WorldWideScience

Sample records for network nn techniques

  1. Neural networks (NN applied to the commercial properties valuation

    Directory of Open Access Journals (Sweden)

    J. M. Núñez Tabales

    2017-03-01

    Full Text Available Several agents, such as buyers and sellers, or local or tax authorities need to estimate the value of properties. There are different approaches to obtain the market price of a dwelling. Many papers have been produced in the academic literature for such purposes, but, these are, almost always, oriented to estimate hedonic prices of residential properties, such as houses or apartments. Here these methodologies are used in the field of estimate market price of commercial premises, using AI techniques. A case study is developed in Cordova —city in the South of Spain—. Neural Networks are an attractive alternative to the traditional hedonic modelling approaches, as they are better adapted to non-linearities of causal relationships and they also produce smaller valuation errors. It is also possible, from the NN model, to obtain implicit prices associated to the main attributes that can explain the variability of the market price of commercial properties.

  2. Hidden Neural Networks: A Framework for HMM/NN Hybrids

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric; Krogh, Anders Stærmose

    1997-01-01

    This paper presents a general framework for hybrids of hidden Markov models (HMM) and neural networks (NN). In the new framework called hidden neural networks (HNN) the usual HMM probability parameters are replaced by neural network outputs. To ensure a probabilistic interpretation the HNN is nor...... HMMs on TIMIT continuous speech recognition benchmarks. On the task of recognizing five broad phoneme classes an accuracy of 84% is obtained compared to 76% for a standard HMM. Additionally, we report a preliminary result of 69% accuracy on the TIMIT 39 phoneme task...

  3. Recognition of decays of charged tracks with neural network techniques

    International Nuclear Information System (INIS)

    Stimpfl-Abele, G.

    1991-01-01

    We developed neural-network learning techniques for the recognition of decays of charged tracks using a feed-forward network with error back-propagation. Two completely different methods are described in detail and their efficiencies for several NN architectures are compared with conventional methods. Excellent results are obtained. (orig.)

  4. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    DEFF Research Database (Denmark)

    Nielsen, Morten; Lund, Ole

    2009-01-01

    this binding event. RESULTS: Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data...

  5. Ionospheric scintillation forecasting model based on NN-PSO technique

    Science.gov (United States)

    Sridhar, M.; Venkata Ratnam, D.; Padma Raju, K.; Sai Praharsha, D.; Saathvika, K.

    2017-09-01

    The forecasting and modeling of ionospheric scintillation effects are crucial for precise satellite positioning and navigation applications. In this paper, a Neural Network model, trained using Particle Swarm Optimization (PSO) algorithm, has been implemented for the prediction of amplitude scintillation index (S4) observations. The Global Positioning System (GPS) and Ionosonde data available at Darwin, Australia (12.4634° S, 130.8456° E) during 2013 has been considered. The correlation analysis between GPS S4 and Ionosonde drift velocities (hmf2 and fof2) data has been conducted for forecasting the S4 values. The results indicate that forecasted S4 values closely follow the measured S4 values for both the quiet and disturbed conditions. The outcome of this work will be useful for understanding the ionospheric scintillation phenomena over low latitude regions.

  6. Reformulated Neural Network (ReNN): a New Alternative for Data-driven Modelling in Hydrology and Water Resources Engineering

    Science.gov (United States)

    Razavi, S.; Tolson, B.; Burn, D.; Seglenieks, F.

    2012-04-01

    Reformulated Neural Network (ReNN) has been recently developed as an efficient and more effective alternative to feedforward multi-layer perceptron (MLP) neural networks [Razavi, S., and Tolson, B. A. (2011). "A new formulation for feedforward neural networks." IEEE Transactions on Neural Networks, 22(10), 1588-1598, DOI: 1510.1109/TNN.2011.2163169]. This presentation initially aims to introduce the ReNN to the water resources community and then demonstrates ReNN applications to water resources related problems. ReNN is essentially equivalent to a single-hidden-layer MLP neural network but defined on a new set of network variables which is more effective than the traditional set of network weights and biases. The main features of the new network variables are that they are geometrically interpretable and each variable has a distinct role in forming the network response. ReNN is more efficiently trained as it has a less complex error response surface. In addition to the ReNN training efficiency, the interpretability of the ReNN variables enables the users to monitor and understand the internal behaviour of the network while training. Regularization in the ReNN response can be also directly measured and controlled. This feature improves the generalization ability of the network. The appeal of the ReNN is demonstrated with two ReNN applications to water resources engineering problems. In the first application, the ReNN is used to model the rainfall-runoff relationships in multiple watersheds in the Great Lakes basin located in northeastern North America. Modelling inflows to the Great Lakes are of great importance to the management of the Great Lakes system. Due to the lack of some detailed physical data about existing control structures in many subwatersheds of this huge basin, the data-driven approach to modelling such as the ReNN are required to replace predictions from a physically-based rainfall runoff model. Unlike traditional MLPs, the ReNN does not necessarily

  7. The European Narcolepsy Network (EU-NN) database

    DEFF Research Database (Denmark)

    Khatami, Ramin; Luca, Gianina; Baumann, Christian R

    2016-01-01

    Narcolepsy with cataplexy is a rare disease with an estimated prevalence of 0.02% in European populations. Narcolepsy shares many features of rare disorders, in particular the lack of awareness of the disease with serious consequences for healthcare supply. Similar to other rare diseases, only a ......, identification of post-marketing medication side-effects, and will contribute to improve clinical trial designs and provide facilities to further develop phase III trials....... Narcolepsy Network is introduced. The database structure, standardization of data acquisition and quality control procedures are described, and an overview provided of the first 1079 patients from 18 European specialized centres. Due to its standardization this continuously increasing data pool is most...

  8. Underwater Acoustic Networking Techniques

    CERN Document Server

    Otnes, Roald; Casari, Paolo; Goetz, Michael; Husøy, Thor; Nissen, Ivor; Rimstad, Knut; van Walree, Paul; Zorzi, Michele

    2012-01-01

    This literature study presents an overview of underwater acoustic networking. It provides a background and describes the state of the art of all networking facets that are relevant for underwater applications. This report serves both as an introduction to the subject and as a summary of existing protocols, providing support and inspiration for the development of network architectures.

  9. NN-align. An artificial neural network-based alignment algorithm for MHC class II peptide binding prediction

    Directory of Open Access Journals (Sweden)

    Lund Ole

    2009-09-01

    Full Text Available Abstract Background The major histocompatibility complex (MHC molecule plays a central role in controlling the adaptive immune response to infections. MHC class I molecules present peptides derived from intracellular proteins to cytotoxic T cells, whereas MHC class II molecules stimulate cellular and humoral immunity through presentation of extracellularly derived peptides to helper T cells. Identification of which peptides will bind a given MHC molecule is thus of great importance for the understanding of host-pathogen interactions, and large efforts have been placed in developing algorithms capable of predicting this binding event. Results Here, we present a novel artificial neural network-based method, NN-align that allows for simultaneous identification of the MHC class II binding core and binding affinity. NN-align is trained using a novel training algorithm that allows for correction of bias in the training data due to redundant binding core representation. Incorporation of information about the residues flanking the peptide-binding core is shown to significantly improve the prediction accuracy. The method is evaluated on a large-scale benchmark consisting of six independent data sets covering 14 human MHC class II alleles, and is demonstrated to outperform other state-of-the-art MHC class II prediction methods. Conclusion The NN-align method is competitive with the state-of-the-art MHC class II peptide binding prediction algorithms. The method is publicly available at http://www.cbs.dtu.dk/services/NetMHCII-2.0.

  10. Supervised Classification of Agricultural Land Cover Using a Modified k-NN Technique (MNN and Landsat Remote Sensing Imagery

    Directory of Open Access Journals (Sweden)

    Karsten Schulz

    2009-11-01

    Full Text Available Nearest neighbor techniques are commonly used in remote sensing, pattern recognition and statistics to classify objects into a predefined number of categories based on a given set of predictors. These techniques are especially useful for highly nonlinear relationship between the variables. In most studies the distance measure is adopted a priori. In contrast we propose a general procedure to find an adaptive metric that combines a local variance reducing technique and a linear embedding of the observation space into an appropriate Euclidean space. To illustrate the application of this technique, two agricultural land cover classifications using mono-temporal and multi-temporal Landsat scenes are presented. The results of the study, compared with standard approaches used in remote sensing such as maximum likelihood (ML or k-Nearest Neighbor (k-NN indicate substantial improvement with regard to the overall accuracy and the cardinality of the calibration data set. Also, using MNN in a soft/fuzzy classification framework demonstrated to be a very useful tool in order to derive critical areas that need some further attention and investment concerning additional calibration data.

  11. Emerging wireless networks concepts, techniques and applications

    CERN Document Server

    Makaya, Christian

    2011-01-01

    An authoritative collection of research papers and surveys, Emerging Wireless Networks: Concepts, Techniques, and Applications explores recent developments in next-generation wireless networks (NGWNs) and mobile broadband networks technologies, including 4G (LTE, WiMAX), 3G (UMTS, HSPA), WiFi, mobile ad hoc networks, mesh networks, and wireless sensor networks. Focusing on improving the performance of wireless networks and provisioning better quality of service and quality of experience for users, it reports on the standards of different emerging wireless networks, applications, and service fr

  12. A neural network technique for remeshing of bone microstructure.

    Science.gov (United States)

    Fischer, Anath; Holdstein, Yaron

    2012-01-01

    Today, there is major interest within the biomedical community in developing accurate noninvasive means for the evaluation of bone microstructure and bone quality. Recent improvements in 3D imaging technology, among them development of micro-CT and micro-MRI scanners, allow in-vivo 3D high-resolution scanning and reconstruction of large specimens or even whole bone models. Thus, the tendency today is to evaluate bone features using 3D assessment techniques rather than traditional 2D methods. For this purpose, high-quality meshing methods are required. However, the 3D meshes produced from current commercial systems usually are of low quality with respect to analysis and rapid prototyping. 3D model reconstruction of bone is difficult due to the complexity of bone microstructure. The small bone features lead to a great deal of neighborhood ambiguity near each vertex. The relatively new neural network method for mesh reconstruction has the potential to create or remesh 3D models accurately and quickly. A neural network (NN), which resembles an artificial intelligence (AI) algorithm, is a set of interconnected neurons, where each neuron is capable of making an autonomous arithmetic calculation. Moreover, each neuron is affected by its surrounding neurons through the structure of the network. This paper proposes an extension of the growing neural gas (GNN) neural network technique for remeshing a triangular manifold mesh that represents bone microstructure. This method has the advantage of reconstructing the surface of a genus-n freeform object without a priori knowledge regarding the original object, its topology, or its shape.

  13. Blockmodeling techniques for complex networks

    Science.gov (United States)

    Ball, Brian Joseph

    The class of network models known as stochastic blockmodels has recently been gaining popularity. In this dissertation, we present new work that uses blockmodels to answer questions about networks. We create a blockmodel based on the idea of link communities, which naturally gives rise to overlapping vertex communities. We derive a fast and accurate algorithm to fit the model to networks. This model can be related to another blockmodel, which allows the method to efficiently find nonoverlapping communities as well. We then create a heuristic based on the link community model whose use is to find the correct number of communities in a network. The heuristic is based on intuitive corrections to likelihood ratio tests. It does a good job finding the correct number of communities in both real networks and synthetic networks generated from the link communities model. Two commonly studied types of networks are citation networks, where research papers cite other papers, and coauthorship networks, where authors are connected if they've written a paper together. We study a multi-modal network from a large dataset of Physics publications that is the combination of the two, allowing for directed links between papers as citations, and an undirected edge between a scientist and a paper if they helped to write it. This allows for new insights on the relation between social interaction and scientific production. We also have the publication dates of papers, which lets us track our measures over time. Finally, we create a stochastic model for ranking vertices in a semi-directed network. The probability of connection between two vertices depends on the difference of their ranks. When this model is fit to high school friendship networks, the ranks appear to correspond with a measure of social status. Students have reciprocated and some unreciprocated edges with other students of closely similar rank that correspond to true friendship, and claim an aspirational friendship with a much

  14. Modeling ionospheric foF 2 response during geomagnetic storms using neural network and linear regression techniques

    Science.gov (United States)

    Tshisaphungo, Mpho; Habarulema, John Bosco; McKinnell, Lee-Anne

    2018-06-01

    In this paper, the modeling of the ionospheric foF 2 changes during geomagnetic storms by means of neural network (NN) and linear regression (LR) techniques is presented. The results will lead to a valuable tool to model the complex ionospheric changes during disturbed days in an operational space weather monitoring and forecasting environment. The storm-time foF 2 data during 1996-2014 from Grahamstown (33.3°S, 26.5°E), South Africa ionosonde station was used in modeling. In this paper, six storms were reserved to validate the models and hence not used in the modeling process. We found that the performance of both NN and LR models is comparable during selected storms which fell within the data period (1996-2014) used in modeling. However, when validated on storm periods beyond 1996-2014, the NN model gives a better performance (R = 0.62) compared to LR model (R = 0.56) for a storm that reached a minimum Dst index of -155 nT during 19-23 December 2015. We also found that both NN and LR models are capable of capturing the ionospheric foF 2 responses during two great geomagnetic storms (28 October-1 November 2003 and 6-12 November 2004) which have been demonstrated to be difficult storms to model in previous studies.

  15. Performance Monitoring Techniques Supporting Cognitive Optical Networking

    DEFF Research Database (Denmark)

    Caballero Jambrina, Antonio; Borkowski, Robert; Zibar, Darko

    2013-01-01

    High degree of heterogeneity of future optical networks, such as services with different quality-of-transmission requirements, modulation formats and switching techniques, will pose a challenge for the control and optimization of different parameters. Incorporation of cognitive techniques can help...... to solve this issue by realizing a network that can observe, act, learn and optimize its performance, taking into account end-to-end goals. In this letter we present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive...... Heterogeneous Reconfigurable Optical Network. We focus on the approaches developed in the project for optical performance monitoring, which enable the feedback from the physical layer to the cognitive decision system by providing accurate description of the performance of the established lightpaths....

  16. Environmental pollutants monitoring network using nuclear techniques

    International Nuclear Information System (INIS)

    Cohen, D.D.

    1994-01-01

    The Australian Nuclear Science and Technology Organisation (ANSTO) in collaboration with the NSW Environment Protection Authority (EPA), Pacific Power and the Universities of NSW and Macquarie has established a large area fine aerosol sampling network covering nearly 60,000 square kilometres of NSW with 25 fine particle samplers. This network known as ASP commenced sampling on 1 July 1991. The cyclone sampler at each site has a 2.5 μm particle diameter cut off and samples for 24 hours using a stretched Teflon filter for each day. Accelerator-based Ion Beam Analysis(IBA) techniques are well suited to analyse the thousands of filter papers a year that originate from such a large scale aerosol sampling network. These techniques are fast multi-elemental and, for the most part, non-destructive so other analytical methods such as neutron activation and ion chromatography can be performed afterwards. Currently ANSTO receives 300 filters per month from this network for analysis using its accelerator based ion beam techniques on a 3 MV Van de Graaff accelerator. One week a month of accelerator time is dedicated to this analysis. This paper described the four simultaneous accelerator based IBA techniques used at ANSTO, to analyse for the following 24 elements H, C, N, O, F, Na, Al, Si, P, S, Cl, K, Ca, Ti, V, Cr, Mn, Fe, Cu, Ni, Co, Zn, Br and Pb. Each analysis requires only a few minutes of accelerator running time to complete. 15 refs., 9 figs

  17. GPU-FS-kNN: a software tool for fast and scalable kNN computation using GPUs.

    Directory of Open Access Journals (Sweden)

    Ahmed Shamsul Arefin

    Full Text Available BACKGROUND: The analysis of biological networks has become a major challenge due to the recent development of high-throughput techniques that are rapidly producing very large data sets. The exploding volumes of biological data are craving for extreme computational power and special computing facilities (i.e. super-computers. An inexpensive solution, such as General Purpose computation based on Graphics Processing Units (GPGPU, can be adapted to tackle this challenge, but the limitation of the device internal memory can pose a new problem of scalability. An efficient data and computational parallelism with partitioning is required to provide a fast and scalable solution to this problem. RESULTS: We propose an efficient parallel formulation of the k-Nearest Neighbour (kNN search problem, which is a popular method for classifying objects in several fields of research, such as pattern recognition, machine learning and bioinformatics. Being very simple and straightforward, the performance of the kNN search degrades dramatically for large data sets, since the task is computationally intensive. The proposed approach is not only fast but also scalable to large-scale instances. Based on our approach, we implemented a software tool GPU-FS-kNN (GPU-based Fast and Scalable k-Nearest Neighbour for CUDA enabled GPUs. The basic approach is simple and adaptable to other available GPU architectures. We observed speed-ups of 50-60 times compared with CPU implementation on a well-known breast microarray study and its associated data sets. CONCLUSION: Our GPU-based Fast and Scalable k-Nearest Neighbour search technique (GPU-FS-kNN provides a significant performance improvement for nearest neighbour computation in large-scale networks. Source code and the software tool is available under GNU Public License (GPL at https://sourceforge.net/p/gpufsknn/.

  18. A STUDY ON NETWORK SECURITY TECHNIQUES

    OpenAIRE

    Dr.T.Hemalatha; Dr.G.Rashita Banu; Dr.Murtaza Ali

    2016-01-01

    Internet plays a vital role in our day today life. Data security in web application has become very crucial. The usage of internet becomes more and more in recent years. Through internet the information’s can be shared through many social networks like Facebook, twitter, LinkedIn, blogs etc. There is chance of hacking the data while sharing from one to one. To prevent the data being hacked there are so many techniques such as Digital Signature, Cryptography, Digital watermarking, Data Sanit...

  19. Estimating surface soil moisture from SMAP observations using a Neural Network technique.

    Science.gov (United States)

    Kolassa, J; Reichle, R H; Liu, Q; Alemohammad, S H; Gentine, P; Aida, K; Asanuma, J; Bircher, S; Caldwell, T; Colliander, A; Cosh, M; Collins, C Holifield; Jackson, T J; Martínez-Fernández, J; McNairn, H; Pacheco, A; Thibeault, M; Walker, J P

    2018-01-01

    A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to March 2017 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observing System Model version 5 (GEOS-5) land modeling system, and Moderate Resolution Imaging Spectroradiometer-based vegetation water content. The NN was trained on GEOS-5 soil moisture target data, making the NN estimates consistent with the GEOS-5 climatology, such that they may ultimately be assimilated into this model without further bias correction. Evaluated against in situ soil moisture measurements, the average unbiased root mean square error (ubRMSE), correlation and anomaly correlation of the NN retrievals were 0.037 m 3 m -3 , 0.70 and 0.66, respectively, against SMAP core validation site measurements and 0.026 m 3 m -3 , 0.58 and 0.48, respectively, against International Soil Moisture Network (ISMN) measurements. At the core validation sites, the NN retrievals have a significantly higher skill than the GEOS-5 model estimates and a slightly lower correlation skill than the SMAP Level-2 Passive (L2P) product. The feasibility of the NN method was reflected by a lower ubRMSE compared to the L2P retrievals as well as a higher skill when ancillary parameters in physically-based retrievals were uncertain. Against ISMN measurements, the skill of the two retrieval products was more comparable. A triple collocation analysis against Advanced Microwave Scanning Radiometer 2 (AMSR2) and Advanced Scatterometer (ASCAT) soil moisture retrievals showed that the NN and L2P retrieval errors have a similar spatial distribution, but the NN retrieval errors are generally lower in densely vegetated regions and transition zones.

  20. Reliability Analysis Techniques for Communication Networks in Nuclear Power Plant

    International Nuclear Information System (INIS)

    Lim, T. J.; Jang, S. C.; Kang, H. G.; Kim, M. C.; Eom, H. S.; Lee, H. J.

    2006-09-01

    The objectives of this project is to investigate and study existing reliability analysis techniques for communication networks in order to develop reliability analysis models for nuclear power plant's safety-critical networks. It is necessary to make a comprehensive survey of current methodologies for communication network reliability. Major outputs of this study are design characteristics of safety-critical communication networks, efficient algorithms for quantifying reliability of communication networks, and preliminary models for assessing reliability of safety-critical communication networks

  1. Techniques Used in String Matching for Network Security

    OpenAIRE

    Jamuna Bhandari

    2014-01-01

    String matching also known as pattern matching is one of primary concept for network security. In this area the effectiveness and efficiency of string matching algorithms is important for applications in network security such as network intrusion detection, virus detection, signature matching and web content filtering system. This paper presents brief review on some of string matching techniques used for network security.

