

2017-01-01

The purpose of this research is to develop and apply the artificial neural network (ANN) with extreme learning machine (ELM) to forecast gross domestic product (GDP) growth rate. The economic growth forecasting was analyzed based on agriculture, manufacturing, industry and services value added in GDP. The results were compared with ANN with back propagation (BP) learning approach since BP could be considered as conventional learning methodology. The reliability of the computational models was accessed based on simulation results and using several statistical indicators. Based on results, it was shown that ANN with ELM learning methodology can be applied effectively in applications of GDP forecasting.

Flow regime identification methodology with MCNP-X code and artificial neural network

International Nuclear Information System (INIS)

Salgado, Cesar M.; Instituto de Engenharia Nuclear; Schirru, Roberto; Brandao, Luis E.B.; Pereira, Claudio M.N.A.

2009-01-01

This paper presents flow regimes identification methodology in multiphase system in annular, stratified and homogeneous oil-water-gas regimes. The principle is based on recognition of the pulse height distributions (PHD) from gamma-ray with supervised artificial neural network (ANN) systems. The detection geometry simulation comprises of two NaI(Tl) detectors and a dual-energy gamma-ray source. The measurement of scattered radiation enables the dual modality densitometry (DMD) measurement principle to be explored. Its basic principle is to combine the measurement of scattered and transmitted radiation in order to acquire information about the different flow regimes. The PHDs obtained by the detectors were used as input to ANN. The data sets required for training and testing the ANN were generated by the MCNP-X code from static and ideal theoretical models of multiphase systems. The ANN correctly identified the three different flow regimes for all data set evaluated. The results presented show that PHDs examined by ANN may be applied in the successfully flow regime identification. (author)

Rainfall prediction methodology with binary multilayer perceptron neural networks

Science.gov (United States)

Esteves, João Trevizoli; de Souza Rolim, Glauco; Ferraudo, Antonio Sergio

2018-05-01

Precipitation, in short periods of time, is a phenomenon associated with high levels of uncertainty and variability. Given its nature, traditional forecasting techniques are expensive and computationally demanding. This paper presents a soft computing technique to forecast the occurrence of rainfall in short ranges of time by artificial neural networks (ANNs) in accumulated periods from 3 to 7 days for each climatic season, mitigating the necessity of predicting its amount. With this premise it is intended to reduce the variance, rise the bias of data and lower the responsibility of the model acting as a filter for quantitative models by removing subsequent occurrences of zeros values of rainfall which leads to bias the and reduces its performance. The model were developed with time series from ten agriculturally relevant regions in Brazil, these places are the ones with the longest available weather time series and and more deficient in accurate climate predictions, it was available 60 years of daily mean air temperature and accumulated precipitation which were used to estimate the potential evapotranspiration and water balance; these were the variables used as inputs for the ANNs models. The mean accuracy of the model for all the accumulated periods were 78% on summer, 71% on winter 62% on spring and 56% on autumn, it was identified that the effect of continentality, the effect of altitude and the volume of normal precipitation, have an direct impact on the accuracy of the ANNs. The models have peak performance in well defined seasons, but looses its accuracy in transitional seasons and places under influence of macro-climatic and mesoclimatic effects, which indicates that this technique can be used to indicate the eminence of rainfall with some limitations.

The quest for a Quantum Neural Network

OpenAIRE

Schuld, M.; Sinayskiy, I.; Petruccione, F.

2014-01-01

With the overwhelming success in the field of quantum information in the last decades, the "quest" for a Quantum Neural Network (QNN) model began in order to combine quantum computing with the striking properties of neural computing. This article presents a systematic approach to QNN research, which so far consists of a conglomeration of ideas and proposals. It outlines the challenge of combining the nonlinear, dissipative dynamics of neural computing and the linear, unitary dynamics of quant...

A Comparison of the Methodological Quality of Articles in Computer Science Education Journals and Conference Proceedings

Science.gov (United States)

Randolph, Justus J.; Julnes, George; Bednarik, Roman; Sutinen, Erkki

2007-01-01

In this study we empirically investigate the claim that articles published in computer science education journals are more methodologically sound than articles published in computer science education conference proceedings. A random sample of 352 articles was selected from those articles published in major computer science education forums between…

Computer-assisted detection of colonic polyps with CT colonography using neural networks and binary classification trees

International Nuclear Information System (INIS)

Jerebko, Anna K.; Summers, Ronald M.; Malley, James D.; Franaszek, Marek; Johnson, C. Daniel

2003-01-01

Detection of colonic polyps in CT colonography is problematic due to complexities of polyp shape and the surface of the normal colon. Published results indicate the feasibility of computer-aided detection of polyps but better classifiers are needed to improve specificity. In this paper we compare the classification results of two approaches: neural networks and recursive binary trees. As our starting point we collect surface geometry information from three-dimensional reconstruction of the colon, followed by a filter based on selected variables such as region density, Gaussian and average curvature and sphericity. The filter returns sites that are candidate polyps, based on earlier work using detection thresholds, to which the neural nets or the binary trees are applied. A data set of 39 polyps from 3 to 25 mm in size was used in our investigation. For both neural net and binary trees we use tenfold cross-validation to better estimate the true error rates. The backpropagation neural net with one hidden layer trained with Levenberg-Marquardt algorithm achieved the best results: sensitivity 90% and specificity 95% with 16 false positives per study

The prediction in computer color matching of dentistry based on GA+BP neural network.

Science.gov (United States)

Li, Haisheng; Lai, Long; Chen, Li; Lu, Cheng; Cai, Qiang

2015-01-01

Although the use of computer color matching can reduce the influence of subjective factors by technicians, matching the color of a natural tooth with a ceramic restoration is still one of the most challenging topics in esthetic prosthodontics. Back propagation neural network (BPNN) has already been introduced into the computer color matching in dentistry, but it has disadvantages such as unstable and low accuracy. In our study, we adopt genetic algorithm (GA) to optimize the initial weights and threshold values in BPNN for improving the matching precision. To our knowledge, we firstly combine the BPNN with GA in computer color matching in dentistry. Extensive experiments demonstrate that the proposed method improves the precision and prediction robustness of the color matching in restorative dentistry.

Evaluation of dose from kV cone-beam computed tomography during radiotherapy: a comparison of methodologies

Science.gov (United States)

Buckley, J.; Wilkinson, D.; Malaroda, A.; Metcalfe, P.

2017-01-01

Three alternative methodologies to the Computed-Tomography Dose Index for the evaluation of Cone-Beam Computed Tomography dose are compared, the Cone-Beam Dose Index, IAEA Human Health Report No. 5 recommended methodology and the AAPM Task Group 111 recommended methodology. The protocols were evaluated for Pelvis and Thorax scan modes on Varian® On-Board Imager and Truebeam kV XI imaging systems. The weighted planar average dose was highest for the AAPM methodology across all scans, with the CBDI being the second highest overall. A 17.96% and 1.14% decrease from the TG-111 protocol to the IAEA and CBDI protocols for the Pelvis mode and 18.15% and 13.10% decrease for the Thorax mode were observed for the XI system. For the OBI system, the variation was 16.46% and 7.14% for Pelvis mode and 15.93% to the CBDI protocol in Thorax mode respectively.

Binary Factorization in Hopfield-Like Neural Networks: Single-Step Approximation and Computer Simulations

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Sirota, A.M.; Húsek, Dušan; Muraviev, I. P.

2004-01-01

Roč. 14, č. 2 (2004), s. 139-152 ISSN 1210-0552 R&D Projects: GA ČR GA201/01/1192 Grant - others:BARRANDE(EU) 99010-2/99053; Intellectual computer Systems(EU) Grant 2.45 Institutional research plan: CEZ:AV0Z1030915 Keywords : nonlinear binary factor analysis * feature extraction * recurrent neural network * Single-Step approximation * neurodynamics simulation * attraction basins * Hebbian learning * unsupervised learning * neuroscience * brain function modeling Subject RIV: BA - General Mathematics

Use of artificial neural networks (computer analysis) in the diagnosis of microcalcifications on mammography

International Nuclear Information System (INIS)

Markopoulos, Christos; Kouskos, Efstratios; Koufopoulos, Konstantinos; Kyriakou, Vasiliki; Gogas, John

2001-01-01

Introduction/objective: the purpose of this study was to evaluate a computer based method for differentiating malignant from benign clustered microcalcifications, comparing it with the performance of three physicians. Methods and material: materials for the study are 240 suspicious microcalcifications on mammograms from 220 female patients who underwent breast biopsy, following hook wire localization under mammographic guidance. The histologic findings were malignant in 108 cases (45%) and benign in 132 cases (55%). Those clusters were analyzed by a computer program and eight features of the calcifications (density, number, area, brightness, diameter average, distance average, proximity average, perimeter compacity average) were quantitatively estimated by a specific artificial neural network. Human input was limited to initial identification of the calcifications. Three physicians-observers were also evaluated for the malignant or benign nature of the clustered microcalcifications. Results: the performance of the artificial network was evaluated by receiver operating characteristics (ROC) curves. ROC curves were also generated for the performance of each observer and for the three observers as a group. The ROC curves for the computer and for the physicians were compared and the results are:area under the curve (AUC) value for computer is 0.937, for physician-1 is 0.746, for physician-2 is 0.785, for physician-3 is 0.835 and for physicians as a group is 0.810. The results of the Student's t-test for paired data showed statistically significant difference between the artificial neural network and the physicians' performance, independently and as a group. Discussion and conclusion: our study showed that computer analysis achieves statistically significantly better performance than that of physicians in the classification of malignant and benign calcifications. This method, after further evaluation and improvement, may help radiologists and breast surgeons in better

Handwritten Digits Recognition Using Neural Computing

Directory of Open Access Journals (Sweden)

Călin Enăchescu

2009-12-01

Full Text Available In this paper we present a method for the recognition of handwritten digits and a practical implementation of this method for real-time recognition. A theoretical framework for the neural networks used to classify the handwritten digits is also presented.The classification task is performed using a Convolutional Neural Network (CNN. CNN is a special type of multy-layer neural network, being trained with an optimized version of the back-propagation learning algorithm.CNN is designed to recognize visual patterns directly from pixel images with minimal preprocessing, being capable to recognize patterns with extreme variability (such as handwritten characters, and with robustness to distortions and simple geometric transformations.The main contributions of this paper are related to theoriginal methods for increasing the efficiency of the learning algorithm by preprocessing the images before the learning process and a method for increasing the precision and performance for real-time applications, by removing the non useful information from the background.By combining these strategies we have obtained an accuracy of 96.76%, using as training set the NIST (National Institute of Standards and Technology database.

Neural network feedforward control of a closed-circuit wind tunnel

Science.gov (United States)

Sutcliffe, Peter

Accurate control of wind-tunnel test conditions can be dramatically enhanced using feedforward control architectures which allow operating conditions to be maintained at a desired setpoint through the use of mathematical models as the primary source of prediction. However, as the desired accuracy of the feedforward prediction increases, the model complexity also increases, so that an ever increasing computational load is incurred. This drawback can be avoided by employing a neural network that is trained offline using the output of a high fidelity wind-tunnel mathematical model, so that the neural network can rapidly reproduce the predictions of the model with a greatly reduced computational overhead. A novel neural network database generation method, developed through the use of fractional factorial arrays, was employed such that a neural network can accurately predict wind-tunnel parameters across a wide range of operating conditions whilst trained upon a highly efficient database. The subsequent network was incorporated into a Neural Network Model Predictive Control (NNMPC) framework to allow an optimised output schedule capable of providing accurate control of the wind-tunnel operating parameters. Facilitation of an optimised path through the solution space is achieved through the use of a chaos optimisation algorithm such that a more globally optimum solution is likely to be found with less computational expense than the gradient descent method. The parameters associated with the NNMPC such as the control horizon are determined through the use of a Taguchi methodology enabling the minimum number of experiments to be carried out to determine the optimal combination. The resultant NNMPC scheme was employed upon the Hessert Low Speed Wind Tunnel at the University of Notre Dame to control the test-section temperature such that it follows a pre-determined reference trajectory during changes in the test-section velocity. Experimental testing revealed that the

Artificial Neural Networks for Reducing Computational Effort in Active Truncated Model Testing of Mooring Lines

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Voie, Per Erlend Torbergsen; Høgsberg, Jan Becker

2015-01-01

simultaneously, this method is very demanding in terms of numerical efficiency and computational power. Therefore, this method has not yet proved to be feasible. It has recently been shown how a hybrid method combining classical numerical models and artificial neural networks (ANN) can provide a dramatic...... prior to the experiment and with a properly trained ANN it is no problem to obtain accurate simulations much faster than real time-without any need for large computational capacity. The present study demonstrates how this hybrid method can be applied to the active truncated experiments yielding a system...

Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study

International Nuclear Information System (INIS)

Benedetti, Miriam; Cesarotti, Vittorio; Introna, Vito; Serranti, Jacopo

2016-01-01

Highlights: • A methodology to enable energy consumption control automation is proposed. • The methodology is based on the use of Artificial Neural Networks. • A method to control the accuracy of the model over time is proposed. • Two methods to enable automatic retraining of the network are proposed. • Retraining methods are evaluated on their accuracy over time. - Abstract: Energy consumption control in energy intensive companies is always more considered as a critical activity to continuously improve energy performance. It undoubtedly requires a huge effort in data gathering and analysis, and the amount of these data together with the scarceness of human resources devoted to Energy Management activities who could maintain and update the analyses’ output are often the main barriers to its diffusion in companies. Advanced tools such as software based on machine learning techniques are therefore the key to overcome these barriers and allow an easy but accurate control. This type of systems is able to solve complex problems obtaining reliable results over time, but not to understand when the reliability of the results is declining (a common situation considering energy using systems, often undergoing structural changes) and to automatically adapt itself using a limited amount of training data, so that a completely automatic application is not yet available and the automatic energy consumption control using intelligent systems is still a challenge. This paper presents a whole new approach to energy consumption control, proposing a methodology based on Artificial Neural Networks (ANNs) and aimed at creating an automatic energy consumption control system. First of all, three different structures of neural networks are proposed and trained using a huge amount of data. Three different performance indicators are then used to identify the most suitable structure, which is implemented to create an energy consumption control tool. In addition, considering that

Biomaterials and computation: a strategic alliance to investigate emergent responses of neural cells.

Science.gov (United States)

Sergi, Pier Nicola; Cavalcanti-Adam, Elisabetta Ada

2017-03-28

Topographical and chemical cues drive migration, outgrowth and regeneration of neurons in different and crucial biological conditions. In the natural extracellular matrix, their influences are so closely coupled that they result in complex cellular responses. As a consequence, engineered biomaterials are widely used to simplify in vitro conditions, disentangling intricate in vivo behaviours, and narrowing the investigation on particular emergent responses. Nevertheless, how topographical and chemical cues affect the emergent response of neural cells is still unclear, thus in silico models are used as additional tools to reproduce and investigate the interactions between cells and engineered biomaterials. This work aims at presenting the synergistic use of biomaterials-based experiments and computation as a strategic way to promote the discovering of complex neural responses as well as to allow the interactions between cells and biomaterials to be quantitatively investigated, fostering a rational design of experiments.

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

Hardware Acceleration of Adaptive Neural Algorithms.

Energy Technology Data Exchange (ETDEWEB)

James, Conrad D. [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

2017-11-01

As tradit ional numerical computing has faced challenges, researchers have turned towards alternative computing approaches to reduce power - per - computation metrics and improve algorithm performance. Here, we describe an approach towards non - conventional computing that strengthens the connection between machine learning and neuroscience concepts. The Hardware Acceleration of Adaptive Neural Algorithms (HAANA) project ha s develop ed neural machine learning algorithms and hardware for applications in image processing and cybersecurity. While machine learning methods are effective at extracting relevant features from many types of data, the effectiveness of these algorithms degrades when subjected to real - world conditions. Our team has generated novel neural - inspired approa ches to improve the resiliency and adaptability of machine learning algorithms. In addition, we have also designed and fabricated hardware architectures and microelectronic devices specifically tuned towards the training and inference operations of neural - inspired algorithms. Finally, our multi - scale simulation framework allows us to assess the impact of microelectronic device properties on algorithm performance.

The Use of Neural Network to Recognize the Parts of the Computer Motherboard

OpenAIRE

Abbas M. Ali; S. D. Gore; Musaab AL-Sarierah

2005-01-01

This study suggests a new approach of learning which utilizes the techniques of computer vision to recognize the parts inside the motherboard. The main thrust is to identify different parts of the motherboard using a Hopfield Neural Network. The outcome of the net is compared with the objects stored in the database. The proposed scheme is implemented using bottom -up approach, where steps like edge detection, spatial filtering, image masking..etc are performed in sequence. the scheme is simul...

Neural Network Optimization of Ligament Stiffnesses for the Enhanced Predictive Ability of a Patient-Specific, Computational Foot/Ankle Model.

Science.gov (United States)

Chande, Ruchi D; Wayne, Jennifer S

2017-09-01

Computational models of diarthrodial joints serve to inform the biomechanical function of these structures, and as such, must be supplied appropriate inputs for performance that is representative of actual joint function. Inputs for these models are sourced from both imaging modalities as well as literature. The latter is often the source of mechanical properties for soft tissues, like ligament stiffnesses; however, such data are not always available for all the soft tissues nor is it known for patient-specific work. In the current research, a method to improve the ligament stiffness definition for a computational foot/ankle model was sought with the greater goal of improving the predictive ability of the computational model. Specifically, the stiffness values were optimized using artificial neural networks (ANNs); both feedforward and radial basis function networks (RBFNs) were considered. Optimal networks of each type were determined and subsequently used to predict stiffnesses for the foot/ankle model. Ultimately, the predicted stiffnesses were considered reasonable and resulted in enhanced performance of the computational model, suggesting that artificial neural networks can be used to optimize stiffness inputs.

Metamodels for Computer-Based Engineering Design: Survey and Recommendations

Science.gov (United States)

Simpson, Timothy W.; Peplinski, Jesse; Koch, Patrick N.; Allen, Janet K.

1997-01-01

The use of statistical techniques to build approximations of expensive computer analysis codes pervades much of todays engineering design. These statistical approximations, or metamodels, are used to replace the actual expensive computer analyses, facilitating multidisciplinary, multiobjective optimization and concept exploration. In this paper we review several of these techniques including design of experiments, response surface methodology, Taguchi methods, neural networks, inductive learning, and kriging. We survey their existing application in engineering design and then address the dangers of applying traditional statistical techniques to approximate deterministic computer analysis codes. We conclude with recommendations for the appropriate use of statistical approximation techniques in given situations and how common pitfalls can be avoided.

Neural network approach in multichannel auditory event-related potential analysis.

Science.gov (United States)

Wu, F Y; Slater, J D; Ramsay, R E

1994-04-01

Even though there are presently no clearly defined criteria for the assessment of P300 event-related potential (ERP) abnormality, it is strongly indicated through statistical analysis that such criteria exist for classifying control subjects and patients with diseases resulting in neuropsychological impairment such as multiple sclerosis (MS). We have demonstrated the feasibility of artificial neural network (ANN) methods in classifying ERP waveforms measured at a single channel (Cz) from control subjects and MS patients. In this paper, we report the results of multichannel ERP analysis and a modified network analysis methodology to enhance automation of the classification rule extraction process. The proposed methodology significantly reduces the work of statistical analysis. It also helps to standardize the criteria of P300 ERP assessment and facilitate the computer-aided analysis on neuropsychological functions.

Analytical methodology for safety validation of computer controlled subsystems. Volume 1 : state-of-the-art and assessment of safety verification/validation methodologies

Science.gov (United States)

1995-09-01

This report describes the development of a methodology designed to assure that a sufficiently high level of safety is achieved and maintained in computer-based systems which perform safety critical functions in high-speed rail or magnetic levitation ...

Ensemble Nonlinear Autoregressive Exogenous Artificial Neural Networks for Short-Term Wind Speed and Power Forecasting.

Science.gov (United States)

Men, Zhongxian; Yee, Eugene; Lien, Fue-Sang; Yang, Zhiling; Liu, Yongqian

2014-01-01

Short-term wind speed and wind power forecasts (for a 72 h period) are obtained using a nonlinear autoregressive exogenous artificial neural network (ANN) methodology which incorporates either numerical weather prediction or high-resolution computational fluid dynamics wind field information as an exogenous input. An ensemble approach is used to combine the predictions from many candidate ANNs in order to provide improved forecasts for wind speed and power, along with the associated uncertainties in these forecasts. More specifically, the ensemble ANN is used to quantify the uncertainties arising from the network weight initialization and from the unknown structure of the ANN. All members forming the ensemble of neural networks were trained using an efficient particle swarm optimization algorithm. The results of the proposed methodology are validated using wind speed and wind power data obtained from an operational wind farm located in Northern China. The assessment demonstrates that this methodology for wind speed and power forecasting generally provides an improvement in predictive skills when compared to the practice of using an "optimal" weight vector from a single ANN while providing additional information in the form of prediction uncertainty bounds.

Advanced Applications of Neural Networks and Artificial Intelligence: A Review

OpenAIRE

Koushal Kumar; Gour Sundar Mitra Thakur

2012-01-01

Artificial Neural Network is a branch of Artificial intelligence and has been accepted as a new computing technology in computer science fields. This paper reviews the field of Artificial intelligence and focusing on recent applications which uses Artificial Neural Networks (ANN’s) and Artificial Intelligence (AI). It also considers the integration of neural networks with other computing methods Such as fuzzy logic to enhance the interpretation ability of data. Artificial Neural Networks is c...

Parametric optimization for floating drum anaerobic bio-digester using Response Surface Methodology and Artificial Neural Network

Directory of Open Access Journals (Sweden)

S. Sathish

2016-12-01

Full Text Available The main purpose of this study to increase the optimal conditions for biogas yield from anaerobic digestion of agricultural waste (Rice Straw using Response Surface Methodology (RSM and Artificial Neural Network (ANN. In the development of predictive models temperature, pH, substrate concentration and agitation time are conceived as model variables. The experimental results show that the liner model terms of temperature, substrate concentration and pH, agitation time have significance of interactive effects (p < 0.05. The results manifest that the optimum process parameters affected on biogas yield increase from the ANN model when compared to RSM model. The ANN model indicates that it is much more accurate and reckons the values of maximum biogas yield when compared to RSM model.

Computationally based methodology for reengineering the high-level waste planning process at SRS

International Nuclear Information System (INIS)

Paul, P.K.; Gregory, M.V.; Wells, M.N.

1997-01-01

The Savannah River Site (SRS) has started processing its legacy of 34 million gallons of high-level radioactive waste into its final disposable form. The SRS high-level waste (HLW) complex consists of 51 waste storage tanks, 3 evaporators, 6 waste treatment operations, and 2 waste disposal facilities. It is estimated that processing wastes to clean up all tanks will take 30+ yr of operation. Integrating all the highly interactive facility operations through the entire life cycle in an optimal fashion-while meeting all the budgetary, regulatory, and operational constraints and priorities-is a complex and challenging planning task. The waste complex operating plan for the entire time span is periodically published as an SRS report. A computationally based integrated methodology has been developed that has streamlined the planning process while showing how to run the operations at economically and operationally optimal conditions. The integrated computational model replaced a host of disconnected spreadsheet calculations and the analysts' trial-and-error solutions using various scenario choices. This paper presents the important features of the integrated computational methodology and highlights the parameters that are core components of the planning process

A Neural Networks Based Operation Guidance System for Procedure Presentation and Validation

International Nuclear Information System (INIS)

Seung, Kun Mo; Lee, Seung Jun; Seong, Poong Hyun

2006-01-01

In this paper, a neural network based operator support system is proposed to reduce operator's errors in abnormal situations in nuclear power plants (NPPs). There are many complicated situations, in which regular and suitable operations should be done by operators accordingly. In order to regulate and validate operators' operations, it is necessary to develop an operator support system which includes computer based procedures with the functions for operation validation. Many computerized procedures systems (CPS) have been recently developed. Focusing on the human machine interface (HMI) design and procedures' computerization, most of CPSs used various methodologies to enhance system's convenience, reliability and accessibility. Other than only showing procedures, the proposed system integrates a simple CPS and an operation validation system (OVS) by using artificial neural network (ANN) for operational permission and quantitative evaluation

Program Helps Simulate Neural Networks

Science.gov (United States)

Villarreal, James; Mcintire, Gary

1993-01-01

Neural Network Environment on Transputer System (NNETS) computer program provides users high degree of flexibility in creating and manipulating wide variety of neural-network topologies at processing speeds not found in conventional computing environments. Supports back-propagation and back-propagation-related algorithms. Back-propagation algorithm used is implementation of Rumelhart's generalized delta rule. NNETS developed on INMOS Transputer(R). Predefines back-propagation network, Jordan network, and reinforcement network to assist users in learning and defining own networks. Also enables users to configure other neural-network paradigms from NNETS basic architecture. Small portion of software written in OCCAM(R) language.

11th International Conference on Computer and Information Science

CERN Document Server

Computer and Information 2012

2012-01-01

The series "Studies in Computational Intelligence" (SCI) publishes new developments and advances in the various areas of computational intelligence – quickly and with a high quality. The intent is to cover the theory, applications, and design methods of computational intelligence, as embedded in the fields of engineering, computer science, physics and life science, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in computational intelligence spanning the areas of neural networks, connectionist systems, genetic algorithms, evolutionary computation, artificial intelligence, cellular automata, self-organizing systems, soft computing, fuzzy systems, and hybrid intelligent systems. Critical to both contributors and readers are the short publication time and world-wide distribution - this permits a rapid and broad dissemination of research results.   The purpose of the 11th IEEE/ACIS International Conference on Computer and Information Science (ICIS 2012...

Artificial neural network methodology: Application to predict magnetic properties of nanocrystalline alloys

International Nuclear Information System (INIS)

Hamzaoui, R.; Cherigui, M.; Guessasma, S.; ElKedim, O.; Fenineche, N.

2009-01-01

This paper is dedicated to the optimization of magnetic properties of iron based magnetic materials with regard to milling and coating process conditions using artificial neural network methodology. Fe-20 wt.% Ni and Fe-6.5 wt.% Si, alloys were obtained using two high-energy ball milling technologies, namely a planetary ball mill P4 vario ball mill from Fritsch and planetary ball mill from Retch. Further processing of Fe-Si powder allowed the spraying of the feedstock material using high-velocity oxy-fuel (HVOF) process to obtain a relatively dense coating. Input parameters were the disc Ω and vial ω speed rotations for the milling technique, and spray distance and oxygen flow rate in the case of coating process. Two main magnetic parameters are optimized namely the saturation magnetization and the coercivity. Predicted results depict clearly coupled effects of input parameters to vary magnetic parameters. In particular, the increase of saturation magnetization is correlated to the increase of the product Ωω (shock power) and the product of spray parameters. Largest coercivity values are correlated to the increase of the ratio Ω/ω (shock mode process) and the increase of the product of spray parameters.

Artificial Neural Networks in Evaluation and Optimization of Modified Release Solid Dosage Forms

Directory of Open Access Journals (Sweden)

Zorica Djurić

2012-10-01

Full Text Available Implementation of the Quality by Design (QbD approach in pharmaceutical development has compelled researchers in the pharmaceutical industry to employ Design of Experiments (DoE as a statistical tool, in product development. Among all DoE techniques, response surface methodology (RSM is the one most frequently used. Progress of computer science has had an impact on pharmaceutical development as well. Simultaneous with the implementation of statistical methods, machine learning tools took an important place in drug formulation. Twenty years ago, the first papers describing application of artificial neural networks in optimization of modified release products appeared. Since then, a lot of work has been done towards implementation of new techniques, especially Artificial Neural Networks (ANN in modeling of production, drug release and drug stability of modified release solid dosage forms. The aim of this paper is to review artificial neural networks in evaluation and optimization of modified release solid dosage forms.

Computer aided detection of ureteral stones in thin slice computed tomography volumes using Convolutional Neural Networks.

Science.gov (United States)

Längkvist, Martin; Jendeberg, Johan; Thunberg, Per; Loutfi, Amy; Lidén, Mats

2018-06-01

Computed tomography (CT) is the method of choice for diagnosing ureteral stones - kidney stones that obstruct the ureter. The purpose of this study is to develop a computer aided detection (CAD) algorithm for identifying a ureteral stone in thin slice CT volumes. The challenge in CAD for urinary stones lies in the similarity in shape and intensity of stones with non-stone structures and how to efficiently deal with large high-resolution CT volumes. We address these challenges by using a Convolutional Neural Network (CNN) that works directly on the high resolution CT volumes. The method is evaluated on a large data base of 465 clinically acquired high-resolution CT volumes of the urinary tract with labeling of ureteral stones performed by a radiologist. The best model using 2.5D input data and anatomical information achieved a sensitivity of 100% and an average of 2.68 false-positives per patient on a test set of 88 scans. Copyright © 2018 The Authors. Published by Elsevier Ltd.. All rights reserved.

[Computer aided diagnosis model for lung tumor based on ensemble convolutional neural network].

Science.gov (United States)

Wang, Yuanyuan; Zhou, Tao; Lu, Huiling; Wu, Cuiying; Yang, Pengfei

2017-08-01

The convolutional neural network (CNN) could be used on computer-aided diagnosis of lung tumor with positron emission tomography (PET)/computed tomography (CT), which can provide accurate quantitative analysis to compensate for visual inertia and defects in gray-scale sensitivity, and help doctors diagnose accurately. Firstly, parameter migration method is used to build three CNNs (CT-CNN, PET-CNN, and PET/CT-CNN) for lung tumor recognition in CT, PET, and PET/CT image, respectively. Then, we aimed at CT-CNN to obtain the appropriate model parameters for CNN training through analysis the influence of model parameters such as epochs, batchsize and image scale on recognition rate and training time. Finally, three single CNNs are used to construct ensemble CNN, and then lung tumor PET/CT recognition was completed through relative majority vote method and the performance between ensemble CNN and single CNN was compared. The experiment results show that the ensemble CNN is better than single CNN on computer-aided diagnosis of lung tumor.

Is Neural Activity Detected by ERP-Based Brain-Computer Interfaces Task Specific?

Directory of Open Access Journals (Sweden)

Markus A Wenzel

Full Text Available Brain-computer interfaces (BCIs that are based on event-related potentials (ERPs can estimate to which stimulus a user pays particular attention. In typical BCIs, the user silently counts the selected stimulus (which is repeatedly presented among other stimuli in order to focus the attention. The stimulus of interest is then inferred from the electroencephalogram (EEG. Detecting attention allocation implicitly could be also beneficial for human-computer interaction (HCI, because it would allow software to adapt to the user's interest. However, a counting task would be inappropriate for the envisaged implicit application in HCI. Therefore, the question was addressed if the detectable neural activity is specific for silent counting, or if it can be evoked also by other tasks that direct the attention to certain stimuli.Thirteen people performed a silent counting, an arithmetic and a memory task. The tasks required the subjects to pay particular attention to target stimuli of a random color. The stimulus presentation was the same in all three tasks, which allowed a direct comparison of the experimental conditions.Classifiers that were trained to detect the targets in one task, according to patterns present in the EEG signal, could detect targets in all other tasks (irrespective of some task-related differences in the EEG.The neural activity detected by the classifiers is not strictly task specific but can be generalized over tasks and is presumably a result of the attention allocation or of the augmented workload. The results may hold promise for the transfer of classification algorithms from BCI research to implicit relevance detection in HCI.

Neural computation and particle accelerators research, technology and applications

CERN Document Server

D'Arras, Horace

2010-01-01

This book discusses neural computation, a network or circuit of biological neurons and relatedly, particle accelerators, a scientific instrument which accelerates charged particles such as protons, electrons and deuterons. Accelerators have a very broad range of applications in many industrial fields, from high energy physics to medical isotope production. Nuclear technology is one of the fields discussed in this book. The development that has been reached by particle accelerators in energy and particle intensity has opened the possibility to a wide number of new applications in nuclear technology. This book reviews the applications in the nuclear energy field and the design features of high power neutron sources are explained. Surface treatments of niobium flat samples and superconducting radio frequency cavities by a new technique called gas cluster ion beam are also studied in detail, as well as the process of electropolishing. Furthermore, magnetic devises such as solenoids, dipoles and undulators, which ...

Vein matching using artificial neural network in vein authentication systems

Science.gov (United States)

Noori Hoshyar, Azadeh; Sulaiman, Riza

2011-10-01

Personal identification technology as security systems is developing rapidly. Traditional authentication modes like key; password; card are not safe enough because they could be stolen or easily forgotten. Biometric as developed technology has been applied to a wide range of systems. According to different researchers, vein biometric is a good candidate among other biometric traits such as fingerprint, hand geometry, voice, DNA and etc for authentication systems. Vein authentication systems can be designed by different methodologies. All the methodologies consist of matching stage which is too important for final verification of the system. Neural Network is an effective methodology for matching and recognizing individuals in authentication systems. Therefore, this paper explains and implements the Neural Network methodology for finger vein authentication system. Neural Network is trained in Matlab to match the vein features of authentication system. The Network simulation shows the quality of matching as 95% which is a good performance for authentication system matching.

Evolution of teaching and evaluation methodologies: The experience in the computer programming course at the Universidad Nacional de Colombia

Directory of Open Access Journals (Sweden)

Jonatan Gomez Perdomo

2014-05-01

Full Text Available In this paper, we present the evolution of a computer-programming course at the Universidad Nacional de Colombia (UNAL. The teaching methodology has evolved from a linear and non-standardized methodology to a flexible, non-linear and student-centered methodology. Our methodology uses an e-learning platform that supports the learning process by offering students and professors custom navigation between the content and material in an interactive way (book chapters, exercises, videos. Moreover, the platform is open access, and approximately 900 students from the university take this course each term. However, our evaluation methodology has evolved from static evaluations based on paper tests to an online process based on computer adaptive testing (CAT that chooses the questions to ask a student and assigns the student a grade according to the student’s ability.

A methodology for the design of experiments in computational intelligence with multiple regression models.

Science.gov (United States)

Fernandez-Lozano, Carlos; Gestal, Marcos; Munteanu, Cristian R; Dorado, Julian; Pazos, Alejandro

2016-01-01

The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A methodology for the design of experiments in computational intelligence with multiple regression models

Directory of Open Access Journals (Sweden)

Carlos Fernandez-Lozano

2016-12-01

Full Text Available The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the field of Computational Intelligence and especially on a correct comparison between the different results provided for different methods, as those techniques are complex systems that require further study to be fully understood. A methodology commonly accepted in Computational intelligence is implemented in an R package called RRegrs. This package includes ten simple and complex regression models to carry out predictive modeling using Machine Learning and well-known regression algorithms. The framework for experimental design presented herein is evaluated and validated against RRegrs. Our results are different for three out of five state-of-the-art simple datasets and it can be stated that the selection of the best model according to our proposal is statistically significant and relevant. It is of relevance to use a statistical approach to indicate whether the differences are statistically significant using this kind of algorithms. Furthermore, our results with three real complex datasets report different best models than with the previously published methodology. Our final goal is to provide a complete methodology for the use of different steps in order to compare the results obtained in Computational Intelligence problems, as well as from other fields, such as for bioinformatics, cheminformatics, etc., given that our proposal is open and modifiable.

A Practical, Robust Methodology for Acquiring New Observation Data Using Computationally Expensive Groundwater Models

Science.gov (United States)

Siade, Adam J.; Hall, Joel; Karelse, Robert N.

2017-11-01

Regional groundwater flow models play an important role in decision making regarding water resources; however, the uncertainty embedded in model parameters and model assumptions can significantly hinder the reliability of model predictions. One way to reduce this uncertainty is to collect new observation data from the field. However, determining where and when to obtain such data is not straightforward. There exist a number of data-worth and experimental design strategies developed for this purpose. However, these studies often ignore issues related to real-world groundwater models such as computational expense, existing observation data, high-parameter dimension, etc. In this study, we propose a methodology, based on existing methods and software, to efficiently conduct such analyses for large-scale, complex regional groundwater flow systems for which there is a wealth of available observation data. The method utilizes the well-established d-optimality criterion, and the minimax criterion for robust sampling strategies. The so-called Null-Space Monte Carlo method is used to reduce the computational burden associated with uncertainty quantification. And, a heuristic methodology, based on the concept of the greedy algorithm, is proposed for developing robust designs with subsets of the posterior parameter samples. The proposed methodology is tested on a synthetic regional groundwater model, and subsequently applied to an existing, complex, regional groundwater system in the Perth region of Western Australia. The results indicate that robust designs can be obtained efficiently, within reasonable computational resources, for making regional decisions regarding groundwater level sampling.

Computational optical tomography using 3-D deep convolutional neural networks

Science.gov (United States)

Nguyen, Thanh; Bui, Vy; Nehmetallah, George

2018-04-01

Deep convolutional neural networks (DCNNs) offer a promising performance for many image processing areas, such as super-resolution, deconvolution, image classification, denoising, and segmentation, with outstanding results. Here, we develop for the first time, to our knowledge, a method to perform 3-D computational optical tomography using 3-D DCNN. A simulated 3-D phantom dataset was first constructed and converted to a dataset of phase objects imaged on a spatial light modulator. For each phase image in the dataset, the corresponding diffracted intensity image was experimentally recorded on a CCD. We then experimentally demonstrate the ability of the developed 3-D DCNN algorithm to solve the inverse problem by reconstructing the 3-D index of refraction distributions of test phantoms from the dataset from their corresponding diffraction patterns.

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Directory of Open Access Journals (Sweden)

Alma Y. Alanis

2013-01-01

Full Text Available This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF improved using particle swarm optimization (PSO to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

Towards semen quality assessment using neural networks

DEFF Research Database (Denmark)

Linneberg, Christian; Salamon, P.; Svarer, C.

1994-01-01

The paper presents the methodology and results from a neural net based classification of human sperm head morphology. The methodology uses a preprocessing scheme in which invariant Fourier descriptors are lumped into “energy” bands. The resulting networks are pruned using optimal brain damage. Pe...

Computer-Aided Methodology for Syndromic Strabismus Diagnosis.

Science.gov (United States)

Sousa de Almeida, João Dallyson; Silva, Aristófanes Corrêa; Teixeira, Jorge Antonio Meireles; Paiva, Anselmo Cardoso; Gattass, Marcelo

2015-08-01

Strabismus is a pathology that affects approximately 4 % of the population, causing aesthetic problems reversible at any age and irreversible sensory alterations that modify the vision mechanism. The Hirschberg test is one type of examination for detecting this pathology. Computer-aided detection/diagnosis is being used with relative success to aid health professionals. Nevertheless, the routine use of high-tech devices for aiding ophthalmological diagnosis and therapy is not a reality within the subspecialty of strabismus. Thus, this work presents a methodology to aid in diagnosis of syndromic strabismus through digital imaging. Two hundred images belonging to 40 patients previously diagnosed by an specialist were tested. The method was demonstrated to be 88 % accurate in esotropias identification (ET), 100 % for exotropias (XT), 80.33 % for hypertropias (HT), and 83.33 % for hypotropias (HoT). The overall average error was 5.6Δ and 3.83Δ for horizontal and vertical deviations, respectively, against the measures presented by the specialist.

On the Reduction of Computational Complexity of Deep Convolutional Neural Networks

Directory of Open Access Journals (Sweden)

Partha Maji

2018-04-01

Full Text Available Deep convolutional neural networks (ConvNets, which are at the heart of many new emerging applications, achieve remarkable performance in audio and visual recognition tasks. Unfortunately, achieving accuracy often implies significant computational costs, limiting deployability. In modern ConvNets it is typical for the convolution layers to consume the vast majority of computational resources during inference. This has made the acceleration of these layers an important research area in academia and industry. In this paper, we examine the effects of co-optimizing the internal structures of the convolutional layers and underlying implementation of fundamental convolution operation. We demonstrate that a combination of these methods can have a big impact on the overall speedup of a ConvNet, achieving a ten-fold increase over baseline. We also introduce a new class of fast one-dimensional (1D convolutions for ConvNets using the Toom–Cook algorithm. We show that our proposed scheme is mathematically well-grounded, robust, and does not require any time-consuming retraining, while still achieving speedups solely from convolutional layers with no loss in baseline accuracy.

Real-space mapping of topological invariants using artificial neural networks

Science.gov (United States)

Carvalho, D.; García-Martínez, N. A.; Lado, J. L.; Fernández-Rossier, J.

2018-03-01

Topological invariants allow one to characterize Hamiltonians, predicting the existence of topologically protected in-gap modes. Those invariants can be computed by tracing the evolution of the occupied wave functions under twisted boundary conditions. However, those procedures do not allow one to calculate a topological invariant by evaluating the system locally, and thus require information about the wave functions in the whole system. Here we show that artificial neural networks can be trained to identify the topological order by evaluating a local projection of the density matrix. We demonstrate this for two different models, a one-dimensional topological superconductor and a two-dimensional quantum anomalous Hall state, both with spatially modulated parameters. Our neural network correctly identifies the different topological domains in real space, predicting the location of in-gap states. By combining a neural network with a calculation of the electronic states that uses the kernel polynomial method, we show that the local evaluation of the invariant can be carried out by evaluating a local quantity, in particular for systems without translational symmetry consisting of tens of thousands of atoms. Our results show that supervised learning is an efficient methodology to characterize the local topology of a system.

A computational framework for ultrastructural mapping of neural circuitry.

Directory of Open Access Journals (Sweden)

James R Anderson

2009-03-01

Full Text Available Circuitry mapping of metazoan neural systems is difficult because canonical neural regions (regions containing one or more copies of all components are large, regional borders are uncertain, neuronal diversity is high, and potential network topologies so numerous that only anatomical ground truth can resolve them. Complete mapping of a specific network requires synaptic resolution, canonical region coverage, and robust neuronal classification. Though transmission electron microscopy (TEM remains the optimal tool for network mapping, the process of building large serial section TEM (ssTEM image volumes is rendered difficult by the need to precisely mosaic distorted image tiles and register distorted mosaics. Moreover, most molecular neuronal class markers are poorly compatible with optimal TEM imaging. Our objective was to build a complete framework for ultrastructural circuitry mapping. This framework combines strong TEM-compliant small molecule profiling with automated image tile mosaicking, automated slice-to-slice image registration, and gigabyte-scale image browsing for volume annotation. Specifically we show how ultrathin molecular profiling datasets and their resultant classification maps can be embedded into ssTEM datasets and how scripted acquisition tools (SerialEM, mosaicking and registration (ir-tools, and large slice viewers (MosaicBuilder, Viking can be used to manage terabyte-scale volumes. These methods enable large-scale connectivity analyses of new and legacy data. In well-posed tasks (e.g., complete network mapping in retina, terabyte-scale image volumes that previously would require decades of assembly can now be completed in months. Perhaps more importantly, the fusion of molecular profiling, image acquisition by SerialEM, ir-tools volume assembly, and data viewers/annotators also allow ssTEM to be used as a prospective tool for discovery in nonneural systems and a practical screening methodology for neurogenetics. Finally

ANALYSIS OF EFFECTIVENESS OF METHODOLOGICAL SYSTEM FOR PROBABILITY AND STOCHASTIC PROCESSES COMPUTER-BASED LEARNING FOR PRE-SERVICE ENGINEERS

Directory of Open Access Journals (Sweden)

E. Chumak

2015-04-01

Full Text Available The author substantiates that only methodological training systems of mathematical disciplines with implementation of information and communication technologies (ICT can meet the requirements of modern educational paradigm and make possible to increase the educational efficiency. Due to this fact, the necessity of developing the methodology of theory of probability and stochastic processes computer-based learning for pre-service engineers is underlined in the paper. The results of the experimental study for analysis of the efficiency of methodological system of theory of probability and stochastic processes computer-based learning for pre-service engineers are shown. The analysis includes three main stages: ascertaining, searching and forming. The key criteria of the efficiency of designed methodological system are the level of probabilistic and stochastic skills of students and their learning motivation. The effect of implementing the methodological system of probability theory and stochastic processes computer-based learning on the level of students’ IT literacy is shown in the paper. The expanding of the range of objectives of ICT applying by students is described by author. The level of formation of students’ learning motivation on the ascertaining and forming stages of the experiment is analyzed. The level of intrinsic learning motivation for pre-service engineers is defined on these stages of the experiment. For this purpose, the methodology of testing the students’ learning motivation in the chosen specialty is presented in the paper. The increasing of intrinsic learning motivation of the experimental group students (E group against the control group students (C group is demonstrated.

Computational intelligence in automotive applications

Energy Technology Data Exchange (ETDEWEB)

Prokhorov, Danil (ed.) [Toyota Motor Engineering and Manufacturing (TEMA), Ann Arbor, MI (United States). Toyota Technical Center

2008-07-01

What is computational intelligence (CI)? Traditionally, CI is understood as a collection of methods from the fields of neural networks (NN), fuzzy logic and evolutionary computation. This edited volume is the first of its kind, suitable to automotive researchers, engineers and students. It provides a representative sample of contemporary CI activities in the area of automotive technology. The volume consists of 13 chapters, including but not limited to these topics: vehicle diagnostics and vehicle system safety, control of vehicular systems, quality control of automotive processes, driver state estimation, safety of pedestrians, intelligent vehicles. All chapters contain overviews of state of the art, and several chapters illustrate their methodologies on examples of real-world systems. About the Editor: Danil Prokhorov began his technical career in St. Petersburg, Russia, after graduating with Honors from Saint Petersburg State University of Aerospace Instrumentation in 1992 (MS in Robotics). He worked as a research engineer in St. Petersburg Institute for Informatics and Automation, one of the institutes of the Russian Academy of Sciences. He came to the US in late 1993 for Ph.D. studies. He became involved in automotive research in 1995 when he was a Summer intern at Ford Scientific Research Lab in Dearborn, MI. Upon his graduation from the EE Department of Texas Tech University, Lubbock, in 1997, he joined Ford to pursue application-driven research on neural networks and other machine learning algorithms. While at Ford, he took part in several production-bound projects including neural network based engine misfire detection. Since 2005 he is with Toyota Technical Center, Ann Arbor, MI, overseeing important mid- and long-term research projects in computational intelligence. (orig.)

Computational Methodologies for Developing Structure–Morphology–Performance Relationships in Organic Solar Cells: A Protocol Review

KAUST Repository

Do, Khanh; Ravva, Mahesh Kumar; Wang, Tonghui; Bredas, Jean-Luc

2016-01-01

We outline a step-by-step protocol that incorporates a number of theoretical and computational methodologies to evaluate the structural and electronic properties of pi-conjugated semiconducting materials in the condensed phase. Our focus

Encoding neural and synaptic functionalities in electron spin: A pathway to efficient neuromorphic computing

Science.gov (United States)

Sengupta, Abhronil; Roy, Kaushik

2017-12-01

Present day computers expend orders of magnitude more computational resources to perform various cognitive and perception related tasks that humans routinely perform every day. This has recently resulted in a seismic shift in the field of computation where research efforts are being directed to develop a neurocomputer that attempts to mimic the human brain by nanoelectronic components and thereby harness its efficiency in recognition problems. Bridging the gap between neuroscience and nanoelectronics, this paper attempts to provide a review of the recent developments in the field of spintronic device based neuromorphic computing. Description of various spin-transfer torque mechanisms that can be potentially utilized for realizing device structures mimicking neural and synaptic functionalities is provided. A cross-layer perspective extending from the device to the circuit and system level is presented to envision the design of an All-Spin neuromorphic processor enabled with on-chip learning functionalities. Device-circuit-algorithm co-simulation framework calibrated to experimental results suggest that such All-Spin neuromorphic systems can potentially achieve almost two orders of magnitude energy improvement in comparison to state-of-the-art CMOS implementations.

Neural networks and applications tutorial

Science.gov (United States)

Guyon, I.

1991-09-01

The importance of neural networks has grown dramatically during this decade. While only a few years ago they were primarily of academic interest, now dozens of companies and many universities are investigating the potential use of these systems and products are beginning to appear. The idea of building a machine whose architecture is inspired by that of the brain has roots which go far back in history. Nowadays, technological advances of computers and the availability of custom integrated circuits, permit simulations of hundreds or even thousands of neurons. In conjunction, the growing interest in learning machines, non-linear dynamics and parallel computation spurred renewed attention in artificial neural networks. Many tentative applications have been proposed, including decision systems (associative memories, classifiers, data compressors and optimizers), or parametric models for signal processing purposes (system identification, automatic control, noise canceling, etc.). While they do not always outperform standard methods, neural network approaches are already used in some real world applications for pattern recognition and signal processing tasks. The tutorial is divided into six lectures, that where presented at the Third Graduate Summer Course on Computational Physics (September 3-7, 1990) on Parallel Architectures and Applications, organized by the European Physical Society: (1) Introduction: machine learning and biological computation. (2) Adaptive artificial neurons (perceptron, ADALINE, sigmoid units, etc.): learning rules and implementations. (3) Neural network systems: architectures, learning algorithms. (4) Applications: pattern recognition, signal processing, etc. (5) Elements of learning theory: how to build networks which generalize. (6) A case study: a neural network for on-line recognition of handwritten alphanumeric characters.

Neural overlap in processing music and speech

Science.gov (United States)

Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L.

2015-01-01

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. PMID:25646513

MoPCoM Methodology: Focus on Models of Computation

Science.gov (United States)

Koudri, Ali; Champeau, Joël; Le Lann, Jean-Christophe; Leilde, Vincent

Today, developments of Real Time Embedded Systems have to face new challenges. On the one hand, Time-To-Market constraints require a reliable development process allowing quick design space exploration. On the other hand, rapidly developing technology, as stated by Moore's law, requires techniques to handle the resulting productivity gap. In a previous paper, we have presented our Model Based Engineering methodology addressing those issues. In this paper, we make a focus on Models of Computation design and analysis. We illustrate our approach on a Cognitive Radio System development implemented on an FPGA. This work is part of the MoPCoM research project gathering academic and industrial organizations (http://www.mopcom.fr).

Safety of High Speed Ground Transportation Systems : Analytical Methodology for Safety Validation of Computer Controlled Subsystems : Volume 2. Development of a Safety Validation Methodology

Science.gov (United States)

1995-01-01

This report describes the development of a methodology designed to assure that a sufficiently high level of safety is achieved and maintained in computer-based systems which perform safety cortical functions in high-speed rail or magnetic levitation ...

An alternative methodology for planning computer class where teaching means are used

Directory of Open Access Journals (Sweden)

Maria del Carmen Carrillo Hernández

2016-06-01

Full Text Available Teaching subject of Informatics II, is provided in the fourth year of teaching career Informática- Labor Education, one of the objectives of it is to develop skills in students that allow plan and structure independently, original and creative, Computer class, where the use of computer media education is the guiding element from which students acquire knowledge. Professional practice have been identified limitations in this regard, with the aim of contributing to the development of these skills, the authors of this article propose an alternative methodology that will guide teachers of this subject, to lead the process learning learning so that the goals are met the program guides.

Rough – Granular Computing knowledge discovery models

Directory of Open Access Journals (Sweden)

Mohammed M. Eissa

2016-11-01

Full Text Available Medical domain has become one of the most important areas of research in order to richness huge amounts of medical information about the symptoms of diseases and how to distinguish between them to diagnose it correctly. Knowledge discovery models play vital role in refinement and mining of medical indicators to help medical experts to settle treatment decisions. This paper introduces four hybrid Rough – Granular Computing knowledge discovery models based on Rough Sets Theory, Artificial Neural Networks, Genetic Algorithm and Rough Mereology Theory. A comparative analysis of various knowledge discovery models that use different knowledge discovery techniques for data pre-processing, reduction, and data mining supports medical experts to extract the main medical indicators, to reduce the misdiagnosis rates and to improve decision-making for medical diagnosis and treatment. The proposed models utilized two medical datasets: Coronary Heart Disease dataset and Hepatitis C Virus dataset. The main purpose of this paper was to explore and evaluate the proposed models based on Granular Computing methodology for knowledge extraction according to different evaluation criteria for classification of medical datasets. Another purpose is to make enhancement in the frame of KDD processes for supervised learning using Granular Computing methodology.

Computational Models of Neuron-Astrocyte Interactions Lead to Improved Efficacy in the Performance of Neural Networks

Science.gov (United States)

Alvarellos-González, Alberto; Pazos, Alejandro; Porto-Pazos, Ana B.

2012-01-01

The importance of astrocytes, one part of the glial system, for information processing in the brain has recently been demonstrated. Regarding information processing in multilayer connectionist systems, it has been shown that systems which include artificial neurons and astrocytes (Artificial Neuron-Glia Networks) have well-known advantages over identical systems including only artificial neurons. Since the actual impact of astrocytes in neural network function is unknown, we have investigated, using computational models, different astrocyte-neuron interactions for information processing; different neuron-glia algorithms have been implemented for training and validation of multilayer Artificial Neuron-Glia Networks oriented toward classification problem resolution. The results of the tests performed suggest that all the algorithms modelling astrocyte-induced synaptic potentiation improved artificial neural network performance, but their efficacy depended on the complexity of the problem. PMID:22649480

Artificial neural network and response surface methodology modeling in mass transfer parameters predictions during osmotic dehydration of Carica papaya L.

Directory of Open Access Journals (Sweden)

J. Prakash Maran

2013-09-01

Full Text Available In this study, a comparative approach was made between artificial neural network (ANN and response surface methodology (RSM to predict the mass transfer parameters of osmotic dehydration of papaya. The effects of process variables such as temperature, osmotic solution concentration and agitation speed on water loss, weight reduction, and solid gain during osmotic dehydration were investigated using a three-level three-factor Box-Behnken experimental design. Same design was utilized to train a feed-forward multilayered perceptron (MLP ANN with back-propagation algorithm. The predictive capabilities of the two methodologies were compared in terms of root mean square error (RMSE, mean absolute error (MAE, standard error of prediction (SEP, model predictive error (MPE, chi square statistic (χ2, and coefficient of determination (R2 based on the validation data set. The results showed that properly trained ANN model is found to be more accurate in prediction as compared to RSM model.

Neural overlap in processing music and speech.

Science.gov (United States)

Peretz, Isabelle; Vuvan, Dominique; Lagrois, Marie-Élaine; Armony, Jorge L

2015-03-19

Neural overlap in processing music and speech, as measured by the co-activation of brain regions in neuroimaging studies, may suggest that parts of the neural circuitries established for language may have been recycled during evolution for musicality, or vice versa that musicality served as a springboard for language emergence. Such a perspective has important implications for several topics of general interest besides evolutionary origins. For instance, neural overlap is an important premise for the possibility of music training to influence language acquisition and literacy. However, neural overlap in processing music and speech does not entail sharing neural circuitries. Neural separability between music and speech may occur in overlapping brain regions. In this paper, we review the evidence and outline the issues faced in interpreting such neural data, and argue that converging evidence from several methodologies is needed before neural overlap is taken as evidence of sharing. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

The Neural-fuzzy Thermal Error Compensation Controller on CNC Machining Center

Science.gov (United States)

Tseng, Pai-Chung; Chen, Shen-Len

The geometric errors and structural thermal deformation are factors that influence the machining accuracy of Computer Numerical Control (CNC) machining center. Therefore, researchers pay attention to thermal error compensation technologies on CNC machine tools. Some real-time error compensation techniques have been successfully demonstrated in both laboratories and industrial sites. The compensation results still need to be enhanced. In this research, the neural-fuzzy theory has been conducted to derive a thermal prediction model. An IC-type thermometer has been used to detect the heat sources temperature variation. The thermal drifts are online measured by a touch-triggered probe with a standard bar. A thermal prediction model is then derived by neural-fuzzy theory based on the temperature variation and the thermal drifts. A Graphic User Interface (GUI) system is also built to conduct the user friendly operation interface with Insprise C++ Builder. The experimental results show that the thermal prediction model developed by neural-fuzzy theory methodology can improve machining accuracy from 80µm to 3µm. Comparison with the multi-variable linear regression analysis the compensation accuracy is increased from ±10µm to ±3µm.

A novel role for visual perspective cues in the neural computation of depth.

Science.gov (United States)

Kim, HyungGoo R; Angelaki, Dora E; DeAngelis, Gregory C

2015-01-01

As we explore a scene, our eye movements add global patterns of motion to the retinal image, complicating visual motion produced by self-motion or moving objects. Conventionally, it has been assumed that extraretinal signals, such as efference copy of smooth pursuit commands, are required to compensate for the visual consequences of eye rotations. We consider an alternative possibility: namely, that the visual system can infer eye rotations from global patterns of image motion. We visually simulated combinations of eye translation and rotation, including perspective distortions that change dynamically over time. We found that incorporating these 'dynamic perspective' cues allowed the visual system to generate selectivity for depth sign from motion parallax in macaque cortical area MT, a computation that was previously thought to require extraretinal signals regarding eye velocity. Our findings suggest neural mechanisms that analyze global patterns of visual motion to perform computations that require knowledge of eye rotations.

Thermal sensation prediction by soft computing methodology.

Science.gov (United States)

Jović, Srđan; Arsić, Nebojša; Vilimonović, Jovana; Petković, Dalibor

2016-12-01

Thermal comfort in open urban areas is very factor based on environmental point of view. Therefore it is need to fulfill demands for suitable thermal comfort during urban planning and design. Thermal comfort can be modeled based on climatic parameters and other factors. The factors are variables and they are changed throughout the year and days. Therefore there is need to establish an algorithm for thermal comfort prediction according to the input variables. The prediction results could be used for planning of time of usage of urban areas. Since it is very nonlinear task, in this investigation was applied soft computing methodology in order to predict the thermal comfort. The main goal was to apply extreme leaning machine (ELM) for forecasting of physiological equivalent temperature (PET) values. Temperature, pressure, wind speed and irradiance were used as inputs. The prediction results are compared with some benchmark models. Based on the results ELM can be used effectively in forecasting of PET. Copyright © 2016 Elsevier Ltd. All rights reserved.