  2. The Network Protocol Analysis Technique in Snort

    Science.gov (United States)

    Wu, Qing-Xiu

    Network protocol analysis is a network sniffer to capture data for further analysis and understanding of the technical means necessary packets. Network sniffing is intercepted by packet assembly binary format of the original message content. In order to obtain the information contained. Required based on TCP / IP protocol stack protocol specification. Again to restore the data packets at protocol format and content in each protocol layer. Actual data transferred, as well as the application tier.

  3. Cognitive Heterogeneous Reconfigurable Optical Networks (CHRON): Enabling Technologies and Techniques

    DEFF Research Database (Denmark)

    Tafur Monroy, Idelfonso; Zibar, Darko; Guerrero Gonzalez, Neil

    2011-01-01

    We present the approach of cognition applied to heterogeneous optical networks developed in the framework of the EU project CHRON: Cognitive Heterogeneous Reconfigurable Optical Network. We introduce and discuss in particular the technologies and techniques that will enable a cognitive optical...... network to observe, act, learn and optimizes its performance, taking into account its high degree of heterogeneity with respect to quality of service, transmission and switching techniques....

  4. Techniques for Intelligence Analysis of Networks

    National Research Council Canada - National Science Library

    Cares, Jeffrey R

    2005-01-01

    ...) there are significant intelligence analysis manifestations of these properties; and (4) a more satisfying theory of Networked Competition than currently exists for NCW/NCO is emerging from this research...

  5. Criminal Network Investigation: Processes, Tools, and Techniques

    DEFF Research Database (Denmark)

    Petersen, Rasmus Rosenqvist

    important challenge for criminal network investigation, despite the massive attention it receives from research and media. Challenges such as the investigation process, the context of the investigation, human factors such as thinking and creativity, and political decisions and legal laws are all challenges...... that could mean the success or failure of criminal network investigations. % include commission reports as indications of process related problems .. to "play a little politics" !! Information, process, and human factors, are challenges we find to be addressable by software system support. Based on those......Criminal network investigations such as police investigations, intelligence analysis, and investigative journalism involve a range of complex knowledge management processes and tasks. Criminal network investigators collect, process, and analyze information related to a specific target to create...

  6. Geophysical worldwide networks: basic concepts and techniques

    International Nuclear Information System (INIS)

    Ruzie, G.; Baubron, G.

    1997-01-01

    The detection of nuclear explosions around the globe requires the setting up of networks of sensors on a worldwide basis. Such equipment should be able to transmit on-line data in real-time or pseudo real-time to a center or processing centers. The high level of demanded reliability for the data (generally better than 99 %) also has an impact on the accuracy and precision of the sensors and the communications technology, as well as the systems used for on-line checking. In the light of these requirements, DAM has developed a data gathering network based on the principle of VSTA duplex links which ensures the on-line transmission of data and operational parameters towards the Processing Centre via a hub. In the other direction, the Centre can act on a number of parameters in order to correct them if necessary, or notify the local maintenance team. To optimize the reliability of the main components of this system, the detection stations as well as their associated beacons have low consumption and can be supplied by solar panels, thus facilitating the installation of the networks. The seismic network on the French national territory is composed of 40 stations built on the principles outlined above. In order to gather data from stations established outside France, DAM is planning to use an analogue system to transmit data in on-line as well as off-line mode. (authors)

  7. Power Minimization techniques for Networked Data Centers

    International Nuclear Information System (INIS)

    Low, Steven; Tang, Kevin

    2011-01-01

    Our objective is to develop a mathematical model to optimize energy consumption at multiple levels in networked data centers, and develop abstract algorithms to optimize not only individual servers, but also coordinate the energy consumption of clusters of servers within a data center and across geographically distributed data centers to minimize the overall energy cost and consumption of brown energy of an enterprise. In this project, we have formulated a variety of optimization models, some stochastic others deterministic, and have obtained a variety of qualitative results on the structural properties, robustness, and scalability of the optimal policies. We have also systematically derived from these models decentralized algorithms to optimize energy efficiency, analyzed their optimality and stability properties. Finally, we have conducted preliminary numerical simulations to illustrate the behavior of these algorithms. We draw the following conclusion. First, there is a substantial opportunity to minimize both the amount and the cost of electricity consumption in a network of datacenters, by exploiting the fact that traffic load, electricity cost, and availability of renewable generation fluctuate over time and across geographical locations. Judiciously matching these stochastic processes can optimize the tradeoff between brown energy consumption, electricity cost, and response time. Second, given the stochastic nature of these three processes, real-time dynamic feedback should form the core of any optimization strategy. The key is to develop decentralized algorithms that can be implemented at different parts of the network as simple, local algorithms that coordinate through asynchronous message passing.

  8. Outlier Detection Techniques For Wireless Sensor Networks: A Survey

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2008-01-01

    In the field of wireless sensor networks, measurements that significantly deviate from the normal pattern of sensed data are considered as outliers. The potential sources of outliers include noise and errors, events, and malicious attacks on the network. Traditional outlier detection techniques are

  9. Survey of Green Radio Communications Networks: Techniques and Recent Advances

    Directory of Open Access Journals (Sweden)

    Mohammed H. Alsharif

    2013-01-01

    Full Text Available Energy efficiency in cellular networks has received significant attention from both academia and industry because of the importance of reducing the operational expenditures and maintaining the profitability of cellular networks, in addition to making these networks “greener.” Because the base station is the primary energy consumer in the network, efforts have been made to study base station energy consumption and to find ways to improve energy efficiency. In this paper, we present a brief review of the techniques that have been used recently to improve energy efficiency, such as energy-efficient power amplifier techniques, time-domain techniques, cell switching, management of the physical layer through multiple-input multiple-output (MIMO management, heterogeneous network architectures based on Micro-Pico-Femtocells, cell zooming, and relay techniques. In addition, this paper discusses the advantages and disadvantages of each technique to contribute to a better understanding of each of the techniques and thereby offer clear insights to researchers about how to choose the best ways to reduce energy consumption in future green radio networks.

  10. Real-time network traffic classification technique for wireless local area networks based on compressed sensing

    Science.gov (United States)

    Balouchestani, Mohammadreza

    2017-05-01

    Network traffic or data traffic in a Wireless Local Area Network (WLAN) is the amount of network packets moving across a wireless network from each wireless node to another wireless node, which provide the load of sampling in a wireless network. WLAN's Network traffic is the main component for network traffic measurement, network traffic control and simulation. Traffic classification technique is an essential tool for improving the Quality of Service (QoS) in different wireless networks in the complex applications such as local area networks, wireless local area networks, wireless personal area networks, wireless metropolitan area networks, and wide area networks. Network traffic classification is also an essential component in the products for QoS control in different wireless network systems and applications. Classifying network traffic in a WLAN allows to see what kinds of traffic we have in each part of the network, organize the various kinds of network traffic in each path into different classes in each path, and generate network traffic matrix in order to Identify and organize network traffic which is an important key for improving the QoS feature. To achieve effective network traffic classification, Real-time Network Traffic Classification (RNTC) algorithm for WLANs based on Compressed Sensing (CS) is presented in this paper. The fundamental goal of this algorithm is to solve difficult wireless network management problems. The proposed architecture allows reducing False Detection Rate (FDR) to 25% and Packet Delay (PD) to 15 %. The proposed architecture is also increased 10 % accuracy of wireless transmission, which provides a good background for establishing high quality wireless local area networks.

  11. NEW TECHNIQUES APPLIED IN ECONOMICS. ARTIFICIAL NEURAL NETWORK

    Directory of Open Access Journals (Sweden)

    Constantin Ilie

    2009-05-01

    Full Text Available The present paper has the objective to inform the public regarding the use of new techniques for the modeling, simulate and forecast of system from different field of activity. One of those techniques is Artificial Neural Network, one of the artificial in

  12. Neural network scatter correction technique for digital radiography

    International Nuclear Information System (INIS)

    Boone, J.M.

    1990-01-01

    This paper presents a scatter correction technique based on artificial neural networks. The technique utilizes the acquisition of a conventional digital radiographic image, coupled with the acquisition of a multiple pencil beam (micro-aperture) digital image. Image subtraction results in a sparsely sampled estimate of the scatter component in the image. The neural network is trained to develop a causal relationship between image data on the low-pass filtered open field image and the sparsely sampled scatter image, and then the trained network is used to correct the entire image (pixel by pixel) in a manner which is operationally similar to but potentially more powerful than convolution. The technique is described and is illustrated using clinical primary component images combined with scatter component images that are realistically simulated using the results from previously reported Monte Carlo investigations. The results indicate that an accurate scatter correction can be realized using this technique

  13. Around the laboratories: Rutherford: Successful tests on bubble chamber target technique; Stanford (SLAC): New storage rings proposal; Berkeley: The HAPPE project to examine cosmic rays with superconducting magnets; The 60th birthday of Professor N.N. Bogolyubov; Argonne: Performance of the automatic film measuring system POLLY II

    CERN Multimedia

    1969-01-01

    Around the laboratories: Rutherford: Successful tests on bubble chamber target technique; Stanford (SLAC): New storage rings proposal; Berkeley: The HAPPE project to examine cosmic rays with superconducting magnets; The 60th birthday of Professor N.N. Bogolyubov; Argonne: Performance of the automatic film measuring system POLLY II

  14. Development of nuclear power plant diagnosis technique using neural networks

    International Nuclear Information System (INIS)

    Horiguchi, Masahiro; Fukawa, Naohiro; Nishimura, Kazuo

    1991-01-01

    A nuclear power plant diagnosis technique has been developed, called transient phenomena analysis, which employs neural network. The neural networks identify malfunctioning equipment by recognizing the pattern of main plant parameters, making it possible to locate the cause of an abnormality when a plant is in a transient state. In a case where some piece of equipment shows abnormal behavior, many plant parameters either directly or indirectly related to that equipment change simultaneously. When an abrupt change in a plant parameter is detected, changes in the 49 main plant parameters are classified into three types and a characteristic change pattern consisting of 49 data is defined. The neural networks then judge the cause of the abnormality from this pattern. This neural-network-based technique can recognize 100 patterns that are characterized by the causes of plant abnormality. (author)

  15. Neural Networks for the Extraction of the ΛC Signal in p-Pb collisions at 
 √sNN = 5.02 TeV

    CERN Document Server

    Giampaolo, Alberto

    2016-01-01

    The charmed baryon ΛC is of interest for the characterization of the quark-gluon plasma (QGP) created in Pb-Pb collisions, due to its sensitivity to c-quark thermalization and to the hadronization mechanisms. The measurement in pp an p-Pb collisions is of interest both as a reference for the Pb- Pb result and in the context of recent observations suggesting the possible creation of a QGP in small colliding systems. This project is focused on the study of the extraction of the ΛC signal in p-Pb collisions with the ALICE detector, through the usage of deep learning, a machine learning technique. In a few weeks we were able to reproduce the results of the existing BDT analysis with a simple shallow networks. In the 6 to 8 pT bin, deep networks using low-level variables get close to the performance of the topological variable analysis, but with the architectures tested in this project they do not seem to be able to outperform it.

  16. Advanced network programming principles and techniques : network application programming with Java

    CERN Document Server

    Ciubotaru, Bogdan

    2013-01-01

    Answering the need for an accessible overview of the field, this text/reference presents a manageable introduction to both the theoretical and practical aspects of computer networks and network programming. Clearly structured and easy to follow, the book describes cutting-edge developments in network architectures, communication protocols, and programming techniques and models, supported by code examples for hands-on practice with creating network-based applications. Features: presents detailed coverage of network architectures; gently introduces the reader to the basic ideas underpinning comp

  17. A technique for choosing an option for SDH network upgrade

    Directory of Open Access Journals (Sweden)

    V. A. Bulanov

    2014-01-01

    Full Text Available Rapidly developing data transmission technologies result in making the network equipment modernization inevitable. There are various options to upgrade the SDH networks, for example, by increasing the capacity of network overloaded sites, the entire network capacity by replacement of the equipment or by creation of a parallel network, by changing the network structure with the organization of multilevel hierarchy of a network, etc. All options vary in a diversity of parameters starting with the solution cost and ending with the labor intensiveness of their realization. Thus, there are no certain standard approaches to the rules to choose an option for the network development. The article offers the technique for choosing the SHD network upgrade based on method of expert evaluations using as a tool the software complex that allows us to have quickly the quantitative characteristics of proposed network option. The technique is as follows:1. Forming a perspective matrix of services inclination to the SDH networks.2. Developing the several possible options for a network modernization.3. Formation of the list of criteria and a definition of indicators to characterize them by two groups, namely costs of the option implementation and arising losses; positive effect from the option introduction.4. Criteria weight coefficients purpose.5. Indicators value assessment within each criterion for each option by each expert. Rationing of the obtained values of indicators in relation to the maximum value of an indicator among all options.6. Calculating the integrated indicators of for each option by criteria groups.7. Creating a set of Pareto by drawing two criteria groups of points, which correspond to all options in the system of coordinates on the plane. Option choice.In implementation of point 2 the indicators derivation owing to software complex plays a key role. This complex should produce a structure of the network equipment, types of multiplexer sections

  18. Design on intelligent gateway technique in home network

    Science.gov (United States)

    Hu, Zhonggong; Feng, Xiancheng

    2008-12-01

    Based on digitization, multimedia, mobility, wide band, real-time interaction and so on,family networks, because can provide diverse and personalized synthesis service in information, correspondence work, entertainment, education and health care and so on, are more and more paid attention by the market. The family network product development has become the focus of the related industry. In this paper,the concept of the family network and the overall reference model of the family network are introduced firstly.Then the core techniques and the correspondence standard related with the family network are proposed.The key analysis is made for the function of family gateway, the function module of the software,the key technologies to client side software architecture and the trend of development of the family network entertainment seeing and hearing service and so on. Product present situation of the family gateway and the future trend of development, application solution of the digital family service are introduced. The development of the family network product bringing about the digital family network industry is introduced finally.It causes the development of software industries,such as communication industry,electrical appliances industry, computer and game and so on.It also causes the development of estate industry.

  19. 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...

  20. An optimization planning technique for Suez Canal Network in Egypt

    Energy Technology Data Exchange (ETDEWEB)

    Abou El-Ela, A.A.; El-Zeftawy, A.A.; Allam, S.M.; Atta, Gasir M. [Electrical Engineering Dept., Faculty of Eng., Shebin El-Kom (Egypt)

    2010-02-15

    This paper introduces a proposed optimization technique POT for predicting the peak load demand and planning of transmission line systems. Many of traditional methods have been presented for long-term load forecasting of electrical power systems. But, the results of these methods are approximated. Therefore, the artificial neural network (ANN) technique for long-term peak load forecasting is modified and discussed as a modern technique in long-term load forecasting. The modified technique is applied on the Egyptian electrical network dependent on its historical data to predict the electrical peak load demand forecasting up to year 2017. This technique is compared with extrapolation of trend curves as a traditional method. The POT is applied also to obtain the optimal planning of transmission lines for the 220 kV of Suez Canal Network (SCN) using the ANN technique. The minimization of the transmission network costs are considered as an objective function, while the transmission lines (TL) planning constraints are satisfied. Zafarana site on the Red Sea coast is considered as an optimal site for installing big wind farm (WF) units in Egypt. So, the POT is applied to plan both the peak load and the electrical transmission of SCN with and without considering WF to develop the impact of WF units on the electrical transmission system of Egypt, considering the reliability constraints which were taken as a separate model in the previous techniques. The application on SCN shows the capability and the efficiently of the proposed techniques to obtain the predicting peak load demand and the optimal planning of transmission lines of SCN up to year 2017. (author)

  1. On Applicability of Network Coding Technique for 6LoWPAN-based Sensor Networks.

    Science.gov (United States)

    Amanowicz, Marek; Krygier, Jaroslaw

    2018-05-26

    In this paper, the applicability of the network coding technique in 6LoWPAN-based sensor multihop networks is examined. The 6LoWPAN is one of the standards proposed for the Internet of Things architecture. Thus, we can expect the significant growth of traffic in such networks, which can lead to overload and decrease in the sensor network lifetime. The authors propose the inter-session network coding mechanism that can be implemented in resource-limited sensor motes. The solution reduces the overall traffic in the network, and in consequence, the energy consumption is decreased. Used procedures take into account deep header compressions of the native 6LoWPAN packets and the hop-by-hop changes of the header structure. Applied simplifications reduce signaling traffic that is typically occurring in network coding deployments, keeping the solution usefulness for the wireless sensor networks with limited resources. The authors validate the proposed procedures in terms of end-to-end packet delay, packet loss ratio, traffic in the air, total energy consumption, and network lifetime. The solution has been tested in a real wireless sensor network. The results confirm the efficiency of the proposed technique, mostly in delay-tolerant sensor networks.

  2. On the theory of coupled πNN-NN systems

    International Nuclear Information System (INIS)

    Machavariani, A.I.

    1982-01-01

    On the basis of the deuteron and δ asobar as a one-particle state, a version of relativistic equations for coupled πNN and NN systems is obtained. It is demonstrated that, if one neglects the non-pole term of the pion-nucleon Green function in the (3.3) resonance region, the three-body equations reduce to a set of equations for the two-body amplitudes of transitions between the πd, NN and Nδ channels

  3. Optical supervised filtering technique based on Hopfield neural network

    Science.gov (United States)

    Bal, Abdullah

    2004-11-01

    Hopfield neural network is commonly preferred for optimization problems. In image segmentation, conventional Hopfield neural networks (HNN) are formulated as a cost-function-minimization problem to perform gray level thresholding on the image histogram or the pixels' gray levels arranged in a one-dimensional array [R. Sammouda, N. Niki, H. Nishitani, Pattern Rec. 30 (1997) 921-927; K.S. Cheng, J.S. Lin, C.W. Mao, IEEE Trans. Med. Imag. 15 (1996) 560-567; C. Chang, P. Chung, Image and Vision comp. 19 (2001) 669-678]. In this paper, a new high speed supervised filtering technique is proposed for image feature extraction and enhancement problems by modifying the conventional HNN. The essential improvement in this technique is to use 2D convolution operation instead of weight-matrix multiplication. Thereby, neural network based a new filtering technique has been obtained that is required just 3 × 3 sized filter mask matrix instead of large size weight coefficient matrix. Optical implementation of the proposed filtering technique is executed easily using the joint transform correlator. The requirement of non-negative data for optical implementation is provided by bias technique to convert the bipolar data to non-negative data. Simulation results of the proposed optical supervised filtering technique are reported for various feature extraction problems such as edge detection, corner detection, horizontal and vertical line extraction, and fingerprint enhancement.

  4. Comparing Generative Adversarial Network Techniques for Image Creation and Modification

    NARCIS (Netherlands)

    Pieters, Mathijs; Wiering, Marco

    2018-01-01

    Generative adversarial networks (GANs) have demonstrated to be successful at generating realistic real-world images. In this paper we compare various GAN techniques, both supervised and unsupervised. The effects on training stability of different objective functions are compared. We add an encoder

  5. MIMO Techniques for Jamming Threat Suppression in Vehicular Networks

    Directory of Open Access Journals (Sweden)

    Dimitrios Kosmanos

    2016-01-01

    Full Text Available Vehicular ad hoc networks have emerged as a promising field of research and development, since they will be able to accommodate a variety of applications, ranging from infotainment to traffic management and road safety. A specific security-related concern that vehicular ad hoc networks face is how to keep communication alive in the presence of radio frequency jamming, especially during emergency situations. Multiple Input Multiple Output techniques are proven to be able to improve some crucial parameters of vehicular communications such as communication range and throughput. In this article, we investigate how Multiple Input Multiple Output techniques can be used in vehicular ad hoc networks as active defense mechanisms in order to avoid jamming threats. For this reason, a variation of spatial multiplexing is proposed, namely, vSP4, which achieves not only high throughput but also a stable diversity gain upon the interference of a malicious jammer.