New methodologies for calculation of flight parameters on reduced scale wings models in wind tunnel =

Science.gov (United States)

Ben Mosbah, Abdallah

In order to improve the qualities of wind tunnel tests, and the tools used to perform aerodynamic tests on aircraft wings in the wind tunnel, new methodologies were developed and tested on rigid and flexible wings models. A flexible wing concept is consists in replacing a portion (lower and/or upper) of the skin with another flexible portion whose shape can be changed using an actuation system installed inside of the wing. The main purpose of this concept is to improve the aerodynamic performance of the aircraft, and especially to reduce the fuel consumption of the airplane. Numerical and experimental analyses were conducted to develop and test the methodologies proposed in this thesis. To control the flow inside the test sections of the Price-Paidoussis wind tunnel of LARCASE, numerical and experimental analyses were performed. Computational fluid dynamics calculations have been made in order to obtain a database used to develop a new hybrid methodology for wind tunnel calibration. This approach allows controlling the flow in the test section of the Price-Paidoussis wind tunnel. For the fast determination of aerodynamic parameters, new hybrid methodologies were proposed. These methodologies were used to control flight parameters by the calculation of the drag, lift and pitching moment coefficients and by the calculation of the pressure distribution around an airfoil. These aerodynamic coefficients were calculated from the known airflow conditions such as angles of attack, the mach and the Reynolds numbers. In order to modify the shape of the wing skin, electric actuators were installed inside the wing to get the desired shape. These deformations provide optimal profiles according to different flight conditions in order to reduce the fuel consumption. A controller based on neural networks was implemented to obtain desired displacement actuators. A metaheuristic algorithm was used in hybridization with neural networks, and support vector machine approaches and their

Goal-directed behaviour and instrumental devaluation: a neural system-level computational model

Directory of Open Access Journals (Sweden)

Francesco Mannella

2016-10-01

Full Text Available Devaluation is the key experimental paradigm used to demonstrate the presence of instrumental behaviours guided by goals in mammals. We propose a neural system-level computational model to address the question of which brain mechanisms allow the current value of rewards to control instrumental actions. The model pivots on and shows the computational soundness of the hypothesis for which the internal representation of instrumental manipulanda (e.g., levers activate the representation of rewards (or `action-outcomes', e.g. foods while attributing to them a value which depends on the current internal state of the animal (e.g., satiation for some but not all foods. The model also proposes an initial hypothesis of the integrated system of key brain components supporting this process and allowing the recalled outcomes to bias action selection: (a the sub-system formed by the basolateral amygdala and insular cortex acquiring the manipulanda-outcomes associations and attributing the current value to the outcomes; (b the three basal ganglia-cortical loops selecting respectively goals, associative sensory representations, and actions; (c the cortico-cortical and striato-nigro-striatal neural pathways supporting the selection, and selection learning, of actions based on habits and goals. The model reproduces and integrates the results of different devaluation experiments carried out with control rats and rats with pre- and post-training lesions of the basolateral amygdala, the nucleus accumbens core, the prelimbic cortex, and the dorso-medial striatum. The results support the soundness of the hypotheses of the model and show its capacity to integrate, at the system-level, the operations of the key brain structures underlying devaluation. Based on its hypotheses and predictions, the model also represents an operational framework to support the design and analysis of new experiments on the motivational aspects of goal-directed behaviour.

An evolutionary computing frame work toward object extraction from satellite images

Directory of Open Access Journals (Sweden)

P.V. Arun

2013-12-01

Full Text Available Image interpretation domains have witnessed the application of many intelligent methodologies over the past decade; however the effective use of evolutionary computing techniques for feature detection has been less explored. In this paper, we critically analyze the possibility of using cellular neural network for accurate feature detection. Contextual knowledge has been effectively represented by incorporating spectral and spatial aspects using adaptive kernel strategy. Developed methodology has been compared with traditional approaches in an object based context and investigations revealed that considerable success has been achieved with the procedure. Intelligent interpretation, automatic interpolation, and effective contextual representations are the features of the system.

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

Detection of white matter lesion regions in MRI using SLIC0 and convolutional neural network.

Science.gov (United States)

Diniz, Pedro Henrique Bandeira; Valente, Thales Levi Azevedo; Diniz, João Otávio Bandeira; Silva, Aristófanes Corrêa; Gattass, Marcelo; Ventura, Nina; Muniz, Bernardo Carvalho; Gasparetto, Emerson Leandro

2018-04-19

White matter lesions are non-static brain lesions that have a prevalence rate up to 98% in the elderly population. Because they may be associated with several brain diseases, it is important that they are detected as soon as possible. Magnetic Resonance Imaging (MRI) provides three-dimensional data with the possibility to detect and emphasize contrast differences in soft tissues, providing rich information about the human soft tissue anatomy. However, the amount of data provided for these images is far too much for manual analysis/interpretation, representing a difficult and time-consuming task for specialists. This work presents a computational methodology capable of detecting regions of white matter lesions of the brain in MRI of FLAIR modality. The techniques highlighted in this methodology are SLIC0 clustering for candidate segmentation and convolutional neural networks for candidate classification. The methodology proposed here consists of four steps: (1) images acquisition, (2) images preprocessing, (3) candidates segmentation and (4) candidates classification. The methodology was applied on 91 magnetic resonance images provided by DASA, and achieved an accuracy of 98.73%, specificity of 98.77% and sensitivity of 78.79% with 0.005 of false positives, without any false positives reduction technique, in detection of white matter lesion regions. It is demonstrated the feasibility of the analysis of brain MRI using SLIC0 and convolutional neural network techniques to achieve success in detection of white matter lesions regions. Copyright © 2018. Published by Elsevier B.V.

The Study of Learners' Preference for Visual Complexity on Small Screens of Mobile Computers Using Neural Networks

Science.gov (United States)

Wang, Lan-Ting; Lee, Kun-Chou

2014-01-01

The vision plays an important role in educational technologies because it can produce and communicate quite important functions in teaching and learning. In this paper, learners' preference for the visual complexity on small screens of mobile computers is studied by neural networks. The visual complexity in this study is divided into five…

Current Trend Towards Using Soft Computing Approaches to Phase Synchronization in Communication Systems

Science.gov (United States)

Drake, Jeffrey T.; Prasad, Nadipuram R.

1999-01-01

This paper surveys recent advances in communications that utilize soft computing approaches to phase synchronization. Soft computing, as opposed to hard computing, is a collection of complementary methodologies that act in producing the most desirable control, decision, or estimation strategies. Recently, the communications area has explored the use of the principal constituents of soft computing, namely, fuzzy logic, neural networks, and genetic algorithms, for modeling, control, and most recently for the estimation of phase in phase-coherent communications. If the receiver in a digital communications system is phase-coherent, as is often the case, phase synchronization is required. Synchronization thus requires estimation and/or control at the receiver of an unknown or random phase offset.

Neural Systems Laboratory

Data.gov (United States)

Federal Laboratory Consortium — As part of the Electrical and Computer Engineering Department and The Institute for System Research, the Neural Systems Laboratory studies the functionality of the...

Hardware implementation of stochastic spiking neural networks.

Science.gov (United States)

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

2012-08-01

Spiking Neural Networks, the last generation of Artificial Neural Networks, are characterized by its bio-inspired nature and by a higher computational capacity with respect to other neural models. In real biological neurons, stochastic processes represent an important mechanism of neural behavior and are responsible of its special arithmetic capabilities. In this work we present a simple hardware implementation of spiking neurons that considers this probabilistic nature. The advantage of the proposed implementation is that it is fully digital and therefore can be massively implemented in Field Programmable Gate Arrays. The high computational capabilities of the proposed model are demonstrated by the study of both feed-forward and recurrent networks that are able to implement high-speed signal filtering and to solve complex systems of linear equations.

Application of artificial neural networks to identify equilibration in computer simulations

Science.gov (United States)

Leibowitz, Mitchell H.; Miller, Evan D.; Henry, Michael M.; Jankowski, Eric

2017-11-01

Determining which microstates generated by a thermodynamic simulation are representative of the ensemble for which sampling is desired is a ubiquitous, underspecified problem. Artificial neural networks are one type of machine learning algorithm that can provide a reproducible way to apply pattern recognition heuristics to underspecified problems. Here we use the open-source TensorFlow machine learning library and apply it to the problem of identifying which hypothetical observation sequences from a computer simulation are “equilibrated” and which are not. We generate training populations and test populations of observation sequences with embedded linear and exponential correlations. We train a two-neuron artificial network to distinguish the correlated and uncorrelated sequences. We find that this simple network is good enough for > 98% accuracy in identifying exponentially-decaying energy trajectories from molecular simulations.

Novel stability criteria for uncertain delayed Cohen-Grossberg neural networks using discretized Lyapunov functional

International Nuclear Information System (INIS)

Souza, Fernando O.; Palhares, Reinaldo M.; Ekel, Petr Ya.

2009-01-01

This paper deals with the stability analysis of delayed uncertain Cohen-Grossberg neural networks (CGNN). The proposed methodology consists in obtaining new robust stability criteria formulated as linear matrix inequalities (LMIs) via the Lyapunov-Krasovskii theory. Particularly one stability criterion is derived from the selection of a parameter-dependent Lyapunov-Krasovskii functional, which allied with the Gu's discretization technique and a simple strategy that decouples the system matrices from the functional matrices, assures a less conservative stability condition. Two computer simulations are presented to support the improved theoretical results.

Computer assisted diagnosis in renal nuclear medicine: rationale, methodology and interpretative criteria for diuretic renography

Science.gov (United States)

Taylor, Andrew T; Garcia, Ernest V

2014-01-01

The goal of artificial intelligence, expert systems, decision support systems and computer assisted diagnosis (CAD) in imaging is the development and implementation of software to assist in the detection and evaluation of abnormalities, to alert physicians to cognitive biases, to reduce intra and inter-observer variability and to facilitate the interpretation of studies at a faster rate and with a higher level of accuracy. These developments are needed to meet the challenges resulting from a rapid increase in the volume of diagnostic imaging studies coupled with a concurrent increase in the number and complexity of images in each patient data. The convergence of an expanding knowledge base and escalating time constraints increases the likelihood of physician errors. Errors are even more likely when physicians interpret low volume studies such as 99mTc-MAG3 diuretic scans where imagers may have had limited training or experience. Decision support systems include neural networks, case-based reasoning, expert systems and statistical systems. iRENEX (renal expert) is an expert system for diuretic renography that uses a set of rules obtained from human experts to analyze a knowledge base of both clinical parameters and quantitative parameters derived from the renogram. Initial studies have shown that the interpretations provided by iRENEX are comparable to the interpretations of a panel of experts. iRENEX provides immediate patient specific feedback at the time of scan interpretation, can be queried to provide the reasons for its conclusions and can be used as an educational tool to teach trainees to better interpret renal scans. iRENEX also has the capacity to populate a structured reporting module and generate a clear and concise impression based on the elements contained in the report; adherence to the procedural and data entry components of the structured reporting module assures and documents procedural competency. Finally, although the focus is CAD applied to

Remedial Action Assessment System: A computer-based methodology for conducting feasibility studies

International Nuclear Information System (INIS)

White, M.K.; Buelt, J.L.; Stottlemyre, J.A.

1991-02-01

Because of the complexity and number of potential waste sites facing the US Department of Energy (DOE) for potential cleanup, DOE is supporting the development of a computer-based methodology to streamline the remedial investigation/feasibility study process. The Remedial Action Assessment System (RAAS), can be used for screening, linking, and evaluating established technology processes in support of conducting feasibility studies. It is also intended to do the same in support of corrective measures studies. The user interface employs menus, windows, help features, and graphical information while RAAS is in operation. Object-oriented programming is used to link unit processes into sets of compatible processes that form appropriate remedial alternatives. Once the remedial alternatives are formed, the RAAS methodology can evaluate them in terms of effectiveness, implementability, and cost. RAAS will access a user-selected risk assessment code to determine the reduction of risk after remedial action by each recommended alternative. The methodology will also help determine the implementability of the remedial alternatives at a site and access cost estimating tools to provide estimates of capital, operating, and maintenance costs. This paper presents the characteristics of two RAAS prototypes currently being developed. These include the RAAS Technology Information System, which accesses graphical, tabular and textual information about technologies, and the main RAAS methodology, which screens, links, and evaluates remedial technologies. 4 refs., 3 figs., 1 tab

Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms

Directory of Open Access Journals (Sweden)

Ahmad Shokuh Saljoughi

2018-01-01

Full Text Available Today, cloud computing has become popular among users in organizations and companies. Security and efficiency are the two major issues facing cloud service providers and their customers. Since cloud computing is a virtual pool of resources provided in an open environment (Internet, cloud-based services entail security risks. Detection of intrusions and attacks through unauthorized users is one of the biggest challenges for both cloud service providers and cloud users. In the present study, artificial intelligence techniques, e.g. MLP Neural Network sand particle swarm optimization algorithm, were used to detect intrusion and attacks. The methods were tested for NSL-KDD, KDD-CUP datasets. The results showed improved accuracy in detecting attacks and intrusions by unauthorized users.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Science.gov (United States)

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

We provide a novel refined attractor-based complexity measurement for Boolean recurrent neural networks that represents an assessment of their computational power in terms of the significance of their attractor dynamics. This complexity measurement is achieved by first proving a computational equivalence between Boolean recurrent neural networks and some specific class of -automata, and then translating the most refined classification of -automata to the Boolean neural network context. As a result, a hierarchical classification of Boolean neural networks based on their attractive dynamics is obtained, thus providing a novel refined attractor-based complexity measurement for Boolean recurrent neural networks. These results provide new theoretical insights to the computational and dynamical capabilities of neural networks according to their attractive potentialities. An application of our findings is illustrated by the analysis of the dynamics of a simplified model of the basal ganglia-thalamocortical network simulated by a Boolean recurrent neural network. This example shows the significance of measuring network complexity, and how our results bear new founding elements for the understanding of the complexity of real brain circuits. PMID:24727866

A high-speed analog neural processor

NARCIS (Netherlands)

Masa, P.; Masa, Peter; Hoen, Klaas; Hoen, Klaas; Wallinga, Hans

1994-01-01

Targeted at high-energy physics research applications, our special-purpose analog neural processor can classify up to 70 dimensional vectors within 50 nanoseconds. The decision-making process of the implemented feedforward neural network enables this type of computation to tolerate weight

International Conference on Artificial Neural Networks (ICANN)

CERN Document Server

Mladenov, Valeri; Kasabov, Nikola; Artificial Neural Networks : Methods and Applications in Bio-/Neuroinformatics

2015-01-01

The book reports on the latest theories on artificial neural networks, with a special emphasis on bio-neuroinformatics methods. It includes twenty-three papers selected from among the best contributions on bio-neuroinformatics-related issues, which were presented at the International Conference on Artificial Neural Networks, held in Sofia, Bulgaria, on September 10-13, 2013 (ICANN 2013). The book covers a broad range of topics concerning the theory and applications of artificial neural networks, including recurrent neural networks, super-Turing computation and reservoir computing, double-layer vector perceptrons, nonnegative matrix factorization, bio-inspired models of cell communities, Gestalt laws, embodied theory of language understanding, saccadic gaze shifts and memory formation, and new training algorithms for Deep Boltzmann Machines, as well as dynamic neural networks and kernel machines. It also reports on new approaches to reinforcement learning, optimal control of discrete time-delay systems, new al...

Efficient Monte Carlo sampling of inverse problems using a neural network-based forward—applied to GPR crosshole traveltime inversion

Science.gov (United States)

Hansen, T. M.; Cordua, K. S.

2017-12-01

Probabilistically formulated inverse problems can be solved using Monte Carlo-based sampling methods. In principle, both advanced prior information, based on for example, complex geostatistical models and non-linear forward models can be considered using such methods. However, Monte Carlo methods may be associated with huge computational costs that, in practice, limit their application. This is not least due to the computational requirements related to solving the forward problem, where the physical forward response of some earth model has to be evaluated. Here, it is suggested to replace a numerical complex evaluation of the forward problem, with a trained neural network that can be evaluated very fast. This will introduce a modeling error that is quantified probabilistically such that it can be accounted for during inversion. This allows a very fast and efficient Monte Carlo sampling of the solution to an inverse problem. We demonstrate the methodology for first arrival traveltime inversion of crosshole ground penetrating radar data. An accurate forward model, based on 2-D full-waveform modeling followed by automatic traveltime picking, is replaced by a fast neural network. This provides a sampling algorithm three orders of magnitude faster than using the accurate and computationally expensive forward model, and also considerably faster and more accurate (i.e. with better resolution), than commonly used approximate forward models. The methodology has the potential to dramatically change the complexity of non-linear and non-Gaussian inverse problems that have to be solved using Monte Carlo sampling techniques.

A Reconfigurable and Biologically Inspired Paradigm for Computation Using Network-On-Chip and Spiking Neural Networks

Directory of Open Access Journals (Sweden)

Jim Harkin

2009-01-01

Full Text Available FPGA devices have emerged as a popular platform for the rapid prototyping of biological Spiking Neural Networks (SNNs applications, offering the key requirement of reconfigurability. However, FPGAs do not efficiently realise the biologically plausible neuron and synaptic models of SNNs, and current FPGA routing structures cannot accommodate the high levels of interneuron connectivity inherent in complex SNNs. This paper highlights and discusses the current challenges of implementing scalable SNNs on reconfigurable FPGAs. The paper proposes a novel field programmable neural network architecture (EMBRACE, incorporating low-power analogue spiking neurons, interconnected using a Network-on-Chip architecture. Results on the evaluation of the EMBRACE architecture using the XOR benchmark problem are presented, and the performance of the architecture is discussed. The paper also discusses the adaptability of the EMBRACE architecture in supporting fault tolerant computing.

PECULIARITIES OF USING THE METHODOLOGY DISTANCE LEARNING OF THE SUBJECT «ENGINEERING AND COMPUTER GRAPHICS» FOR STUDENTS STUDYING BY CORRESPONDENCE

OpenAIRE

Olena V. Slobodianiuk

2010-01-01

A great part of the distance course of the subject «Engineering and Computer Graphics» (ECG) placed in Internet looks as an electronic manual. But the distance training process has a complicated structure and combines not only studying theoretical material but also collaboration between students and a teacher, a work in group. The methodology distance learning of ECG is proposed. This methodology developed and researched on Faculty the engineering and computer graphics of Vinnitsa National Te...

Artificial neural networks in NDT

International Nuclear Information System (INIS)

Abdul Aziz Mohamed

2001-01-01

Artificial neural networks, simply known as neural networks, have attracted considerable interest in recent years largely because of a growing recognition of the potential of these computational paradigms as powerful alternative models to conventional pattern recognition or function approximation techniques. The neural networks approach is having a profound effect on almost all fields, and has been utilised in fields Where experimental inter-disciplinary work is being carried out. Being a multidisciplinary subject with a broad knowledge base, Nondestructive Testing (NDT) or Nondestructive Evaluation (NDE) is no exception. This paper explains typical applications of neural networks in NDT/NDE. Three promising types of neural networks are highlighted, namely, back-propagation, binary Hopfield and Kohonen's self-organising maps. (Author)

Spiking Neural Networks Based on OxRAM Synapses for Real-Time Unsupervised Spike Sorting.

Science.gov (United States)

Werner, Thilo; Vianello, Elisa; Bichler, Olivier; Garbin, Daniele; Cattaert, Daniel; Yvert, Blaise; De Salvo, Barbara; Perniola, Luca

2016-01-01

In this paper, we present an alternative approach to perform spike sorting of complex brain signals based on spiking neural networks (SNN). The proposed architecture is suitable for hardware implementation by using resistive random access memory (RRAM) technology for the implementation of synapses whose low latency (spike sorting. This offers promising advantages to conventional spike sorting techniques for brain-computer interfaces (BCI) and neural prosthesis applications. Moreover, the ultra-low power consumption of the RRAM synapses of the spiking neural network (nW range) may enable the design of autonomous implantable devices for rehabilitation purposes. We demonstrate an original methodology to use Oxide based RRAM (OxRAM) as easy to program and low energy (Spike Timing Dependent Plasticity. Real spiking data have been recorded both intra- and extracellularly from an in-vitro preparation of the Crayfish sensory-motor system and used for validation of the proposed OxRAM based SNN. This artificial SNN is able to identify, learn, recognize and distinguish between different spike shapes in the input signal with a recognition rate about 90% without any supervision.

Deep learning for computational chemistry

Energy Technology Data Exchange (ETDEWEB)

Goh, Garrett B. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Hodas, Nathan O. [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354; Vishnu, Abhinav [Advanced Computing, Mathematics, and Data Division, Pacific Northwest National Laboratory, 902 Battelle Blvd Richland Washington 99354

2017-03-08

The rise and fall of artificial neural networks is well documented in the scientific literature of both the fields of computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on “deep” neural networks. Within the last few years, we have seen the transformative impact of deep learning the computer science domain, notably in speech recognition and computer vision, to the extent that the majority of practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties as compared to traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including QSAR, virtual screening, protein structure modeling, QM calculations, materials synthesis and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non neural networks state-of-the-art models across disparate research topics, and deep neural network based models often exceeded the “glass ceiling” expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a useful tool and may grow into a pivotal role for various challenges in the computational chemistry field.

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making

Directory of Open Access Journals (Sweden)

Bryan C. Daniels

2017-06-01

Full Text Available A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a “coding duality” in which there are accumulation and consensus formation processes distinguished by different timescales.

Dual Coding Theory Explains Biphasic Collective Computation in Neural Decision-Making.

Science.gov (United States)

Daniels, Bryan C; Flack, Jessica C; Krakauer, David C

2017-01-01

A central question in cognitive neuroscience is how unitary, coherent decisions at the whole organism level can arise from the distributed behavior of a large population of neurons with only partially overlapping information. We address this issue by studying neural spiking behavior recorded from a multielectrode array with 169 channels during a visual motion direction discrimination task. It is well known that in this task there are two distinct phases in neural spiking behavior. Here we show Phase I is a distributed or incompressible phase in which uncertainty about the decision is substantially reduced by pooling information from many cells. Phase II is a redundant or compressible phase in which numerous single cells contain all the information present at the population level in Phase I, such that the firing behavior of a single cell is enough to predict the subject's decision. Using an empirically grounded dynamical modeling framework, we show that in Phase I large cell populations with low redundancy produce a slow timescale of information aggregation through critical slowing down near a symmetry-breaking transition. Our model indicates that increasing collective amplification in Phase II leads naturally to a faster timescale of information pooling and consensus formation. Based on our results and others in the literature, we propose that a general feature of collective computation is a "coding duality" in which there are accumulation and consensus formation processes distinguished by different timescales.

A proposed methodology for computational fluid dynamics code verification, calibration, and validation

Science.gov (United States)

Aeschliman, D. P.; Oberkampf, W. L.; Blottner, F. G.

Verification, calibration, and validation (VCV) of Computational Fluid Dynamics (CFD) codes is an essential element of the code development process. The exact manner in which code VCV activities are planned and conducted, however, is critically important. It is suggested that the way in which code validation, in particular, is often conducted--by comparison to published experimental data obtained for other purposes--is in general difficult and unsatisfactory, and that a different approach is required. This paper describes a proposed methodology for CFD code VCV that meets the technical requirements and is philosophically consistent with code development needs. The proposed methodology stresses teamwork and cooperation between code developers and experimentalists throughout the VCV process, and takes advantage of certain synergisms between CFD and experiment. A novel approach to uncertainty analysis is described which can both distinguish between and quantify various types of experimental error, and whose attributes are used to help define an appropriate experimental design for code VCV experiments. The methodology is demonstrated with an example of laminar, hypersonic, near perfect gas, 3-dimensional flow over a sliced sphere/cone of varying geometrical complexity.

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

Handbook of research on P2P and grid systems for service-oriented computing : models, methodologies, and applications

NARCIS (Netherlands)

Antonopoulos, N.; Exarchakos, G.; Li, Maozhen; Liotta, A.

2010-01-01

Introduction: Service-oriented computing is a popular design methodology for large scale business computing systems. A significant number of companies aim to reap the benefit of cost reduction by realizing B2B and B2C processes on large-scale SOA-compliant software system platforms. Peer-to-Peer

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

Energy Technology Data Exchange (ETDEWEB)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

2006-07-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

Neutron spectrometry and dosimetry by means of Bonner spheres system and artificial neural networks applying robust design of artificial neural networks

International Nuclear Information System (INIS)

Martinez B, M.R.; Ortiz R, J.M.; Vega C, H.R.

2006-01-01

An Artificial Neural Network has been designed, trained and tested to unfold neutron spectra and simultaneously to calculate equivalent doses. A set of 187 neutron spectra compiled by the International Atomic Energy Agency and 13 equivalent doses were used in the artificial neural network designed, trained and tested. In order to design the neural network was used the robust design of artificial neural networks methodology, which assures that the quality of the neural networks takes into account from the design stage. Unless previous works, here, for first time a group of neural networks were designed and trained to unfold 187 neutron spectra and at the same time to calculate 13 equivalent doses, starting from the count rates coming from the Bonner spheres system by using a systematic and experimental strategy. (Author)

A New Screening Methodology for Improved Oil Recovery Processes Using Soft-Computing Techniques

Science.gov (United States)

Parada, Claudia; Ertekin, Turgay

2010-05-01

The first stage of production of any oil reservoir involves oil displacement by natural drive mechanisms such as solution gas drive, gas cap drive and gravity drainage. Typically, improved oil recovery (IOR) methods are applied to oil reservoirs that have been depleted naturally. In more recent years, IOR techniques are applied to reservoirs even before their natural energy drive is exhausted by primary depletion. Descriptive screening criteria for IOR methods are used to select the appropriate recovery technique according to the fluid and rock properties. This methodology helps in assessing the most suitable recovery process for field deployment of a candidate reservoir. However, the already published screening guidelines neither provide information about the expected reservoir performance nor suggest a set of project design parameters, which can be used towards the optimization of the process. In this study, artificial neural networks (ANN) are used to build a high-performance neuro-simulation tool for screening different improved oil recovery techniques: miscible injection (CO2 and N2), waterflooding and steam injection processes. The simulation tool consists of proxy models that implement a multilayer cascade feedforward back propagation network algorithm. The tool is intended to narrow the ranges of possible scenarios to be modeled using conventional simulation, reducing the extensive time and energy spent in dynamic reservoir modeling. A commercial reservoir simulator is used to generate the data to train and validate the artificial neural networks. The proxy models are built considering four different well patterns with different well operating conditions as the field design parameters. Different expert systems are developed for each well pattern. The screening networks predict oil production rate and cumulative oil production profiles for a given set of rock and fluid properties, and design parameters. The results of this study show that the networks are

A methodology to develop computational phantoms with adjustable posture for WBC calibration

International Nuclear Information System (INIS)

Fonseca, T C Ferreira; Vanhavere, F; Bogaerts, R; Hunt, John

2014-01-01

A Whole Body Counter (WBC) is a facility to routinely assess the internal contamination of exposed workers, especially in the case of radiation release accidents. The calibration of the counting device is usually done by using anthropomorphic physical phantoms representing the human body. Due to such a challenge of constructing representative physical phantoms a virtual calibration has been introduced. The use of computational phantoms and the Monte Carlo method to simulate radiation transport have been demonstrated to be a worthy alternative. In this study we introduce a methodology developed for the creation of realistic computational voxel phantoms with adjustable posture for WBC calibration. The methodology makes use of different software packages to enable the creation and modification of computational voxel phantoms. This allows voxel phantoms to be developed on demand for the calibration of different WBC configurations. This in turn helps to study the major source of uncertainty associated with the in vivo measurement routine which is the difference between the calibration phantoms and the real persons being counted. The use of realistic computational phantoms also helps the optimization of the counting measurement. Open source codes such as MakeHuman and Blender software packages have been used for the creation and modelling of 3D humanoid characters based on polygonal mesh surfaces. Also, a home-made software was developed whose goal is to convert the binary 3D voxel grid into a MCNPX input file. This paper summarizes the development of a library of phantoms of the human body that uses two basic phantoms called MaMP and FeMP (Male and Female Mesh Phantoms) to create a set of male and female phantoms that vary both in height and in weight. Two sets of MaMP and FeMP phantoms were developed and used for efficiency calibration of two different WBC set-ups: the Doel NPP WBC laboratory and AGM laboratory of SCK-CEN in Mol, Belgium. (paper)

A methodology to develop computational phantoms with adjustable posture for WBC calibration

Science.gov (United States)

Ferreira Fonseca, T. C.; Bogaerts, R.; Hunt, John; Vanhavere, F.

2014-11-01

A Whole Body Counter (WBC) is a facility to routinely assess the internal contamination of exposed workers, especially in the case of radiation release accidents. The calibration of the counting device is usually done by using anthropomorphic physical phantoms representing the human body. Due to such a challenge of constructing representative physical phantoms a virtual calibration has been introduced. The use of computational phantoms and the Monte Carlo method to simulate radiation transport have been demonstrated to be a worthy alternative. In this study we introduce a methodology developed for the creation of realistic computational voxel phantoms with adjustable posture for WBC calibration. The methodology makes use of different software packages to enable the creation and modification of computational voxel phantoms. This allows voxel phantoms to be developed on demand for the calibration of different WBC configurations. This in turn helps to study the major source of uncertainty associated with the in vivo measurement routine which is the difference between the calibration phantoms and the real persons being counted. The use of realistic computational phantoms also helps the optimization of the counting measurement. Open source codes such as MakeHuman and Blender software packages have been used for the creation and modelling of 3D humanoid characters based on polygonal mesh surfaces. Also, a home-made software was developed whose goal is to convert the binary 3D voxel grid into a MCNPX input file. This paper summarizes the development of a library of phantoms of the human body that uses two basic phantoms called MaMP and FeMP (Male and Female Mesh Phantoms) to create a set of male and female phantoms that vary both in height and in weight. Two sets of MaMP and FeMP phantoms were developed and used for efficiency calibration of two different WBC set-ups: the Doel NPP WBC laboratory and AGM laboratory of SCK-CEN in Mol, Belgium.

Development of Fuzzy Logic and Soft Computing Methodologies

Science.gov (United States)

Zadeh, L. A.; Yager, R.

1999-01-01

Our earlier research on computing with words (CW) has led to a new direction in fuzzy logic which points to a major enlargement of the role of natural languages in information processing, decision analysis and control. This direction is based on the methodology of computing with words and embodies a new theory which is referred to as the computational theory of perceptions (CTP). An important feature of this theory is that it can be added to any existing theory - especially to probability theory, decision analysis, and control - and enhance the ability of the theory to deal with real-world problems in which the decision-relevant information is a mixture of measurements and perceptions. The new direction is centered on an old concept - the concept of a perception - a concept which plays a central role in human cognition. The ability to reason with perceptions perceptions of time, distance, force, direction, shape, intent, likelihood, truth and other attributes of physical and mental objects - underlies the remarkable human capability to perform a wide variety of physical and mental tasks without any measurements and any computations. Everyday examples of such tasks are parking a car, driving in city traffic, cooking a meal, playing golf and summarizing a story. Perceptions are intrinsically imprecise. Imprecision of perceptions reflects the finite ability of sensory organs and ultimately, the brain, to resolve detail and store information. More concretely, perceptions are both fuzzy and granular, or, for short, f-granular. Perceptions are f-granular in the sense that: (a) the boundaries of perceived classes are not sharply defined; and (b) the elements of classes are grouped into granules, with a granule being a clump of elements drawn together by indistinguishability, similarity. proximity or functionality. F-granularity of perceptions may be viewed as a human way of achieving data compression. In large measure, scientific progress has been, and continues to be

Complex computation in the retina

Science.gov (United States)

Deshmukh, Nikhil Rajiv

Elucidating the general principles of computation in neural circuits is a difficult problem requiring both a tractable model circuit as well as sophisticated measurement tools. This thesis advances our understanding of complex computation in the salamander retina and its underlying circuitry and furthers the development of advanced tools to enable detailed study of neural circuits. The retina provides an ideal model system for neural circuits in general because it is capable of producing complex representations of the visual scene, and both its inputs and outputs are accessible to the experimenter. Chapter 2 describes the biophysical mechanisms that give rise to the omitted stimulus response in retinal ganglion cells described in Schwartz et al., (2007) and Schwartz and Berry, (2008). The extra response to omitted flashes is generated at the input to bipolar cells, and is separable from the characteristic latency shift of the OSR apparent in ganglion cells, which must occur downstream in the circuit. Chapter 3 characterizes the nonlinearities at the first synapse of the ON pathway in response to high contrast flashes and develops a phenomenological model that captures the effect of synaptic activation and intracellular signaling dynamics on flash responses. This work is the first attempt to model the dynamics of the poorly characterized mGluR6 transduction cascade unique to ON bipolar cells, and explains the second lobe of the biphasic flash response. Complementary to the study of neural circuits, recent advances in wafer-scale photolithography have made possible new devices to measure the electrical and mechanical properties of neurons. Chapter 4 reports a novel piezoelectric sensor that facilitates the simultaneous measurement of electrical and mechanical signals in neural tissue. This technology could reveal the relationship between the electrical activity of neurons and their local mechanical environment, which is critical to the study of mechanoreceptors

Premonitory urges and tics in Tourette syndrome: computational mechanisms and neural correlates.

Science.gov (United States)

Conceição, Vasco A; Dias, Ângelo; Farinha, Ana C; Maia, Tiago V

2017-10-01

Tourette syndrome is characterized by open motor behaviors - tics - but another crucial aspect of the disorder is the presence of premonitory urges: uncomfortable sensations that typically precede tics and are temporarily alleviated by tics. We review the evidence implicating the somatosensory cortices and the insula in premonitory urges and the motor cortico-basal ganglia-thalamo-cortical loop in tics. We consider how these regions interact during tic execution, suggesting that the insula plays an important role as a nexus linking the sensory and emotional character of premonitory urges with their translation into tics. We also consider how these regions interact during tic learning, integrating the neural evidence with a computational perspective on how premonitory-urge alleviation reinforces tics. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimation of Conditional Quantile using Neural Networks

DEFF Research Database (Denmark)

Kulczycki, P.; Schiøler, Henrik

1999-01-01

The problem of estimating conditional quantiles using neural networks is investigated here. A basic structure is developed using the methodology of kernel estimation, and a theory guaranteeing con-sistency on a mild set of assumptions is provided. The constructed structure constitutes a basis...... for the design of a variety of different neural networks, some of which are considered in detail. The task of estimating conditional quantiles is related to Bayes point estimation whereby a broad range of applications within engineering, economics and management can be suggested. Numerical results illustrating...... the capabilities of the elaborated neural network are also given....

[Optimization of calcium alginate floating microspheres loading aspirin by artificial neural networks and response surface methodology].

Science.gov (United States)

Zhang, An-yang; Fan, Tian-yuan

2010-04-18

To investigate the preparation and optimization of calcium alginate floating microspheres loading aspirin. A model was used to predict the in vitro release of aspirin and optimize the formulation by artificial neural networks (ANNs) and response surface methodology (RSM). The amounts of the material in the formulation were used as inputs, while the release and floating rate of the microspheres were used as outputs. The performances of ANNs and RSM were compared. ANNs were more accurate in prediction. There was no significant difference between ANNs and RSM in optimization. Approximately 90% of the optimized microspheres could float on the artificial gastric juice over 4 hours. 42.12% of aspirin was released in 60 min, 60.97% in 120 min and 78.56% in 240 min. The release of the drug from the microspheres complied with Higuchi equation. The aspirin floating microspheres with satisfying in vitro release were prepared successfully by the methods of ANNs and RSM.

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

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture

Directory of Open Access Journals (Sweden)

Xiaopu Zhang

2018-06-01

Full Text Available Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR. The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN and long short-term memory (LSTM is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96% with less transmitted data (about 90% was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

An Efficient Neural-Network-Based Microseismic Monitoring Platform for Hydraulic Fracture on an Edge Computing Architecture.

Science.gov (United States)

Zhang, Xiaopu; Lin, Jun; Chen, Zubin; Sun, Feng; Zhu, Xi; Fang, Gengfa

2018-06-05

Microseismic monitoring is one of the most critical technologies for hydraulic fracturing in oil and gas production. To detect events in an accurate and efficient way, there are two major challenges. One challenge is how to achieve high accuracy due to a poor signal-to-noise ratio (SNR). The other one is concerned with real-time data transmission. Taking these challenges into consideration, an edge-computing-based platform, namely Edge-to-Center LearnReduce, is presented in this work. The platform consists of a data center with many edge components. At the data center, a neural network model combined with convolutional neural network (CNN) and long short-term memory (LSTM) is designed and this model is trained by using previously obtained data. Once the model is fully trained, it is sent to edge components for events detection and data reduction. At each edge component, a probabilistic inference is added to the neural network model to improve its accuracy. Finally, the reduced data is delivered to the data center. Based on experiment results, a high detection accuracy (over 96%) with less transmitted data (about 90%) was achieved by using the proposed approach on a microseismic monitoring system. These results show that the platform can simultaneously improve the accuracy and efficiency of microseismic monitoring.

Mechanisms of Neurofeedback: A Computation-theoretic Approach.

Science.gov (United States)

Davelaar, Eddy J

2018-05-15

Neurofeedback training is a form of brain training in which information about a neural measure is fed back to the trainee who is instructed to increase or decrease the value of that particular measure. This paper focuses on electroencephalography (EEG) neurofeedback in which the neural measures of interest are the brain oscillations. To date, the neural mechanisms that underlie successful neurofeedback training are still unexplained. Such an understanding would benefit researchers, funding agencies, clinicians, regulatory bodies, and insurance firms. Based on recent empirical work, an emerging theory couched firmly within computational neuroscience is proposed that advocates a critical role of the striatum in modulating EEG frequencies. The theory is implemented as a computer simulation of peak alpha upregulation, but in principle any frequency band at one or more electrode sites could be addressed. The simulation successfully learns to increase its peak alpha frequency and demonstrates the influence of threshold setting - the threshold that determines whether positive or negative feedback is provided. Analyses of the model suggest that neurofeedback can be likened to a search process that uses importance sampling to estimate the posterior probability distribution over striatal representational space, with each representation being associated with a distribution of values of the target EEG band. The model provides an important proof of concept to address pertinent methodological questions about how to understand and improve EEG neurofeedback success. Copyright © 2017 IBRO. Published by Elsevier Ltd. All rights reserved.

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning.

Science.gov (United States)

Liu, Yang; Yang, Jie; Huang, Yuan; Xu, Lixiong; Li, Siguang; Qi, Man

2015-01-01

Artificial neural networks (ANNs) have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Biologically based neural circuit modelling for the study of fear learning and extinction

Science.gov (United States)

Nair, Satish S.; Paré, Denis; Vicentic, Aleksandra

2016-11-01

The neuronal systems that promote protective defensive behaviours have been studied extensively using Pavlovian conditioning. In this paradigm, an initially neutral-conditioned stimulus is paired with an aversive unconditioned stimulus leading the subjects to display behavioural signs of fear. Decades of research into the neural bases of this simple behavioural paradigm uncovered that the amygdala, a complex structure comprised of several interconnected nuclei, is an essential part of the neural circuits required for the acquisition, consolidation and expression of fear memory. However, emerging evidence from the confluence of electrophysiological, tract tracing, imaging, molecular, optogenetic and chemogenetic methodologies, reveals that fear learning is mediated by multiple connections between several amygdala nuclei and their distributed targets, dynamical changes in plasticity in local circuit elements as well as neuromodulatory mechanisms that promote synaptic plasticity. To uncover these complex relations and analyse multi-modal data sets acquired from these studies, we argue that biologically realistic computational modelling, in conjunction with experiments, offers an opportunity to advance our understanding of the neural circuit mechanisms of fear learning and to address how their dysfunction may lead to maladaptive fear responses in mental disorders.

Deep learning for computational chemistry.

Science.gov (United States)

Goh, Garrett B; Hodas, Nathan O; Vishnu, Abhinav

2017-06-15

The rise and fall of artificial neural networks is well documented in the scientific literature of both computer science and computational chemistry. Yet almost two decades later, we are now seeing a resurgence of interest in deep learning, a machine learning algorithm based on multilayer neural networks. Within the last few years, we have seen the transformative impact of deep learning in many domains, particularly in speech recognition and computer vision, to the extent that the majority of expert practitioners in those field are now regularly eschewing prior established models in favor of deep learning models. In this review, we provide an introductory overview into the theory of deep neural networks and their unique properties that distinguish them from traditional machine learning algorithms used in cheminformatics. By providing an overview of the variety of emerging applications of deep neural networks, we highlight its ubiquity and broad applicability to a wide range of challenges in the field, including quantitative structure activity relationship, virtual screening, protein structure prediction, quantum chemistry, materials design, and property prediction. In reviewing the performance of deep neural networks, we observed a consistent outperformance against non-neural networks state-of-the-art models across disparate research topics, and deep neural network-based models often exceeded the "glass ceiling" expectations of their respective tasks. Coupled with the maturity of GPU-accelerated computing for training deep neural networks and the exponential growth of chemical data on which to train these networks on, we anticipate that deep learning algorithms will be a valuable tool for computational chemistry. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Neural networks - Potential appplication in the nuclear industry

International Nuclear Information System (INIS)

Yiftah, S.

1989-01-01

Neural networks are an emerging technology which is perceived to have potential for solving complex computation problems which cannot be solved by standard computational methods. One such example is the inverse kinematics problem which is considered to be the most difficult problem in robotics. In 1986, only one neural network modelling tool was available, now there are about twenty offered commercially by various companies in North America

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Directory of Open Access Journals (Sweden)

Rohit Shukla

2018-03-01

Full Text Available Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines.

Computing Generalized Matrix Inverse on Spiking Neural Substrate

Science.gov (United States)

Shukla, Rohit; Khoram, Soroosh; Jorgensen, Erik; Li, Jing; Lipasti, Mikko; Wright, Stephen

2018-01-01

Emerging neural hardware substrates, such as IBM's TrueNorth Neurosynaptic System, can provide an appealing platform for deploying numerical algorithms. For example, a recurrent Hopfield neural network can be used to find the Moore-Penrose generalized inverse of a matrix, thus enabling a broad class of linear optimizations to be solved efficiently, at low energy cost. However, deploying numerical algorithms on hardware platforms that severely limit the range and precision of representation for numeric quantities can be quite challenging. This paper discusses these challenges and proposes a rigorous mathematical framework for reasoning about range and precision on such substrates. The paper derives techniques for normalizing inputs and properly quantizing synaptic weights originating from arbitrary systems of linear equations, so that solvers for those systems can be implemented in a provably correct manner on hardware-constrained neural substrates. The analytical model is empirically validated on the IBM TrueNorth platform, and results show that the guarantees provided by the framework for range and precision hold under experimental conditions. Experiments with optical flow demonstrate the energy benefits of deploying a reduced-precision and energy-efficient generalized matrix inverse engine on the IBM TrueNorth platform, reflecting 10× to 100× improvement over FPGA and ARM core baselines. PMID:29593483

Software development methodology for computer based I&C systems of prototype fast breeder reactor

International Nuclear Information System (INIS)

Manimaran, M.; Shanmugam, A.; Parimalam, P.; Murali, N.; Satya Murty, S.A.V.

2015-01-01

Highlights: • Software development methodology adopted for computer based I&C systems of PFBR is detailed. • Constraints imposed as part of software requirements and coding phase are elaborated. • Compliance to safety and security requirements are described. • Usage of CASE (Computer Aided Software Engineering) tools during software design, analysis and testing phase are explained. - Abstract: Prototype Fast Breeder Reactor (PFBR) is sodium cooled reactor which is in the advanced stage of construction in Kalpakkam, India. Versa Module Europa bus based Real Time Computer (RTC) systems are deployed for Instrumentation & Control of PFBR. RTC systems have to perform safety functions within the stipulated time which calls for highly dependable software. Hence, well defined software development methodology is adopted for RTC systems starting from the requirement capture phase till the final validation of the software product. V-model is used for software development. IEC 60880 standard and AERB SG D-25 guideline are followed at each phase of software development. Requirements documents and design documents are prepared as per IEEE standards. Defensive programming strategies are followed for software development using C language. Verification and validation (V&V) of documents and software are carried out at each phase by independent V&V committee. Computer aided software engineering tools are used for software modelling, checking for MISRA C compliance and to carry out static and dynamic analysis. Various software metrics such as cyclomatic complexity, nesting depth and comment to code are checked. Test cases are generated using equivalence class partitioning, boundary value analysis and cause and effect graphing techniques. System integration testing is carried out wherein functional and performance requirements of the system are monitored

Software development methodology for computer based I&C systems of prototype fast breeder reactor

Energy Technology Data Exchange (ETDEWEB)

Manimaran, M., E-mail: maran@igcar.gov.in; Shanmugam, A.; Parimalam, P.; Murali, N.; Satya Murty, S.A.V.

2015-10-15

Highlights: • Software development methodology adopted for computer based I&C systems of PFBR is detailed. • Constraints imposed as part of software requirements and coding phase are elaborated. • Compliance to safety and security requirements are described. • Usage of CASE (Computer Aided Software Engineering) tools during software design, analysis and testing phase are explained. - Abstract: Prototype Fast Breeder Reactor (PFBR) is sodium cooled reactor which is in the advanced stage of construction in Kalpakkam, India. Versa Module Europa bus based Real Time Computer (RTC) systems are deployed for Instrumentation & Control of PFBR. RTC systems have to perform safety functions within the stipulated time which calls for highly dependable software. Hence, well defined software development methodology is adopted for RTC systems starting from the requirement capture phase till the final validation of the software product. V-model is used for software development. IEC 60880 standard and AERB SG D-25 guideline are followed at each phase of software development. Requirements documents and design documents are prepared as per IEEE standards. Defensive programming strategies are followed for software development using C language. Verification and validation (V&V) of documents and software are carried out at each phase by independent V&V committee. Computer aided software engineering tools are used for software modelling, checking for MISRA C compliance and to carry out static and dynamic analysis. Various software metrics such as cyclomatic complexity, nesting depth and comment to code are checked. Test cases are generated using equivalence class partitioning, boundary value analysis and cause and effect graphing techniques. System integration testing is carried out wherein functional and performance requirements of the system are monitored.

Reconstruction of sub-surface archaeological remains from magnetic data using neural computing.

Science.gov (United States)

Bescoby, D. J.; Cawley, G. C.; Chroston, P. N.

2003-04-01

The remains of a former Roman colonial settlement, once part of the classical city of Butrint in southern Albania have been the subject of a high resolution magnetic survey using a caesium-vapour magnetometer. The survey revealed the surviving remains of an extensive planned settlement and a number of outlying buildings, today buried beneath over 0.5 m of alluvial deposits. The aim of the current research is to derive a sub-surface model from the magnetic survey measurements, allowing an enhanced archaeological interpretation of the data. Neural computing techniques are used to perform the non-linear mapping between magnetic data and corresponding sub-surface model parameters. The adoption of neural computing paradigms potentially holds several advantages over other modelling techniques, allowing fast solutions for complex data, while having a high tolerance to noise. A multi-layer perceptron network with a feed-forward architecture is trained to estimate the shape and burial depth of wall foundations using a series of representative models as training data. Parameters used to forward model the training data sets are derived from a number of trial trench excavations targeted over features identified by the magnetic survey. The training of the network was optimized by first applying it to synthetic test data of known source parameters. Pre-processing of the network input data, including the use of a rotationally invariant transform, enhanced network performance and the efficiency of the training data. The approach provides good results when applied to real magnetic data, accurately predicting the depths and layout of wall foundations within the former settlement, verified by subsequent excavation. The resulting sub-surface model is derived from the averaged outputs of a ‘committee’ of five networks, trained with individualized training sets. Fuzzy logic inference has also been used to combine individual network outputs through correlation with data from a second

Cognitive methodology for forecasting oil and gas industry using pattern-based neural information technologies

Science.gov (United States)

Gafurov, O.; Gafurov, D.; Syryamkin, V.

2018-05-01

The paper analyses a field of computer science formed at the intersection of such areas of natural science as artificial intelligence, mathematical statistics, and database theory, which is referred to as "Data Mining" (discovery of knowledge in data). The theory of neural networks is applied along with classical methods of mathematical analysis and numerical simulation. The paper describes the technique protected by the patent of the Russian Federation for the invention “A Method for Determining Location of Production Wells during the Development of Hydrocarbon Fields” [1–3] and implemented using the geoinformation system NeuroInformGeo. There are no analogues in domestic and international practice. The paper gives an example of comparing the forecast of the oil reservoir quality made by the geophysicist interpreter using standard methods and the forecast of the oil reservoir quality made using this technology. The technical result achieved shows the increase of efficiency, effectiveness, and ecological compatibility of development of mineral deposits and discovery of a new oil deposit.

Wind turbine power coefficient estimation by soft computing methodologies: Comparative study

International Nuclear Information System (INIS)

Shamshirband, Shahaboddin; Petković, Dalibor; Saboohi, Hadi; Anuar, Nor Badrul; Inayat, Irum; Akib, Shatirah; Ćojbašić, Žarko; Nikolić, Vlastimir; Mat Kiah, Miss Laiha; Gani, Abdullah

2014-01-01

Highlights: • Variable speed operation of wind turbine to increase power generation. • Changeability and fluctuation of wind has to be accounted. • To build an effective prediction model of wind turbine power coefficient. • The impact of the variation in the blade pitch angle and tip speed ratio. • Support vector regression methodology application as predictive methodology. - Abstract: Wind energy has become a large contender of traditional fossil fuel energy, particularly with the successful operation of multi-megawatt sized wind turbines. However, reasonable wind speed is not adequately sustainable everywhere to build an economical wind farm. In wind energy conversion systems, one of the operational problems is the changeability and fluctuation of wind. In most cases, wind speed can vacillate rapidly. Hence, quality of produced energy becomes an important problem in wind energy conversion plants. Several control techniques have been applied to improve the quality of power generated from wind turbines. In this study, the polynomial and radial basis function (RBF) are applied as the kernel function of support vector regression (SVR) to estimate optimal power coefficient value of the wind turbines. Instead of minimizing the observed training error, SVR p oly and SVR r bf attempt to minimize the generalization error bound so as to achieve generalized performance. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVR approach in compare to other soft computing methodologies

Computational Vibrational Spectroscopy of glycine in aqueous solution - Fundamental considerations towards feasible methodologies

Science.gov (United States)

Lutz, Oliver M. D.; Messner, Christoph B.; Hofer, Thomas S.; Canaval, Lorenz R.; Bonn, Guenther K.; Huck, Christian W.

2014-05-01

In this work, the mid-infrared spectrum of aqueous glycine is predicted by a number of computational approaches. Velocity autocorrelation functions are applied to ab initio QMCF-MD and QM/MM-MD simulations in order to obtain IR power spectra. Furthermore, continuum solvation model augmented geometry optimizations are studied by anharmonic calculations relying on the PT2-VSCF and the VPT2 formalism. In this context, the potential based EFP hydration technique is discussed and the importance of a Monte Carlo search in conjunction with PT2-VSCF calculations is critically assessed. All results are directly compared to newly recorded experimental FT-IR spectroscopic data, elucidating the qualities of the respective methodology. Moreover, the computational approaches are discussed regarding their usefulness for the interpretation of experimental spectra.

A Design Methodology for Efficient Implementation of Deconvolutional Neural Networks on an FPGA

OpenAIRE

Zhang, Xinyu; Das, Srinjoy; Neopane, Ojash; Kreutz-Delgado, Ken

2017-01-01

In recent years deep learning algorithms have shown extremely high performance on machine learning tasks such as image classification and speech recognition. In support of such applications, various FPGA accelerator architectures have been proposed for convolutional neural networks (CNNs) that enable high performance for classification tasks at lower power than CPU and GPU processors. However, to date, there has been little research on the use of FPGA implementations of deconvolutional neural...

MapReduce Based Parallel Neural Networks in Enabling Large Scale Machine Learning

Directory of Open Access Journals (Sweden)

Yang Liu

2015-01-01

Full Text Available Artificial neural networks (ANNs have been widely used in pattern recognition and classification applications. However, ANNs are notably slow in computation especially when the size of data is large. Nowadays, big data has received a momentum from both industry and academia. To fulfill the potentials of ANNs for big data applications, the computation process must be speeded up. For this purpose, this paper parallelizes neural networks based on MapReduce, which has become a major computing model to facilitate data intensive applications. Three data intensive scenarios are considered in the parallelization process in terms of the volume of classification data, the size of the training data, and the number of neurons in the neural network. The performance of the parallelized neural networks is evaluated in an experimental MapReduce computer cluster from the aspects of accuracy in classification and efficiency in computation.

Methodological Interactionism : Theory and Application to the Firm and to the Building of Trust

NARCIS (Netherlands)

Nooteboom, B.

2007-01-01

Recent insights from the ‘embodied cognition’ perspective in cognitive science, supported by neural research, provide a basis for a ‘methodological interactionism’ that transcends both the methodological individualism of economics and the methodological collectivism of (some) sociology, and is

FOREWORD: Computational methodologies for designing materials Computational methodologies for designing materials

Science.gov (United States)

Rahman, Talat S.

2009-02-01

It would be fair to say that in the past few decades, theory and computer modeling have played a major role in elucidating the microscopic factors that dictate the properties of functional novel materials. Together with advances in experimental techniques, theoretical methods are becoming increasingly capable of predicting properties of materials at different length scales, thereby bringing in sight the long-sought goal of designing material properties according to need. Advances in computer technology and their availability at a reasonable cost around the world have made tit all the more urgent to disseminate what is now known about these modern computational techniques. In this special issue on computational methodologies for materials by design we have tried to solicit articles from authors whose works collectively represent the microcosm of developments in the area. This turned out to be a difficult task for a variety of reasons, not the least of which is space limitation in this special issue. Nevertheless, we gathered twenty articles that represent some of the important directions in which theory and modeling are proceeding in the general effort to capture the ability to produce materials by design. The majority of papers presented here focus on technique developments that are expected to uncover further the fundamental processes responsible for material properties, and for their growth modes and morphological evolutions. As for material properties, some of the articles here address the challenges that continue to emerge from attempts at accurate descriptions of magnetic properties, of electronically excited states, and of sparse matter, all of which demand new looks at density functional theory (DFT). I should hasten to add that much of the success in accurate computational modeling of materials emanates from the remarkable predictive power of DFT, without which we would not be able to place the subject on firm theoretical grounds. As we know and will also

Neural Parallel Engine: A toolbox for massively parallel neural signal processing.

Science.gov (United States)

Tam, Wing-Kin; Yang, Zhi

2018-05-01

Large-scale neural recordings provide detailed information on neuronal activities and can help elicit the underlying neural mechanisms of the brain. However, the computational burden is also formidable when we try to process the huge data stream generated by such recordings. In this study, we report the development of Neural Parallel Engine (NPE), a toolbox for massively parallel neural signal processing on graphical processing units (GPUs). It offers a selection of the most commonly used routines in neural signal processing such as spike detection and spike sorting, including advanced algorithms such as exponential-component-power-component (EC-PC) spike detection and binary pursuit spike sorting. We also propose a new method for detecting peaks in parallel through a parallel compact operation. Our toolbox is able to offer a 5× to 110× speedup compared with its CPU counterparts depending on the algorithms. A user-friendly MATLAB interface is provided to allow easy integration of the toolbox into existing workflows. Previous efforts on GPU neural signal processing only focus on a few rudimentary algorithms, are not well-optimized and often do not provide a user-friendly programming interface to fit into existing workflows. There is a strong need for a comprehensive toolbox for massively parallel neural signal processing. A new toolbox for massively parallel neural signal processing has been created. It can offer significant speedup in processing signals from large-scale recordings up to thousands of channels. Copyright © 2018 Elsevier B.V. All rights reserved.

Computational Methodologies for Developing Structure–Morphology–Performance Relationships in Organic Solar Cells: A Protocol Review

KAUST Repository

Do, Khanh

2016-09-08

We outline a step-by-step protocol that incorporates a number of theoretical and computational methodologies to evaluate the structural and electronic properties of pi-conjugated semiconducting materials in the condensed phase. Our focus is on methodologies appropriate for the characterization, at the molecular level, of the morphology in blend systems consisting of an electron donor and electron acceptor, of importance for understanding the performance properties of bulk-heterojunction organic solar cells. The protocol is formulated as an introductory manual for investigators who aim to study the bulk-heterojunction morphology in molecular details, thereby facilitating the development of structure morphology property relationships when used in tandem with experimental results.

The feasibility of using neural networks to obtain cross sections from electron swarm data

International Nuclear Information System (INIS)

Morgan, W.L.

1991-01-01

This paper reports that although still more a curiosity than an accepted technique in computational modeling, the very new field of neural computing is beginning to find applications in physics. Presented in some background on neural computing and a discussion on the use of neural networks to obtain electron-impact cross sections from measured drift velocities, characteristic energies, and other swarm data. This is what is known as an inverse problem, a class of problems for which neural networks may be frequently superior to other numerical algorithms. Momentum transfer cross sections obtained for a model problem and for xenon using a neural network are presented

Quantum neural networks: Current status and prospects for development

Science.gov (United States)

Altaisky, M. V.; Kaputkina, N. E.; Krylov, V. A.

2014-11-01

The idea of quantum artificial neural networks, first formulated in [34], unites the artificial neural network concept with the quantum computation paradigm. Quantum artificial neural networks were first systematically considered in the PhD thesis by T. Menneer (1998). Based on the works of Menneer and Narayanan [42, 43], Kouda, Matsui, and Nishimura [35, 36], Altaisky [2, 68], Zhou [67], and others, quantum-inspired learning algorithms for neural networks were developed, and are now used in various training programs and computer games [29, 30]. The first practically realizable scaled hardware-implemented model of the quantum artificial neural network is obtained by D-Wave Systems, Inc. [33]. It is a quantum Hopfield network implemented on the basis of superconducting quantum interference devices (SQUIDs). In this work we analyze possibilities and underlying principles of an alternative way to implement quantum neural networks on the basis of quantum dots. A possibility of using quantum neural network algorithms in automated control systems, associative memory devices, and in modeling biological and social networks is examined.

Type-2 fuzzy neural networks and their applications

CERN Document Server

Aliev, Rafik Aziz

2014-01-01

This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientis

A neural-fuzzy approach to classify the ecological status in surface waters

International Nuclear Information System (INIS)

Ocampo-Duque, William; Schuhmacher, Marta; Domingo, Jose L.