  6. High Dimensional Modulation and MIMO Techniques for Access Networks

    DEFF Research Database (Denmark)

    Binti Othman, Maisara

    Exploration of advanced modulation formats and multiplexing techniques for next generation optical access networks are of interest as promising solutions for delivering multiple services to end-users. This thesis addresses this from two different angles: high dimensionality carrierless...... the capacity per wavelength of the femto-cell network. Bit rate up to 1.59 Gbps with fiber-wireless transmission over 1 m air distance is demonstrated. The results presented in this thesis demonstrate the feasibility of high dimensionality CAP in increasing the number of dimensions and their potentially......) optical access network. 2 X 2 MIMO RoF employing orthogonal frequency division multiplexing (OFDM) with 5.6 GHz RoF signaling over all-vertical cavity surface emitting lasers (VCSEL) WDM passive optical networks (PONs). We have employed polarization division multiplexing (PDM) to further increase...

  7. Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

    Directory of Open Access Journals (Sweden)

    DongHo Kang

    2014-01-01

    Full Text Available Internet of Things (IoT consists of several tiny devices connected together to form a collaborative computing environment. Recently IoT technologies begin to merge with supervisory control and data acquisition (SCADA sensor networks to more efficiently gather and analyze real-time data from sensors in industrial environments. But SCADA sensor networks are becoming more and more vulnerable to cyber-attacks due to increased connectivity. To safely adopt IoT technologies in the SCADA environments, it is important to improve the security of SCADA sensor networks. In this paper we propose a multiple filtering technique based on whitelists to detect illegitimate packets. Our proposed system detects the traffic of network and application protocol attacks with a set of whitelists collected from normal traffic.

  8. Mobility management techniques for the next-generation wireless networks

    Science.gov (United States)

    Sun, Junzhao; Howie, Douglas P.; Sauvola, Jaakko J.

    2001-10-01

    The tremendous demands from social market are pushing the booming development of mobile communications faster than ever before, leading to plenty of new advanced techniques emerging. With the converging of mobile and wireless communications with Internet services, the boundary between mobile personal telecommunications and wireless computer networks is disappearing. Wireless networks of the next generation need the support of all the advances on new architectures, standards, and protocols. Mobility management is an important issue in the area of mobile communications, which can be best solved at the network layer. One of the key features of the next generation wireless networks is all-IP infrastructure. This paper discusses the mobility management schemes for the next generation mobile networks through extending IP's functions with mobility support. A global hierarchical framework model for the mobility management of wireless networks is presented, in which the mobility management is divided into two complementary tasks: macro mobility and micro mobility. As the macro mobility solution, a basic principle of Mobile IP is introduced, together with the optimal schemes and the advances in IPv6. The disadvantages of the Mobile IP on solving the micro mobility problem are analyzed, on the basis of which three main proposals are discussed as the micro mobility solutions for mobile communications, including Hierarchical Mobile IP (HMIP), Cellular IP, and Handoff-Aware Wireless Access Internet Infrastructure (HAWAII). A unified model is also described in which the different micro mobility solutions can coexist simultaneously in mobile networks.

  9. Applications of Graph Spectral Techniques to Water Distribution Network Management

    Directory of Open Access Journals (Sweden)

    Armando di Nardo

    2018-01-01

    Full Text Available Cities depend on multiple heterogeneous, interconnected infrastructures to provide safe water to consumers. Given this complexity, efficient numerical techniques are needed to support optimal control and management of a water distribution network (WDN. This paper introduces a holistic analysis framework to support water utilities on the decision making process for an efficient supply management. The proposal is based on graph spectral techniques that take advantage of eigenvalues and eigenvectors properties of matrices that are associated with graphs. Instances of these matrices are the adjacency matrix and the Laplacian, among others. The interest for this application is to work on a graph that specifically represents a WDN. This is a complex network that is made by nodes corresponding to water sources and consumption points and links corresponding to pipes and valves. The aim is to face new challenges on urban water supply, ranging from computing approximations for network performance assessment to setting device positioning for efficient and automatic WDN division into district metered areas. It is consequently created a novel tool-set of graph spectral techniques adapted to improve main water management tasks and to simplify the identification of water losses through the definition of an optimal network partitioning. Two WDNs are used to analyze the proposed methodology. Firstly, the well-known network of C-Town is investigated for benchmarking of the proposed graph spectral framework. This allows for comparing the obtained results with others coming from previously proposed approaches in literature. The second case-study corresponds to an operational network. It shows the usefulness and optimality of the proposal to effectively manage a WDN.

  10. Iterated interactions method. Realistic NN potential

    International Nuclear Information System (INIS)

    Gorbatov, A.M.; Skopich, V.L.; Kolganova, E.A.

    1991-01-01

    The method of iterated potential is tested in the case of realistic fermionic systems. As a base for comparison calculations of the 16 O system (using various versions of realistic NN potentials) by means of the angular potential-function method as well as operators of pairing correlation were used. The convergence of genealogical series is studied for the central Malfliet-Tjon potential. In addition the mathematical technique of microscopical calculations is improved: new equations for correlators in odd states are suggested and the technique of leading terms was applied for the first time to calculations of heavy p-shell nuclei in the basis of angular potential functions

  11. Microstructural characterization of materials by neural network technique

    Energy Technology Data Exchange (ETDEWEB)

    Barat, P. [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Chatterjee, A., E-mail: arnomitra@veccal.ernet.i [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Mukherjee, P.; Gayathri, N. [Variable Energy Cyclotron Centre, 1/AF Bidhan Nagar, Kolkata 700064 (India); Jayakumar, T.; Raj, Baldev [Indira Gandhi Centre for Atomic Research, Kalpakkam 603102 (India)

    2010-11-15

    Ultrasonic signals received by pulse echo technique from plane parallel Zircaloy 2 samples of fixed thickness and of three different microstructures, were subjected to signal analysis, as conventional parameters like velocity and attenuation could not reliably discriminate them. The signals, obtained from these samples, were first sampled and digitized. Modified Karhunen Loeve Transform was used to reduce their dimensionality. A multilayered feed forward Artificial Neural Network was trained using a few signals in their reduced domain from the three different microstructures. The rest of the signals from the three samples with different microstructures were classified satisfactorily using this network.

  12. Cooperative Technique Based on Sensor Selection in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    ISLAM, M. R.

    2009-02-01

    Full Text Available An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs are used to form a Multiple Input Single Output (MISO structure wirelessly connected with a Network Capable Application Processor (NCAP. Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for evaluating derived parameters. The results show that the selected MISO structure outperforms the unselected MISO structure and it shows energy efficient performance than SISO structure after a certain distance.

  13. Congestion estimation technique in the optical network unit registration process.

    Science.gov (United States)

    Kim, Geunyong; Yoo, Hark; Lee, Dongsoo; Kim, Youngsun; Lim, Hyuk

    2016-07-01

    We present a congestion estimation technique (CET) to estimate the optical network unit (ONU) registration success ratio for the ONU registration process in passive optical networks. An optical line terminal (OLT) estimates the number of collided ONUs via the proposed scheme during the serial number state. The OLT can obtain congestion level among ONUs to be registered such that this information may be exploited to change the size of a quiet window to decrease the collision probability. We verified the efficiency of the proposed method through simulation and experimental results.

  14. Dynamical graph theory networks techniques for the analysis of sparse connectivity networks in dementia

    Science.gov (United States)

    Tahmassebi, Amirhessam; Pinker-Domenig, Katja; Wengert, Georg; Lobbes, Marc; Stadlbauer, Andreas; Romero, Francisco J.; Morales, Diego P.; Castillo, Encarnacion; Garcia, Antonio; Botella, Guillermo; Meyer-Bäse, Anke

    2017-05-01

    Graph network models in dementia have become an important computational technique in neuroscience to study fundamental organizational principles of brain structure and function of neurodegenerative diseases such as dementia. The graph connectivity is reflected in the connectome, the complete set of structural and functional connections of the graph network, which is mostly based on simple Pearson correlation links. In contrast to simple Pearson correlation networks, the partial correlations (PC) only identify direct correlations while indirect associations are eliminated. In addition to this, the state-of-the-art techniques in brain research are based on static graph theory, which is unable to capture the dynamic behavior of the brain connectivity, as it alters with disease evolution. We propose a new research avenue in neuroimaging connectomics based on combining dynamic graph network theory and modeling strategies at different time scales. We present the theoretical framework for area aggregation and time-scale modeling in brain networks as they pertain to disease evolution in dementia. This novel paradigm is extremely powerful, since we can derive both static parameters pertaining to node and area parameters, as well as dynamic parameters, such as system's eigenvalues. By implementing and analyzing dynamically both disease driven PC-networks and regular concentration networks, we reveal differences in the structure of these network that play an important role in the temporal evolution of this disease. The described research is key to advance biomedical research on novel disease prediction trajectories and dementia therapies.

  15. Optimal deep neural networks for sparse recovery via Laplace techniques

    OpenAIRE

    Limmer, Steffen; Stanczak, Slawomir

    2017-01-01

    This paper introduces Laplace techniques for designing a neural network, with the goal of estimating simplex-constraint sparse vectors from compressed measurements. To this end, we recast the problem of MMSE estimation (w.r.t. a pre-defined uniform input distribution) as the problem of computing the centroid of some polytope that results from the intersection of the simplex and an affine subspace determined by the measurements. Owing to the specific structure, it is shown that the centroid ca...

  16. NN-SITE: A remote monitoring testbed facility

    International Nuclear Information System (INIS)

    Kadner, S.; White, R.; Roman, W.; Sheely, K.; Puckett, J.; Ystesund, K.

    1997-01-01

    DOE, Aquila Technologies, LANL and SNL recently launched collaborative efforts to create a Non-Proliferation Network Systems Integration and Test (NN-Site, pronounced N-Site) facility. NN-Site will focus on wide area, local area, and local operating level network connectivity including Internet access. This facility will provide thorough and cost-effective integration, testing and development of information connectivity among diverse operating systems and network topologies prior to full-scale deployment. In concentrating on instrument interconnectivity, tamper indication, and data collection and review, NN-Site will facilitate efforts of equipment providers and system integrators in deploying systems that will meet nuclear non-proliferation and safeguards objectives. The following will discuss the objectives of ongoing remote monitoring efforts, as well as the prevalent policy concerns. An in-depth discussion of the Non-Proliferation Network Systems Integration and Test facility (NN-Site) will illuminate the role that this testbed facility can perform in meeting the objectives of remote monitoring efforts, and its potential contribution in promoting eventual acceptance of remote monitoring systems in facilities worldwide

  17. Statistical techniques to extract information during SMAP soil moisture assimilation

    Science.gov (United States)

    Kolassa, J.; Reichle, R. H.; Liu, Q.; Alemohammad, S. H.; Gentine, P.

    2017-12-01

    Statistical techniques permit the retrieval of soil moisture estimates in a model climatology while retaining the spatial and temporal signatures of the satellite observations. As a consequence, the need for bias correction prior to an assimilation of these estimates is reduced, which could result in a more effective use of the independent information provided by the satellite observations. In this study, a statistical neural network (NN) retrieval algorithm is calibrated using SMAP brightness temperature observations and modeled soil moisture estimates (similar to those used to calibrate the SMAP Level 4 DA system). Daily values of surface soil moisture are estimated using the NN and then assimilated into the NASA Catchment model. The skill of the assimilation estimates is assessed based on a comprehensive comparison to in situ measurements from the SMAP core and sparse network sites as well as the International Soil Moisture Network. The NN retrieval assimilation is found to significantly improve the model skill, particularly in areas where the model does not represent processes related to agricultural practices. Additionally, the NN method is compared to assimilation experiments using traditional bias correction techniques. The NN retrieval assimilation is found to more effectively use the independent information provided by SMAP resulting in larger model skill improvements than assimilation experiments using traditional bias correction techniques.

  18. A neural network image reconstruction technique for electrical impedance tomography

    International Nuclear Information System (INIS)

    Adler, A.; Guardo, R.

    1994-01-01

    Reconstruction of Images in Electrical Impedance Tomography requires the solution of a nonlinear inverse problem on noisy data. This problem is typically ill-conditioned and requires either simplifying assumptions or regularization based on a priori knowledge. This paper presents a reconstruction algorithm using neural network techniques which calculates a linear approximation of the inverse problem directly from finite element simulations of the forward problem. This inverse is adapted to the geometry of the medium and the signal-to-noise ratio (SNR) used during network training. Results show good conductivity reconstruction where measurement SNR is similar to the training conditions. The advantages of this method are its conceptual simplicity and ease of implementation, and the ability to control the compromise between the noise performance and resolution of the image reconstruction

  19. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.

    2010-11-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine the location of the source using the direct and the relayed signal at the destination. We derive the Cramer-Rao lower bound (CRLB) expressions separately for x and y coordinates of the location estimate. We analyze the effects of cognitive behaviour of the relay on the performance of the proposed method. We also discuss and quantify the reliability of the location estimate using the proposed technique if the source is not stationary. The overall performance of the proposed method is presented through simulations. ©2010 IEEE.

  20. Performance of artificial neural networks and genetical evolved artificial neural networks unfolding techniques

    International Nuclear Information System (INIS)

    Ortiz R, J. M.; Martinez B, M. R.; Vega C, H. R.; Gallego D, E.; Lorente F, A.; Mendez V, R.; Los Arcos M, J. M.; Guerrero A, J. E.

    2011-01-01

    With the Bonner spheres spectrometer neutron spectrum is obtained through an unfolding procedure. Monte Carlo methods, Regularization, Parametrization, Least-squares, and Maximum Entropy are some of the techniques utilized for unfolding. In the last decade methods based on Artificial Intelligence Technology have been used. Approaches based on Genetic Algorithms and Artificial Neural Networks (Ann) have been developed in order to overcome the drawbacks of previous techniques. Nevertheless the advantages of Ann still it has some drawbacks mainly in the design process of the network, vg the optimum selection of the architectural and learning Ann parameters. In recent years the use of hybrid technologies, combining Ann and genetic algorithms, has been utilized to. In this work, several Ann topologies were trained and tested using Ann and Genetically Evolved Artificial Neural Networks in the aim to unfold neutron spectra using the count rates of a Bonner sphere spectrometer. Here, a comparative study of both procedures has been carried out. (Author)

  1. Display techniques for dynamic network data in transportation GIS

    Energy Technology Data Exchange (ETDEWEB)

    Ganter, J.H.; Cashwell, J.W.

    1994-05-01

    Interest in the characteristics of urban street networks is increasing at the same time new monitoring technologies are delivering detailed traffic data. These emerging streams of data may lead to the dilemma that airborne remote sensing has faced: how to select and access the data, and what meaning is hidden in them? computer-assisted visualization techniques are needed to portray these dynamic data. Of equal importance are controls that let the user filter, symbolize, and replay the data to reveal patterns and trends over varying time spans. We discuss a prototype software system that addresses these requirements.

  2. Particle identification using artificial neural networks at BESIII

    International Nuclear Information System (INIS)

    Qin Gang; Lv Junguang; Bian Jianming; Chinese Academy of Sciences, Beijing

    2008-01-01

    A multilayered perceptrons' neural network technique has been applied in the particle identification at BESIII. The networks are trained in each sub-detector level. The NN output of sub-detectors can be sent to a sequential network or be constructed as PDFs for a likelihood. Good muon-ID, electron-ID and hadron-ID are obtained from the networks by using the simulated Monte Carlo samples. (authors)

  3. The role of neural networks in nuclear power plant safety systems

    International Nuclear Information System (INIS)

    Boger, Z.

    1993-01-01

    Neural networks (NN) techniques have been applied in recent years to many systems by researchers in the nuclear power industry, mainly for modeling and sensor validation. Recent results are reviewed, including new directions in applications to control systems, safety analysis, and ''virtual'' instruments. As new fast learning algorithms become available, large systems may be learned effectively, even with few training examples. The nuclear industry hesitates to include NN in safety related systems, but it seems that the obstacles could be overcome with the demonstration of successful applications, even from other industries. Coupling of full-scale reactor simulators, as fault database generators, with neural networks learning should be explored. The integration of Expert System technology with NN should improve the Validation and Verification tasks, and also help overcome psychological barriers. It may prove that the potential of NN to help operators, compared with the existing and proposed alternatives, outweigh the risks. (author). 58 refs, 2 figs

  4. Exploiting Social Media Sensor Networks through Novel Data Fusion Techniques

    Energy Technology Data Exchange (ETDEWEB)

    Kouri, Tina [Sandia National Lab. (SNL-NM), Albuquerque, NM (United States)

    2017-11-01

    Unprecedented amounts of data are continuously being generated by sensors (“hard” data) and by humans (“soft” data), and this data needs to be exploited to its full potential. The first step in exploiting this data is determine how the hard and soft data are related to each other. In this project we fuse hard and soft data, using the attributes of each (e.g., time and space), to gain more information about interesting events. Next, we attempt to use social networking textual data to predict the present (i.e., predict that an interesting event is occurring and details about the event) using data mining, machine learning, natural language processing, and text analysis techniques.

  5. Distributed cluster management techniques for unattended ground sensor networks

    Science.gov (United States)

    Essawy, Magdi A.; Stelzig, Chad A.; Bevington, James E.; Minor, Sharon

    2005-05-01

    Smart Sensor Networks are becoming important target detection and tracking tools. The challenging problems in such networks include the sensor fusion, data management and communication schemes. This work discusses techniques used to distribute sensor management and multi-target tracking responsibilities across an ad hoc, self-healing cluster of sensor nodes. Although miniaturized computing resources possess the ability to host complex tracking and data fusion algorithms, there still exist inherent bandwidth constraints on the RF channel. Therefore, special attention is placed on the reduction of node-to-node communications within the cluster by minimizing unsolicited messaging, and distributing the sensor fusion and tracking tasks onto local portions of the network. Several challenging problems are addressed in this work including track initialization and conflict resolution, track ownership handling, and communication control optimization. Emphasis is also placed on increasing the overall robustness of the sensor cluster through independent decision capabilities on all sensor nodes. Track initiation is performed using collaborative sensing within a neighborhood of sensor nodes, allowing each node to independently determine if initial track ownership should be assumed. This autonomous track initiation prevents the formation of duplicate tracks while eliminating the need for a central "management" node to assign tracking responsibilities. Track update is performed as an ownership node requests sensor reports from neighboring nodes based on track error covariance and the neighboring nodes geo-positional location. Track ownership is periodically recomputed using propagated track states to determine which sensing node provides the desired coverage characteristics. High fidelity multi-target simulation results are presented, indicating the distribution of sensor management and tracking capabilities to not only reduce communication bandwidth consumption, but to also

  6. Calibration Technique of the Irradiated Thermocouple using Artificial Neural Network

    Energy Technology Data Exchange (ETDEWEB)

    Hong, Jin Tae; Joung, Chang Young; Ahn, Sung Ho; Yang, Tae Ho; Heo, Sung Ho; Jang, Seo Yoon [KAERI, Daejeon (Korea, Republic of)

    2016-05-15

    To correct the signals, the degradation rate of sensors needs to be analyzed, and re-calibration of sensors should be followed periodically. In particular, because thermocouples instrumented in the nuclear fuel rod are degraded owing to the high neutron fluence generated from the nuclear fuel, the periodic re-calibration process is necessary. However, despite the re-calibration of the thermocouple, the measurement error will be increased until next re-calibration. In this study, based on the periodically calibrated temperature - voltage data, an interpolation technique using the artificial neural network will be introduced to minimize the calibration error of the C-type thermocouple under the irradiation test. The test result shows that the calculated voltages derived from the interpolation function have good agreement with the experimental sampling data, and they also accurately interpolate the voltages at arbitrary temperature and neutron fluence. That is, once the reference data is obtained by experiments, it is possible to accurately calibrate the voltage signal at a certain neutron fluence and temperature using an artificial neural network.

  7. Applications of neural networks to the studies of phase transitions of two-dimensional Potts models

    Science.gov (United States)

    Li, C.-D.; Tan, D.-R.; Jiang, F.-J.

    2018-04-01

    We study the phase transitions of two-dimensional (2D) Q-states Potts models on the square lattice, using the first principles Monte Carlo (MC) simulations as well as the techniques of neural networks (NN). We demonstrate that the ideas from NN can be adopted to study these considered phase transitions efficiently. In particular, even with a simple NN constructed in this investigation, we are able to obtain the relevant information of the nature of these phase transitions, namely whether they are first order or second order. Our results strengthen the potential applicability of machine learning in studying various states of matters. Subtlety of applying NN techniques to investigate many-body systems is briefly discussed as well.

  8. Segmentation of isolated MR images: development and comparison of neuronal networks

    International Nuclear Information System (INIS)

    Paredes, R.; Robles, M.; Marti-Bonmati, L.; Masia, L.

    1998-01-01

    Segmentation defines the capacity to differentiate among types of tissues. In MR. it is frequently applied to volumetric determinations. Digital images can be segmented in a number of ways; neuronal networks (NN) can be employed for this purpose. Our objective was to develop algorithms for automatic segmentation using NN and apply them to central nervous system MR images. The segmentation obtained with NN was compared with that resulting from other procedures (region-growing and K means). Each NN consisted of two layers: one based on unsupervised training, which was utilized for image segmentation in sets of K, and a second layer associating each set obtained by the preceding layer with the real set corresponding to the previously segmented objective image. This NN was trained with previously segmented images with supervised regions-growing algorithms and automatic K means. Thus, 4 different segmentation were obtained: region-growing, K means, NN with region-growing and NN with K means. The tissue volumes corresponding to cerebrospinal fluid, gray matter and white matter obtained with the 4 techniques were compared and the most representative segmented image was selected qualitatively by averaging the visual perception of 3 radiologists. The segmentation that best corresponded to the visual perception of the radiologists was that consisting of trained NN with region-growing. In comparison, the other 3 algorithms presented low percentage differences (mean, 3.44%). The mean percentage error for the 3 tissues from these algorithms was lower for region-growing segmentation (2.34%) than for trained NN with K means (3.31%) and for automatic K-means segmentation (4.66%). Thus, NN are reliable in the automation of isolated MR image segmentation. (Author) 12 refs

  9. The application of neural networks with artificial intelligence technique in the modeling of industrial processes

    International Nuclear Information System (INIS)

    Saini, K. K.; Saini, Sanju

    2008-01-01

    Neural networks are a relatively new artificial intelligence technique that emulates the behavior of biological neural systems in digital software or hardware. These networks can 'learn', automatically, complex relationships among data. This feature makes the technique very useful in modeling processes for which mathematical modeling is difficult or impossible. The work described here outlines some examples of the application of neural networks with artificial intelligence technique in the modeling of industrial processes.

  10. An RSS based location estimation technique for cognitive relay networks

    KAUST Repository

    Qaraqe, Khalid A.; Hussain, Syed Imtiaz; Ç elebi, Hasari Burak; Abdallah, Mohamed M.; Alouini, Mohamed-Slim

    2010-01-01

    In this paper, a received signal strength (RSS) based location estimation method is proposed for a cooperative wireless relay network where the relay is a cognitive radio. We propose a method for the considered cognitive relay network to determine

  11. A fuzzy network module extraction technique for gene expression data

    Indian Academy of Sciences (India)

    2014-05-01

    expression network from the distance matrix. The distance matrix is .... mental process, cellular component assembly involved in ..... the molecules are present in the network. User can ... hsa05213:Endometrial cancer. 24. 0.07.

  12. The summarize of the technique about proactive network security protection

    International Nuclear Information System (INIS)

    Liu Baoxu; Li Xueying; Cao Aijuan; Yu Chuansong; Xu Rongsheng

    2003-01-01

    The proactive protection measures and the traditional passive security protection tools are complementarities each other. It also can supply the conventional network security protection system and enhance its capability of the security protection. Based upon sorts of existing network security technologies, this article analyses and summarizes the technologies, functions and the development directions of some key proactive network security protection tools. (authors)

  13. Reinforcement learning techniques for controlling resources in power networks

    Science.gov (United States)

    Kowli, Anupama Sunil

    As power grids transition towards increased reliance on renewable generation, energy storage and demand response resources, an effective control architecture is required to harness the full functionalities of these resources. There is a critical need for control techniques that recognize the unique characteristics of the different resources and exploit the flexibility afforded by them to provide ancillary services to the grid. The work presented in this dissertation addresses these needs. Specifically, new algorithms are proposed, which allow control synthesis in settings wherein the precise distribution of the uncertainty and its temporal statistics are not known. These algorithms are based on recent developments in Markov decision theory, approximate dynamic programming and reinforcement learning. They impose minimal assumptions on the system model and allow the control to be "learned" based on the actual dynamics of the system. Furthermore, they can accommodate complex constraints such as capacity and ramping limits on generation resources, state-of-charge constraints on storage resources, comfort-related limitations on demand response resources and power flow limits on transmission lines. Numerical studies demonstrating applications of these algorithms to practical control problems in power systems are discussed. Results demonstrate how the proposed control algorithms can be used to improve the performance and reduce the computational complexity of the economic dispatch mechanism in a power network. We argue that the proposed algorithms are eminently suitable to develop operational decision-making tools for large power grids with many resources and many sources of uncertainty.

  14. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection

    Directory of Open Access Journals (Sweden)

    Declan T. Delaney

    2016-12-01

    Full Text Available No single network solution for Internet of Things (IoT networks can provide the required level of Quality of Service (QoS for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  15. A Framework to Implement IoT Network Performance Modelling Techniques for Network Solution Selection.

    Science.gov (United States)

    Delaney, Declan T; O'Hare, Gregory M P

    2016-12-01

    No single network solution for Internet of Things (IoT) networks can provide the required level of Quality of Service (QoS) for all applications in all environments. This leads to an increasing number of solutions created to fit particular scenarios. Given the increasing number and complexity of solutions available, it becomes difficult for an application developer to choose the solution which is best suited for an application. This article introduces a framework which autonomously chooses the best solution for the application given the current deployed environment. The framework utilises a performance model to predict the expected performance of a particular solution in a given environment. The framework can then choose an apt solution for the application from a set of available solutions. This article presents the framework with a set of models built using data collected from simulation. The modelling technique can determine with up to 85% accuracy the solution which performs the best for a particular performance metric given a set of solutions. The article highlights the fractured and disjointed practice currently in place for examining and comparing communication solutions and aims to open a discussion on harmonising testing procedures so that different solutions can be directly compared and offers a framework to achieve this within IoT networks.

  16. Neural Network-Based Coronary Heart Disease Risk Prediction Using Feature Correlation Analysis

    Directory of Open Access Journals (Sweden)

    Jae Kwon Kim

    2017-01-01

    Full Text Available Background. Of the machine learning techniques used in predicting coronary heart disease (CHD, neural network (NN is popularly used to improve performance accuracy. Objective. Even though NN-based systems provide meaningful results based on clinical experiments, medical experts are not satisfied with their predictive performances because NN is trained in a “black-box” style. Method. We sought to devise an NN-based prediction of CHD risk using feature correlation analysis (NN-FCA using two stages. First, the feature selection stage, which makes features acceding to the importance in predicting CHD risk, is ranked, and second, the feature correlation analysis stage, during which one learns about the existence of correlations between feature relations and the data of each NN predictor output, is determined. Result. Of the 4146 individuals in the Korean dataset evaluated, 3031 had low CHD risk and 1115 had CHD high risk. The area under the receiver operating characteristic (ROC curve of the proposed model (0.749 ± 0.010 was larger than the Framingham risk score (FRS (0.393 ± 0.010. Conclusions. The proposed NN-FCA, which utilizes feature correlation analysis, was found to be better than FRS in terms of CHD risk prediction. Furthermore, the proposed model resulted in a larger ROC curve and more accurate predictions of CHD risk in the Korean population than the FRS.

  17. Discrimination of panti p → tanti t events by a neural network classifier

    International Nuclear Information System (INIS)

    Cherubini, A.; Odorico, R.

    1992-01-01

    Neural network and conventional statistical techniques are compared in the problem of discriminating panti p→tanti t events, with top quarks decaying into anything, from the associated hadronic background at the energy of the Fermilab collider. The NN we develop for this sake is an improved version of Kohonen's learning vector quantization scheme. Performance of the NN as a tanti t event classifier is found to be less satisfactory than that achievable by statistical methods. We conclude that the probable reasons for that are: i) The NN approach presents advantages only when dealing with event distributions in the feature space which substantially differ from Gaussians; ii) NN's require much larger training sets of events than statistical discrimination in order to give comparable results. (orig.)

  18. Improved Space Surveillance Network (SSN) Scheduling using Artificial Intelligence Techniques

    Science.gov (United States)

    Stottler, D.

    There are close to 20,000 cataloged manmade objects in space, the large majority of which are not active, functioning satellites. These are tracked by phased array and mechanical radars and ground and space-based optical telescopes, collectively known as the Space Surveillance Network (SSN). A better SSN schedule of observations could, using exactly the same legacy sensor resources, improve space catalog accuracy through more complementary tracking, provide better responsiveness to real-time changes, better track small debris in low earth orbit (LEO) through efficient use of applicable sensors, efficiently track deep space (DS) frequent revisit objects, handle increased numbers of objects and new types of sensors, and take advantage of future improved communication and control to globally optimize the SSN schedule. We have developed a scheduling algorithm that takes as input the space catalog and the associated covariance matrices and produces a globally optimized schedule for each sensor site as to what objects to observe and when. This algorithm is able to schedule more observations with the same sensor resources and have those observations be more complementary, in terms of the precision with which each orbit metric is known, to produce a satellite observation schedule that, when executed, minimizes the covariances across the entire space object catalog. If used operationally, the results would be significantly increased accuracy of the space catalog with fewer lost objects with the same set of sensor resources. This approach inherently can also trade-off fewer high priority tasks against more lower-priority tasks, when there is benefit in doing so. Currently the project has completed a prototyping and feasibility study, using open source data on the SSN's sensors, that showed significant reduction in orbit metric covariances. The algorithm techniques and results will be discussed along with future directions for the research.

  19. Wireless multimedia sensor networks on reconfigurable hardware information reduction techniques

    CERN Document Server

    Ang, Li-minn; Chew, Li Wern; Yeong, Lee Seng; Chia, Wai Chong

    2013-01-01

    Traditional wireless sensor networks (WSNs) capture scalar data such as temperature, vibration, pressure, or humidity. Motivated by the success of WSNs and also with the emergence of new technology in the form of low-cost image sensors, researchers have proposed combining image and audio sensors with WSNs to form wireless multimedia sensor networks (WMSNs).

  20. NET European Network on Neutron Techniques Standardization for Structural Integrity

    International Nuclear Information System (INIS)

    Youtsos, A.

    2004-01-01

    Improved performance and safety of European energy production systems is essential for providing safe, clean and inexpensive electricity to the citizens of the enlarged EU. The state of the art in assessing internal stresses, micro-structure and defects in welded nuclear components -as well as their evolution due to complex thermo-mechanical loads and irradiation exposure -needs to be improved before relevant structural integrity assessment code requirements can safely become less conservative. This is valid for both experimental characterization techniques and predictive numerical algorithms. In the course of the last two decades neutron methods have proven to be excellent means for providing valuable information required in structural integrity assessment of advanced engineering applications. However, the European industry is hampered from broadly using neutron research due to lack of harmonised and standardized testing methods. 35 European major industrial and research/academic organizations have joined forces, under JRC coordination, to launch the NET European Network on Neutron Techniques Standardization for Structural Integrity in May 2002. The NET collaborative research initiative aims at further development and harmonisation of neutron scattering methods, in support of structural integrity assessment. This is pursued through a number of testing round robin campaigns on neutron diffraction and small angle neutron scattering - SANS and supported by data provided by other more conventional destructive and non-destructive methods, such as X-ray diffraction and deep and surface hole drilling. NET also strives to develop more reliable and harmonized simulation procedures for the prediction of residual stress and damage in steel welded power plant components. This is pursued through a number of computational round robin campaigns based on advanced FEM techniques, and on reliable data obtained by such novel and harmonized experimental methods. The final goal of

  1. Models of πNN interactions

    International Nuclear Information System (INIS)

    Lee, T.S.H.

    1988-01-01

    A πNN model inspired by Quantum Chromodynamics is presented. The model gives an accurate fit to the most recent Arndt NN phase shifts up to 1 GeV and can be applied to study intermediate- and high-energy nuclear reactions. 20 refs., 2 figs

  2. Coarse-grain bandwidth estimation techniques for large-scale network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, E.

    In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-and-forward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.

  3. Coarse-Grain Bandwidth Estimation Techniques for Large-Scale Space Network

    Science.gov (United States)

    Cheung, Kar-Ming; Jennings, Esther

    2013-01-01

    In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-andforward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.

  4. Adverse Outcome Pathway Network Analyses: Techniques and benchmarking the AOPwiki

    Science.gov (United States)

    Abstract: As the community of toxicological researchers, risk assessors, and risk managers adopt the adverse outcome pathway (AOP) paradigm for organizing toxicological knowledge, the number and diversity of adverse outcome pathways and AOP networks are continuing to grow. This ...

  5. Memory Compression Techniques for Network Address Management in MPI

    Energy Technology Data Exchange (ETDEWEB)

    Guo, Yanfei; Archer, Charles J.; Blocksome, Michael; Parker, Scott; Bland, Wesley; Raffenetti, Ken; Balaji, Pavan

    2017-05-29

    MPI allows applications to treat processes as a logical collection of integer ranks for each MPI communicator, while internally translating these logical ranks into actual network addresses. In current MPI implementations the management and lookup of such network addresses use memory sizes that are proportional to the number of processes in each communicator. In this paper, we propose a new mechanism, called AV-Rankmap, for managing such translation. AV-Rankmap takes advantage of logical patterns in rank-address mapping that most applications naturally tend to have, and it exploits the fact that some parts of network address structures are naturally more performance critical than others. It uses this information to compress the memory used for network address management. We demonstrate that AV-Rankmap can achieve performance similar to or better than that of other MPI implementations while using significantly less memory.

  6. A Technique for Presenting a Deceptive Dynamic Network Topology

    Science.gov (United States)

    2013-03-01

    more complicated network topologies in our experiments. We used a Watts- Strogatz [35] model to generate a synthetic topology for experimentation due...generated Watts- Strogatz model except for the intelligent router and the web server. The actual router used does not impact the results of our experiment...library for the Python [43] programming language. NetworkX provides two features useful for this experiment. It was used to generate a Watts- Strogatz model

  7. Investigation of tt in the full hadronic final state at CDF with a neural network approach

    CERN Document Server

    Sidoti, A; Busetto, G; Castro, A; Dusini, S; Lazzizzera, I; Wyss, J

    2001-01-01

    In this work we present the results of a neural network (NN) approach to the measurement of the tt production cross-section and top mass in the all-hadronic channel, analyzing data collected at the Collider Detector at Fermilab (CDF) experiment. We have used a hardware implementation of a feedforward neural network, TOTEM, the product of a collaboration of INFN (Istituto Nazionale Fisica Nucleare)-IRST (Istituto per la Ricerca Scientifica e Tecnologica)-University of Trento, Italy. Particular attention has been paid to the evaluation of the systematics specifically related to the NN approach. The results are consistent with those obtained at CDF by conventional data selection techniques. (38 refs).

  8. Hybrid NN/SVM Computational System for Optimizing Designs

    Science.gov (United States)

    Rai, Man Mohan

    2009-01-01

    A computational method and system based on a hybrid of an artificial neural network (NN) and a support vector machine (SVM) (see figure) has been conceived as a means of maximizing or minimizing an objective function, optionally subject to one or more constraints. Such maximization or minimization could be performed, for example, to optimize solve a data-regression or data-classification problem or to optimize a design associated with a response function. A response function can be considered as a subset of a response surface, which is a surface in a vector space of design and performance parameters. A typical example of a design problem that the method and system can be used to solve is that of an airfoil, for which a response function could be the spatial distribution of pressure over the airfoil. In this example, the response surface would describe the pressure distribution as a function of the operating conditions and the geometric parameters of the airfoil. The use of NNs to analyze physical objects in order to optimize their responses under specified physical conditions is well known. NN analysis is suitable for multidimensional interpolation of data that lack structure and enables the representation and optimization of a succession of numerical solutions of increasing complexity or increasing fidelity to the real world. NN analysis is especially useful in helping to satisfy multiple design objectives. Feedforward NNs can be used to make estimates based on nonlinear mathematical models. One difficulty associated with use of a feedforward NN arises from the need for nonlinear optimization to determine connection weights among input, intermediate, and output variables. It can be very expensive to train an NN in cases in which it is necessary to model large amounts of information. Less widely known (in comparison with NNs) are support vector machines (SVMs), which were originally applied in statistical learning theory. In terms that are necessarily

  9. Intrusion detection techniques for plant-wide network in a nuclear power plant

    International Nuclear Information System (INIS)

    Rajasekhar, P.; Shrikhande, S.V.; Biswas, B.B.; Patil, R.K.

    2012-01-01

    Nuclear power plants have a lot of critical data to be sent to the operator workstations. A plant wide integrated communication network, with high throughput, determinism and redundancy, is required between the workstations and the field. Switched Ethernet network is a promising prospect for such an integrated communication network. But for such an integrated system, intrusion is a major issue. Hence the network should have an intrusion detection system to make the network data secure and enhance the network availability. Intrusion detection is the process of monitoring the events occurring in a network and analyzing them for signs of possible incidents, which are violations or imminent threats of violation of network security policies, acceptable user policies, or standard security practices. This paper states the various intrusion detection techniques and approaches which are applicable for analysis of a plant wide network. (author)

  10. Multiclass Boosting with Adaptive Group-Based kNN and Its Application in Text Categorization

    Directory of Open Access Journals (Sweden)

    Lei La

    2012-01-01

    Full Text Available AdaBoost is an excellent committee-based tool for classification. However, its effectiveness and efficiency in multiclass categorization face the challenges from methods based on support vector machine (SVM, neural networks (NN, naïve Bayes, and k-nearest neighbor (kNN. This paper uses a novel multi-class AdaBoost algorithm to avoid reducing the multi-class classification problem to multiple two-class classification problems. This novel method is more effective. In addition, it keeps the accuracy advantage of existing AdaBoost. An adaptive group-based kNN method is proposed in this paper to build more accurate weak classifiers and in this way control the number of basis classifiers in an acceptable range. To further enhance the performance, weak classifiers are combined into a strong classifier through a double iterative weighted way and construct an adaptive group-based kNN boosting algorithm (AGkNN-AdaBoost. We implement AGkNN-AdaBoost in a Chinese text categorization system. Experimental results showed that the classification algorithm proposed in this paper has better performance both in precision and recall than many other text categorization methods including traditional AdaBoost. In addition, the processing speed is significantly enhanced than original AdaBoost and many other classic categorization algorithms.

  11. Synchronization of uncertain time-varying network based on sliding mode control technique

    Science.gov (United States)

    Lü, Ling; Li, Chengren; Bai, Suyuan; Li, Gang; Rong, Tingting; Gao, Yan; Yan, Zhe

    2017-09-01

    We research synchronization of uncertain time-varying network based on sliding mode control technique. The sliding mode control technique is first modified so that it can be applied to network synchronization. Further, by choosing the appropriate sliding surface, the identification law of uncertain parameter, the adaptive law of the time-varying coupling matrix element and the control input of network are designed, it is sure that the uncertain time-varying network can synchronize effectively the synchronization target. At last, we perform some numerical simulations to demonstrate the effectiveness of the proposed results.

  12. Developing Visualization Techniques for Semantics-based Information Networks

    Science.gov (United States)

    Keller, Richard M.; Hall, David R.

    2003-01-01

    Information systems incorporating complex network structured information spaces with a semantic underpinning - such as hypermedia networks, semantic networks, topic maps, and concept maps - are being deployed to solve some of NASA s critical information management problems. This paper describes some of the human interaction and navigation problems associated with complex semantic information spaces and describes a set of new visual interface approaches to address these problems. A key strategy is to leverage semantic knowledge represented within these information spaces to construct abstractions and views that will be meaningful to the human user. Human-computer interaction methodologies will guide the development and evaluation of these approaches, which will benefit deployed NASA systems and also apply to information systems based on the emerging Semantic Web.

  13. Data mining techniques in sensor networks summarization, interpolation and surveillance

    CERN Document Server

    Appice, Annalisa; Fumarola, Fabio; Malerba, Donato

    2013-01-01

    Sensor networks comprise of a number of sensors installed across a spatially distributed network, which gather information and periodically feed a central server with the measured data. The server monitors the data, issues possible alarms and computes fast aggregates. As data analysis requests may concern both present and past data, the server is forced to store the entire stream. But the limited storage capacity of a server may reduce the amount of data stored on the disk. One solution is to compute summaries of the data as it arrives, and to use these summaries to interpolate the real data.