2007-01-01

A methodology based on a hybrid approach that combines fuzzy inference systems and artificial neural networks has been used to classify ecological status in surface waters. This methodology has been proposed to deal efficiently with the non-linearity and highly subjective nature of variables involved in this serious problem. Ecological status has been assessed with biological, hydro-morphological, and physicochemical indicators. A data set collected from 378 sampling sites in the Ebro river basin has been used to train and validate the hybrid model. Up to 97.6% of sampling sites have been correctly classified with neural-fuzzy models. Such performance resulted very competitive when compared with other classification algorithms. With non-parametric classification-regression trees and probabilistic neural networks, the predictive capacities were 90.7% and 97.0%, respectively. The proposed methodology can support decision-makers in evaluation and classification of ecological status, as required by the EU Water Framework Directive. - Fuzzy inference systems can be used as environmental classifiers

Towards a cognitive robotics methodology for reward-based decision-making: dynamical systems modelling of the Iowa Gambling Task

Science.gov (United States)

Lowe, Robert; Ziemke, Tom

2010-09-01

The somatic marker hypothesis (SMH) posits that the role of emotions and mental states in decision-making manifests through bodily responses to stimuli of import to the organism's welfare. The Iowa Gambling Task (IGT), proposed by Bechara and Damasio in the mid-1990s, has provided the major source of empirical validation to the role of somatic markers in the service of flexible and cost-effective decision-making in humans. In recent years the IGT has been the subject of much criticism concerning: (1) whether measures of somatic markers reveal that they are important for decision-making as opposed to behaviour preparation; (2) the underlying neural substrate posited as critical to decision-making of the type relevant to the task; and (3) aspects of the methodological approach used, particularly on the canonical version of the task. In this paper, a cognitive robotics methodology is proposed to explore a dynamical systems approach as it applies to the neural computation of reward-based learning and issues concerning embodiment. This approach is particularly relevant in light of a strongly emerging alternative hypothesis to the SMH, the reversal learning hypothesis, which links, behaviourally and neurocomputationally, a number of more or less complex reward-based decision-making tasks, including the 'A-not-B' task - already subject to dynamical systems investigations with a focus on neural activation dynamics. It is also suggested that the cognitive robotics methodology may be used to extend systematically the IGT benchmark to more naturalised, but nevertheless controlled, settings that might better explore the extent to which the SMH, and somatic states per se, impact on complex decision-making.

Project of computer program for designing the steel with the assumed CCT diagram

OpenAIRE

S. Malara; J. Trzaska; L.A. Dobrzański

2007-01-01

Purpose: The aim of this paper was developing a project of computer aided method for designing the chemicalcomposition of steel with the assumed CCT diagram.Design/methodology/approach: The purpose has been achieved in four stages. At the first stage characteristicpoints of CCT diagram have been determined. At the second stage neural networks have been developed, andnext CCT diagram terms of similarity have been worked out- at the third one. In the last one steel chemicalcomposition optimizat...

Comparison of 2D and 3D neural induction methods for the generation of neural progenitor cells from human induced pluripotent stem cells

DEFF Research Database (Denmark)

Chandrasekaran, Abinaya; Avci, Hasan; Ochalek, Anna

2017-01-01

Neural progenitor cells (NPCs) from human induced pluripotent stem cells (hiPSCs) are frequently induced using 3D culture methodologies however, it is unknown whether spheroid-based (3D) neural induction is actually superior to monolayer (2D) neural induction. Our aim was to compare the efficiency......), cortical layer (TBR1, CUX1) and glial markers (SOX9, GFAP, AQP4). Electron microscopy demonstrated that both methods resulted in morphologically similar neural rosettes. However, quantification of NPCs derived from 3D neural induction exhibited an increase in the number of PAX6/NESTIN double positive cells...... the electrophysiological properties between the two induction methods. In conclusion, 3D neural induction increases the yield of PAX6+/NESTIN+ cells and gives rise to neurons with longer neurites, which might be an advantage for the production of forebrain cortical neurons, highlighting the potential of 3D neural...

Emerging phenomena in neural networks with dynamic synapses and their computational implications

Directory of Open Access Journals (Sweden)

Joaquin J. eTorres

2013-04-01

Full Text Available In this paper we review our research on the effect and computational role of dynamical synapses on feed-forward and recurrent neural networks. Among others, we report on the appearance of a new class of dynamical memories which result from the destabilisation of learned memory attractors. This has important consequences for dynamic information processing allowing the system to sequentially access the information stored in the memories under changing stimuli. Although storage capacity of stable memories also decreases, our study demonstrated the positive effect of synaptic facilitation to recover maximum storage capacity and to enlarge the capacity of the system for memory recall in noisy conditions. Possibly, the new dynamical behaviour can be associated with the voltage transitions between up and down states observed in cortical areas in the brain. We investigated the conditions for which the permanence times in the up state are power-law distributed, which is a sign for criticality, and concluded that the experimentally observed large variability of permanence times could be explained as the result of noisy dynamic synapses with large recovery times. Finally, we report how short-term synaptic processes can transmit weak signals throughout more than one frequency range in noisy neural networks, displaying a kind of stochastic multi-resonance. This effect is due to competition between activity-dependent synaptic fluctuations (due to dynamic synapses and the existence of neuron firing threshold which adapts to the incoming mean synaptic input.

Introduction to Concepts in Artificial Neural Networks

Science.gov (United States)

Niebur, Dagmar

1995-01-01

This introduction to artificial neural networks summarizes some basic concepts of computational neuroscience and the resulting models of artificial neurons. The terminology of biological and artificial neurons, biological and machine learning and neural processing is introduced. The concepts of supervised and unsupervised learning are explained with examples from the power system area. Finally, a taxonomy of different types of neurons and different classes of artificial neural networks is presented.

Modular Neural Tile Architecture for Compact Embedded Hardware Spiking Neural Network

NARCIS (Netherlands)

Pande, Sandeep; Morgan, Fearghal; Cawley, Seamus; Bruintjes, Tom; Smit, Gerardus Johannes Maria; McGinley, Brian; Carrillo, Snaider; Harkin, Jim; McDaid, Liam

2013-01-01

Biologically-inspired packet switched network on chip (NoC) based hardware spiking neural network (SNN) architectures have been proposed as an embedded computing platform for classification, estimation and control applications. Storage of large synaptic connectivity (SNN topology) information in

Random noise effects in pulse-mode digital multilayer neural networks.

Science.gov (United States)

Kim, Y C; Shanblatt, M A

1995-01-01

A pulse-mode digital multilayer neural network (DMNN) based on stochastic computing techniques is implemented with simple logic gates as basic computing elements. The pulse-mode signal representation and the use of simple logic gates for neural operations lead to a massively parallel yet compact and flexible network architecture, well suited for VLSI implementation. Algebraic neural operations are replaced by stochastic processes using pseudorandom pulse sequences. The distributions of the results from the stochastic processes are approximated using the hypergeometric distribution. Synaptic weights and neuron states are represented as probabilities and estimated as average pulse occurrence rates in corresponding pulse sequences. A statistical model of the noise (error) is developed to estimate the relative accuracy associated with stochastic computing in terms of mean and variance. Computational differences are then explained by comparison to deterministic neural computations. DMNN feedforward architectures are modeled in VHDL using character recognition problems as testbeds. Computational accuracy is analyzed, and the results of the statistical model are compared with the actual simulation results. Experiments show that the calculations performed in the DMNN are more accurate than those anticipated when Bernoulli sequences are assumed, as is common in the literature. Furthermore, the statistical model successfully predicts the accuracy of the operations performed in the DMNN.

Evolving RBF neural networks for adaptive soft-sensor design.

Science.gov (United States)

Alexandridis, Alex

2013-12-01

This work presents an adaptive framework for building soft-sensors based on radial basis function (RBF) neural network models. The adaptive fuzzy means algorithm is utilized in order to evolve an RBF network, which approximates the unknown system based on input-output data from it. The methodology gradually builds the RBF network model, based on two separate levels of adaptation: On the first level, the structure of the hidden layer is modified by adding or deleting RBF centers, while on the second level, the synaptic weights are adjusted with the recursive least squares with exponential forgetting algorithm. The proposed approach is tested on two different systems, namely a simulated nonlinear DC Motor and a real industrial reactor. The results show that the produced soft-sensors can be successfully applied to model the two nonlinear systems. A comparison with two different adaptive modeling techniques, namely a dynamic evolving neural-fuzzy inference system (DENFIS) and neural networks trained with online backpropagation, highlights the advantages of the proposed methodology.

Metodología basada en redes neurales para interpretación de la resistividad del suelo en zonas urbanas. Methodology based on neural networks for earth resistivity interpretation in congested urban areas

Directory of Open Access Journals (Sweden)

Miguel Martínez Lozano

2015-04-01

Full Text Available Uno de los problemas que se afronta durante el diseño de un sistema de puesta a tierra (PAT en zonas urbanas, es la obtención de los parámetros eléctricos del suelo, ya que los métodos tradicionales de medición de resistividad no se pueden aplicar por problemas de espacio. En este trabajo, se plantea una alternativa para la obtención de la resistividad, mediante sondeo eléctrico introduciendo una barra vertical y registrando cómo varía la resistencia de PAT del electrodo en función de la profundidad; con esos resultados se desarrolla una metodología basada en redes neurales, para estimar los parámetros del suelo para un modelo biestratificado del mismo. Se estudia por tanto, el tema de las complicaciones de medición en zonas urbanas y se plantea en detalle la metodología empleando técnicas modernas de medición y procesamiento. Los resultados obtenidos, tanto en fase de simulación digital como con ensayos de campo, muestran la validez.  One of the main troubles for grounding system design in an electrical installation on congested urban areas is obtaining the soil parameters, since the traditional measurements techniques are not applicable due to the limited space. In the present work, an alternative procedure based on introducing a driven rod into the soil and registering the variation of ground resistance versus the depth, is presented; with the field measurements obtained, a procedure were evaluated to estimate the soil parameters in a simplified bi-stratified model (two vertical layers using a trained neural network to minimize the effort and time to obtain the respective results. The trouble about the measurement and estimation of electrical soil properties in congested urban areas is solved with the detailed methodology presented, based on non conventional measurement techniques and computational processing. The results obtained during both digital simulation and field measurements, demonstrates the validity of the

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

Science.gov (United States)

Cárdenas, Jhon; Orjuela-Cañón, Alvaro D.; Cerquera, Alexander; Ravelo, Antonio

2017-11-01

Different studies have used Transfer Entropy (TE) and Granger Causality (GC) computation to quantify interconnection between physiological systems. These methods have disadvantages in parametrization and availability in analytic formulas to evaluate the significance of the results. Other inconvenience is related with the assumptions in the distribution of the models generated from the data. In this document, the authors present a way to measure the causality that connect the Central Nervous System (CNS) and the Cardiac System (CS) in people diagnosed with obstructive sleep apnea syndrome (OSA) before and during treatment with continuous positive air pressure (CPAP). For this purpose, artificial neural networks were used to obtain models for GC computation, based on time series of normalized powers calculated from electrocardiography (EKG) and electroencephalography (EEG) signals recorded in polysomnography (PSG) studies.

A computational methodology for formulating gasoline surrogate fuels with accurate physical and chemical kinetic properties

KAUST Repository

Ahmed, Ahfaz

2015-03-01

Gasoline is the most widely used fuel for light duty automobile transportation, but its molecular complexity makes it intractable to experimentally and computationally study the fundamental combustion properties. Therefore, surrogate fuels with a simpler molecular composition that represent real fuel behavior in one or more aspects are needed to enable repeatable experimental and computational combustion investigations. This study presents a novel computational methodology for formulating surrogates for FACE (fuels for advanced combustion engines) gasolines A and C by combining regression modeling with physical and chemical kinetics simulations. The computational methodology integrates simulation tools executed across different software platforms. Initially, the palette of surrogate species and carbon types for the target fuels were determined from a detailed hydrocarbon analysis (DHA). A regression algorithm implemented in MATLAB was linked to REFPROP for simulation of distillation curves and calculation of physical properties of surrogate compositions. The MATLAB code generates a surrogate composition at each iteration, which is then used to automatically generate CHEMKIN input files that are submitted to homogeneous batch reactor simulations for prediction of research octane number (RON). The regression algorithm determines the optimal surrogate composition to match the fuel properties of FACE A and C gasoline, specifically hydrogen/carbon (H/C) ratio, density, distillation characteristics, carbon types, and RON. The optimal surrogate fuel compositions obtained using the present computational approach was compared to the real fuel properties, as well as with surrogate compositions available in the literature. Experiments were conducted within a Cooperative Fuels Research (CFR) engine operating under controlled autoignition (CAI) mode to compare the formulated surrogates against the real fuels. Carbon monoxide measurements indicated that the proposed surrogates

The Effects of GABAergic Polarity Changes on Episodic Neural Network Activity in Developing Neural Systems

Directory of Open Access Journals (Sweden)

Wilfredo Blanco

2017-09-01

Full Text Available Early in development, neural systems have primarily excitatory coupling, where even GABAergic synapses are excitatory. Many of these systems exhibit spontaneous episodes of activity that have been characterized through both experimental and computational studies. As development progress the neural system goes through many changes, including synaptic remodeling, intrinsic plasticity in the ion channel expression, and a transformation of GABAergic synapses from excitatory to inhibitory. What effect each of these, and other, changes have on the network behavior is hard to know from experimental studies since they all happen in parallel. One advantage of a computational approach is that one has the ability to study developmental changes in isolation. Here, we examine the effects of GABAergic synapse polarity change on the spontaneous activity of both a mean field and a neural network model that has both glutamatergic and GABAergic coupling, representative of a developing neural network. We find some intuitive behavioral changes as the GABAergic neurons go from excitatory to inhibitory, shared by both models, such as a decrease in the duration of episodes. We also find some paradoxical changes in the activity that are only present in the neural network model. In particular, we find that during early development the inter-episode durations become longer on average, while later in development they become shorter. In addressing this unexpected finding, we uncover a priming effect that is particularly important for a small subset of neurons, called the “intermediate neurons.” We characterize these neurons and demonstrate why they are crucial to episode initiation, and why the paradoxical behavioral change result from priming of these neurons. The study illustrates how even arguably the simplest of developmental changes that occurs in neural systems can present non-intuitive behaviors. It also makes predictions about neural network behavioral changes

A proposal to order the neutron data set in neutron spectrometry using the RDANN methodology

Energy Technology Data Exchange (ETDEWEB)

Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

2006-07-01

A new proposal to order a neutron data set in the design process of artificial neural networks in the neutron spectrometry field is presented for first time. The robust design of artificial neural networks methodology was applied to 187 neutron spectra data set compiled by the International Atomic Energy Agency. Four cases of grouping the neutron spectra were considered and around 1000 different neural networks were designed, trained and tested with different net topologies each one. After carrying out the systematic methodology for all the cases, it was determined that the best neural network topology that produced the best reconstructed neutron spectra was case with 187 neutron spectra data set, determining that the best neural network topology is: 7 entrance neurons, 14 neurons in a hidden layer and 31 neurons in the exit layer, with a value of 0.1 in the learning rate and 0.1 in the moment. (Author)

A proposal to order the neutron data set in neutron spectrometry using the RDANN methodology

International Nuclear Information System (INIS)

Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R.

2006-01-01

A new proposal to order a neutron data set in the design process of artificial neural networks in the neutron spectrometry field is presented for first time. The robust design of artificial neural networks methodology was applied to 187 neutron spectra data set compiled by the International Atomic Energy Agency. Four cases of grouping the neutron spectra were considered and around 1000 different neural networks were designed, trained and tested with different net topologies each one. After carrying out the systematic methodology for all the cases, it was determined that the best neural network topology that produced the best reconstructed neutron spectra was case with 187 neutron spectra data set, determining that the best neural network topology is: 7 entrance neurons, 14 neurons in a hidden layer and 31 neurons in the exit layer, with a value of 0.1 in the learning rate and 0.1 in the moment. (Author)

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Ammar Daskin

2018-01-01

The learning process for multilayered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow–Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, these iterative formulas result in terms formed by the principal components of the weight matrix, namely, the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the pha...

SuperSpike: Supervised Learning in Multilayer Spiking Neural Networks.

Science.gov (United States)

Zenke, Friedemann; Ganguli, Surya

2018-04-13

A vast majority of computation in the brain is performed by spiking neural networks. Despite the ubiquity of such spiking, we currently lack an understanding of how biological spiking neural circuits learn and compute in vivo, as well as how we can instantiate such capabilities in artificial spiking circuits in silico. Here we revisit the problem of supervised learning in temporally coding multilayer spiking neural networks. First, by using a surrogate gradient approach, we derive SuperSpike, a nonlinear voltage-based three-factor learning rule capable of training multilayer networks of deterministic integrate-and-fire neurons to perform nonlinear computations on spatiotemporal spike patterns. Second, inspired by recent results on feedback alignment, we compare the performance of our learning rule under different credit assignment strategies for propagating output errors to hidden units. Specifically, we test uniform, symmetric, and random feedback, finding that simpler tasks can be solved with any type of feedback, while more complex tasks require symmetric feedback. In summary, our results open the door to obtaining a better scientific understanding of learning and computation in spiking neural networks by advancing our ability to train them to solve nonlinear problems involving transformations between different spatiotemporal spike time patterns.

A non-penalty recurrent neural network for solving a class of constrained optimization problems.

Science.gov (United States)

Hosseini, Alireza

2016-01-01

In this paper, we explain a methodology to analyze convergence of some differential inclusion-based neural networks for solving nonsmooth optimization problems. For a general differential inclusion, we show that if its right hand-side set valued map satisfies some conditions, then solution trajectory of the differential inclusion converges to optimal solution set of its corresponding in optimization problem. Based on the obtained methodology, we introduce a new recurrent neural network for solving nonsmooth optimization problems. Objective function does not need to be convex on R(n) nor does the new neural network model require any penalty parameter. We compare our new method with some penalty-based and non-penalty based models. Moreover for differentiable cases, we implement circuit diagram of the new neural network. Copyright © 2015 Elsevier Ltd. All rights reserved.

Methodology for Developing Hydrological Models Based on an Artificial Neural Network to Establish an Early Warning System in Small Catchments

Directory of Open Access Journals (Sweden)

Ivana Sušanj

2016-01-01

Full Text Available In some situations, there is no possibility of hazard mitigation, especially if the hazard is induced by water. Thus, it is important to prevent consequences via an early warning system (EWS to announce the possible occurrence of a hazard. The aim and objective of this paper are to investigate the possibility of implementing an EWS in a small-scale catchment and to develop a methodology for developing a hydrological prediction model based on an artificial neural network (ANN as an essential part of the EWS. The methodology is implemented in the case study of the Slani Potok catchment, which is historically recognized as a hazard-prone area, by establishing continuous monitoring of meteorological and hydrological parameters to collect data for the training, validation, and evaluation of the prediction capabilities of the ANN model. The model is validated and evaluated by visual and common calculation approaches and a new evaluation for the assessment. This new evaluation is proposed based on the separation of the observed data into classes based on the mean data value and the percentages of classes above or below the mean data value as well as on the performance of the mean absolute error.

Application of neural network to CT

International Nuclear Information System (INIS)

Ma, Xiao-Feng; Takeda, Tatsuoki

1999-01-01

This paper presents a new method for two-dimensional image reconstruction by using a multilayer neural network. Multilayer neural networks are extensively investigated and practically applied to solution of various problems such as inverse problems or time series prediction problems. From learning an input-output mapping from a set of examples, neural networks can be regarded as synthesizing an approximation of multidimensional function (that is, solving the problem of hypersurface reconstruction, including smoothing and interpolation). From this viewpoint, neural networks are well suited to the solution of CT image reconstruction. Though a conventionally used object function of a neural network is composed of a sum of squared errors of the output data, we can define an object function composed of a sum of residue of an integral equation. By employing an appropriate line integral for this integral equation, we can construct a neural network that can be used for CT. We applied this method to some model problems and obtained satisfactory results. As it is not necessary to discretized the integral equation using this reconstruction method, therefore it is application to the problem of complicated geometrical shapes is also feasible. Moreover, in neural networks, interpolation is performed quite smoothly, as a result, inverse mapping can be achieved smoothly even in case of including experimental and numerical errors, However, use of conventional back propagation technique for optimization leads to an expensive computation cost. To overcome this drawback, 2nd order optimization methods or parallel computing will be applied in future. (J.P.N.)

Soft Computing Methods for Disulfide Connectivity Prediction.

Science.gov (United States)

Márquez-Chamorro, Alfonso E; Aguilar-Ruiz, Jesús S

2015-01-01

The problem of protein structure prediction (PSP) is one of the main challenges in structural bioinformatics. To tackle this problem, PSP can be divided into several subproblems. One of these subproblems is the prediction of disulfide bonds. The disulfide connectivity prediction problem consists in identifying which nonadjacent cysteines would be cross-linked from all possible candidates. Determining the disulfide bond connectivity between the cysteines of a protein is desirable as a previous step of the 3D PSP, as the protein conformational search space is highly reduced. The most representative soft computing approaches for the disulfide bonds connectivity prediction problem of the last decade are summarized in this paper. Certain aspects, such as the different methodologies based on soft computing approaches (artificial neural network or support vector machine) or features of the algorithms, are used for the classification of these methods.

Using Brain Stimulation to Disentangle Neural Correlates of Conscious Vision

Directory of Open Access Journals (Sweden)

Tom Alexander de Graaf

2014-09-01

Full Text Available Research into the neural correlates of consciousness (NCCs has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as fMRI or EEG do not always afford inference on the role these brain processes play in conscious vision. Such empirical neural correlates of consciousness could reflect neural prerequisites, neural consequences, or neural substrates of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical neural correlates of consciousness.

Hardware for soft computing and soft computing for hardware

CERN Document Server

Nedjah, Nadia

2014-01-01

Single and Multi-Objective Evolutionary Computation (MOEA),  Genetic Algorithms (GAs), Artificial Neural Networks (ANNs), Fuzzy Controllers (FCs), Particle Swarm Optimization (PSO) and Ant colony Optimization (ACO) are becoming omnipresent in almost every intelligent system design. Unfortunately, the application of the majority of these techniques is complex and so requires a huge computational effort to yield useful and practical results. Therefore, dedicated hardware for evolutionary, neural and fuzzy computation is a key issue for designers. With the spread of reconfigurable hardware such as FPGAs, digital as well as analog hardware implementations of such computation become cost-effective. The idea behind this book is to offer a variety of hardware designs for soft computing techniques that can be embedded in any final product. Also, to introduce the successful application of soft computing technique to solve many hard problem encountered during the design of embedded hardware designs. Reconfigurable em...

Determination of phase diagrams via computer simulation: methodology and applications to water, electrolytes and proteins

International Nuclear Information System (INIS)

Vega, C; Sanz, E; Abascal, J L F; Noya, E G

2008-01-01

In this review we focus on the determination of phase diagrams by computer simulation, with particular attention to the fluid-solid and solid-solid equilibria. The methodology to compute the free energy of solid phases will be discussed. In particular, the Einstein crystal and Einstein molecule methodologies are described in a comprehensive way. It is shown that both methodologies yield the same free energies and that free energies of solid phases present noticeable finite size effects. In fact, this is the case for hard spheres in the solid phase. Finite size corrections can be introduced, although in an approximate way, to correct for the dependence of the free energy on the size of the system. The computation of free energies of solid phases can be extended to molecular fluids. The procedure to compute free energies of solid phases of water (ices) will be described in detail. The free energies of ices Ih, II, III, IV, V, VI, VII, VIII, IX, XI and XII will be presented for the SPC/E and TIP4P models of water. Initial coexistence points leading to the determination of the phase diagram of water for these two models will be provided. Other methods to estimate the melting point of a solid, such as the direct fluid-solid coexistence or simulations of the free surface of the solid, will be discussed. It will be shown that the melting points of ice Ih for several water models, obtained from free energy calculations, direct coexistence simulations and free surface simulations agree within their statistical uncertainty. Phase diagram calculations can indeed help to improve potential models of molecular fluids. For instance, for water, the potential model TIP4P/2005 can be regarded as an improved version of TIP4P. Here we will review some recent work on the phase diagram of the simplest ionic model, the restricted primitive model. Although originally devised to describe ionic liquids, the model is becoming quite popular to describe the behavior of charged colloids

Computational optimization of biodiesel combustion using response surface methodology

Directory of Open Access Journals (Sweden)

Ganji Prabhakara Rao

2017-01-01

Full Text Available The present work focuses on optimization of biodiesel combustion phenomena through parametric approach using response surface methodology. Physical properties of biodiesel play a vital role for accurate simulations of the fuel spray, atomization, combustion, and emission formation processes. Typically methyl based biodiesel consists of five main types of esters: methyl palmitate, methyl oleate, methyl stearate, methyl linoleate, and methyl linolenate in its composition. Based on the amount of methyl esters present the properties of pongamia bio-diesel and its blends were estimated. CONVERGETM computational fluid dynamics software was used to simulate the fuel spray, turbulence and combustion phenomena. The simulation responses such as indicated specific fuel consumption, NOx, and soot were analyzed using design of experiments. Regression equations were developed for each of these responses. The optimum parameters were found out to be compression ratio – 16.75, start of injection – 21.9° before top dead center, and exhaust gas re-circulation – 10.94%. Results have been compared with baseline case.

Efficacy of an artificial neural network-based approach to endoscopic ultrasound elastography in diagnosis of focal pancreatic masses.

Science.gov (United States)

Săftoiu, Adrian; Vilmann, Peter; Gorunescu, Florin; Janssen, Jan; Hocke, Michael; Larsen, Michael; Iglesias-Garcia, Julio; Arcidiacono, Paolo; Will, Uwe; Giovannini, Marc; Dietrich, Cristoph F; Havre, Roald; Gheorghe, Cristian; McKay, Colin; Gheonea, Dan Ionuţ; Ciurea, Tudorel

2012-01-01

By using strain assessment, real-time endoscopic ultrasound (EUS) elastography provides additional information about a lesion's characteristics in the pancreas. We assessed the accuracy of real-time EUS elastography in focal pancreatic lesions using computer-aided diagnosis by artificial neural network analysis. We performed a prospective, blinded, multicentric study at of 258 patients (774 recordings from EUS elastography) who were diagnosed with chronic pancreatitis (n = 47) or pancreatic adenocarcinoma (n = 211) from 13 tertiary academic medical centers in Europe (the European EUS Elastography Multicentric Study Group). We used postprocessing software analysis to compute individual frames of elastography movies recorded by retrieving hue histogram data from a dynamic sequence of EUS elastography into a numeric matrix. The data then were analyzed in an extended neural network analysis, to automatically differentiate benign from malignant patterns. The neural computing approach had 91.14% training accuracy (95% confidence interval [CI], 89.87%-92.42%) and 84.27% testing accuracy (95% CI, 83.09%-85.44%). These results were obtained using the 10-fold cross-validation technique. The statistical analysis of the classification process showed a sensitivity of 87.59%, a specificity of 82.94%, a positive predictive value of 96.25%, and a negative predictive value of 57.22%. Moreover, the corresponding area under the receiver operating characteristic curve was 0.94 (95% CI, 0.91%-0.97%), which was significantly higher than the values obtained by simple mean hue histogram analysis, for which the area under the receiver operating characteristic was 0.85. Use of the artificial intelligence methodology via artificial neural networks supports the medical decision process, providing fast and accurate diagnoses. Copyright © 2012 AGA Institute. Published by Elsevier Inc. All rights reserved.

A computational model incorporating neural stem cell dynamics reproduces glioma incidence across the lifespan in the human population.

Directory of Open Access Journals (Sweden)

Roman Bauer

Full Text Available Glioma is the most common form of primary brain tumor. Demographically, the risk of occurrence increases until old age. Here we present a novel computational model to reproduce the probability of glioma incidence across the lifespan. Previous mathematical models explaining glioma incidence are framed in a rather abstract way, and do not directly relate to empirical findings. To decrease this gap between theory and experimental observations, we incorporate recent data on cellular and molecular factors underlying gliomagenesis. Since evidence implicates the adult neural stem cell as the likely cell-of-origin of glioma, we have incorporated empirically-determined estimates of neural stem cell number, cell division rate, mutation rate and oncogenic potential into our model. We demonstrate that our model yields results which match actual demographic data in the human population. In particular, this model accounts for the observed peak incidence of glioma at approximately 80 years of age, without the need to assert differential susceptibility throughout the population. Overall, our model supports the hypothesis that glioma is caused by randomly-occurring oncogenic mutations within the neural stem cell population. Based on this model, we assess the influence of the (experimentally indicated decrease in the number of neural stem cells and increase of cell division rate during aging. Our model provides multiple testable predictions, and suggests that different temporal sequences of oncogenic mutations can lead to tumorigenesis. Finally, we conclude that four or five oncogenic mutations are sufficient for the formation of glioma.

A Quantum Implementation Model for Artificial Neural Networks

OpenAIRE

Daskin, Ammar

2016-01-01

The learning process for multi layered neural networks with many nodes makes heavy demands on computational resources. In some neural network models, the learning formulas, such as the Widrow-Hoff formula, do not change the eigenvectors of the weight matrix while flatting the eigenvalues. In infinity, this iterative formulas result in terms formed by the principal components of the weight matrix: i.e., the eigenvectors corresponding to the non-zero eigenvalues. In quantum computing, the phase...

Prediction of the Thermal Conductivity of Refrigerants by Computational Methods and Artificial Neural Network.

Science.gov (United States)

Ghaderi, Forouzan; Ghaderi, Amir H; Ghaderi, Noushin; Najafi, Bijan

2017-01-01

Background: The thermal conductivity of fluids can be calculated by several computational methods. However, these methods are reliable only at the confined levels of density, and there is no specific computational method for calculating thermal conductivity in the wide ranges of density. Methods: In this paper, two methods, an Artificial Neural Network (ANN) approach and a computational method established upon the Rainwater-Friend theory, were used to predict the value of thermal conductivity in all ranges of density. The thermal conductivity of six refrigerants, R12, R14, R32, R115, R143, and R152 was predicted by these methods and the effectiveness of models was specified and compared. Results: The results show that the computational method is a usable method for predicting thermal conductivity at low levels of density. However, the efficiency of this model is considerably reduced in the mid-range of density. It means that this model cannot be used at density levels which are higher than 6. On the other hand, the ANN approach is a reliable method for thermal conductivity prediction in all ranges of density. The best accuracy of ANN is achieved when the number of units is increased in the hidden layer. Conclusion: The results of the computational method indicate that the regular dependence between thermal conductivity and density at higher densities is eliminated. It can develop a nonlinear problem. Therefore, analytical approaches are not able to predict thermal conductivity in wide ranges of density. Instead, a nonlinear approach such as, ANN is a valuable method for this purpose.

Application of Artificial Neural Network and Response Surface Methodology in Modeling of Surface Roughness in WS2 Solid Lubricant Assisted MQL Turning of Inconel 718

Science.gov (United States)

Maheshwera Reddy Paturi, Uma; Devarasetti, Harish; Abimbola Fadare, David; Reddy Narala, Suresh Kumar

2018-04-01

In the present paper, the artificial neural network (ANN) and response surface methodology (RSM) are used in modeling of surface roughness in WS2 (tungsten disulphide) solid lubricant assisted minimal quantity lubrication (MQL) machining. The real time MQL turning of Inconel 718 experimental data considered in this paper was available in the literature [1]. In ANN modeling, performance parameters such as mean square error (MSE), mean absolute percentage error (MAPE) and average error in prediction (AEP) for the experimental data were determined based on Levenberg–Marquardt (LM) feed forward back propagation training algorithm with tansig as transfer function. The MATLAB tool box has been utilized in training and testing of neural network model. Neural network model with three input neurons, one hidden layer with five neurons and one output neuron (3-5-1 architecture) is found to be most confidence and optimal. The coefficient of determination (R2) for both the ANN and RSM model were seen to be 0.998 and 0.982 respectively. The surface roughness predictions from ANN and RSM model were related with experimentally measured values and found to be in good agreement with each other. However, the prediction efficacy of ANN model is relatively high when compared with RSM model predictions.

Computer-aided diagnosis of prostate cancer using a deep convolutional neural network from multiparametric MRI.

Science.gov (United States)

Song, Yang; Zhang, Yu-Dong; Yan, Xu; Liu, Hui; Zhou, Minxiong; Hu, Bingwen; Yang, Guang

2018-04-16

Deep learning is the most promising methodology for automatic computer-aided diagnosis of prostate cancer (PCa) with multiparametric MRI (mp-MRI). To develop an automatic approach based on deep convolutional neural network (DCNN) to classify PCa and noncancerous tissues (NC) with mp-MRI. Retrospective. In all, 195 patients with localized PCa were collected from a PROSTATEx database. In total, 159/17/19 patients with 444/48/55 observations (215/23/23 PCas and 229/25/32 NCs) were randomly selected for training/validation/testing, respectively. T 2 -weighted, diffusion-weighted, and apparent diffusion coefficient images. A radiologist manually labeled the regions of interest of PCas and NCs and estimated the Prostate Imaging Reporting and Data System (PI-RADS) scores for each region. Inspired by VGG-Net, we designed a patch-based DCNN model to distinguish between PCa and NCs based on a combination of mp-MRI data. Additionally, an enhanced prediction method was used to improve the prediction accuracy. The performance of DCNN prediction was tested using a receiver operating characteristic (ROC) curve, and the area under the ROC curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were calculated. Moreover, the predicted result was compared with the PI-RADS score to evaluate its clinical value using decision curve analysis. Two-sided Wilcoxon signed-rank test with statistical significance set at 0.05. The DCNN produced excellent diagnostic performance in distinguishing between PCa and NC for testing datasets with an AUC of 0.944 (95% confidence interval: 0.876-0.994), sensitivity of 87.0%, specificity of 90.6%, PPV of 87.0%, and NPV of 90.6%. The decision curve analysis revealed that the joint model of PI-RADS and DCNN provided additional net benefits compared with the DCNN model and the PI-RADS scheme. The proposed DCNN-based model with enhanced prediction yielded high performance in statistical analysis, suggesting

Parallel consensual neural networks.

Science.gov (United States)

Benediktsson, J A; Sveinsson, J R; Ersoy, O K; Swain, P H

1997-01-01

A new type of a neural-network architecture, the parallel consensual neural network (PCNN), is introduced and applied in classification/data fusion of multisource remote sensing and geographic data. The PCNN architecture is based on statistical consensus theory and involves using stage neural networks with transformed input data. The input data are transformed several times and the different transformed data are used as if they were independent inputs. The independent inputs are first classified using the stage neural networks. The output responses from the stage networks are then weighted and combined to make a consensual decision. In this paper, optimization methods are used in order to weight the outputs from the stage networks. Two approaches are proposed to compute the data transforms for the PCNN, one for binary data and another for analog data. The analog approach uses wavelet packets. The experimental results obtained with the proposed approach show that the PCNN outperforms both a conjugate-gradient backpropagation neural network and conventional statistical methods in terms of overall classification accuracy of test data.

EnergiTools. A methodology for performance monitoring and diagnosis

International Nuclear Information System (INIS)

Ancion, P.; Bastien, R.; Ringdahl, K.

2000-01-01

EnergiTools is a performance monitoring and diagnostic tool that combines the power of on-line process data acquisition with advanced diagnosis methodologies. Analytical models based on thermodynamic principles are combined with neural networks to validate sensor data and to estimate missing or faulty measurements. Advanced diagnostic technologies are then applied to point out potential faults and areas to be investigated further. The diagnosis methodologies are based on Bayesian belief networks. Expert knowledge is captured in the form of the fault-symptom relationships and includes historical information as the likelihood of faults and symptoms. The methodology produces the likelihood of component failure root causes using the expert knowledge base. EnergiTools is used at Ringhals nuclear power plants. It has led to the diagnosis of various performance issues. Three case studies based on this plant data and model are presented and illustrate the diagnosis support methodologies implemented in EnergiTools . In the first case, the analytical data qualification technique points out several faulty measurements. The application of a neural network for the estimation of the nuclear reactor power by interpreting several plant indicators is then illustrated. The use of the Bayesian belief networks is finally described. (author)

Potential usefulness of an artificial neural network for assessing ventricular size

International Nuclear Information System (INIS)

Fukuda, Haruyuki; Nakajima, Hideyuki; Usuki, Noriaki; Saiwai, Shigeo; Miyamoto, Takeshi; Inoue, Yuichi; Onoyama, Yasuto.

1995-01-01

An artificial neural network approach was applied to assess ventricular size from computed tomograms. Three layer, feed-forward neural networks with a back propagation algorithm were designed to distinguish between three degree of enlargement of the ventricles on the basis of patient's age and six items of computed tomographic information. Data for training and testing the neural network were created with computed tomograms of the brains selected at random from daily examinations. Four radiologists decided by mutual consent subjectively based on their experience whether the ventricles were within normal limits, slightly enlarged, or enlarged for the patient's age. The data for training was obtained from 38 patients. The data for testing was obtained from 47 other patients. The performance of the neural network trained using the data for training was evaluated by the rate of correct answers to the data for testing. The valid solution ratio to response of the test data obtained from the trained neural networks was more than 90% for all conditions in this study. The solutions were completely valid in the neural networks with two or three units at the hidden layer with 2,200 learning iterations, and with two units at the hidden layer with 11,000 learning iterations. The squared error decreased remarkably in the range from 0 to 500 learning iterations, and was close to a contrast over two thousand learning iterations. The neural network with a hidden layer having two or three units showed high decision performance. The preliminary results strongly suggest that the neural network approach has potential utility in computer-aided estimation of enlargement of the ventricles. (author)

Assessing neural activity related to decision-making through flexible odds ratio curves and their derivatives.

Science.gov (United States)

Roca-Pardiñas, Javier; Cadarso-Suárez, Carmen; Pardo-Vazquez, Jose L; Leboran, Victor; Molenberghs, Geert; Faes, Christel; Acuña, Carlos

2011-06-30

It is well established that neural activity is stochastically modulated over time. Therefore, direct comparisons across experimental conditions and determination of change points or maximum firing rates are not straightforward. This study sought to compare temporal firing probability curves that may vary across groups defined by different experimental conditions. Odds-ratio (OR) curves were used as a measure of comparison, and the main goal was to provide a global test to detect significant differences of such curves through the study of their derivatives. An algorithm is proposed that enables ORs based on generalized additive models, including factor-by-curve-type interactions to be flexibly estimated. Bootstrap methods were used to draw inferences from the derivatives curves, and binning techniques were applied to speed up computation in the estimation and testing processes. A simulation study was conducted to assess the validity of these bootstrap-based tests. This methodology was applied to study premotor ventral cortex neural activity associated with decision-making. The proposed statistical procedures proved very useful in revealing the neural activity correlates of decision-making in a visual discrimination task. Copyright © 2011 John Wiley & Sons, Ltd.

Iris Data Classification Using Quantum Neural Networks

International Nuclear Information System (INIS)

Sahni, Vishal; Patvardhan, C.

2006-01-01

Quantum computing is a novel paradigm that promises to be the future of computing. The performance of quantum algorithms has proved to be stunning. ANN within the context of classical computation has been used for approximation and classification tasks with some success. This paper presents an idea of quantum neural networks along with the training algorithm and its convergence property. It synergizes the unique properties of quantum bits or qubits with the various techniques in vogue in neural networks. An example application of Fisher's Iris data set, a benchmark classification problem has also been presented. The results obtained amply demonstrate the classification capabilities of the quantum neuron and give an idea of their promising capabilities

22nd Italian Workshop on Neural Nets

CERN Document Server

Bassis, Simone; Esposito, Anna; Morabito, Francesco

2013-01-01

This volume collects a selection of contributions which has been presented at the 22nd Italian Workshop on Neural Networks, the yearly meeting of the Italian Society for Neural Networks (SIREN). The conference was held in Italy, Vietri sul Mare (Salerno), during May 17-19, 2012. The annual meeting of SIREN is sponsored by International Neural Network Society (INNS), European Neural Network Society (ENNS) and IEEE Computational Intelligence Society (CIS). The book – as well as the workshop-  is organized in three main components, two special sessions and a group of regular sessions featuring different aspects and point of views of artificial neural networks and natural intelligence, also including applications of present compelling interest.

Neural Network with Local Memory for Nuclear Reactor Power Level Control

International Nuclear Information System (INIS)

Uluyol, Oender; Ragheb, Magdi; Tsoukalas, Lefteri

2001-01-01

A methodology is introduced for a neural network with local memory called a multilayered local output gamma feedback (LOGF) neural network within the paradigm of locally-recurrent globally-feedforward neural networks. It appears to be well-suited for the identification, prediction, and control tasks in highly dynamic systems; it allows for the presentation of different timescales through incorporation of a gamma memory. A learning algorithm based on the backpropagation-through-time approach is derived. The spatial and temporal weights of the network are iteratively optimized for a given problem using the derived learning algorithm. As a demonstration of the methodology, it is applied to the task of power level control of a nuclear reactor at different fuel cycle conditions. The results demonstrate that the LOGF neural network controller outperforms the classical as well as the state feedback-assisted classical controllers for reactor power level control by showing a better tracking of the demand power, improving the fuel and exit temperature responses, and by performing robustly in different fuel cycle and power level conditions

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

Neural components of altruistic punishment

Directory of Open Access Journals (Sweden)

Emily eDu

2015-02-01

Full Text Available Altruistic punishment, which occurs when an individual incurs a cost to punish in response to unfairness or a norm violation, may play a role in perpetuating cooperation. The neural correlates underlying costly punishment have only recently begun to be explored. Here we review the current state of research on the neural basis of altruism from the perspectives of costly punishment, emphasizing the importance of characterizing elementary neural processes underlying a decision to punish. In particular, we emphasize three cognitive processes that contribute to the decision to altruistically punish in most scenarios: inequity aversion, cost-benefit calculation, and social reference frame to distinguish self from others. Overall, we argue for the importance of understanding the neural correlates of altruistic punishment with respect to the core computations necessary to achieve a decision to punish.

Pulsed neural networks consisting of single-flux-quantum spiking neurons

International Nuclear Information System (INIS)

Hirose, T.; Asai, T.; Amemiya, Y.

2007-01-01

An inhibitory pulsed neural network was developed for brain-like information processing, by using single-flux-quantum (SFQ) circuits. It consists of spiking neuron devices that are coupled to each other through all-to-all inhibitory connections. The network selects neural activity. The operation of the neural network was confirmed by computer simulation. SFQ neuron devices can imitate the operation of the inhibition phenomenon of neural networks

Computations Underlying Social Hierarchy Learning: Distinct Neural Mechanisms for Updating and Representing Self-Relevant Information.

Science.gov (United States)

Kumaran, Dharshan; Banino, Andrea; Blundell, Charles; Hassabis, Demis; Dayan, Peter

2016-12-07

Knowledge about social hierarchies organizes human behavior, yet we understand little about the underlying computations. Here we show that a Bayesian inference scheme, which tracks the power of individuals, better captures behavioral and neural data compared with a reinforcement learning model inspired by rating systems used in games such as chess. We provide evidence that the medial prefrontal cortex (MPFC) selectively mediates the updating of knowledge about one's own hierarchy, as opposed to that of another individual, a process that underpinned successful performance and involved functional interactions with the amygdala and hippocampus. In contrast, we observed domain-general coding of rank in the amygdala and hippocampus, even when the task did not require it. Our findings reveal the computations underlying a core aspect of social cognition and provide new evidence that self-relevant information may indeed be afforded a unique representational status in the brain. Copyright © 2016 The Authors. Published by Elsevier Inc. All rights reserved.

NeuroMEMS: Neural Probe Microtechnologies

Directory of Open Access Journals (Sweden)

Sam Musallam

2008-10-01

Full Text Available Neural probe technologies have already had a significant positive effect on our understanding of the brain by revealing the functioning of networks of biological neurons. Probes are implanted in different areas of the brain to record and/or stimulate specific sites in the brain. Neural probes are currently used in many clinical settings for diagnosis of brain diseases such as seizers, epilepsy, migraine, Alzheimer’s, and dementia. We find these devices assisting paralyzed patients by allowing them to operate computers or robots using their neural activity. In recent years, probe technologies were assisted by rapid advancements in microfabrication and microelectronic technologies and thus are enabling highly functional and robust neural probes which are opening new and exciting avenues in neural sciences and brain machine interfaces. With a wide variety of probes that have been designed, fabricated, and tested to date, this review aims to provide an overview of the advances and recent progress in the microfabrication techniques of neural probes. In addition, we aim to highlight the challenges faced in developing and implementing ultralong multi-site recording probes that are needed to monitor neural activity from deeper regions in the brain. Finally, we review techniques that can improve the biocompatibility of the neural probes to minimize the immune response and encourage neural growth around the electrodes for long term implantation studies.

Livermore Big Artificial Neural Network Toolkit

Energy Technology Data Exchange (ETDEWEB)

2016-07-01

LBANN is a toolkit that is designed to train artificial neural networks efficiently on high performance computing architectures. It is optimized to take advantages of key High Performance Computing features to accelerate neural network training. Specifically it is optimized for low-latency, high bandwidth interconnects, node-local NVRAM, node-local GPU accelerators, and high bandwidth parallel file systems. It is built on top of the open source Elemental distributed-memory dense and spars-direct linear algebra and optimization library that is released under the BSD license. The algorithms contained within LBANN are drawn from the academic literature and implemented to work within a distributed-memory framework.

Design of a computation tool for neutron spectrometry and dosimetry through evolutionary neural networks

International Nuclear Information System (INIS)

Ortiz R, J. M.; Vega C, H. R.; Martinez B, M. R.; Gallego, E.

2009-10-01

The neutron dosimetry is one of the most complicated tasks of radiation protection, due to it is a complex technique and highly dependent of neutron energy. One of the first devices used to perform neutron spectrometry is the system known as spectrometric system of Bonner spheres, that continuous being one of spectrometers most commonly used. This system has disadvantages such as: the components weight, the low resolution of spectrum, long and drawn out procedure for the spectra reconstruction, which require an expert user in system management, the need of use a reconstruction code as BUNKIE, SAND, etc., which are based on an iterative reconstruction algorithm and whose greatest inconvenience is that for the spectrum reconstruction, are needed to provide to system and initial spectrum as close as possible to the desired spectrum get. Consequently, researchers have mentioned the need to developed alternative measurement techniques to improve existing monitoring systems for workers. Among these alternative techniques have been reported several reconstruction procedures based on artificial intelligence techniques such as genetic algorithms, artificial neural networks and hybrid systems of evolutionary artificial neural networks using genetic algorithms. However, the use of these techniques in the nuclear science area is not free of problems, so it has been suggested that more research is conducted in such a way as to solve these disadvantages. Because they are emerging technologies, there are no tools for the results analysis, so in this paper we present first the design of a computation tool that allow to analyze the neutron spectra and equivalent doses, obtained through the hybrid technology of neural networks and genetic algorithms. This tool provides an user graphical environment, friendly, intuitive and easy of operate. The speed of program operation is high, executing the analysis in a few seconds, so it may storage and or print the obtained information for

Beam standardization and dosimetric methodology in computed tomography

International Nuclear Information System (INIS)

Maia, Ana Figueiredo

2005-01-01

Special ionization chambers, named pencil ionization chambers, are used in dosimetric procedures in computed tomography beams (CT). In this work, an extensive study about pencil ionization chambers was performed, as a contribution to the accuracy of the dosimetric procedures in CT beams. The international scientific community has recently been discussing the need of the establishment of a specific calibration procedure for CT ionization chambers, once these chambers present special characteristics that differentiate them from other ionization chambers used in diagnostic radiology beams. In this work, an adequate calibration procedure for pencil ionization chambers was established at the Calibration Laboratory, of the Institute de Pesquisas Energeticas e Nucleares, in accordance with the most recent international recommendations. Two calibration methodologies were tested and analyzed by comparative studies. Moreover, a new extended length parallel plate ionization chamber, with a transversal section very similar to pencil ionization chambers, was developed. The operational characteristics of this chamber were determined and the results obtained showed that its behaviour is adequate as a reference system in CT standard beams. Two other studies were performed during this work, both using CT ionization chambers. The first study was about the performance of a pencil ionization chamber in standard radiation beams of several types and energies, and the results showed that this chamber presents satisfactory behaviour in other radiation qualities as of diagnostic radiology, mammography and radiotherapy. In the second study, a tandem system for verification of hal'-value layer variations in CT equipment, using a pencil ionization chamber, was developed. Because of the X rays tube rotation, the determination of half-value layers in computed tomography equipment is not an easy task, and it is usually not performed within quality control programs. (author)

Parameter estimation using compensatory neural networks

Indian Academy of Sciences (India)

of interconnections among neurons but also reduces the total computing time for training. The suggested model has properties of the basic neuron ..... Engelbrecht A P, Cloete I, Geldenhuys J, Zurada J M 1995 Automatic scaling using gamma learning for feedforward neural networks. From natural to artificial computing.

Dynamical systems, attractors, and neural circuits.

Science.gov (United States)

Miller, Paul

2016-01-01

Biology is the study of dynamical systems. Yet most of us working in biology have limited pedagogical training in the theory of dynamical systems, an unfortunate historical fact that can be remedied for future generations of life scientists. In my particular field of systems neuroscience, neural circuits are rife with nonlinearities at all levels of description, rendering simple methodologies and our own intuition unreliable. Therefore, our ideas are likely to be wrong unless informed by good models. These models should be based on the mathematical theories of dynamical systems since functioning neurons are dynamic-they change their membrane potential and firing rates with time. Thus, selecting the appropriate type of dynamical system upon which to base a model is an important first step in the modeling process. This step all too easily goes awry, in part because there are many frameworks to choose from, in part because the sparsely sampled data can be consistent with a variety of dynamical processes, and in part because each modeler has a preferred modeling approach that is difficult to move away from. This brief review summarizes some of the main dynamical paradigms that can arise in neural circuits, with comments on what they can achieve computationally and what signatures might reveal their presence within empirical data. I provide examples of different dynamical systems using simple circuits of two or three cells, emphasizing that any one connectivity pattern is compatible with multiple, diverse functions.

A neural population model incorporating dopaminergic neurotransmission during complex voluntary behaviors.

Directory of Open Access Journals (Sweden)

Stefan Fürtinger

2014-11-01

Full Text Available Assessing brain activity during complex voluntary motor behaviors that require the recruitment of multiple neural sites is a field of active research. Our current knowledge is primarily based on human brain imaging studies that have clear limitations in terms of temporal and spatial resolution. We developed a physiologically informed non-linear multi-compartment stochastic neural model to simulate functional brain activity coupled with neurotransmitter release during complex voluntary behavior, such as speech production. Due to its state-dependent modulation of neural firing, dopaminergic neurotransmission plays a key role in the organization of functional brain circuits controlling speech and language and thus has been incorporated in our neural population model. A rigorous mathematical proof establishing existence and uniqueness of solutions to the proposed model as well as a computationally efficient strategy to numerically approximate these solutions are presented. Simulated brain activity during the resting state and sentence production was analyzed using functional network connectivity, and graph theoretical techniques were employed to highlight differences between the two conditions. We demonstrate that our model successfully reproduces characteristic changes seen in empirical data between the resting state and speech production, and dopaminergic neurotransmission evokes pronounced changes in modeled functional connectivity by acting on the underlying biological stochastic neural model. Specifically, model and data networks in both speech and rest conditions share task-specific network features: both the simulated and empirical functional connectivity networks show an increase in nodal influence and segregation in speech over the resting state. These commonalities confirm that dopamine is a key neuromodulator of the functional connectome of speech control. Based on reproducible characteristic aspects of empirical data, we suggest a number

Hierarchical modular granular neural networks with fuzzy aggregation

CERN Document Server

Sanchez, Daniela

2016-01-01

In this book, a new method for hybrid intelligent systems is proposed. The proposed method is based on a granular computing approach applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC) approach, thus resulting in a new concept of MNNs; modular granular neural networks (MGNNs). In addition fuzzy logic (FL) and hierarchical genetic algorithms (HGAs) are techniques used in this research work to improve results. These techniques are chosen because in other works have demonstrated to be a good option, and in the case of MNNs and HGAs, these techniques allow to improve the results obtained than with their conventional versions; respectively artificial neural networks and genetic algorithms.

Application of code scaling, applicability and uncertainty methodology to large break LOCA analysis of two loop PWR

International Nuclear Information System (INIS)

Mavko, B.; Stritar, A.; Prosek, A.

1993-01-01

In NED 119, No. 1 (May 1990) a series of six papers published by a Technical Program Group presented a new methodology for the safety evaluation of emergency core cooling systems in nuclear power plants. This paper describes the application of that new methodology to the LB LOCA analysis of the two loop Westinghouse power plant. Results of the original work were used wherever possible, so that the analysis was finished in less than one man year of work. Steam generator plugging level and safety injection flow rate were used as additional uncertainty parameters, which had not been used in the original work. The computer code RELAP5/MOD2 was used. Response surface was generated by the regression analysis and by the artificial neural network like Optimal Statistical Estimator method. Results were compared also to the analytical calculation. (orig.)

Dynamic decomposition of spatiotemporal neural signals.

Directory of Open Access Journals (Sweden)

Luca Ambrogioni

2017-05-01

Full Text Available Neural signals are characterized by rich temporal and spatiotemporal dynamics that reflect the organization of cortical networks. Theoretical research has shown how neural networks can operate at different dynamic ranges that correspond to specific types of information processing. Here we present a data analysis framework that uses a linearized model of these dynamic states in order to decompose the measured neural signal into a series of components that capture both rhythmic and non-rhythmic neural activity. The method is based on stochastic differential equations and Gaussian process regression. Through computer simulations and analysis of magnetoencephalographic data, we demonstrate the efficacy of the method in identifying meaningful modulations of oscillatory signals corrupted by structured temporal and spatiotemporal noise. These results suggest that the method is particularly suitable for the analysis and interpretation of complex temporal and spatiotemporal neural signals.

Computational vision

CERN Document Server

Wechsler, Harry

1990-01-01

The book is suitable for advanced courses in computer vision and image processing. In addition to providing an overall view of computational vision, it contains extensive material on topics that are not usually covered in computer vision texts (including parallel distributed processing and neural networks) and considers many real applications.

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.

Optimal Brain Surgeon on Artificial Neural Networks in

DEFF Research Database (Denmark)

Christiansen, Niels Hørbye; Job, Jonas Hultmann; Klyver, Katrine

2012-01-01

It is shown how the procedure know as optimal brain surgeon can be used to trim and optimize artificial neural networks in nonlinear structural dynamics. Beside optimizing the neural network, and thereby minimizing computational cost in simulation, the surgery procedure can also serve as a quick...

Methodology and computer program for applying improved, inelastic ERR for the design of mine layouts on planar reefs.

CSIR Research Space (South Africa)

Spottiswoode, SM

2002-08-01

Full Text Available and the visco-plastic models of Napier and Malan (1997) and Malan (2002). Methodologies and a computer program (MINF) are developed during this project that write synthetic catalogues of seismic events to simulate the rock response to mining...

Optimization of artificial neural networks for the reconstruction of the neutrons spectrum and their equivalent doses

International Nuclear Information System (INIS)

Reyes A, A.; Ortiz R, J. M.; Reyes H, A.; Castaneda M, R.; Solis S, L. O.; Vega C, H. R.

2014-08-01

In this work was used the robust design methodology of artificial neural networks to determine a good topology of net able to solve with efficiency the problems of neutrons spectrometry and dosimetry. For the design of the topology of optimized net 36 different net architectures based on an orthogonal arrangement with a configuration L 9 (3 4 ), L 4 (3 2 ) were trained. For the training of the neural networks, was used a computer code developed in the ambient of Mat lab programming, which automates the process and analysis of the information, reducing the time used in this activity considerably for the investigator. For the training of the propagation nets forward was utilized a neutrons spectrum compendium published by the International Atomic Energy Agency, where of the total 80% was used for the training and 20% for the test, it trained with an inverse propagation algorithm being the entrance data the count rates corresponding to the 7 spheres of the spectrometric system of Bonner spheres, as exit data, the neural network obtains the neutrons spectrum expressed in 60 energy groups and are calculated of simultaneous way 15 dosimetric quantities. (Author)

Neurosecurity: security and privacy for neural devices.

Science.gov (United States)

Denning, Tamara; Matsuoka, Yoky; Kohno, Tadayoshi

2009-07-01

An increasing number of neural implantable devices will become available in the near future due to advances in neural engineering. This discipline holds the potential to improve many patients' lives dramatically by offering improved-and in some cases entirely new-forms of rehabilitation for conditions ranging from missing limbs to degenerative cognitive diseases. The use of standard engineering practices, medical trials, and neuroethical evaluations during the design process can create systems that are safe and that follow ethical guidelines; unfortunately, none of these disciplines currently ensure that neural devices are robust against adversarial entities trying to exploit these devices to alter, block, or eavesdrop on neural signals. The authors define "neurosecurity"-a version of computer science security principles and methods applied to neural engineering-and discuss why neurosecurity should be a critical consideration in the design of future neural devices.

Color sensor and neural processor on one chip

Science.gov (United States)

Fiesler, Emile; Campbell, Shannon R.; Kempem, Lother; Duong, Tuan A.

1998-10-01

Low-cost, compact, and robust color sensor that can operate in real-time under various environmental conditions can benefit many applications, including quality control, chemical sensing, food production, medical diagnostics, energy conservation, monitoring of hazardous waste, and recycling. Unfortunately, existing color sensor are either bulky and expensive or do not provide the required speed and accuracy. In this publication we describe the design of an accurate real-time color classification sensor, together with preprocessing and a subsequent neural network processor integrated on a single complementary metal oxide semiconductor (CMOS) integrated circuit. This one-chip sensor and information processor will be low in cost, robust, and mass-producible using standard commercial CMOS processes. The performance of the chip and the feasibility of its manufacturing is proven through computer simulations based on CMOS hardware parameters. Comparisons with competing methodologies show a significantly higher performance for our device.