  14. Future view of electric power information processing techniques. Architecture techniques for power supply communication network

    Energy Technology Data Exchange (ETDEWEB)

    Tanaka, Keisuke

    1988-06-20

    Present situations of a power supply communication are described, and the future trend of a power supply information network is reviewed. For the improvement of a transmission efficiency and quality and a cost benefit for the power supply communication, the introduction of digital networks has been promoted. As for a protection information network, since there is the difference between a required communication quality of system protection information and that of power supply operation information, the individual digital network configuration is expected, in addition, the increasing of image information transmission for monitoring is also estimated. As for a business information network, the construction of a broad-band switched network is expected with increasing of image transmission needs such as a television meeting. Furthermore, the expansion to a power supply ISDN which is possible to connect between a telephone, facsimile and data terminal, to exchange various media and to connect between networks is expected with higher communication services in the protection and business network. However, for its practical use, the standardization of various interfaces will become essential. (3 figs, 1 tab)

  15. Combined techniques for network measurements at accelerator facilities

    International Nuclear Information System (INIS)

    Pschorn, I.

    1999-01-01

    Usually network measurements at GSi (Gesellschaft fur Schwerionen forschung) are carried out by employing the Leica tachymeter TC2002K etc. Due to time constraints and the fact that GSi possesses only one of these selected, high precision total-stations, it was suddenly necessary to think about employing a Laser tracker as the major instrument for a reference network measurement. The idea was to compare the different instruments and to proof if it is possible at all to carry out a precise network measurement using a laser tracker. In the end the SMX Tracker4500 combined with Leica NA3000 for network measurements at GSi, Darmstadt and at BESSY Il, Berlin (both located in Germany) was applied. A few results are shown in the following chapters. A new technology in 3D metrology came up. Some ideas of applying these new tools in the field of accelerator measurements are given. Finally aspects of calibration and checking the performance of the employed high precision instrument are pointed out in this paper. (author)

  16. Address autoconfiguration in wireless ad hoc networks : Protocols and techniques

    NARCIS (Netherlands)

    Cempaka Wangi, N.I.; Prasad, R.V.; Jacobsson, M.; Niemegeers, I.

    2008-01-01

    With the advent of smaller devices having higher computational capacity and wireless communication capabilities, the world is becoming completely networked. Although, the mobile nature of these devices provides ubiquitous services, it also poses many challenges. In this article, we look in depth at

  17. Evaluating Automatic Pools Distribution Techniques for Self-Configured Networks

    NARCIS (Netherlands)

    Gomes, Reinaldo; de O. Schmidt, Ricardo

    NextGeneration of Networks (NGN) is one of the most important research topics of the last decade. Current Internet is not capable of supporting new users and operators’ demands and a new structure will be necessary to them. In this context many solutions might be necessary: from architectural

  18. Knapsack--TOPSIS Technique for Vertical Handover in Heterogeneous Wireless Network.

    Directory of Open Access Journals (Sweden)

    E M Malathy

    Full Text Available In a heterogeneous wireless network, handover techniques are designed to facilitate anywhere/anytime service continuity for mobile users. Consistent best-possible access to a network with widely varying network characteristics requires seamless mobility management techniques. Hence, the vertical handover process imposes important technical challenges. Handover decisions are triggered for continuous connectivity of mobile terminals. However, bad network selection and overload conditions in the chosen network can cause fallout in the form of handover failure. In order to maintain the required Quality of Service during the handover process, decision algorithms should incorporate intelligent techniques. In this paper, a new and efficient vertical handover mechanism is implemented using a dynamic programming method from the operation research discipline. This dynamic programming approach, which is integrated with the Technique to Order Preference by Similarity to Ideal Solution (TOPSIS method, provides the mobile user with the best handover decisions. Moreover, in this proposed handover algorithm a deterministic approach which divides the network into zones is incorporated into the network server in order to derive an optimal solution. The study revealed that this method is found to achieve better performance and QoS support to users and greatly reduce the handover failures when compared to the traditional TOPSIS method. The decision arrived at the zone gateway using this operational research analytical method (known as the dynamic programming knapsack approach together with Technique to Order Preference by Similarity to Ideal Solution yields remarkably better results in terms of the network performance measures such as throughput and delay.

  19. Artificial neural network modeling of DDGS flowability with varying process and storage parameters

    Science.gov (United States)

    Neural Network (NN) modeling techniques were used to predict flowability behavior in distillers dried grains with solubles (DDGS) prepared with varying CDS (10, 15, and 20%, wb), drying temperature (100, 200, and 300°C), cooling temperature (-12, 0, and 35°C) and cooling time (0 and 1 month) levels....

  20. QoS Provisioning Techniques for Future Fiber-Wireless (FiWi Access Networks

    Directory of Open Access Journals (Sweden)

    Martin Maier

    2010-04-01

    Full Text Available A plethora of enabling optical and wireless access-metro network technologies have been emerging that can be used to build future-proof bimodal fiber-wireless (FiWi networks. Hybrid FiWi networks aim at providing wired and wireless quad-play services over the same infrastructure simultaneously and hold great promise to mitigate the digital divide and change the way we live and work by replacing commuting with teleworking. After overviewing enabling optical and wireless network technologies and their QoS provisioning techniques, we elaborate on enabling radio-over-fiber (RoF and radio-and-fiber (R&F technologies. We describe and investigate new QoS provisioning techniques for future FiWi networks, ranging from traffic class mapping, scheduling, and resource management to advanced aggregation techniques, congestion control, and layer-2 path selection algorithms.

  1. Detection of Two-Level Inverter Open-Circuit Fault Using a Combined DWT-NN Approach

    Directory of Open Access Journals (Sweden)

    Bilal Djamel Eddine Cherif

    2018-01-01

    Full Text Available Three-phase static converters with voltage structure are widely used in many industrial systems. In order to prevent the propagation of the fault to other components of the system and ensure continuity of service in the event of a failure of the converter, efficient and rapid methods of detection and localization must be implemented. This paper work addresses a diagnostic technique based on the discrete wavelet transform (DWT algorithm and the approach of neural network (NN, for the detection of an inverter IGBT open-circuit switch fault. To illustrate the merits of the technique and validate the results, experimental tests are conducted using a built voltage inverter fed induction motor. The inverter is controlled by the SVM control strategy.

  2. Swarm intelligence techniques for optimization and management tasks insensor networks

    OpenAIRE

    Hernández Pibernat, Hugo

    2012-01-01

    Premi extraordinari doctorat curs 2011-2012, àmbit Enginyeria de les TIC The main contributions of this thesis are located in the domain of wireless sensor netorks. More in detail, we introduce energyaware algorithms and protocols in the context of the following topics: self-synchronized duty-cycling in networks with energy harvesting capabilities, distributed graph coloring and minimum energy broadcasting with realistic antennas. In the following, we review the research conducted...

  3. Evaluation of Techniques to Detect Significant Network Performance Problems using End-to-End Active Network Measurements

    Energy Technology Data Exchange (ETDEWEB)

    Cottrell, R.Les; Logg, Connie; Chhaparia, Mahesh; /SLAC; Grigoriev, Maxim; /Fermilab; Haro, Felipe; /Chile U., Catolica; Nazir, Fawad; /NUST, Rawalpindi; Sandford, Mark

    2006-01-25

    End-to-End fault and performance problems detection in wide area production networks is becoming increasingly hard as the complexity of the paths, the diversity of the performance, and dependency on the network increase. Several monitoring infrastructures are built to monitor different network metrics and collect monitoring information from thousands of hosts around the globe. Typically there are hundreds to thousands of time-series plots of network metrics which need to be looked at to identify network performance problems or anomalous variations in the traffic. Furthermore, most commercial products rely on a comparison with user configured static thresholds and often require access to SNMP-MIB information, to which a typical end-user does not usually have access. In our paper we propose new techniques to detect network performance problems proactively in close to realtime and we do not rely on static thresholds and SNMP-MIB information. We describe and compare the use of several different algorithms that we have implemented to detect persistent network problems using anomalous variations analysis in real end-to-end Internet performance measurements. We also provide methods and/or guidance for how to set the user settable parameters. The measurements are based on active probes running on 40 production network paths with bottlenecks varying from 0.5Mbits/s to 1000Mbit/s. For well behaved data (no missed measurements and no very large outliers) with small seasonal changes most algorithms identify similar events. We compare the algorithms' robustness with respect to false positives and missed events especially when there are large seasonal effects in the data. Our proposed techniques cover a wide variety of network paths and traffic patterns. We also discuss the applicability of the algorithms in terms of their intuitiveness, their speed of execution as implemented, and areas of applicability. Our encouraging results compare and evaluate the accuracy of our

  4. Review Of Prevention Techniques For Denial Of Service DOS Attacks In Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Poonam Rolla

    2015-08-01

    Full Text Available Wireless Sensor Networks comprised of several tiny sensor nodes which are densely deployed over the region to monitor the environmental conditions. These sensor nodes have certain design issues out of which security is the main predominant factor as it effects the whole lifetime of network. DDoS Distributed denial of service attack floods unnecessary packets in the sensor network. A review on DDoS attacks and their prevention techniques have been done in this paper.

  5. Investigation of tt-bar in the full hadronic final state at CDF with a neural network approach

    International Nuclear Information System (INIS)

    Sidoti, A.; Azzi, P.; Busetto, G.; Castro, A.; Dusini, S.; Lazzizzera, I.; Wyss, J.L.

    2001-01-01

    In this work we present the results of a neural network (NN) approach to the measurement of the tt-bar production cross-section and top mass in the all-hadronic channel, analyzing data collected at the Collider Detector at Fermilab (CDF) experiment. We have used a hardware implementation of a feed forward neural network, TOTEM, the product of a collaboration of INFN (Istituto Nazionale Fisica Nucleare) - IRST (Istituto per la Ricerca Scientifica e Tecnologica) - University of Trento, Italy. Particular attention has been paid to the evaluation of the systematics specifically related to the NN approach. The results are consistent with those obtained at CDF by conventional data selection techniques

  6. Spin correlations in NN-NNπ reactions

    International Nuclear Information System (INIS)

    Davison, N.E.

    1990-01-01

    This paper reports that even after years of intensive work on the coupled NN-NNπ reactions, there are still some remarkable simple things that we do not know about the NN interactions at a few hundred MeV notably: Do dibaryon resonances exist, and if so, what are their masses and widths? How much isospin I = O interaction is there in the np channel? Why is the microscopic description of such a basic process as single pion production so elusive?

  7. Auditing information structures in organizations: A review of data collection techniques for network analysis

    NARCIS (Netherlands)

    Koning, K.H.; de Jong, Menno D.T.

    2005-01-01

    Network analysis is one of the current techniques for investigating organizational communication. Despite the amount of how-to literature about using network analysis to assess information flows and relationships in organizations, little is known about the methodological strengths and weaknesses of

  8. Social Learning Network Analysis Model to Identify Learning Patterns Using Ontology Clustering Techniques and Meaningful Learning

    Science.gov (United States)

    Firdausiah Mansur, Andi Besse; Yusof, Norazah

    2013-01-01

    Clustering on Social Learning Network still not explored widely, especially when the network focuses on e-learning system. Any conventional methods are not really suitable for the e-learning data. SNA requires content analysis, which involves human intervention and need to be carried out manually. Some of the previous clustering techniques need…

  9. Novel anti-jamming technique for OCDMA network through FWM in SOA based wavelength converter

    Science.gov (United States)

    Jyoti, Vishav; Kaler, R. S.

    2013-06-01

    In this paper, we propose a novel anti-jamming technique for optical code division multiple access (OCDMA) network through four wave mixing (FWM) in semiconductor optical amplifier (SOA) based wavelength converter. OCDMA signal can be easily jammed with high power jamming signal. It is shown that wavelength conversion through four wave mixing in SOA has improved capability of jamming resistance. It is observed that jammer has no effect on OCDMA network even at high jamming powers by using the proposed technique.

  10. Controller tuning of district heating networks using experiment design techniques

    International Nuclear Information System (INIS)

    Dobos, Laszlo; Abonyi, Janos

    2011-01-01

    There are various governmental policies aimed at reducing the dependence on fossil fuels for space heating and the reduction in its associated emission of greenhouse gases. DHNs (District heating networks) could provide an efficient method for house and space heating by utilizing residual industrial waste heat. In such systems, heat is produced and/or thermally upgraded in a central plant and then distributed to the end users through a pipeline network. The control strategies of these networks are rather difficult thanks to the non-linearity of the system and the strong interconnection between the controlled variables. That is why a NMPC (non-linear model predictive controller) could be applied to be able to fulfill the heat demand of the consumers. The main objective of this paper is to propose a tuning method for the applied NMPC to fulfill the control goal as soon as possible. The performance of the controller is characterized by an economic cost function based on pre-defined operation ranges. A methodology from the field of experiment design is applied to tune the model predictive controller to reach the best performance. The efficiency of the proposed methodology is proven throughout a case study of a simulated NMPC controlled DHN. -- Highlights: → To improve the energetic and economic efficiency of a DHN an appropriate control system is necessary. → The time consumption of transitions can be shortened with the proper control system. → A NLMPC is proposed as control system. → The NLMPC is tuned by utilization of simplex methodology, using an economic oriented cost function. → The proposed NLMPC needs a detailed model of the DHN based on the physical description.

  11. A new cascade NN based method to short-term load forecast in deregulated electricity market

    International Nuclear Information System (INIS)

    Kouhi, Sajjad; Keynia, Farshid

    2013-01-01

    Highlights: • We are proposed a new hybrid cascaded NN based method and WT to short-term load forecast in deregulated electricity market. • An efficient preprocessor consist of normalization and shuffling of signals is presented. • In order to select the best inputs, a two-stage feature selection is presented. • A new cascaded structure consist of three cascaded NNs is used as forecaster. - Abstract: Short-term load forecasting (STLF) is a major discussion in efficient operation of power systems. The electricity load is a nonlinear signal with time dependent behavior. The area of electricity load forecasting has still essential need for more accurate and stable load forecast algorithm. To improve the accuracy of prediction, a new hybrid forecast strategy based on cascaded neural network is proposed for STLF. This method is consists of wavelet transform, an intelligent two-stage feature selection, and cascaded neural network. The feature selection is used to remove the irrelevant and redundant inputs. The forecast engine is composed of three cascaded neural network (CNN) structure. This cascaded structure can be efficiently extract input/output mapping function of the nonlinear electricity load data. Adjustable parameters of the intelligent feature selection and CNN is fine-tuned by a kind of cross-validation technique. The proposed STLF is tested on PJM and New York electricity markets. It is concluded from the result, the proposed algorithm is a robust forecast method

  12. Classification of remotely sensed data using OCR-inspired neural network techniques. [Optical Character Recognition

    Science.gov (United States)

    Kiang, Richard K.

    1992-01-01

    Neural networks have been applied to classifications of remotely sensed data with some success. To improve the performance of this approach, an examination was made of how neural networks are applied to the optical character recognition (OCR) of handwritten digits and letters. A three-layer, feedforward network, along with techniques adopted from OCR, was used to classify Landsat-4 Thematic Mapper data. Good results were obtained. To overcome the difficulties that are characteristic of remote sensing applications and to attain significant improvements in classification accuracy, a special network architecture may be required.

  13. Multivariate correlation analysis technique based on euclidean distance map for network traffic characterization

    NARCIS (Netherlands)

    Tan, Zhiyuan; Jamdagni, Aruna; He, Xiangjian; Nanda, Priyadarsi; Liu, Ren Ping; Qing, Sihan; Susilo, Willy; Wang, Guilin; Liu, Dongmei

    2011-01-01

    The quality of feature has significant impact on the performance of detection techniques used for Denial-of-Service (DoS) attack. The features that fail to provide accurate characterization for network traffic records make the techniques suffer from low accuracy in detection. Although researches

  14. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    AlQuerm, Ismail A.

    2018-02-21

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks such as the upcoming 5G. Energy efficiency in the future 5G network is one of the essential problems that needs consideration due to the interference and heterogeneity of the network topology. Smart resource allocation, environmental adaptivity, user-awareness and energy efficiency are essential features in the future networks. It is important to support these features at different networks topologies with various applications. Cognitive radio has been found to be the paradigm that is able to satisfy the above requirements. It is a very interdisciplinary topic that incorporates flexible system architectures, machine learning, context awareness and cooperative networking. Mitola’s vision about cognitive radio intended to build context-sensitive smart radios that are able to adapt to the wireless environment conditions while maintaining quality of service support for different applications. Artificial intelligence techniques including heuristics algorithms and machine learning are the shining tools that are employed to serve the new vision of cognitive radio. In addition, these techniques show a potential to be utilized in an efficient resource allocation for the upcoming 5G networks’ structures such as heterogeneous multi-tier 5G networks and heterogeneous cloud radio access networks due to their capability to allocate resources according to real-time data analytics. In this thesis, we study cognitive radio from a system point of view focusing closely on architectures, artificial intelligence techniques that can enable intelligent radio resource allocation and efficient radio parameters reconfiguration. We propose a modular cognitive resource management architecture, which facilitates a development of flexible control for

  15. Simultaneous fitting of a potential-energy surface and its corresponding force fields using feedforward neural networks

    Science.gov (United States)

    Pukrittayakamee, A.; Malshe, M.; Hagan, M.; Raff, L. M.; Narulkar, R.; Bukkapatnum, S.; Komanduri, R.

    2009-04-01

    An improved neural network (NN) approach is presented for the simultaneous development of accurate potential-energy hypersurfaces and corresponding force fields that can be utilized to conduct ab initio molecular dynamics and Monte Carlo studies on gas-phase chemical reactions. The method is termed as combined function derivative approximation (CFDA). The novelty of the CFDA method lies in the fact that although the NN has only a single output neuron that represents potential energy, the network is trained in such a way that the derivatives of the NN output match the gradient of the potential-energy hypersurface. Accurate force fields can therefore be computed simply by differentiating the network. Both the computed energies and the gradients are then accurately interpolated using the NN. This approach is superior to having the gradients appear in the output layer of the NN because it greatly simplifies the required architecture of the network. The CFDA permits weighting of function fitting relative to gradient fitting. In every test that we have run on six different systems, CFDA training (without a validation set) has produced smaller out-of-sample testing error than early stopping (with a validation set) or Bayesian regularization (without a validation set). This indicates that CFDA training does a better job of preventing overfitting than the standard methods currently in use. The training data can be obtained using an empirical potential surface or any ab initio method. The accuracy and interpolation power of the method have been tested for the reaction dynamics of H+HBr using an analytical potential. The results show that the present NN training technique produces more accurate fits to both the potential-energy surface as well as the corresponding force fields than the previous methods. The fitting and interpolation accuracy is so high (rms error=1.2 cm-1) that trajectories computed on the NN potential exhibit point-by-point agreement with corresponding

  16. Reliability assessment of restructured power systems using reliability network equivalent and pseudo-sequential simulation techniques

    International Nuclear Information System (INIS)

    Ding, Yi; Wang, Peng; Goel, Lalit; Billinton, Roy; Karki, Rajesh

    2007-01-01

    This paper presents a technique to evaluate reliability of a restructured power system with a bilateral market. The proposed technique is based on the combination of the reliability network equivalent and pseudo-sequential simulation approaches. The reliability network equivalent techniques have been implemented in the Monte Carlo simulation procedure to reduce the computational burden of the analysis. Pseudo-sequential simulation has been used to increase the computational efficiency of the non-sequential simulation method and to model the chronological aspects of market trading and system operation. Multi-state Markov models for generation and transmission systems are proposed and implemented in the simulation. A new load shedding scheme is proposed during generation inadequacy and network congestion to minimize the load curtailment. The IEEE reliability test system (RTS) is used to illustrate the technique. (author)

  17. Broadcast Expenses Controlling Techniques in Mobile Ad-hoc Networks: A Survey

    Directory of Open Access Journals (Sweden)

    Naeem Ahmad

    2016-07-01

    Full Text Available The blind flooding of query packets in route discovery more often characterizes the broadcast storm problem, exponentially increases energy consumption of intermediate nodes and congests the entire network. In such a congested network, the task of establishing the path between resources may become very complex and unwieldy. An extensive research work has been done in this area to improve the route discovery phase of routing protocols by reducing broadcast expenses. The purpose of this study is to provide a comparative analysis of existing broadcasting techniques for the route discovery phase, in order to bring about an efficient broadcasting technique for determining the route with minimum conveying nodes in ad-hoc networks. The study is designed to highlight the collective merits and demerits of such broadcasting techniques along with certain conclusions that would contribute to the choice of broadcasting techniques.