Topology influences performance in the associative memory neural networks

International Nuclear Information System (INIS)

Lu Jianquan; He Juan; Cao Jinde; Gao Zhiqiang

2006-01-01

To explore how topology affects performance within Hopfield-type associative memory neural networks (AMNNs), we studied the computational performance of the neural networks with regular lattice, random, small-world, and scale-free structures. In this Letter, we found that the memory performance of neural networks obtained through asynchronous updating from 'larger' nodes to 'smaller' nodes are better than asynchronous updating in random order, especially for the scale-free topology. The computational performance of associative memory neural networks linked by the above-mentioned network topologies with the same amounts of nodes (neurons) and edges (synapses) were studied respectively. Along with topologies becoming more random and less locally disordered, we will see that the performance of associative memory neural network is quite improved. By comparing, we show that the regular lattice and random network form two extremes in terms of patterns stability and retrievability. For a network, its patterns stability and retrievability can be largely enhanced by adding a random component or some shortcuts to its structured component. According to the conclusions of this Letter, we can design the associative memory neural networks with high performance and minimal interconnect requirements

Data systems and computer science: Neural networks base R/T program overview

Science.gov (United States)

Gulati, Sandeep

1991-01-01

The research base, in the U.S. and abroad, for the development of neural network technology is discussed. The technical objectives are to develop and demonstrate adaptive, neural information processing concepts. The leveraging of external funding is also discussed.

RDANN a new methodology to solve the neutron spectra unfolding problem

Energy Technology Data Exchange (ETDEWEB)

Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R. [UAZ, Av. Ramon Lopez Velarde No. 801, 98000 Zacatecas (Mexico)

2006-07-01

The optimization processes known as Taguchi method and DOE methodology are applied to the design, training and testing of Artificial Neural Networks in the neutron spectrometry field, which offer potential benefits in the evaluation of the behavior of the net as well as the ability to examine the interaction of the weights and neurons inside the same one. In this work, the Robust Design of Artificial Neural Networks methodology is used to solve the neutron spectra unfolding problem, designing, training and testing an ANN using a set of 187 neutron spectra compiled by the International Atomic Energy Agency, to obtain the better neutron spectra unfolded from the Bonner spheres spectrometer's count rates. (Author)

RDANN a new methodology to solve the neutron spectra unfolding problem

International Nuclear Information System (INIS)

Ortiz R, J.M.; Martinez B, M.R.; Vega C, H.R.

2006-01-01

The optimization processes known as Taguchi method and DOE methodology are applied to the design, training and testing of Artificial Neural Networks in the neutron spectrometry field, which offer potential benefits in the evaluation of the behavior of the net as well as the ability to examine the interaction of the weights and neurons inside the same one. In this work, the Robust Design of Artificial Neural Networks methodology is used to solve the neutron spectra unfolding problem, designing, training and testing an ANN using a set of 187 neutron spectra compiled by the International Atomic Energy Agency, to obtain the better neutron spectra unfolded from the Bonner spheres spectrometer's count rates. (Author)

Enhancing neural-network performance via assortativity

International Nuclear Information System (INIS)

Franciscis, Sebastiano de; Johnson, Samuel; Torres, Joaquin J.

2011-01-01

The performance of attractor neural networks has been shown to depend crucially on the heterogeneity of the underlying topology. We take this analysis a step further by examining the effect of degree-degree correlations - assortativity - on neural-network behavior. We make use of a method recently put forward for studying correlated networks and dynamics thereon, both analytically and computationally, which is independent of how the topology may have evolved. We show how the robustness to noise is greatly enhanced in assortative (positively correlated) neural networks, especially if it is the hub neurons that store the information.

Locally optimal extracellular stimulation for chaotic desynchronization of neural populations.

Science.gov (United States)

Wilson, Dan; Moehlis, Jeff

2014-10-01

We use optimal control theory to design a methodology to find locally optimal stimuli for desynchronization of a model of neurons with extracellular stimulation. This methodology yields stimuli which lead to positive Lyapunov exponents, and hence desynchronizes a neural population. We analyze this methodology in the presence of interneuron coupling to make predictions about the strength of stimulation required to overcome synchronizing effects of coupling. This methodology suggests a powerful alternative to pulsatile stimuli for deep brain stimulation as it uses less energy than pulsatile stimuli, and could eliminate the time consuming tuning process.

Abstract computation in schizophrenia detection through artificial neural network based systems.

Science.gov (United States)

Cardoso, L; Marins, F; Magalhães, R; Marins, N; Oliveira, T; Vicente, H; Abelha, A; Machado, J; Neves, J

2015-01-01

Schizophrenia stands for a long-lasting state of mental uncertainty that may bring to an end the relation among behavior, thought, and emotion; that is, it may lead to unreliable perception, not suitable actions and feelings, and a sense of mental fragmentation. Indeed, its diagnosis is done over a large period of time; continuos signs of the disturbance persist for at least 6 (six) months. Once detected, the psychiatrist diagnosis is made through the clinical interview and a series of psychic tests, addressed mainly to avoid the diagnosis of other mental states or diseases. Undeniably, the main problem with identifying schizophrenia is the difficulty to distinguish its symptoms from those associated to different untidiness or roles. Therefore, this work will focus on the development of a diagnostic support system, in terms of its knowledge representation and reasoning procedures, based on a blended of Logic Programming and Artificial Neural Networks approaches to computing, taking advantage of a novel approach to knowledge representation and reasoning, which aims to solve the problems associated in the handling (i.e., to stand for and reason) of defective information.

Determination of penetration depth at high velocity impact using finite element method and artificial neural network tools

Directory of Open Access Journals (Sweden)

Namık KılıÇ

2015-06-01

Full Text Available Determination of ballistic performance of an armor solution is a complicated task and evolved significantly with the application of finite element methods (FEM in this research field. The traditional armor design studies performed with FEM requires sophisticated procedures and intensive computational effort, therefore simpler and accurate numerical approaches are always worthwhile to decrease armor development time. This study aims to apply a hybrid method using FEM simulation and artificial neural network (ANN analysis to approximate ballistic limit thickness for armor steels. To achieve this objective, a predictive model based on the artificial neural networks is developed to determine ballistic resistance of high hardness armor steels against 7.62 mm armor piercing ammunition. In this methodology, the FEM simulations are used to create training cases for Multilayer Perceptron (MLP three layer networks. In order to validate FE simulation methodology, ballistic shot tests on 20 mm thickness target were performed according to standard Stanag 4569. Afterwards, the successfully trained ANN(s is used to predict the ballistic limit thickness of 500 HB high hardness steel armor. Results show that even with limited number of data, FEM-ANN approach can be used to predict ballistic penetration depth with adequate accuracy.

Multi-criteria decision making development of ion chromatographic method for determination of inorganic anions in oilfield waters based on artificial neural networks retention model.

Science.gov (United States)

Stefanović, Stefica Cerjan; Bolanča, Tomislav; Luša, Melita; Ukić, Sime; Rogošić, Marko

2012-02-24

This paper describes the development of ad hoc methodology for determination of inorganic anions in oilfield water, since their composition often significantly differs from the average (concentration of components and/or matrix). Therefore, fast and reliable method development has to be performed in order to ensure the monitoring of desired properties under new conditions. The method development was based on computer assisted multi-criteria decision making strategy. The used criteria were: maximal value of objective functions used, maximal robustness of the separation method, minimal analysis time, and maximal retention distance between two nearest components. Artificial neural networks were used for modeling of anion retention. The reliability of developed method was extensively tested by the validation of performance characteristics. Based on validation results, the developed method shows satisfactory performance characteristics, proving the successful application of computer assisted methodology in the described case study. Copyright © 2011 Elsevier B.V. All rights reserved.

Computational Evolutionary Methodology for Knowledge Discovery and Forecasting in Epidemiology and Medicine

International Nuclear Information System (INIS)

Rao, Dhananjai M.; Chernyakhovsky, Alexander; Rao, Victoria

2008-01-01

Humanity is facing an increasing number of highly virulent and communicable diseases such as avian influenza. Researchers believe that avian influenza has potential to evolve into one of the deadliest pandemics. Combating these diseases requires in-depth knowledge of their epidemiology. An effective methodology for discovering epidemiological knowledge is to utilize a descriptive, evolutionary, ecological model and use bio-simulations to study and analyze it. These types of bio-simulations fall under the category of computational evolutionary methods because the individual entities participating in the simulation are permitted to evolve in a natural manner by reacting to changes in the simulated ecosystem. This work describes the application of the aforementioned methodology to discover epidemiological knowledge about avian influenza using a novel eco-modeling and bio-simulation environment called SEARUMS. The mathematical principles underlying SEARUMS, its design, and the procedure for using SEARUMS are discussed. The bio-simulations and multi-faceted case studies conducted using SEARUMS elucidate its ability to pinpoint timelines, epicenters, and socio-economic impacts of avian influenza. This knowledge is invaluable for proactive deployment of countermeasures in order to minimize negative socioeconomic impacts, combat the disease, and avert a pandemic

A modified dynamic evolving neural-fuzzy approach to modeling customer satisfaction for affective design.

Science.gov (United States)

Kwong, C K; Fung, K Y; Jiang, Huimin; Chan, K Y; Siu, Kin Wai Michael

2013-01-01

Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1) the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS) failed to run due to a large number of inputs; (2) the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

A Modified Dynamic Evolving Neural-Fuzzy Approach to Modeling Customer Satisfaction for Affective Design

Directory of Open Access Journals (Sweden)

C. K. Kwong

2013-01-01

Full Text Available Affective design is an important aspect of product development to achieve a competitive edge in the marketplace. A neural-fuzzy network approach has been attempted recently to model customer satisfaction for affective design and it has been proved to be an effective one to deal with the fuzziness and non-linearity of the modeling as well as generate explicit customer satisfaction models. However, such an approach to modeling customer satisfaction has two limitations. First, it is not suitable for the modeling problems which involve a large number of inputs. Second, it cannot adapt to new data sets, given that its structure is fixed once it has been developed. In this paper, a modified dynamic evolving neural-fuzzy approach is proposed to address the above mentioned limitations. A case study on the affective design of mobile phones was conducted to illustrate the effectiveness of the proposed methodology. Validation tests were conducted and the test results indicated that: (1 the conventional Adaptive Neuro-Fuzzy Inference System (ANFIS failed to run due to a large number of inputs; (2 the proposed dynamic neural-fuzzy model outperforms the subtractive clustering-based ANFIS model and fuzzy c-means clustering-based ANFIS model in terms of their modeling accuracy and computational effort.

Improvement in the determination of elemental concentrations in PIXE analyses using artificial neural system

International Nuclear Information System (INIS)

Correa, R.; Dinator, M.I.; Morales, J.R.; Miranda, P.A.; Cancino, S.A.; Vila, I.; Requena, I.

2008-01-01

An Artificial Neural System, ANS, has been designed to operate in the analysis of spectra obtained from a PIXE (Proton Induced X-ray Emissions) application. The special designed ANS was used in the calculation of the concentrations of the major elements in the samples. Neural systems using several feed-forward ANN of similar topology working in parallel were trained with error back propagation algorithm using sets of spectra of known elemental concentrations. Following the training phase of the neural networks, other PIXE spectra were analyzed with this methodology providing unknown elemental concentrations. ANS results were compared with results obtained by traditional computer codes like AXIL and GUPIX, obtaining correlations factors close to one. The rather short time required to process each spectrum, of the order of microseconds, allows fast analysis of a large number of samples. Here we present applications of ANS in the PIXE analyses of samples of organic nature like liver, gills and muscle from fishes. ANS results were compared with elemental concentrations obtained in a previous application where a single ANN was used for each analyzed element. PIXE analyses were performed at the Nuclear Physics Laboratory of the University of Chile, using 2.2 MeV proton beams provided by a Van de Graaff accelerator. (author)

Classification of dried vegetables using computer image analysis and artificial neural networks

Science.gov (United States)

Koszela, K.; Łukomski, M.; Mueller, W.; Górna, K.; Okoń, P.; Boniecki, P.; Zaborowicz, M.; Wojcieszak, D.

2017-07-01

In the recent years, there has been a continuously increasing demand for vegetables and dried vegetables. This trend affects the growth of the dehydration industry in Poland helping to exploit excess production. More and more often dried vegetables are used in various sectors of the food industry, both due to their high nutritional qualities and changes in consumers' food preferences. As we observe an increase in consumer awareness regarding a healthy lifestyle and a boom in health food, there is also an increase in the consumption of such food, which means that the production and crop area can increase further. Among the dried vegetables, dried carrots play a strategic role due to their wide application range and high nutritional value. They contain high concentrations of carotene and sugar which is present in the form of crystals. Carrots are also the vegetables which are most often subjected to a wide range of dehydration processes; this makes it difficult to perform a reliable qualitative assessment and classification of this dried product. The many qualitative properties of dried carrots determining their positive or negative quality assessment include colour and shape. The aim of the research was to develop and implement the model of a computer system for the recognition and classification of freeze-dried, convection-dried and microwave vacuum dried products using the methods of computer image analysis and artificial neural networks.

Cerebral methodology based computing to estimate real phenomena from large-scale nuclear simulation

International Nuclear Information System (INIS)

Suzuki, Yoshio

2011-01-01

Our final goal is to estimate real phenomena from large-scale nuclear simulations by using computing processes. Large-scale simulations mean that they include scale variety and physical complexity so that corresponding experiments and/or theories do not exist. In nuclear field, it is indispensable to estimate real phenomena from simulations in order to improve the safety and security of nuclear power plants. Here, the analysis of uncertainty included in simulations is needed to reveal sensitivity of uncertainty due to randomness, to reduce the uncertainty due to lack of knowledge and to lead a degree of certainty by verification and validation (V and V) and uncertainty quantification (UQ) processes. To realize this, we propose 'Cerebral Methodology based Computing (CMC)' as computing processes with deductive and inductive approaches by referring human reasoning processes. Our idea is to execute deductive and inductive simulations contrasted with deductive and inductive approaches. We have established its prototype system and applied it to a thermal displacement analysis of a nuclear power plant. The result shows that our idea is effective to reduce the uncertainty and to get the degree of certainty. (author)

Comparisons of a Quantum Annealing and Classical Computer Neural Net Approach for Inferring Global Annual CO2 Fluxes over Land

Science.gov (United States)

Halem, M.; Radov, A.; Singh, D.

2017-12-01

Investigations of mid to high latitude atmospheric CO2 show growing amplitudes in seasonal variations over the past several decades. Recent high-resolution satellite measurements of CO2 concentration are now available for three years from the Orbiting Carbon Observatory-2. The Atmospheric Radiation Measurement (ARM) program of DOE has been making long-term CO2-flux measurements (in addition to CO2 concentration and an array of other meteorological quantities) at several towers and mobile sites located around the globe at half-hour frequencies. Recent papers have shown CO2 fluxes inferred by assimilating CO2 observations into ecosystem models are largely inconsistent with station observations. An investigation of how the biosphere has reacted to changes in atmospheric CO2 is essential to our understanding of potential climate-vegetation feedbacks. Thus, new approaches for calculating CO2-flux for assimilation into land surface models are necessary for improving the prediction of annual carbon uptake. In this study, we calculate and compare the predicted CO2 fluxes results employing a Feed Forward Backward Propagation Neural Network model on two architectures, (i) an IBM Minsky Computer node and (ii) a hybrid version of the ARC D-Wave quantum annealing computer. We compare the neural net results of predictions of CO2 flux from ARM station data for three different DOE ecosystem sites; an arid plains near Oklahoma City, a northern arctic site at Barrows AL, and a tropical rainforest site in the Amazon. Training times and predictive results for the calculating annual CO2 flux for the two architectures for each of the three sites are presented. Comparative results of predictions as measured by RMSE and MAE are discussed. Plots and correlations of observed vs predicted CO2 flux are also presented for all three sites. We show the estimated training times for quantum and classical calculations when extended to calculating global annual Carbon Uptake over land. We also

Optimality in Microwave-Assisted Drying of Aloe Vera ( Aloe barbadensis Miller) Gel using Response Surface Methodology and Artificial Neural Network Modeling

Science.gov (United States)

Das, Chandan; Das, Arijit; Kumar Golder, Animes

2016-10-01

The present work illustrates the Microwave-Assisted Drying (MWAD) characteristic of aloe vera gel combined with process optimization and artificial neural network modeling. The influence of microwave power (160-480 W), gel quantity (4-8 g) and drying time (1-9 min) on the moisture ratio was investigated. The drying of aloe gel exhibited typical diffusion-controlled characteristics with a predominant interaction between input power and drying time. Falling rate period was observed for the entire MWAD of aloe gel. Face-centered Central Composite Design (FCCD) developed a regression model to evaluate their effects on moisture ratio. The optimal MWAD conditions were established as microwave power of 227.9 W, sample amount of 4.47 g and 5.78 min drying time corresponding to the moisture ratio of 0.15. A computer-stimulated Artificial Neural Network (ANN) model was generated for mapping between process variables and the desired response. `Levenberg-Marquardt Back Propagation' algorithm with 3-5-1 architect gave the best prediction, and it showed a clear superiority over FCCD.

2D neural hardware versus 3D biological ones

Energy Technology Data Exchange (ETDEWEB)

Beiu, V.

1998-12-31

This paper will present important limitations of hardware neural nets as opposed to biological neural nets (i.e. the real ones). The author starts by discussing neural structures and their biological inspirations, while mentioning the simplifications leading to artificial neural nets. Going further, the focus will be on hardware constraints. The author will present recent results for three different alternatives of implementing neural networks: digital, threshold gate, and analog, while the area and the delay will be related to neurons' fan-in and weights' precision. Based on all of these, it will be shown why hardware implementations cannot cope with their biological inspiration with respect to their power of computation: the mapping onto silicon lacking the third dimension of biological nets. This translates into reduced fan-in, and leads to reduced precision. The main conclusion is that one is faced with the following alternatives: (1) try to cope with the limitations imposed by silicon, by speeding up the computation of the elementary silicon neurons; (2) investigate solutions which would allow one to use the third dimension, e.g. using optical interconnections.

Neural networks: Application to medical imaging

Science.gov (United States)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Computational models of neuromodulation.

Science.gov (United States)

Fellous, J M; Linster, C

1998-05-15

Computational modeling of neural substrates provides an excellent theoretical framework for the understanding of the computational roles of neuromodulation. In this review, we illustrate, with a large number of modeling studies, the specific computations performed by neuromodulation in the context of various neural models of invertebrate and vertebrate preparations. We base our characterization of neuromodulations on their computational and functional roles rather than on anatomical or chemical criteria. We review the main framework in which neuromodulation has been studied theoretically (central pattern generation and oscillations, sensory processing, memory and information integration). Finally, we present a detailed mathematical overview of how neuromodulation has been implemented at the single cell and network levels in modeling studies. Overall, neuromodulation is found to increase and control computational complexity.

RNA secondary structure prediction using soft computing.

Science.gov (United States)

Ray, Shubhra Sankar; Pal, Sankar K

2013-01-01

Prediction of RNA structure is invaluable in creating new drugs and understanding genetic diseases. Several deterministic algorithms and soft computing-based techniques have been developed for more than a decade to determine the structure from a known RNA sequence. Soft computing gained importance with the need to get approximate solutions for RNA sequences by considering the issues related with kinetic effects, cotranscriptional folding, and estimation of certain energy parameters. A brief description of some of the soft computing-based techniques, developed for RNA secondary structure prediction, is presented along with their relevance. The basic concepts of RNA and its different structural elements like helix, bulge, hairpin loop, internal loop, and multiloop are described. These are followed by different methodologies, employing genetic algorithms, artificial neural networks, and fuzzy logic. The role of various metaheuristics, like simulated annealing, particle swarm optimization, ant colony optimization, and tabu search is also discussed. A relative comparison among different techniques, in predicting 12 known RNA secondary structures, is presented, as an example. Future challenging issues are then mentioned.

Short-Term Load Forecasting Model Based on Quantum Elman Neural Networks

Directory of Open Access Journals (Sweden)

Zhisheng Zhang

2016-01-01

Full Text Available Short-term load forecasting model based on quantum Elman neural networks was constructed in this paper. The quantum computation and Elman feedback mechanism were integrated into quantum Elman neural networks. Quantum computation can effectively improve the approximation capability and the information processing ability of the neural networks. Quantum Elman neural networks have not only the feedforward connection but also the feedback connection. The feedback connection between the hidden nodes and the context nodes belongs to the state feedback in the internal system, which has formed specific dynamic memory performance. Phase space reconstruction theory is the theoretical basis of constructing the forecasting model. The training samples are formed by means of K-nearest neighbor approach. Through the example simulation, the testing results show that the model based on quantum Elman neural networks is better than the model based on the quantum feedforward neural network, the model based on the conventional Elman neural network, and the model based on the conventional feedforward neural network. So the proposed model can effectively improve the prediction accuracy. The research in the paper makes a theoretical foundation for the practical engineering application of the short-term load forecasting model based on quantum Elman neural networks.

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

Science.gov (United States)

Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

Science.gov (United States)

Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

A dose to curie conversion methodology

International Nuclear Information System (INIS)

Stowe, P.A.

1987-01-01

Development of the computer code RadCAT (Radioactive waste Classification And Tracking) has led to the development of a simple dose rate to curie content conversion methodology for containers with internally distributed radioactive material. It was determined early on that, if possible, the computerized dose rate to curie evaluation model employed in RadCAT should yield the same results as the hand method utilized and specified in plant procedures. A review of current industry practices indicated two distinct types of computational methodologies are presently in use. The most common methods are computer based calculations utilizing complex mathematical models specifically established for various containers geometries. This type of evaluation is tedious, however, and does not lend itself to repetition by hand. The second method of evaluation, therefore, is simplified expressions that sacrifice accuracy for ease of computation, and generally over estimate container curie content. To meet the aforementioned criterion current computer based models were deemed unacceptably complex and hand computational methods to be too inaccurate for serious consideration. The contact dose rate/curie content analysis methodology presented herein provides an equation that is easy to use in hand calculations yet provides accuracy equivalent to other computer based computations

An integrated impact assessment and weighting methodology: evaluation of the environmental consequences of computer display technology substitution.

Science.gov (United States)

Zhou, Xiaoying; Schoenung, Julie M

2007-04-01

Computer display technology is currently in a state of transition, as the traditional technology of cathode ray tubes is being replaced by liquid crystal display flat-panel technology. Technology substitution and process innovation require the evaluation of the trade-offs among environmental impact, cost, and engineering performance attributes. General impact assessment methodologies, decision analysis and management tools, and optimization methods commonly used in engineering cannot efficiently address the issues needed for such evaluation. The conventional Life Cycle Assessment (LCA) process often generates results that can be subject to multiple interpretations, although the advantages of the LCA concept and framework obtain wide recognition. In the present work, the LCA concept is integrated with Quality Function Deployment (QFD), a popular industrial quality management tool, which is used as the framework for the development of our integrated model. The problem of weighting is addressed by using pairwise comparison of stakeholder preferences. Thus, this paper presents a new integrated analytical approach, Integrated Industrial Ecology Function Deployment (I2-EFD), to assess the environmental behavior of alternative technologies in correlation with their performance and economic characteristics. Computer display technology is used as the case study to further develop our methodology through the modification and integration of various quality management tools (e.g., process mapping, prioritization matrix) and statistical methods (e.g., multi-attribute analysis, cluster analysis). Life cycle thinking provides the foundation for our methodology, as we utilize a published LCA report, which stopped at the characterization step, as our starting point. Further, we evaluate the validity and feasibility of our methodology by considering uncertainty and conducting sensitivity analysis.

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

International Nuclear Information System (INIS)

Husam Fayiz, Al Masri

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms. (paper)

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

Neural dynamics in reconfigurable silicon.

Science.gov (United States)

Basu, A; Ramakrishnan, S; Petre, C; Koziol, S; Brink, S; Hasler, P E

2010-10-01

A neuromorphic analog chip is presented that is capable of implementing massively parallel neural computations while retaining the programmability of digital systems. We show measurements from neurons with Hopf bifurcations and integrate and fire neurons, excitatory and inhibitory synapses, passive dendrite cables, coupled spiking neurons, and central pattern generators implemented on the chip. This chip provides a platform for not only simulating detailed neuron dynamics but also uses the same to interface with actual cells in applications such as a dynamic clamp. There are 28 computational analog blocks (CAB), each consisting of ion channels with tunable parameters, synapses, winner-take-all elements, current sources, transconductance amplifiers, and capacitors. There are four other CABs which have programmable bias generators. The programmability is achieved using floating gate transistors with on-chip programming control. The switch matrix for interconnecting the components in CABs also consists of floating-gate transistors. Emphasis is placed on replicating the detailed dynamics of computational neural models. Massive computational area efficiency is obtained by using the reconfigurable interconnect as synaptic weights, resulting in more than 50 000 possible 9-b accurate synapses in 9 mm(2).

High level cognitive information processing in neural networks

Science.gov (United States)

Barnden, John A.; Fields, Christopher A.

1992-01-01

Two related research efforts were addressed: (1) high-level connectionist cognitive modeling; and (2) local neural circuit modeling. The goals of the first effort were to develop connectionist models of high-level cognitive processes such as problem solving or natural language understanding, and to understand the computational requirements of such models. The goals of the second effort were to develop biologically-realistic model of local neural circuits, and to understand the computational behavior of such models. In keeping with the nature of NASA's Innovative Research Program, all the work conducted under the grant was highly innovative. For instance, the following ideas, all summarized, are contributions to the study of connectionist/neural networks: (1) the temporal-winner-take-all, relative-position encoding, and pattern-similarity association techniques; (2) the importation of logical combinators into connection; (3) the use of analogy-based reasoning as a bridge across the gap between the traditional symbolic paradigm and the connectionist paradigm; and (4) the application of connectionism to the domain of belief representation/reasoning. The work on local neural circuit modeling also departs significantly from the work of related researchers. In particular, its concentration on low-level neural phenomena that could support high-level cognitive processing is unusual within the area of biological local circuit modeling, and also serves to expand the horizons of the artificial neural net field.

Using brain stimulation to disentangle neural correlates of conscious vision.

Science.gov (United States)

de Graaf, Tom A; Sack, Alexander T

2014-01-01

Research into the neural correlates of consciousness (NCCs) has blossomed, due to the advent of new and increasingly sophisticated brain research tools. Neuroimaging has uncovered a variety of brain processes that relate to conscious perception, obtained in a range of experimental paradigms. But methods such as functional magnetic resonance imaging or electroencephalography do not always afford inference on the functional role these brain processes play in conscious vision. Such empirical NCCs could reflect neural prerequisites, neural consequences, or neural substrates of a conscious experience. Here, we take a closer look at the use of non-invasive brain stimulation (NIBS) techniques in this context. We discuss and review how NIBS methodology can enlighten our understanding of brain mechanisms underlying conscious vision by disentangling the empirical NCCs.

Non-linear feedback neural networks VLSI implementations and applications

CERN Document Server

Ansari, Mohd Samar

2014-01-01

This book aims to present a viable alternative to the Hopfield Neural Network (HNN) model for analog computation. It is well known that the standard HNN suffers from problems of convergence to local minima, and requirement of a large number of neurons and synaptic weights. Therefore, improved solutions are needed. The non-linear synapse neural network (NoSyNN) is one such possibility and is discussed in detail in this book. This book also discusses the applications in computationally intensive tasks like graph coloring, ranking, and linear as well as quadratic programming. The material in the book is useful to students, researchers and academician working in the area of analog computation.

Developing Scene Understanding Neural Software for Realistic Autonomous Outdoor Missions

Science.gov (United States)

2017-09-01

computer using a single graphics processing unit (GPU). To the best of our knowledge, an implementation of the open-source Python -based AlexNet CNN on...1. Introduction Neurons in the brain enable us to understand scenes by assessing the spatial, temporal, and feature relations of objects in the...effort to use computer neural networks to augment human neural intelligence to improve our scene understanding (Krizhevsky et al. 2012; Zhou et al

Jealousy: novel methods and neural correlates.

Science.gov (United States)

Harmon-Jones, Eddie; Peterson, Carly K; Harris, Christine R

2009-02-01

Because of the difficulties surrounding the evocation of jealousy, past research has relied on reactions to hypothetical scenarios and recall of past experiences of jealousy. Both methodologies have limitations, however. The present research was designed to develop a method of evoking jealousy in the laboratory that would be well controlled, ethically permissible, and psychologically meaningful. Study 1 demonstrated that jealousy could be evoked in a modified version of K. D. Williams' (2007) Cyberball ostracism paradigm in which the rejecting person was computer-generated. Study 2, the first to examine neural activity during the active experience of jealousy, tested whether experienced jealousy was associated with greater relative left or right frontal cortical activation. The findings revealed that the experience of jealousy was correlated with greater relative left frontal cortical activation toward the "sexually" desired partner. This pattern of activation suggests that jealousy is associated with approach motivation. Taken together, the present studies developed a laboratory paradigm for the study of jealousy that should help foster research on one of the most social of emotions. (c) 2009 APA, all rights reserved

Verification of a hybrid adjoint methodology in Titan for single photon emission computed tomography - 316

International Nuclear Information System (INIS)

Royston, K.; Haghighat, A.; Yi, C.

2010-01-01

The hybrid deterministic transport code TITAN is being applied to a Single Photon Emission Computed Tomography (SPECT) simulation of a myocardial perfusion study. The TITAN code's hybrid methodology allows the use of a discrete ordinates solver in the phantom region and a characteristics method solver in the collimator region. Currently we seek to validate the adjoint methodology in TITAN for this application using a SPECT model that has been created in the MCNP5 Monte Carlo code. The TITAN methodology was examined based on the response of a single voxel detector placed in front of the heart with and without collimation. For the case without collimation, the TITAN response for single voxel-sized detector had a -9.96% difference relative to the MCNP5 response. To simulate collimation, the adjoint source was specified in directions located within the collimator acceptance angle. For a single collimator hole with a diameter matching the voxel dimension, a difference of -0.22% was observed. Comparisons to groupings of smaller collimator holes of two different sizes resulted in relative differences of 0.60% and 0.12%. The number of adjoint source directions within an acceptance angle was increased and showed no significant change in accuracy. Our results indicate that the hybrid adjoint methodology of TITAN yields accurate solutions greater than a factor of two faster than MCNP5. (authors)

Mass reconstruction with a neural network

International Nuclear Information System (INIS)

Loennblad, L.; Peterson, C.; Roegnvaldsson, T.