  18. Flash floods warning technique based on wireless communication networks data

    Science.gov (United States)

    David, Noam; Alpert, Pinhas; Messer, Hagit

    2010-05-01

    Flash floods can occur throughout or subsequent to rainfall events, particularly in cases where the precipitation is of high-intensity. Unfortunately, each year these floods cause severe property damage and heavy casualties. At present, there are no sufficient real time flash flood warning facilities found to cope with this phenomenon. Here we show the tremendous potential of flash floods advanced warning based on precipitation measurements of commercial microwave links. As was recently shown, wireless communication networks supply high resolution precipitation measurements at ground level while often being situated in flood prone areas, covering large parts of these hazardous regions. We present the flash flood warning potential of the wireless communication system for two different cases when floods occurred at the Judean desert and at the northern Negev in Israel. In both cases, an advanced warning regarding the hazard could have been announced based on this system. • This research was supported by THE ISRAEL SCIENCE FOUNDATION (grant No. 173/08). This work was also supported by a grant from the Yeshaya Horowitz Association, Jerusalem. Additional support was given by the PROCEMA-BMBF project and by the GLOWA-JR BMBF project.

  19. Prediction and Optimization of Key Performance Indicators in the Production of Stator Core Using a GA-NN Approach

    Science.gov (United States)

    Rajora, M.; Zou, P.; Xu, W.; Jin, L.; Chen, W.; Liang, S. Y.

    2017-12-01

    With the rapidly changing demands of the manufacturing market, intelligent techniques are being used to solve engineering problems due to their ability to handle nonlinear complex problems. For example, in the conventional production of stator cores, it is relied upon experienced engineers to make an initial plan on the number of compensation sheets to be added to achieve uniform pressure distribution throughout the laminations. Additionally, these engineers must use their experience to revise the initial plans based upon the measurements made during the production of stator core. However, this method yields inconsistent results as humans are incapable of storing and analysing large amounts of data. In this article, first, a Neural Network (NN), trained using a hybrid Levenberg-Marquardt (LM) - Genetic Algorithm (GA), is developed to assist the engineers with the decision-making process. Next, the trained NN is used as a fitness function in an optimization algorithm to find the optimal values of the initial compensation sheet plan with the aim of minimizing the required revisions during the production of the stator core.

  20. Greening the networks: a comparative analysis of different energy efficient techniques

    International Nuclear Information System (INIS)

    Arshad, M.J.; Saeed, S.S.

    2014-01-01

    From a room electric bulb to the gigantic backbone networks energy savings have now become a matter of considerable concern. Issues such as resource depletion, global warming, high energy consumptions and environmental threats gave birth to the idea of green networking. Serious efforts have been done in this regard on large scale in the ICT sector. In this work first we give an idea of how and why this modern technology emerged. We then formulate a precise definition of the term green technology. We then discuss some leading techniques which are promising to produce green-results when implemented on real time network systems. These technologies are viewed from different perspectives e.g. hardware implementations, software mechanisms and protocol changing etc. We then compare these techniques based on some pivotal points. The main conclusion is that a detailed comparison is needed for selecting a technology to implement on a network system. (author)

  1. Real-time estimation of lesion depth and control of radiofrequency ablation within ex vivo animal tissues using a neural network.

    Science.gov (United States)

    Wang, Yearnchee Curtis; Chan, Terence Chee-Hung; Sahakian, Alan Varteres

    2018-01-04

    Radiofrequency ablation (RFA), a method of inducing thermal ablation (cell death), is often used to destroy tumours or potentially cancerous tissue. Current techniques for RFA estimation (electrical impedance tomography, Nakagami ultrasound, etc.) require long compute times (≥ 2 s) and measurement devices other than the RFA device. This study aims to determine if a neural network (NN) can estimate ablation lesion depth for control of bipolar RFA using complex electrical impedance - since tissue electrical conductivity varies as a function of tissue temperature - in real time using only the RFA therapy device's electrodes. Three-dimensional, cubic models comprised of beef liver, pork loin or pork belly represented target tissue. Temperature and complex electrical impedance from 72 data generation ablations in pork loin and belly were used for training the NN (403 s on Xeon processor). NN inputs were inquiry depth, starting complex impedance and current complex impedance. Training-validation-test splits were 70%-0%-30% and 80%-10%-10% (overfit test). Once the NN-estimated lesion depth for a margin reached the target lesion depth, RFA was stopped for that margin of tissue. The NN trained to 93% accuracy and an NN-integrated control ablated tissue to within 1.0 mm of the target lesion depth on average. Full 15-mm depth maps were calculated in 0.2 s on a single-core ARMv7 processor. The results show that a NN could make lesion depth estimations in real-time using less in situ devices than current techniques. With the NN-based technique, physicians could deliver quicker and more precise ablation therapy.

  2. Nonlinear System Identification Using Neural Networks Trained with Natural Gradient Descent

    Directory of Open Access Journals (Sweden)

    Ibnkahla Mohamed

    2003-01-01

    Full Text Available We use natural gradient (NG learning neural networks (NNs for modeling and identifying nonlinear systems with memory. The nonlinear system is comprised of a discrete-time linear filter followed by a zero-memory nonlinearity . The NN model is composed of a linear adaptive filter followed by a two-layer memoryless nonlinear NN. A Kalman filter-based technique and a search-and-converge method have been employed for the NG algorithm. It is shown that the NG descent learning significantly outperforms the ordinary gradient descent and the Levenberg-Marquardt (LM procedure in terms of convergence speed and mean squared error (MSE performance.

  3. Coherent network analysis technique for discriminating gravitational-wave bursts from instrumental noise

    International Nuclear Information System (INIS)

    Chatterji, Shourov; Lazzarini, Albert; Stein, Leo; Sutton, Patrick J.; Searle, Antony; Tinto, Massimo

    2006-01-01

    The sensitivity of current searches for gravitational-wave bursts is limited by non-Gaussian, nonstationary noise transients which are common in real detectors. Existing techniques for detecting gravitational-wave bursts assume the output of the detector network to be the sum of a stationary Gaussian noise process and a gravitational-wave signal. These techniques often fail in the presence of noise nonstationarities by incorrectly identifying such transients as possible gravitational-wave bursts. Furthermore, consistency tests currently used to try to eliminate these noise transients are not applicable to general networks of detectors with different orientations and noise spectra. In order to address this problem we introduce a fully coherent consistency test that is robust against noise nonstationarities and allows one to distinguish between gravitational-wave bursts and noise transients in general detector networks. This technique does not require any a priori knowledge of the putative burst waveform

  4. IMPLEMENTATION OF IMPROVED NETWORK LIFETIME TECHNIQUE FOR WSN USING CLUSTER HEAD ROTATION AND SIMULTANEOUS RECEPTION

    Directory of Open Access Journals (Sweden)

    Arun Vasanaperumal

    2015-11-01

    Full Text Available There are number of potential applications of Wireless Sensor Networks (WSNs like wild habitat monitoring, forest fire detection, military surveillance etc. All these applications are constrained for power from a stand along battery power source. So it becomes of paramount importance to conserve the energy utilized from this power source. A lot of efforts have gone into this area recently and it remains as one of the hot research areas. In order to improve network lifetime and reduce average power consumption, this study proposes a novel cluster head selection algorithm. Clustering is the preferred architecture when the numbers of nodes are larger because it results in considerable power savings for large networks as compared to other ones like tree or star. Since majority of the applications generally involve more than 30 nodes, clustering has gained widespread importance and is most used network architecture. The optimum number of clusters is first selected based on the number of nodes in the network. When the network is in operation the cluster heads in a cluster are rotated periodically based on the proposed cluster head selection algorithm to increase the network lifetime. Throughout the network single-hop communication methodology is assumed. This work will serve as an encouragement for further advances in the low power techniques for implementing Wireless Sensor Networks (WSNs.

  5. Effective field theory for NN interactions

    International Nuclear Information System (INIS)

    Tran Duy Khuong; Vo Hanh Phuc

    2003-01-01

    The effective field theory of NN interactions is formulated and the power counting appropriate to this case is reviewed. It is more subtle than in most effective field theories since in the limit that the S-wave NN scattering lengths go to infinity. It is governed by nontrivial fixed point. The leading two body terms in the effective field theory for nucleon self interactions are scale invariant and invariant under Wigner SU(4) spin-isospin symmetry in this limit. Higher body terms with no derivatives (i.e. three and four body terms) are automatically invariant under Wigner symmetry. (author)

  6. A novel wavelet neural network based pathological stage detection technique for an oral precancerous condition

    Science.gov (United States)

    Paul, R R; Mukherjee, A; Dutta, P K; Banerjee, S; Pal, M; Chatterjee, J; Chaudhuri, K; Mukkerjee, K

    2005-01-01

    Aim: To describe a novel neural network based oral precancer (oral submucous fibrosis; OSF) stage detection method. Method: The wavelet coefficients of transmission electron microscopy images of collagen fibres from normal oral submucosa and OSF tissues were used to choose the feature vector which, in turn, was used to train the artificial neural network. Results: The trained network was able to classify normal and oral precancer stages (less advanced and advanced) after obtaining the image as an input. Conclusions: The results obtained from this proposed technique were promising and suggest that with further optimisation this method could be used to detect and stage OSF, and could be adapted for other conditions. PMID:16126873

  7. Application of artificial neural networks with backpropagation technique in the financial data

    Science.gov (United States)

    Jaiswal, Jitendra Kumar; Das, Raja

    2017-11-01

    The propensity of applying neural networks has been proliferated in multiple disciplines for research activities since the past recent decades because of its powerful control with regulatory parameters for pattern recognition and classification. It is also being widely applied for forecasting in the numerous divisions. Since financial data have been readily available due to the involvement of computers and computing systems in the stock market premises throughout the world, researchers have also developed numerous techniques and algorithms to analyze the data from this sector. In this paper we have applied neural network with backpropagation technique to find the data pattern from finance section and prediction for stock values as well.

  8. Applied techniques for high bandwidth data transfers across wide area networks

    International Nuclear Information System (INIS)

    Lee, J.; Gunter, D.; Tierney, B.; Allcock, B.; Bester, J.; Bresnahan, J.; Tuecke, S.

    2001-01-01

    Large distributed systems such as Computational/Data Grids require large amounts of data to be co-located with the computing facilities for processing. From their work developing a scalable distributed network cache, the authors have gained experience with techniques necessary to achieve high data throughput over high bandwidth Wide Area Networks (WAN). The authors discuss several hardware and software design techniques, and then describe their application to an implementation of an enhanced FTP protocol called GridFTP. The authors describe results from the Supercomputing 2000 conference

  9. The Application of Helicopter Rotor Defect Detection Using Wavelet Analysis and Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Jin-Li Sun

    2014-06-01

    Full Text Available When detect the helicopter rotor beam with ultrasonic testing, it is difficult to realize the noise removing and quantitative testing. This paper used the wavelet analysis technique to remove the noise among the ultrasonic detection signal and highlight the signal feature of defect, then drew the curve of defect size and signal amplitude. Based on the relationship of defect size and signal amplitude, a BP neural network was built up and the corresponding estimated value of the simulate defect was obtained by repeating training. It was confirmed that the wavelet analysis and neural network technique met the requirements of practical testing.

  10. Increasing data distribution in BitTorrent networks by using network coding techniques

    DEFF Research Database (Denmark)

    Braun, Patrik János; Sipos, Marton A.; Ekler, Péter

    2015-01-01

    Abstract: Peer-to-peer networks are well known for their benefits when used for sharing data among multiple users. One of the most common protocols for shared data distribution is BitTorrent. Despite its popularity, it has some inefficiencies that affect the speed of the content distribution. In ...

  11. Evidence of tensor correlations in the nuclear many-body system using a modern NN potential

    International Nuclear Information System (INIS)

    Fiase, J.O.; Nkoma, J.S.; Sharmaand, L.K.; Hosaka, A.

    2003-01-01

    In this paper we show evidence of the importance of tensor correlations in the nuclear many-body system by calculating the effective two-body nuclear matrix elements in the frame work of the Lowest-Order Constrained Variational (LOCV) technique with two-body correlation functions using the Reid93 potential. We have achieved this by switching on and off the strength of the tensor correlations, α k . We have found that in order to obtain reasonable agreement with earlier calculations based on the G-matrix theory, we must turn on the strength of the tensor correlations especially in the triplet even (TE) and tensor even (TNE) channels to take the value of approximately, 0.05. As an application, we have estimated the value of the Landau - Migdal parameter, g' NN which we found to be g' NN = 0.65. This compares favorably with the G-matrix calculated value of g' NN = 0.54. (author)

  12. Quantitative workflow based on NN for weighting criteria in landfill suitability mapping

    Science.gov (United States)

    Abujayyab, Sohaib K. M.; Ahamad, Mohd Sanusi S.; Yahya, Ahmad Shukri; Ahmad, Siti Zubaidah; Alkhasawneh, Mutasem Sh.; Aziz, Hamidi Abdul

    2017-10-01

    Our study aims to introduce a new quantitative workflow that integrates neural networks (NNs) and multi criteria decision analysis (MCDA). Existing MCDA workflows reveal a number of drawbacks, because of the reliance on human knowledge in the weighting stage. Thus, new workflow presented to form suitability maps at the regional scale for solid waste planning based on NNs. A feed-forward neural network employed in the workflow. A total of 34 criteria were pre-processed to establish the input dataset for NN modelling. The final learned network used to acquire the weights of the criteria. Accuracies of 95.2% and 93.2% achieved for the training dataset and testing dataset, respectively. The workflow was found to be capable of reducing human interference to generate highly reliable maps. The proposed workflow reveals the applicability of NN in generating landfill suitability maps and the feasibility of integrating them with existing MCDA workflows.

  13. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks.

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-08-18

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

  14. An Autonomous Self-Aware and Adaptive Fault Tolerant Routing Technique for Wireless Sensor Networks

    Science.gov (United States)

    Abba, Sani; Lee, Jeong-A

    2015-01-01

    We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network. PMID:26295236

  15. Network module detection: Affinity search technique with the multi-node topological overlap measure.

    Science.gov (United States)

    Li, Ai; Horvath, Steve

    2009-07-20

    Many clustering procedures only allow the user to input a pairwise dissimilarity or distance measure between objects. We propose a clustering method that can input a multi-point dissimilarity measure d(i1, i2, ..., iP) where the number of points P can be larger than 2. The work is motivated by gene network analysis where clusters correspond to modules of highly interconnected nodes. Here, we define modules as clusters of network nodes with high multi-node topological overlap. The topological overlap measure is a robust measure of interconnectedness which is based on shared network neighbors. In previous work, we have shown that the multi-node topological overlap measure yields biologically meaningful results when used as input of network neighborhood analysis. We adapt network neighborhood analysis for the use of module detection. We propose the Module Affinity Search Technique (MAST), which is a generalized version of the Cluster Affinity Search Technique (CAST). MAST can accommodate a multi-node dissimilarity measure. Clusters grow around user-defined or automatically chosen seeds (e.g. hub nodes). We propose both local and global cluster growth stopping rules. We use several simulations and a gene co-expression network application to argue that the MAST approach leads to biologically meaningful results. We compare MAST with hierarchical clustering and partitioning around medoid clustering. Our flexible module detection method is implemented in the MTOM software which can be downloaded from the following webpage: http://www.genetics.ucla.edu/labs/horvath/MTOM/

  16. A control technique for integration of DG units to the electrical networks

    DEFF Research Database (Denmark)

    Pouresmaeil, Edris; Miguel-Espinar, Carlos; Massot-Campos, Miquel

    2013-01-01

    This paper deals with a multiobjective control technique for integration of distributed generation (DG) resources to the electrical power network. The proposed strategy provides compensation for active, reactive, and harmonic load current components during connection of DG link to the grid...

  17. Mechanical properties of the collagen network in human articular cartilage as measured by osmotic stress technique

    NARCIS (Netherlands)

    Basser, P.J.; Schneiderman, R.; Bank, R.A.; Wachtel, E.; Maroudas, A.

    1998-01-01

    We have used an isotropic osmotic stress technique to assess the swelling pressures of human articular cartilage over a wide range of hydrations in order to determine from these measurements, for the first time, the tensile stress in the collagen network, P(c), as a function of hydration. Osmotic

  18. Overview of the neural network based technique for monitoring of road condition via reconstructed road profiles

    CSIR Research Space (South Africa)

    Ngwangwa, HM

    2008-07-01

    Full Text Available on the road and driver to assess the integrity of road and vehicle infrastructure. In this paper, vehicle vibration data are applied to an artificial neural network to reconstruct the corresponding road surface profiles. The results show that the technique...

  19. Fault diagnosis in nuclear power plants using an artificial neural network technique

    International Nuclear Information System (INIS)

    Chou, H.P.; Prock, J.; Bonfert, J.P.

    1993-01-01

    Application of artificial intelligence (AI) computational techniques, such as expert systems, fuzzy logic, and neural networks in diverse areas has taken place extensively. In the nuclear industry, the intended goal for these AI techniques is to improve power plant operational safety and reliability. As a computerized operator support tool, the artificial neural network (ANN) approach is an emerging technology that currently attracts a large amount of interest. The ability of ANNs to extract the input/output relation of a complicated process and the superior execution speed of a trained ANN motivated this study. The goal was to develop neural networks for sensor and process faults diagnosis with the potential of implementing as a component of a real-time operator support system LYDIA, early sensor and process fault detection and diagnosis

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

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

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

  1. Exploring machine-learning-based control plane intrusion detection techniques in software defined optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Yuqiao; Chen, Haoran; Zhao, Yongli; Zhang, Jie

    2017-12-01

    In software defined optical networks (SDON), the centralized control plane may encounter numerous intrusion threatens which compromise the security level of provisioned services. In this paper, the issue of control plane security is studied and two machine-learning-based control plane intrusion detection techniques are proposed for SDON with properly selected features such as bandwidth, route length, etc. We validate the feasibility and efficiency of the proposed techniques by simulations. Results show an accuracy of 83% for intrusion detection can be achieved with the proposed machine-learning-based control plane intrusion detection techniques.

  2. Renewal of Road Networks Using Map-matching Technique of Trajectories

    Directory of Open Access Journals (Sweden)

    WU Tao

    2017-04-01

    Full Text Available The road network with complete and accurate information, as one of the key foundations of Smart City, bears significance in fields like urban planning, traffic managing and public traveling, et al. However, long manufacturing period of road network data, based on traditional surveying methods, often leaves it in an inconsistent state with the latest situation. Recently, positioning techniques ubiquitously used in mobile devices has been gradually coming into focus for domestic and overseas scholars. Currently, most of approaches, generating or updating road networks from mobile location information, are to compute with GPS trajectory data directly by various algorithms, which lead to expensive consumption of computational resources in case of mass GPS data covering large-scale areas. For this reason, we propose a spiral update strategy of road network data based on map-matching technology, which follows a “identify→analyze→extract→update” process. The main idea is to detect condemned road segments of existing road network data with the help of HMM for each trajectory input, as well as repair them, on the local scale, by extracting new road information from trajectory data.The proposed approach avoids computing on the entire dataset of trajectory data for road segments. Instead, it updates information of existing road network data by means of focalizing on the minimum range of potential condemned segments. We evaluated the performance of our proposals using GPS traces collected on taxies and OpenStreetMap(OSM road networks covering urban areas of Wuhan City.

  3. High capacity fiber optic sensor networks using hybrid multiplexing techniques and their applications

    Science.gov (United States)

    Sun, Qizhen; Li, Xiaolei; Zhang, Manliang; Liu, Qi; Liu, Hai; Liu, Deming

    2013-12-01

    Fiber optic sensor network is the development trend of fiber senor technologies and industries. In this paper, I will discuss recent research progress on high capacity fiber sensor networks with hybrid multiplexing techniques and their applications in the fields of security monitoring, environment monitoring, Smart eHome, etc. Firstly, I will present the architecture of hybrid multiplexing sensor passive optical network (HSPON), and the key technologies for integrated access and intelligent management of massive fiber sensor units. Two typical hybrid WDM/TDM fiber sensor networks for perimeter intrusion monitor and cultural relics security are introduced. Secondly, we propose the concept of "Microstructure-Optical X Domin Refecltor (M-OXDR)" for fiber sensor network expansion. By fabricating smart micro-structures with the ability of multidimensional encoded and low insertion loss along the fiber, the fiber sensor network of simple structure and huge capacity more than one thousand could be achieved. Assisted by the WDM/TDM and WDM/FDM decoding methods respectively, we built the verification systems for long-haul and real-time temperature sensing. Finally, I will show the high capacity and flexible fiber sensor network with IPv6 protocol based hybrid fiber/wireless access. By developing the fiber optic sensor with embedded IPv6 protocol conversion module and IPv6 router, huge amounts of fiber optic sensor nodes can be uniquely addressed. Meanwhile, various sensing information could be integrated and accessed to the Next Generation Internet.

  4. Evaluation of artificial neural network techniques for flow forecasting in the River Yangtze, China

    Directory of Open Access Journals (Sweden)

    C. W. Dawson

    2002-01-01

    Full Text Available While engineers have been quantifying rainfall-runoff processes since the mid-19th century, it is only in the last decade that artificial neural network models have been applied to the same task. This paper evaluates two neural networks in this context: the popular multilayer perceptron (MLP, and the radial basis function network (RBF. Using six-hourly rainfall-runoff data for the River Yangtze at Yichang (upstream of the Three Gorges Dam for the period 1991 to 1993, it is shown that both neural network types can simulate river flows beyond the range of the training set. In addition, an evaluation of alternative RBF transfer functions demonstrates that the popular Gaussian function, often used in RBF networks, is not necessarily the ‘best’ function to use for river flow forecasting. Comparisons are also made between these neural networks and conventional statistical techniques; stepwise multiple linear regression, auto regressive moving average models and a zero order forecasting approach. Keywords: Artificial neural network, multilayer perception, radial basis function, flood forecasting

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

    Directory of Open Access Journals (Sweden)

    H.H. Mohamad

    2013-09-01

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

  6. Evaluation Technique of Chloride Penetration Using Apparent Diffusion Coefficient and Neural Network Algorithm

    Directory of Open Access Journals (Sweden)

    Yun-Yong Kim

    2014-01-01

    Full Text Available Diffusion coefficient from chloride migration test is currently used; however this cannot provide a conventional solution like total chloride contents since it depicts only ion migration velocity in electrical field. This paper proposes a simple analysis technique for chloride behavior using apparent diffusion coefficient from neural network algorithm with time-dependent diffusion phenomena. For this work, thirty mix proportions of high performance concrete are prepared and their diffusion coefficients are obtained after long term-NaCl submerged test. Considering time-dependent diffusion coefficient based on Fick’s 2nd Law and NNA (neural network algorithm, analysis technique for chloride penetration is proposed. The applicability of the proposed technique is verified through the results from accelerated test, long term submerged test, and field investigation results.

  7. Smart techniques in the dynamic spectrum alocation for cognitive wireless networks

    Directory of Open Access Journals (Sweden)

    Camila Salgado

    2016-09-01

    Full Text Available Objective: The objective of this work is to study the applications of different techniques of artificial intelligence and autonomous learning in the dynamic allocation of spectrum for cognitive wireless networks, especially the distributed ones. Method: The development of this work was done through the study and analysis of some of the most relevant publications in current literature through the search in indexed international journals in ISI and Scopus. Results: the most relevant techniques of artificial intelligence and autonomous learning were determined. Also, the ones with more applicability in the allocation of spectrum for cognitive wireless networks were determined, too. . Conclusions: The implementation of a technique, or set of them, depends on the needs in signal processing, compensation in response times, sample availability, storage capacity, learning ability and robustness, among others.

  8. Applied techniques for high bandwidth data transfers across wide area networks

    International Nuclear Information System (INIS)

    Lee, Jason; Gunter, Dan; Tierney, Brian; Allcock, Bill; Bester, Joe; Bresnahan, John; Tuecke, Steve

    2001-01-01

    Large distributed systems such as Computational/Data Grids require large amounts of data to be co-located with the computing facilities for processing. Ensuring that the data is there in time for the computation in today's Internet is a massive problem. From our work developing a scalable distributed network cache, we have gained experience with techniques necessary to achieve high data throughput over high bandwidth Wide Area Networks (WAN). In this paper, we discuss several hardware and software design techniques and issues, and then describe their application to an implementation of an enhanced FTP protocol called GridFTP. We also describe results from two applications using these techniques, which were obtained at the Supercomputing 2000 conference

  9. Computational Intelligence based techniques for islanding detection of distributed generation in distribution network: A review

    International Nuclear Information System (INIS)

    Laghari, J.A.; Mokhlis, H.; Karimi, M.; Bakar, A.H.A.; Mohamad, Hasmaini

    2014-01-01

    Highlights: • Unintentional and intentional islanding, their causes, and solutions are presented. • Remote, passive, active and hybrid islanding detection techniques are discussed. • The limitation of these techniques in accurately detect islanding are discussed. • Computational intelligence techniques ability in detecting islanding is discussed. • Review of ANN, fuzzy logic control, ANFIS, Decision tree techniques is provided. - Abstract: Accurate and fast islanding detection of distributed generation is highly important for its successful operation in distribution networks. Up to now, various islanding detection technique based on communication, passive, active and hybrid methods have been proposed. However, each technique suffers from certain demerits that cause inaccuracies in islanding detection. Computational intelligence based techniques, due to their robustness and flexibility in dealing with complex nonlinear systems, is an option that might solve this problem. This paper aims to provide a comprehensive review of computational intelligence based techniques applied for islanding detection of distributed generation. Moreover, the paper compares the accuracies of computational intelligence based techniques over existing techniques to provide a handful of information for industries and utility researchers to determine the best method for their respective system

  10. Improved Image Encryption for Real-Time Application over Wireless Communication Networks using Hybrid Cryptography Technique

    Directory of Open Access Journals (Sweden)

    Kazeem B. Adedeji

    2016-12-01

    Full Text Available Advances in communication networks have enabled organization to send confidential data such as digital images over wireless networks. However, the broadcast nature of wireless communication channel has made it vulnerable to attack from eavesdroppers. We have developed a hybrid cryptography technique, and we present its application to digital images as a means of improving the security of digital image for transmission over wireless communication networks. The hybrid technique uses a combination of a symmetric (Data Encryption Standard and asymmetric (Rivest Shamir Adleman cryptographic algorithms to secure data to be transmitted between different nodes of a wireless network. Three different image samples of type jpeg, png and jpg were tested using this technique. The results obtained showed that the hybrid system encrypt the images with minimal simulation time, and high throughput. More importantly, there is no relation or information between the original images and their encrypted form, according to Shannon’s definition of perfect security, thereby making the system much more secure.

  11. Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

    KAUST Repository

    Ghazzai, Hakim

    2016-09-16

    Over the last decade, mobile communications have been witnessing a noteworthy increase of data traffic demand that is causing an enormous energy consumption in cellular networks. The reduction of their fossil fuel consumption in addition to the huge energy bills paid by mobile operators is considered as the most important challenges for the next-generation cellular networks. Although most of the proposed studies were focusing on individual physical layer power optimizations, there is a growing necessity to meet the green objective of fifth-generation cellular networks while respecting the user\\'s quality of service. This paper investigates four important techniques that could be exploited separately or together in order to enable wireless operators achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between competitive mobile operators. The presented simulation results measure the gain that could be obtained using these techniques compared with that of traditional scenarios. Finally, this paper discusses the issues and challenges related to the implementations of these techniques in real environments. © 2016 IEEE.

  12. Application of signal processing techniques for islanding detection of distributed generation in distribution network: A review

    International Nuclear Information System (INIS)

    Raza, Safdar; Mokhlis, Hazlie; Arof, Hamzah; Laghari, J.A.; Wang, Li

    2015-01-01

    Highlights: • Pros & cons of conventional islanding detection techniques (IDTs) are discussed. • Signal processing techniques (SPTs) ability in detecting islanding is discussed. • SPTs ability in improving performance of passive techniques are discussed. • Fourier, s-transform, wavelet, HHT & tt-transform based IDTs are reviewed. • Intelligent classifiers (ANN, ANFIS, Fuzzy, SVM) application in SPT are discussed. - Abstract: High penetration of distributed generation resources (DGR) in distribution network provides many benefits in terms of high power quality, efficiency, and low carbon emissions in power system. However, efficient islanding detection and immediate disconnection of DGR is critical in order to avoid equipment damage, grid protection interference, and personnel safety hazards. Islanding detection techniques are mainly classified into remote, passive, active, and hybrid techniques. From these, passive techniques are more advantageous due to lower power quality degradation, lower cost, and widespread usage by power utilities. However, the main limitations of these techniques are that they possess a large non detection zones and require threshold setting. Various signal processing techniques and intelligent classifiers have been used to overcome the limitations of passive islanding. Signal processing techniques, in particular, are adopted due to their versatility, stability, cost effectiveness, and ease of modification. This paper presents a comprehensive overview of signal processing techniques used to improve common passive islanding detection techniques. A performance comparison between the signal processing based islanding detection techniques with existing techniques are also provided. Finally, this paper outlines the relative advantages and limitations of the signal processing techniques in order to provide basic guidelines for researchers and field engineers in determining the best method for their system

  13. GeNN: a code generation framework for accelerated brain simulations

    Science.gov (United States)

    Yavuz, Esin; Turner, James; Nowotny, Thomas

    2016-01-01

    Large-scale numerical simulations of detailed brain circuit models are important for identifying hypotheses on brain functions and testing their consistency and plausibility. An ongoing challenge for simulating realistic models is, however, computational speed. In this paper, we present the GeNN (GPU-enhanced Neuronal Networks) framework, which aims to facilitate the use of graphics accelerators for computational models of large-scale neuronal networks to address this challenge. GeNN is an open source library that generates code to accelerate the execution of network simulations on NVIDIA GPUs, through a flexible and extensible interface, which does not require in-depth technical knowledge from the users. We present performance benchmarks showing that 200-fold speedup compared to a single core of a CPU can be achieved for a network of one million conductance based Hodgkin-Huxley neurons but that for other models the speedup can differ. GeNN is available for Linux, Mac OS X and Windows platforms. The source code, user manual, tutorials, Wiki, in-depth example projects and all other related information can be found on the project website http://genn-team.github.io/genn/.

  14. Comparison of Available Bandwidth Estimation Techniques in Packet-Switched Mobile Networks

    DEFF Research Database (Denmark)

    López Villa, Dimas; Ubeda Castellanos, Carlos; Teyeb, Oumer Mohammed

    2006-01-01

    The relative contribution of the transport network towards the per-user capacity in mobile telecommunication systems is becoming very important due to the ever increasing air-interface data rates. Thus, resource management procedures such as admission, load and handover control can make use...... of information regarding the available bandwidth in the transport network, as it could end up being the bottleneck rather than the air interface. This paper provides a comparative study of three well known available bandwidth estimation techniques, i.e. TOPP, SLoPS and pathChirp, taking into account...

  15. Development of neural network techniques for the analysis of JET ECE data

    International Nuclear Information System (INIS)

    Bartlett, D.V.; Bishop, C.M.

    1993-01-01

    This paper reports on a project currently in progress to develop neutral network techniques for the conversion of JET ECE spectra to electron temperature profiles. The aim is to obtain profiles with reduced measurement uncertainties by incorporating data from the LIDAR Thomson scattering diagnostic in the analysis, while retaining the faster time resolution of the ECE measurements. The properties of neural networks are briefly reviewed, and the reasons for using them in this application are explained. Some preliminary results are presented and the direction of future work is outlined. (orig.)

  16. Wireless network development for the automatic registration of parameters in laboratories of nuclear analytical techniques

    International Nuclear Information System (INIS)

    Tincopa, Jean Pierre; Baltuano, Oscar; Bedregal, Patricia

    2015-01-01

    This paper presents in detail the development of a low-cost wireless network for automatic recording of temperature and relative humidity parameters in the laboratory of nuclear analytical techniques. This prototype has a DHT22 sensor which gives us both parameters with high precision and are automatically read and displayed by a ATmega328P microcontroller. This data is then transmitted through transceivers Xbee Pro S2B forming a mesh network for real time storage using an RTC (Real Time Clock). We present the experimental results obtained in its implementation. (author)

  17. Multi-agent: a technique to implement geo-visualization of networked virtual reality

    Science.gov (United States)

    Lin, Zhiyong; Li, Wenjing; Meng, Lingkui

    2007-06-01

    Networked Virtual Reality (NVR) is a system based on net connected and spatial information shared, whose demands cannot be fully meet by the existing architectures and application patterns of VR to some extent. In this paper, we propose a new architecture of NVR based on Multi-Agent framework. which includes the detailed definition of various agents and their functions and full description of the collaboration mechanism, Through the prototype system test with DEM Data and 3D Models Data, the advantages of Multi-Agent based Networked Virtual Reality System in terms of the data loading time, user response time and scene construction time etc. are verified. First, we introduce the characters of Networked Virtual Realty and the characters of Multi-Agent technique in Section 1. Then we give the architecture design of Networked Virtual Realty based on Multi-Agent in Section 2.The Section 2 content includes the rule of task division, the multi-agent architecture design to implement Networked Virtual Realty and the function of agents. Section 3 shows the prototype implementation according to the design. Finally, Section 4 discusses the benefits of using Multi-Agent to implement geovisualization of Networked Virtual Realty.

  18. Heat control in HVDC resistive divider by PID and NN controllers

    International Nuclear Information System (INIS)

    Yilmaz, S.; Dincer, H.; Eksin, I.; Kalenderli, O.

    2007-01-01

    In this study, a control system is presented that is devised to increase measurement precisions within a prototype high voltage DC resistive divider (HVDC-RD). Since one of the major sources of measurement errors in such devices is the self heating effect, a system controlling the temperature within the high voltage DC resistive divider is devised so that suitable and stable temperature conditions are maintained that, in return, will decrease the measurement errors. The resistive divider system is cooled by oil, and PID and neural network (NN) controllers try to keep the temperature within the prescribed limits. The system to be controlled exhibits a nonlinear character, and therefore, a control approach based on NN controllers is proposed. Thus, a system that can fulfill the various requirements dictated by the designer is constructed. The performance of the NN controller is compared with that of the PID controller developed for the same purpose, and the values of the performance indices indicate the superiority of the NN controller over that of the classical PID controller

  19. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah [University of Medea, Medea (Algeria)

    2015-11-15

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  20. Prediction of the rejection of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes using neural networks

    International Nuclear Information System (INIS)

    Ammi, Yamina; Khaouane, Latifa; Hanini, Salah

    2015-01-01

    This work investigates the use of neural networks in modeling the rejection processes of organic compounds (neutral and ionic) by nanofiltration and reverse osmosis membranes. Three feed-forward neural network (NN) models, characterized by a similar structure (eleven neurons for NN1 and NN2 and twelve neurons for NN3 in the input layer, one hidden layer and one neuron in the output layer), are constructed with the aim of predicting the rejection of organic compounds (neutral and ionic). A set of 956 data points for NN1 and 701 data points for NN2 and NN3 were used to test the neural networks. 80%, 10%, and 10% of the total data were used, respectively, for the training, the validation, and the test of the three models. For the most promising neural network models, the predicted rejection values of the test dataset were compared to measured rejections values; good correlations were found (R= 0.9128 for NN1, R=0.9419 for NN2, and R=0.9527 for NN3). The root mean squared errors for the total dataset were 11.2430% for NN1, 9.0742% for NN2, and 8.2047% for NN3. Furthermore, the comparison between the predicted results and QSAR models shows that the neural network models gave far better.

  1. Material discovery by combining stochastic surface walking global optimization with a neural network.

    Science.gov (United States)

    Huang, Si-Da; Shang, Cheng; Zhang, Xiao-Jie; Liu, Zhi-Pan

    2017-09-01

    While the underlying potential energy surface (PES) determines the structure and other properties of a material, it has been frustrating to predict new materials from theory even with the advent of supercomputing facilities. The accuracy of the PES and the efficiency of PES sampling are two major bottlenecks, not least because of the great complexity of the material PES. This work introduces a "Global-to-Global" approach for material discovery by combining for the first time a global optimization method with neural network (NN) techniques. The novel global optimization method, named the stochastic surface walking (SSW) method, is carried out massively in parallel for generating a global training data set, the fitting of which by the atom-centered NN produces a multi-dimensional global PES; the subsequent SSW exploration of large systems with the analytical NN PES can provide key information on the thermodynamics and kinetics stability of unknown phases identified from global PESs. We describe in detail the current implementation of the SSW-NN method with particular focuses on the size of the global data set and the simultaneous energy/force/stress NN training procedure. An important functional material, TiO 2 , is utilized as an example to demonstrate the automated global data set generation, the improved NN training procedure and the application in material discovery. Two new TiO 2 porous crystal structures are identified, which have similar thermodynamics stability to the common TiO 2 rutile phase and the kinetics stability for one of them is further proved from SSW pathway sampling. As a general tool for material simulation, the SSW-NN method provides an efficient and predictive platform for large-scale computational material screening.

  2. Resonances in the ΣNN system

    International Nuclear Information System (INIS)

    Gibson, B.F.; Afnan, I.R.

    1993-01-01

    We first review certain unique aspects of few-body Α- hypernuclei and then explore the physics of summation threshold in few-body elastic scattering and reactions. In particular, we discuss a predicted enhancement in the Αd cross section near the summation NN threshold in terms of poles in the Τ=0YNN amplitude. A brief discussion of anticipated poles in the Τ=1 amplitudes is also given

  3. Isobar contributions to the NN interaction

    International Nuclear Information System (INIS)

    Bagnoud, X.; Holinde, K.; Machleidt, R.

    1981-01-01

    The fourth-order noniterative 2π-exchange diagrams involving double-isobar intermediate states are evaluated in momentum space, in the framework of noncovariant perturbation theory. The role of these diagrams is studied by making a detailed comparison with (i) the corresponding iterative diagrams and (ii) diagrams involving NN or NΔ intermediate states. Current prescriptions for replacing all time-orderings of those diagrams by (simple) pion-range transition potentials are tested

  4. Under-Frequency Load Shedding Technique Considering Event-Based for an Islanded Distribution Network

    Directory of Open Access Journals (Sweden)

    Hasmaini Mohamad

    2016-06-01

    Full Text Available One of the biggest challenge for an islanding operation is to sustain the frequency stability. A large power imbalance following islanding would cause under-frequency, hence an appropriate control is required to shed certain amount of load. The main objective of this research is to develop an adaptive under-frequency load shedding (UFLS technique for an islanding system. The technique is designed considering an event-based which includes the moment system is islanded and a tripping of any DG unit during islanding operation. A disturbance magnitude is calculated to determine the amount of load to be shed. The technique is modeled by using PSCAD simulation tool. A simulation studies on a distribution network with mini hydro generation is carried out to evaluate the UFLS model. It is performed under different load condition: peak and base load. Results show that the load shedding technique have successfully shed certain amount of load and stabilized the system frequency.

  5. SRAM Design for Wireless Sensor Networks Energy Efficient and Variability Resilient Techniques

    CERN Document Server

    Sharma, Vibhu; Dehaene, Wim

    2013-01-01

    This book features various, ultra low energy, variability resilient SRAM circuit design techniques for wireless sensor network applications. Conventional SRAM design targets area efficiency and high performance at the increased cost of energy consumption, making it unsuitable for computation-intensive sensor node applications.  This book, therefore, guides the reader through different techniques at the circuit level for reducing   energy consumption and increasing the variability resilience. It includes a detailed review of the most efficient circuit design techniques and trade-offs, introduces new memory architecture techniques, sense amplifier circuits and voltage optimization methods for reducing the impact of variability for the advanced technology nodes.    Discusses fundamentals of energy reduction for SRAM circuits and applies them to energy limitation challenges associated with wireless sensor  nodes; Explains impact of variability resilience in reducing the energy consumption; Describes various...

  6. Energy neutral protocol based on hierarchical routing techniques for energy harvesting wireless sensor network

    Science.gov (United States)

    Muhammad, Umar B.; Ezugwu, Absalom E.; Ofem, Paulinus O.; Rajamäki, Jyri; Aderemi, Adewumi O.