1992-01-01

A feed-forward neural network method is developed for reconstructing the invariant mass of hadronic jets appearing in a calorimeter. The approach is illustrated in W→qanti q, where W-bosons are produced in panti p reactions at SPS collider energies. The neural network method yields results that are superior to conventional methods. This neural network application differs from the classification ones in the sense that an analog number (the mass) is computed by the network, rather than a binary decision being made. As a by-product our application clearly demonstrates the need for using 'intelligent' variables in instances when the amount of training instances is limited. (orig.)

Neural Computations for Biosonar Imaging in the Big Brown Bat

Science.gov (United States)

Saillant, Prestor Augusto

1995-11-01

The study of the intimate relationship between space and time has taken many forms, ranging from the Theory of Relativity down to the problem of avoiding traffic jams. However, nowhere has this relationship been more fully developed and exploited than in dolphins and bats, which have the ability to utilize biosonar. This thesis describes research on the behavioral and computational basis of echolocation carried out in order to explore the neural mechanisms which may account for the space-time constructs which are of psychological importance to the big brown bat. The SCAT (Spectrogram Correlation and Transformation) computational model was developed to provide a framework for understanding the computational requirements of FM echolocation as determined from psychophysical experiments (i.e., high resolution imaging) and neurobiological constraints (Saillant et al., 1993). The second part of the thesis consisted in developing a new behavioral paradigm for simultaneously studying acoustic behavior and flight behavior of big brown bats in pursuit of stationary or moving targets. In the third part of the thesis a complete acoustic "artificial bat" was constructed, making use of the SCAT process. The development of the artificial bat allowed us to begin experimentation with real world echoes from various targets, in order to gain a better appreciation for the additional complexities and sources of information encountered by bats in flight. Finally, the continued development of the SCAT model has allowed a deeper understanding of the phenomenon of "time expansion" and of the phenomenon of phase sensitivity in the ultrasonic range. Time expansion, first predicted through the use of the SCAT model, and later found in auditory local evoked potential recordings, opens up a new realm of information processing and representation in the brain which as of yet has not been considered. It seems possible, from the work in the auditory system, that time expansion may provide a novel

Learning from neural control.

Science.gov (United States)

Wang, Cong; Hill, David J

2006-01-01

One of the amazing successes of biological systems is their ability to "learn by doing" and so adapt to their environment. In this paper, first, a deterministic learning mechanism is presented, by which an appropriately designed adaptive neural controller is capable of learning closed-loop system dynamics during tracking control to a periodic reference orbit. Among various neural network (NN) architectures, the localized radial basis function (RBF) network is employed. A property of persistence of excitation (PE) for RBF networks is established, and a partial PE condition of closed-loop signals, i.e., the PE condition of a regression subvector constructed out of the RBFs along a periodic state trajectory, is proven to be satisfied. Accurate NN approximation for closed-loop system dynamics is achieved in a local region along the periodic state trajectory, and a learning ability is implemented during a closed-loop feedback control process. Second, based on the deterministic learning mechanism, a neural learning control scheme is proposed which can effectively recall and reuse the learned knowledge to achieve closed-loop stability and improved control performance. The significance of this paper is that the presented deterministic learning mechanism and the neural learning control scheme provide elementary components toward the development of a biologically-plausible learning and control methodology. Simulation studies are included to demonstrate the effectiveness of the approach.

Deep Gate Recurrent Neural Network

Science.gov (United States)

2016-11-22

and Fred Cummins. Learning to forget: Continual prediction with lstm . Neural computation, 12(10):2451–2471, 2000. Alex Graves. Generating sequences...DSGU) and Simple Gated Unit (SGU), which are structures for learning long-term dependencies. Compared to traditional Long Short-Term Memory ( LSTM ) and...Gated Recurrent Unit (GRU), both structures require fewer parameters and less computation time in sequence classification tasks. Unlike GRU and LSTM

Classes of feedforward neural networks and their circuit complexity

NARCIS (Netherlands)

Shawe-Taylor, John S.; Anthony, Martin H.G.; Kern, Walter

1992-01-01

This paper aims to place neural networks in the context of boolean circuit complexity. We define appropriate classes of feedforward neural networks with specified fan-in, accuracy of computation and depth and using techniques of communication complexity proceed to show that the classes fit into a

Robust model predictive control of nonlinear systems with unmodeled dynamics and bounded uncertainties based on neural networks.

Science.gov (United States)

Yan, Zheng; Wang, Jun

2014-03-01

This paper presents a neural network approach to robust model predictive control (MPC) for constrained discrete-time nonlinear systems with unmodeled dynamics affected by bounded uncertainties. The exact nonlinear model of underlying process is not precisely known, but a partially known nominal model is available. This partially known nonlinear model is first decomposed to an affine term plus an unknown high-order term via Jacobian linearization. The linearization residue combined with unmodeled dynamics is then modeled using an extreme learning machine via supervised learning. The minimax methodology is exploited to deal with bounded uncertainties. The minimax optimization problem is reformulated as a convex minimization problem and is iteratively solved by a two-layer recurrent neural network. The proposed neurodynamic approach to nonlinear MPC improves the computational efficiency and sheds a light for real-time implementability of MPC technology. Simulation results are provided to substantiate the effectiveness and characteristics of the proposed approach.

Evaluating the Visualization of What a Deep Neural Network Has Learned.

Science.gov (United States)

Samek, Wojciech; Binder, Alexander; Montavon, Gregoire; Lapuschkin, Sebastian; Muller, Klaus-Robert

Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and interpret the reasoning embodied in a DNN for a single test image. These methods quantify the "importance" of individual pixels with respect to the classification decision and allow a visualization in terms of a heatmap in pixel/input space. While the usefulness of heatmaps can be judged subjectively by a human, an objective quality measure is missing. In this paper, we present a general methodology based on region perturbation for evaluating ordered collections of pixels such as heatmaps. We compare heatmaps computed by three different methods on the SUN397, ILSVRC2012, and MIT Places data sets. Our main result is that the recently proposed layer-wise relevance propagation algorithm qualitatively and quantitatively provides a better explanation of what made a DNN arrive at a particular classification decision than the sensitivity-based approach or the deconvolution method. We provide theoretical arguments to explain this result and discuss its practical implications. Finally, we investigate the use of heatmaps for unsupervised assessment of the neural network performance.Deep neural networks (DNNs) have demonstrated impressive performance in complex machine learning tasks such as image classification or speech recognition. However, due to their multilayer nonlinear structure, they are not transparent, i.e., it is hard to grasp what makes them arrive at a particular classification or recognition decision, given a new unseen data sample. Recently, several approaches have been proposed enabling one to understand and

Learning Universal Computations with Spikes

Science.gov (United States)

Thalmeier, Dominik; Uhlmann, Marvin; Kappen, Hilbert J.; Memmesheimer, Raoul-Martin

2016-01-01

Providing the neurobiological basis of information processing in higher animals, spiking neural networks must be able to learn a variety of complicated computations, including the generation of appropriate, possibly delayed reactions to inputs and the self-sustained generation of complex activity patterns, e.g. for locomotion. Many such computations require previous building of intrinsic world models. Here we show how spiking neural networks may solve these different tasks. Firstly, we derive constraints under which classes of spiking neural networks lend themselves to substrates of powerful general purpose computing. The networks contain dendritic or synaptic nonlinearities and have a constrained connectivity. We then combine such networks with learning rules for outputs or recurrent connections. We show that this allows to learn even difficult benchmark tasks such as the self-sustained generation of desired low-dimensional chaotic dynamics or memory-dependent computations. Furthermore, we show how spiking networks can build models of external world systems and use the acquired knowledge to control them. PMID:27309381

COMPUTATIONAL ANALYSIS BASED ON ARTIFICIAL NEURAL NETWORKS FOR AIDING IN DIAGNOSING OSTEOARTHRITIS OF THE LUMBAR SPINE

Science.gov (United States)

Veronezi, Carlos Cassiano Denipotti; de Azevedo Simões, Priscyla Waleska Targino; dos Santos, Robson Luiz; da Rocha, Edroaldo Lummertz; Meláo, Suelen; de Mattos, Merisandra Côrtes; Cechinel, Cristian

2015-01-01

Objective: To ascertain the advantages of applying artificial neural networks to recognize patterns on lumbar spine radiographies in order to aid in the process of diagnosing primary osteoarthritis. Methods: This was a cross-sectional descriptive analytical study with a quantitative approach and an emphasis on diagnosis. The training set was composed of images collected between January and July 2009 from patients who had undergone lateral-view digital radiographies of the lumbar spine, which were provided by a radiology clinic located in the municipality of Criciúma (SC). Out of the total of 260 images gathered, those with distortions, those presenting pathological conditions that altered the architecture of the lumbar spine and those with patterns that were difficult to characterize were discarded, resulting in 206 images. The image data base (n = 206) was then subdivided, resulting in 68 radiographies for the training stage, 68 images for tests and 70 for validation. A hybrid neural network based on Kohonen self-organizing maps and on Multilayer Perceptron networks was used. Results: After 90 cycles, the validation was carried out on the best results, achieving accuracy of 62.85%, sensitivity of 65.71% and specificity of 60%. Conclusions: Even though the effectiveness shown was moderate, this study is still innovative. The values show that the technique used has a promising future, pointing towards further studies on image and cycle processing methodology with a larger quantity of radiographies. PMID:27027010

An Algebra-Based Introductory Computational Neuroscience Course with Lab.

Science.gov (United States)

Fink, Christian G

2017-01-01

A course in computational neuroscience has been developed at Ohio Wesleyan University which requires no previous experience with calculus or computer programming, and which exposes students to theoretical models of neural information processing and techniques for analyzing neural data. The exploration of theoretical models of neural processes is conducted in the classroom portion of the course, while data analysis techniques are covered in lab. Students learn to program in MATLAB and are offered the opportunity to conclude the course with a final project in which they explore a topic of their choice within computational neuroscience. Results from a questionnaire administered at the beginning and end of the course indicate significant gains in student facility with core concepts in computational neuroscience, as well as with analysis techniques applied to neural data.

Anomaly detection in an automated safeguards system using neural networks

International Nuclear Information System (INIS)

Whiteson, R.; Howell, J.A.

1992-01-01

An automated safeguards system must be able to detect an anomalous event, identify the nature of the event, and recommend a corrective action. Neural networks represent a new way of thinking about basic computational mechanisms for intelligent information processing. In this paper, we discuss the issues involved in applying a neural network model to the first step of this process: anomaly detection in materials accounting systems. We extend our previous model to a 3-tank problem and compare different neural network architectures and algorithms. We evaluate the computational difficulties in training neural networks and explore how certain design principles affect the problems. The issues involved in building a neural network architecture include how the information flows, how the network is trained, how the neurons in a network are connected, how the neurons process information, and how the connections between neurons are modified. Our approach is based on the demonstrated ability of neural networks to model complex, nonlinear, real-time processes. By modeling the normal behavior of the processes, we can predict how a system should be behaving and, therefore, detect when an abnormality occurs

Deep Learning Neural Networks in Cybersecurity - Managing Malware with AI

OpenAIRE

Rayle, Keith

2017-01-01

There’s a lot of talk about the benefits of deep learning (neural networks) and how it’s the new electricity that will power us into the future. Medical diagnosis, computer vision and speech recognition are all examples of use-cases where neural networks are being applied in our everyday business environment. This begs the question…what are the uses of neural-network applications for cyber security? How does the AI process work when applying neural networks to detect malicious software bombar...

Runoff Modelling in Urban Storm Drainage by Neural Networks

DEFF Research Database (Denmark)

Rasmussen, Michael R.; Brorsen, Michael; Schaarup-Jensen, Kjeld

1995-01-01

A neural network is used to simulate folw and water levels in a sewer system. The calibration of th neural network is based on a few measured events and the network is validated against measureed events as well as flow simulated with the MOUSE model (Lindberg and Joergensen, 1986). The neural...... network is used to compute flow or water level at selected points in the sewer system, and to forecast the flow from a small residential area. The main advantages of the neural network are the build-in self calibration procedure and high speed performance, but the neural network cannot be used to extract...... knowledge of the runoff process. The neural network was found to simulate 150 times faster than e.g. the MOUSE model....

Proposal for an All-Spin Artificial Neural Network: Emulating Neural and Synaptic Functionalities Through Domain Wall Motion in Ferromagnets.

Science.gov (United States)

Sengupta, Abhronil; Shim, Yong; Roy, Kaushik

2016-12-01

Non-Boolean computing based on emerging post-CMOS technologies can potentially pave the way for low-power neural computing platforms. However, existing work on such emerging neuromorphic architectures have either focused on solely mimicking the neuron, or the synapse functionality. While memristive devices have been proposed to emulate biological synapses, spintronic devices have proved to be efficient at performing the thresholding operation of the neuron at ultra-low currents. In this work, we propose an All-Spin Artificial Neural Network where a single spintronic device acts as the basic building block of the system. The device offers a direct mapping to synapse and neuron functionalities in the brain while inter-layer network communication is accomplished via CMOS transistors. To the best of our knowledge, this is the first demonstration of a neural architecture where a single nanoelectronic device is able to mimic both neurons and synapses. The ultra-low voltage operation of low resistance magneto-metallic neurons enables the low-voltage operation of the array of spintronic synapses, thereby leading to ultra-low power neural architectures. Device-level simulations, calibrated to experimental results, was used to drive the circuit and system level simulations of the neural network for a standard pattern recognition problem. Simulation studies indicate energy savings by  ∼  100× in comparison to a corresponding digital/analog CMOS neuron implementation.

Development of methodology and computer programs for the ground response spectrum and the probabilistic seismic hazard analysis

Energy Technology Data Exchange (ETDEWEB)

Kim, Joon Kyoung [Semyung Univ., Research Institute of Industrial Science and Technol , Jecheon (Korea, Republic of)

1996-12-15

Objective of this study is to investigate and develop the methodologies and corresponding computer codes, compatible to the domestic seismological and geological environments, for estimating ground response spectrum and probabilistic seismic hazard. Using the PSHA computer program, the Cumulative Probability Functions(CPDF) and Probability Functions (PDF) of the annual exceedence have been investigated for the analysis of the uncertainty space of the annual probability at ten interested seismic hazard levels (0.1 g to 0.99 g). The cumulative provability functions and provability functions of the annual exceedence have been also compared to those results from the different input parameter spaces.

Gas ultracentrifuge separative parameters modeling using hybrid neural networks

International Nuclear Information System (INIS)

Crus, Maria Ursulina de Lima

2005-01-01

A hybrid neural network is developed for the calculation of the separative performance of an ultracentrifuge. A feed forward neural network is trained to estimate the internal flow parameters of a gas ultracentrifuge, and then these parameters are applied in the diffusion equation. For this study, a 573 experimental data set is used to establish the relation between the separative performance and the controlled variables. The process control variables considered are: the feed flow rate F, the cut θ and the product pressure Pp. The mechanical arrangements consider the radial waste scoop dimension, the rotating baffle size D s and the axial feed location Z E . The methodology was validated through the comparison of the calculated separative performance with experimental values. This methodology may be applied to other processes, just by adapting the phenomenological procedures. (author)

Methodology for Computational Fluid Dynamic Validation for Medical Use: Application to Intracranial Aneurysm.

Science.gov (United States)

Paliwal, Nikhil; Damiano, Robert J; Varble, Nicole A; Tutino, Vincent M; Dou, Zhongwang; Siddiqui, Adnan H; Meng, Hui

2017-12-01

Computational fluid dynamics (CFD) is a promising tool to aid in clinical diagnoses of cardiovascular diseases. However, it uses assumptions that simplify the complexities of the real cardiovascular flow. Due to high-stakes in the clinical setting, it is critical to calculate the effect of these assumptions in the CFD simulation results. However, existing CFD validation approaches do not quantify error in the simulation results due to the CFD solver's modeling assumptions. Instead, they directly compare CFD simulation results against validation data. Thus, to quantify the accuracy of a CFD solver, we developed a validation methodology that calculates the CFD model error (arising from modeling assumptions). Our methodology identifies independent error sources in CFD and validation experiments, and calculates the model error by parsing out other sources of error inherent in simulation and experiments. To demonstrate the method, we simulated the flow field of a patient-specific intracranial aneurysm (IA) in the commercial CFD software star-ccm+. Particle image velocimetry (PIV) provided validation datasets for the flow field on two orthogonal planes. The average model error in the star-ccm+ solver was 5.63 ± 5.49% along the intersecting validation line of the orthogonal planes. Furthermore, we demonstrated that our validation method is superior to existing validation approaches by applying three representative existing validation techniques to our CFD and experimental dataset, and comparing the validation results. Our validation methodology offers a streamlined workflow to extract the "true" accuracy of a CFD solver.

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

Prediction of two-phase mixture density using artificial neural networks

International Nuclear Information System (INIS)

Lombardi, C.; Mazzola, A.

1997-01-01

In nuclear power plants, the density of boiling mixtures has a significant relevance due to its influence on the neutronic balance, the power distribution and the reactor dynamics. Since the determination of the two-phase mixture density on a purely analytical basis is in fact impractical in many situations of interest, heuristic relationships have been developed based on the parameters describing the two-phase system. However, the best or even a good structure for the correlation cannot be determined in advance, also considering that it is usually desired to represent the experimental data with the most compact equation. A possible alternative to empirical correlations is the use of artificial neural networks, which allow one to model complex systems without requiring the explicit formulation of the relationships existing among the variables. In this work, the neural network methodology was applied to predict the density data of two-phase mixtures up-flowing in adiabatic channels under different experimental conditions. The trained network predicts the density data with a root-mean-square error of 5.33%, being ∼ 93% of the data points predicted within 10%. When compared with those of two conventional well-proven correlations, i.e. the Zuber-Findlay and the CISE correlations, the neural network performances are significantly better. In spite of the good accuracy of the neural network predictions, the 'black-box' characteristic of the neural model does not allow an easy physical interpretation of the knowledge integrated in the network weights. Therefore, the neural network methodology has the advantage of not requiring a formal correlation structure and of giving very accurate results, but at the expense of a loss of model transparency. (author)

Artificial Neural Networks For Hadron Hadron Cross-sections

International Nuclear Information System (INIS)

ELMashad, M.; ELBakry, M.Y.; Tantawy, M.; Habashy, D.M.

2011-01-01

In recent years artificial neural networks (ANN ) have emerged as a mature and viable framework with many applications in various areas. Artificial neural networks theory is sometimes used to refer to a branch of computational science that uses neural networks as models to either simulate or analyze complex phenomena and/or study the principles of operation of neural networks analytically. In this work a model of hadron- hadron collision using the ANN technique is present, the hadron- hadron based ANN model calculates the cross sections of hadron- hadron collision. The results amply demonstrate the feasibility of such new technique in extracting the collision features and prove its effectiveness

Computer-Based Cognitive Training for Executive Functions after Stroke: A Systematic Review

Science.gov (United States)

van de Ven, Renate M.; Murre, Jaap M. J.; Veltman, Dick J.; Schmand, Ben A.

2016-01-01

Background: Stroke commonly results in cognitive impairments in working memory, attention, and executive function, which may be restored with appropriate training programs. Our aim was to systematically review the evidence for computer-based cognitive training of executive dysfunctions. Methods: Studies were included if they concerned adults who had suffered stroke or other types of acquired brain injury, if the intervention was computer training of executive functions, and if the outcome was related to executive functioning. We searched in MEDLINE, PsycINFO, Web of Science, and The Cochrane Library. Study quality was evaluated based on the CONSORT Statement. Treatment effect was evaluated based on differences compared to pre-treatment and/or to a control group. Results: Twenty studies were included. Two were randomized controlled trials that used an active control group. The other studies included multiple baselines, a passive control group, or were uncontrolled. Improvements were observed in tasks similar to the training (near transfer) and in tasks dissimilar to the training (far transfer). However, these effects were not larger in trained than in active control groups. Two studies evaluated neural effects and found changes in both functional and structural connectivity. Most studies suffered from methodological limitations (e.g., lack of an active control group and no adjustment for multiple testing) hampering differentiation of training effects from spontaneous recovery, retest effects, and placebo effects. Conclusions: The positive findings of most studies, including neural changes, warrant continuation of research in this field, but only if its methodological limitations are addressed. PMID:27148007

Dynamical networks: Finding, measuring, and tracking neural population activity using network science

Directory of Open Access Journals (Sweden)

Mark D. Humphries

2017-12-01

Full Text Available Systems neuroscience is in a headlong rush to record from as many neurons at the same time as possible. As the brain computes and codes using neuron populations, it is hoped these data will uncover the fundamentals of neural computation. But with hundreds, thousands, or more simultaneously recorded neurons come the inescapable problems of visualizing, describing, and quantifying their interactions. Here I argue that network science provides a set of scalable, analytical tools that already solve these problems. By treating neurons as nodes and their interactions as links, a single network can visualize and describe an arbitrarily large recording. I show that with this description we can quantify the effects of manipulating a neural circuit, track changes in population dynamics over time, and quantitatively define theoretical concepts of neural populations such as cell assemblies. Using network science as a core part of analyzing population recordings will thus provide both qualitative and quantitative advances to our understanding of neural computation.

Beam-orientation customization using an artificial neural network

International Nuclear Information System (INIS)

Rowbottom, C.G.; Webb, S.; Oldham, M.

1999-01-01

A methodology for the constrained customization of coplanar beam orientations in radiotherapy treatment planning using an artificial neural network (ANN) has been developed. The geometry of the patients, with cancer of the prostate, was modelled by reducing the external contour, planning target volume (PTV) and organs at risk (OARs) to a set of cuboids. The coordinates and size of the cuboids were given to the ANN as inputs. A previously developed beam-orientation constrained-customization (BOCC) scheme employing a conventional computer algorithm was used to determine the customized beam orientations in a training set containing 45 patient datasets. Twelve patient datasets not involved in the training of the artificial neural network were used to test whether the ANN was able to map the inputs to customized beam orientations. Improvements from the customized beam orientations were compared with standard treatment plans with fixed gantry angles and plans produced from the BOCC scheme. The ANN produced customized beam orientations within 5 deg. of the BOCC scheme in 62.5% of cases. The average difference in the beam orientations produced by the ANN and the BOCC scheme was 7.7 deg. (±1.7, 1 SD). Compared with the standard treatment plans, the BOCC scheme produced plans with an increase in the average tumour control probability (TCP) of 5.7% (±1.4, 1 SD) whilst the ANN generated plans increased the average TCP by 3.9% (±1.3, 1 SD). Both figures refer to the TCP at a fixed rectal normal tissue complication probability (NTCP) of 1%. In conclusion, even using a very simple model for the geometry of the patient, an ANN was able to produce beam orientations that were similar to those produced by a conventional computer algorithm. (author)

Sensitivity analysis of linear programming problem through a recurrent neural network

Science.gov (United States)

Das, Raja

2017-11-01

In this paper we study the recurrent neural network for solving linear programming problems. To achieve optimality in accuracy and also in computational effort, an algorithm is presented. We investigate the sensitivity analysis of linear programming problem through the neural network. A detailed example is also presented to demonstrate the performance of the recurrent neural network.

Nutrients interaction investigation to improve Monascus purpureus FTC5391 growth rate using Response Surface Methodology and Artificial Neural Network

Directory of Open Access Journals (Sweden)

Mohamad, R.

2013-01-01

Full Text Available Aims: Two vital factors, certain environmental conditions and nutrients as a source of energy are entailed for successful growth and reproduction of microorganisms. Manipulation of nutritional requirement is the simplest and most effectual strategy to stimulate and enhance the activity of microorganisms. Methodology and Results: In this study, response surface methodology (RSM and artificial neural network (ANN were employed to optimize the carbon and nitrogen sources in order to improve growth rate of Monascus purpureus FTC5391,a new local isolate. The best models for optimization of growth rate were a multilayer full feed-forward incremental back propagation network, and a modified response surface model using backward elimination. The optimum condition for cell mass production was: sucrose 2.5%, yeast extract 0.045%, casamino acid 0.275%, sodium nitrate 0.48%, potato starch 0.045%, dextrose 1%, potassium nitrate 0.57%. The experimental cell mass production using this optimal condition was 21 mg/plate/12days, which was 2.2-fold higher than the standard condition (sucrose 5%, yeast extract 0.15%, casamino acid 0.25%, sodium nitrate 0.3%, potato starch 0.2%, dextrose 1%, potassium nitrate 0.3%. Conclusion, significance and impact of study: The results of RSM and ANN showed that all carbon and nitrogen sources tested had significant effect on growth rate (P-value < 0.05. In addition the use of RSM and ANN alongside each other provided a proper growth prediction model.

The neural processing of voluntary completed, real and virtual violent and nonviolent computer game scenarios displaying predefined actions in gamers and nongamers.

Science.gov (United States)

Regenbogen, Christina; Herrmann, Manfred; Fehr, Thorsten

2010-01-01

Studies investigating the effects of violent computer and video game playing have resulted in heterogeneous outcomes. It has been assumed that there is a decreased ability to differentiate between virtuality and reality in people that play these games intensively. FMRI data of a group of young males with (gamers) and without (controls) a history of long-term violent computer game playing experience were obtained during the presentation of computer game and realistic video sequences. In gamers the processing of real violence in contrast to nonviolence produced activation clusters in right inferior frontal, left lingual and superior temporal brain regions. Virtual violence activated a network comprising bilateral inferior frontal, occipital, postcentral, right middle temporal, and left fusiform regions. Control participants showed extended left frontal, insula and superior frontal activations during the processing of real, and posterior activations during the processing of virtual violent scenarios. The data suggest that the ability to differentiate automatically between real and virtual violence has not been diminished by a long-term history of violent video game play, nor have gamers' neural responses to real violence in particular been subject to desensitization processes. However, analyses of individual data indicated that group-related analyses reflect only a small part of actual individual different neural network involvement, suggesting that the consideration of individual learning history is sufficient for the present discussion.

Project of neural network for steel grade selection with the assumed CCT diagram

OpenAIRE

S. Malara; L.A. Dobrzański; J. Trzaska

2008-01-01

Purpose: The aim of this paper was developing a project of neural network for selection of steel grade with the specified CCT diagram – structure and of harness after heat treatment.Design/methodology/approach: The goal has been achieved in the following stages: at the first stage characteristic points of CCT diagram have been determined. At the second stage neural network has been developed and optimized.Findings: The neural network was developed in this paper, that allowed selection of stee...

Applications of neural network to numerical analyses

International Nuclear Information System (INIS)

Takeda, Tatsuoki; Fukuhara, Makoto; Ma, Xiao-Feng; Liaqat, Ali

1999-01-01

Applications of a multi-layer neural network to numerical analyses are described. We are mainly concerned with the computed tomography and the solution of differential equations. In both cases as the objective functions for the training process of the neural network we employed residuals of the integral equation or the differential equations. This is different from the conventional neural network training where sum of the squared errors of the output values is adopted as the objective function. For model problems both the methods gave satisfactory results and the methods are considered promising for some kind of problems. (author)

Energy efficiency optimisation for distillation column using artificial neural network models

International Nuclear Information System (INIS)

Osuolale, Funmilayo N.; Zhang, Jie

2016-01-01

This paper presents a neural network based strategy for the modelling and optimisation of energy efficiency in distillation columns incorporating the second law of thermodynamics. Real-time optimisation of distillation columns based on mechanistic models is often infeasible due to the effort in model development and the large computation effort associated with mechanistic model computation. This issue can be addressed by using neural network models which can be quickly developed from process operation data. The computation time in neural network model evaluation is very short making them ideal for real-time optimisation. Bootstrap aggregated neural networks are used in this study for enhanced model accuracy and reliability. Aspen HYSYS is used for the simulation of the distillation systems. Neural network models for exergy efficiency and product compositions are developed from simulated process operation data and are used to maximise exergy efficiency while satisfying products qualities constraints. Applications to binary systems of methanol-water and benzene-toluene separations culminate in a reduction of utility consumption of 8.2% and 28.2% respectively. Application to multi-component separation columns also demonstrate the effectiveness of the proposed method with a 32.4% improvement in the exergy efficiency. - Highlights: • Neural networks can accurately model exergy efficiency in distillation columns. • Bootstrap aggregated neural network offers improved model prediction accuracy. • Improved exergy efficiency is obtained through model based optimisation. • Reductions of utility consumption by 8.2% and 28.2% were achieved for binary systems. • The exergy efficiency for multi-component distillation is increased by 32.4%.

Methodologies, languages and tools

International Nuclear Information System (INIS)

Amako, Katsuya

1994-01-01

This is a summary of the open-quotes Methodologies, Languages and Toolsclose quotes session in the CHEP'94 conference. All the contributions to methodologies and languages are relevant to the object-oriented approach. Other topics presented are related to various software tools in the down-sized computing environment