    2017-06-01

    Recently, researchers in the field of wireless sensor networks have resorted to energy harvesting techniques that allows energy to be harvested from the ambient environment to power sensor nodes. Using such Energy harvesting techniques together with proper routing protocols, an Energy Neutral state can be achieved so that sensor nodes can run perpetually. In this paper, we propose an Energy Neutral LEACH routing protocol which is an extension to the traditional LEACH protocol. The goal of the proposed protocol is to use Gateway node in each cluster so as to reduce the data transmission ranges of cluster head nodes. Simulation results show that the proposed routing protocol achieves a higher throughput and ensure the energy neutral status of the entire network.

  7. A Visual Analytics Technique for Identifying Heat Spots in Transportation Networks

    Directory of Open Access Journals (Sweden)

    Marian Sorin Nistor

    2016-12-01

    Full Text Available The decision takers of the public transportation system, as part of urban critical infrastructures, need to increase the system resilience. For doing so, we identified analysis tools for biological networks as an adequate basis for visual analytics in that domain. In the paper at hand we therefore translate such methods for transportation systems and show the benefits by applying them on the Munich subway network. Here, visual analytics is used to identify vulnerable stations from different perspectives. The applied technique is presented step by step. Furthermore, the key challenges in applying this technique on transportation systems are identified. Finally, we propose the implementation of the presented features in a management cockpit to integrate the visual analytics mantra for an adequate decision support on transportation systems.

  8. Characterization of ceramic materials using ultrasonic technique in the frequency domain and artificial networks

    International Nuclear Information System (INIS)

    Baroni, D.B.; Bittencourt, M.S.Q.; Pereira, C.M.N.A.

    2008-01-01

    The ceramic material characterization is very important to guarantee its mechanical properties. In the case of nuclear fuel (UO 2 ) the adequate porosity ensures its thermal efficiency and its structural integrity that contribute to the safety at nuclear power plants. The Ultrasound Laboratory of the Nuclear Engineering Institute (LABUS/IEN) has developed a technique to measure the porosity in ceramic materials. This technique uses ultrasound signal in the frequency domain and creates spectrum patterns related to the material porosity. Trained artificial neural networks recognizes these patterns and associates them to the porosities. In this work 20 pellets of Alumina were used with porosities in the same range used in the nuclear fuel (0.70% to 4.25%). In this case the used network was able to recognize the patterns of the pellets and to associate to the porosities with 100% of precision. It was possible to distinguished pellets with a difference of 0.01% of the porosity. (author)

  9. Application of CA and NN for event recognition in experiments DISTO and STREAMER

    International Nuclear Information System (INIS)

    Bussa, M.P.; Ivanov, V.V.; Kisel, I.V.; Pontecorvo, G.B.

    1997-01-01

    An algorithm for charged particle recognition and event identification applying a cellular automaton (CA) model and a multi-layer neural network (NN) has been developed for the DISTO experiment under way at Saturne (Saclay, France). A further development of the model will be applied for particle recognition in the Dubna Streamer Chamber Spectrometer (DSCS) for studying pion-nucleus absorption (experiments DISTO and STREAMER). (orig.)

  10. Study of Li atom diffusion in amorphous Li3PO4 with neural network potential

    Science.gov (United States)

    Li, Wenwen; Ando, Yasunobu; Minamitani, Emi; Watanabe, Satoshi

    2017-12-01

    To clarify atomic diffusion in amorphous materials, which is important in novel information and energy devices, theoretical methods having both reliability and computational speed are eagerly anticipated. In the present study, we applied neural network (NN) potentials, a recently developed machine learning technique, to the study of atom diffusion in amorphous materials, using Li3PO4 as a benchmark material. The NN potential was used together with the nudged elastic band, kinetic Monte Carlo, and molecular dynamics methods to characterize Li vacancy diffusion behavior in the amorphous Li3PO4 model. By comparing these results with corresponding DFT calculations, we found that the average error of the NN potential is 0.048 eV in calculating energy barriers of diffusion paths, and 0.041 eV in diffusion activation energy. Moreover, the diffusion coefficients obtained from molecular dynamics are always consistent with those from ab initio molecular dynamics simulation, while the computation speed of the NN potential is 3-4 orders of magnitude faster than DFT. Lastly, the structure of amorphous Li3PO4 and the ion transport properties in it were studied with the NN potential using a large supercell model containing more than 1000 atoms. The formation of P2O7 units was observed, which is consistent with the experimental characterization. The Li diffusion activation energy was estimated to be 0.55 eV, which agrees well with the experimental measurements.

  11. New Theoretical Analysis of the LRRM Calibration Technique for Vector Network Analyzers

    OpenAIRE

    Purroy Martín, Francesc; Pradell i Cara, Lluís

    2001-01-01

    In this paper, a new theoretical analysis of the four-standards line-reflect-reflect-match (LRRM) vector network-analyzer (VNA) calibration technique is presented. As a result, it is shown that the reference-impedance (to which the LRRM calibration is referred) cannot generally be defined whenever nonideal standards are used. Based on this consideration, a new algorithm to determine the on-wafer match standard is proposed that improves the LRRM calibration accuracy. Experimental verification ...

  12. A Survey of Congestion Control Techniques and Data Link Protocols in Satellite Networks

    OpenAIRE

    Fahmy, Sonia; Jain, Raj; Lu, Fang; Kalyanaraman, Shivkumar

    1998-01-01

    Satellite communication systems are the means of realizing a global broadband integrated services digital network. Due to the statistical nature of the integrated services traffic, the resulting rate fluctuations and burstiness render congestion control a complicated, yet indispensable function. The long propagation delay of the earth-satellite link further imposes severe demands and constraints on the congestion control schemes, as well as the media access control techniques and retransmissi...

  13. Performance evaluation of an importance sampling technique in a Jackson network

    Science.gov (United States)

    brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

    2014-03-01

    Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.

  14. Fault Diagnosis of Power System Based on Improved Genetic Optimized BP-NN

    Directory of Open Access Journals (Sweden)

    Yuan Pu

    2015-01-01

    Full Text Available BP neural network (Back-Propagation Neural Network, BP-NN is one of the most widely neural network models and is applied to fault diagnosis of power system currently. BP neural network has good self-learning and adaptive ability and generalization ability, but the operation process is easy to fall into local minima. Genetic algorithm has global optimization features, and crossover is the most important operation of the Genetic Algorithm. In this paper, we can modify the crossover of traditional Genetic Algorithm, using improved genetic algorithm optimized BP neural network training initial weights and thresholds, to avoid the problem of BP neural network fall into local minima. The results of analysis by an example, the method can efficiently diagnose network fault location, and improve fault-tolerance and grid fault diagnosis effect.

  15. Hybrid Clustering-GWO-NARX neural network technique in predicting stock price

    Science.gov (United States)

    Das, Debashish; Safa Sadiq, Ali; Mirjalili, Seyedali; Noraziah, A.

    2017-09-01

    Prediction of stock price is one of the most challenging tasks due to nonlinear nature of the stock data. Though numerous attempts have been made to predict the stock price by applying various techniques, yet the predicted price is not always accurate and even the error rate is high to some extent. Consequently, this paper endeavours to determine an efficient stock prediction strategy by implementing a combinatorial method of Grey Wolf Optimizer (GWO), Clustering and Non Linear Autoregressive Exogenous (NARX) Technique. The study uses stock data from prominent stock market i.e. New York Stock Exchange (NYSE), NASDAQ and emerging stock market i.e. Malaysian Stock Market (Bursa Malaysia), Dhaka Stock Exchange (DSE). It applies K-means clustering algorithm to determine the most promising cluster, then MGWO is used to determine the classification rate and finally the stock price is predicted by applying NARX neural network algorithm. The prediction performance gained through experimentation is compared and assessed to guide the investors in making investment decision. The result through this technique is indeed promising as it has shown almost precise prediction and improved error rate. We have applied the hybrid Clustering-GWO-NARX neural network technique in predicting stock price. We intend to work with the effect of various factors in stock price movement and selection of parameters. We will further investigate the influence of company news either positive or negative in stock price movement. We would be also interested to predict the Stock indices.

  16. WRHT: A Hybrid Technique for Detection of Wormhole Attack in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rupinder Singh

    2016-01-01

    Full Text Available Wormhole attack is a challenging security threat to wireless sensor networks which results in disrupting most of the routing protocols as this attack can be triggered in different modes. In this paper, WRHT, a wormhole resistant hybrid technique, is proposed, which can detect the presence of wormhole attack in a more optimistic manner than earlier techniques. WRHT is based on the concept of watchdog and Delphi schemes and ensures that the wormhole will not be left untreated in the sensor network. WRHT makes use of the dual wormhole detection mechanism of calculating probability factor time delay probability and packet loss probability of the established path in order to find the value of wormhole presence probability. The nodes in the path are given different ranking and subsequently colors according to their behavior. The most striking feature of WRHT consists of its capacity to defend against almost all categories of wormhole attacks without depending on any required additional hardware such as global positioning system, timing information or synchronized clocks, and traditional cryptographic schemes demanding high computational needs. The experimental results clearly indicate that the proposed technique has significant improvement over the existing wormhole attack detection techniques.

  17. Consistency requirements on Δ contributions to the NN potential

    International Nuclear Information System (INIS)

    Rinat, A.S.

    1982-04-01

    We discuss theories leading to intermediate state NΔ and ΔΔ contributions to Vsub(NN). We focus on the customary addition of Lsub(ΔNπ)' to Lsub(πNN)' in a conventional field theory and argue that overcounting of contributions to tsub(πN) and Vsub(NN) will be the rule. We then discuss the cloudy bag model where a similar interaction naturally arises and which leads to a consistent theory. (author)

  18. Multi-Objective Planning Techniques in Distribution Networks: A Composite Review

    Directory of Open Access Journals (Sweden)

    Syed Ali Abbas Kazmi

    2017-02-01

    Full Text Available Distribution networks (DNWs are facing numerous challenges, notably growing load demands, environmental concerns, operational constraints and expansion limitations with the current infrastructure. These challenges serve as a motivation factor for various distribution network planning (DP strategies, such as timely addressing load growth aiming at prominent objectives such as reliability, power quality, economic viability, system stability and deferring costly reinforcements. The continuous transformation of passive to active distribution networks (ADN needs to consider choices, primarily distributed generation (DG, network topology change, installation of new protection devices and key enablers as planning options in addition to traditional grid reinforcements. Since modern DP (MDP in deregulated market environments includes multiple stakeholders, primarily owners, regulators, operators and consumers, one solution fit for all planning scenarios may not satisfy all these stakeholders. Hence, this paper presents a review of several planning techniques (PTs based on mult-objective optimizations (MOOs in DNWs, aiming at better trade-off solutions among conflicting objectives and satisfying multiple stakeholders. The PTs in the paper spread across four distinct planning classifications including DG units as an alternative to costly reinforcements, capacitors and power electronic devices for ensuring power quality aspects, grid reinforcements, expansions, and upgrades as a separate category and network topology alteration and reconfiguration as a viable planning option. Several research works associated with multi-objective planning techniques (MOPT have been reviewed with relevant models, methods and achieved objectives, abiding with system constraints. The paper also provides a composite review of current research accounts and interdependence of associated components in the respective classifications. The potential future planning areas, aiming at

  19. Focus-based filtering + clustering technique for power-law networks with small world phenomenon

    Science.gov (United States)

    Boutin, François; Thièvre, Jérôme; Hascoët, Mountaz

    2006-01-01

    Realistic interaction networks usually present two main properties: a power-law degree distribution and a small world behavior. Few nodes are linked to many nodes and adjacent nodes are likely to share common neighbors. Moreover, graph structure usually presents a dense core that is difficult to explore with classical filtering and clustering techniques. In this paper, we propose a new filtering technique accounting for a user-focus. This technique extracts a tree-like graph with also power-law degree distribution and small world behavior. Resulting structure is easily drawn with classical force-directed drawing algorithms. It is also quickly clustered and displayed into a multi-level silhouette tree (MuSi-Tree) from any user-focus. We built a new graph filtering + clustering + drawing API and report a case study.

  20. Artificial neural network techniques to improve the ability of optical coherence tomography to detect optic neuritis.

    Science.gov (United States)

    Garcia-Martin, Elena; Herrero, Raquel; Bambo, Maria P; Ara, Jose R; Martin, Jesus; Polo, Vicente; Larrosa, Jose M; Garcia-Feijoo, Julian; Pablo, Luis E

    2015-01-01

    To analyze the ability of Spectralis optical coherence tomography (OCT) to detect multiple sclerosis (MS) and to distinguish MS eyes with antecedent optic neuritis (ON). To analyze the capability of artificial neural network (ANN) techniques to improve the diagnostic precision. MS patients and controls were enrolled (n = 217). OCT was used to determine the 768 retinal nerve fiber layer thicknesses. Sensitivity and specificity were evaluated to test the ability of OCT to discriminate between MS and healthy eyes, and between MS with and without antecedent ON using ANN. Using ANN technique multilayer perceptrons, OCT could detect MS with a sensitivity of 89.3%, a specificity of 87.6%, and a diagnostic precision of 88.5%. Compared with the OCT-provided parameters, the ANN had a better sensitivity-specificity balance. ANN technique improves the capability of Spectralis OCT to detect MS disease and to distinguish MS eyes with or without antecedent ON.

  1. Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm

    Directory of Open Access Journals (Sweden)

    Mi Jeong Kim

    2015-07-01

    Full Text Available In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA-based wireless mesh network (WMN with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

  2. Distributed Synchronization Technique for OFDMA-Based Wireless Mesh Networks Using a Bio-Inspired Algorithm.

    Science.gov (United States)

    Kim, Mi Jeong; Maeng, Sung Joon; Cho, Yong Soo

    2015-07-28

    In this paper, a distributed synchronization technique based on a bio-inspired algorithm is proposed for an orthogonal frequency division multiple access (OFDMA)-based wireless mesh network (WMN) with a time difference of arrival. The proposed time- and frequency-synchronization technique uses only the signals received from the neighbor nodes, by considering the effect of the propagation delay between the nodes. It achieves a fast synchronization with a relatively low computational complexity because it is operated in a distributed manner, not requiring any feedback channel for the compensation of the propagation delays. In addition, a self-organization scheme that can be effectively used to construct 1-hop neighbor nodes is proposed for an OFDMA-based WMN with a large number of nodes. The performance of the proposed technique is evaluated with regard to the convergence property and synchronization success probability using a computer simulation.

  3. Novel stability criteria for fuzzy Hopfield neural networks based on an improved homogeneous matrix polynomials technique

    International Nuclear Information System (INIS)

    Feng Yi-Fu; Zhang Qing-Ling; Feng De-Zhi

    2012-01-01

    The global stability problem of Takagi—Sugeno (T—S) fuzzy Hopfield neural networks (FHNNs) with time delays is investigated. Novel LMI-based stability criteria are obtained by using Lyapunov functional theory to guarantee the asymptotic stability of the FHNNs with less conservatism. Firstly, using both Finsler's lemma and an improved homogeneous matrix polynomial technique, and applying an affine parameter-dependent Lyapunov—Krasovskii functional, we obtain the convergent LMI-based stability criteria. Algebraic properties of the fuzzy membership functions in the unit simplex are considered in the process of stability analysis via the homogeneous matrix polynomials technique. Secondly, to further reduce the conservatism, a new right-hand-side slack variables introducing technique is also proposed in terms of LMIs, which is suitable to the homogeneous matrix polynomials setting. Finally, two illustrative examples are given to show the efficiency of the proposed approaches

  4. Prediction of the electron redundant SinNn fullerenes

    Science.gov (United States)

    Yang, Huihui; Song, Yan; Zhang, Yan; Chen, Hongshan

    2018-05-01

    The stabilities and electronic structures of SimAln-mNn and SinNn (n = 16, 20, m = 12 and n = 24, m = 16) fullerene-like cages have been investigated using density functional method B3LYP and the second-order perturbation theory MP2. The results show that the SimAln-mNn and SinNn fullerenes are more stable than the AlN counterparts. Comparing with the corresponding AlnNn cages, one silicon atom in each Si2N2 square protrudes and the excess electrons reside as lone pair electrons at the outside of the protrudent Si atoms. Analyses on the electronic structures suggest that the Sisbnd N bonds are covalent bonding with strong polarity. The ELF (electron localization function) shows large electron pair probability between Si and N atoms. The orbital interactions between Si and N are stronger than that between Al and N atoms; the overlap integral is 0.40 per Sisbnd N bond in SinNn and 0.34 per Alsbnd N bond in AlnNn. The AIM (atoms in molecule) charges on the Al atoms in AlnNn and SimAln-mNn are 2.37 and 2.40. The charges on the in-plane and protrudent Si atoms are about 2.88 and 1.50 respectively. Considering the large local dipole moments around the protrudent Si atoms, the electrostatic interactions are also favorable to the SiN cages.

  5. Direct nn-scattering at the YAGUAR reactor

    Energy Technology Data Exchange (ETDEWEB)

    Crawford, B.E. [Gettysburg College, Department of Physics, Gettysburg, PA 17325 (United States)]. E-mail: bcrawfor@gettysburg.edu; Furman, W.I. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Howell, C.R. [Duke University and Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States); Lychagin, E.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Levakov, B.G. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Litvin, V.I. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Lyzhin, A.E. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Magda, E.P. [Russian Federal Nuclear Center-All Russian Research Institute of Technical Physics, P.O. Box 245, Snezhinsk 456770 (Russian Federation); Mitchell, G.E. [North Carolina State University, Raleigh, NC 27695-8202 (United States); Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States); Muzichka, A.Yu. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Nekhaev, G.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Sharapov, E.I. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Shvetsov, V.N. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Stephenson, S.L. [Gettysburg College, Department of Physics, Gettysburg, PA 17325 (United States); Strelkov, A.V. [Joint Institute for Nuclear Research, Dubna 141980 (Russian Federation); Tornow, W. [Duke University and Triangle Universities Nuclear Laboratory, Durham, NC 27708-0308 (United States)

    2005-12-15

    The Direct Investigation of a {sub nn} Association (DIANNA) is finalizing the design of a direct measurement of the nn-scattering length to be performed at the YAGUAR reactor in Snezhinsk, Russia. Extensive modeling of the neutron field, nn-scattering kinematics, and sources of detector background have verified the plan for a 3% measurement of a {sub nn}. Measurements of the neutron flux support the neutron field modeling. Initial test measurements of the neutron field inside the underground channel have confirmed calculations of the thermal neutron background.

  6. Network meta-analysis: a technique to gather evidence from direct and indirect comparisons

    Science.gov (United States)

    2017-01-01

    Systematic reviews and pairwise meta-analyses of randomized controlled trials, at the intersection of clinical medicine, epidemiology and statistics, are positioned at the top of evidence-based practice hierarchy. These are important tools to base drugs approval, clinical protocols and guidelines formulation and for decision-making. However, this traditional technique only partially yield information that clinicians, patients and policy-makers need to make informed decisions, since it usually compares only two interventions at the time. In the market, regardless the clinical condition under evaluation, usually many interventions are available and few of them have been studied in head-to-head studies. This scenario precludes conclusions to be drawn from comparisons of all interventions profile (e.g. efficacy and safety). The recent development and introduction of a new technique – usually referred as network meta-analysis, indirect meta-analysis, multiple or mixed treatment comparisons – has allowed the estimation of metrics for all possible comparisons in the same model, simultaneously gathering direct and indirect evidence. Over the last years this statistical tool has matured as technique with models available for all types of raw data, producing different pooled effect measures, using both Frequentist and Bayesian frameworks, with different software packages. However, the conduction, report and interpretation of network meta-analysis still poses multiple challenges that should be carefully considered, especially because this technique inherits all assumptions from pairwise meta-analysis but with increased complexity. Thus, we aim to provide a basic explanation of network meta-analysis conduction, highlighting its risks and benefits for evidence-based practice, including information on statistical methods evolution, assumptions and steps for performing the analysis. PMID:28503228

  7. Interference-Aware Spectrum Sharing Techniques for Next Generation Wireless Networks

    KAUST Repository

    Qaraqe, Marwa Khalid

    2011-11-20

    Background: Reliable high-speed data communication that supports multimedia application for both indoor and outdoor mobile users is a fundamental requirement for next generation wireless networks and requires a dense deployment of physically coexisting network architectures. Due to the limited spectrum availability, a novel interference-aware spectrum-sharing concept is introduced where networks that suffer from congested spectrums (secondary-networks) are allowed to share the spectrum with other networks with available spectrum (primary-networks) under the condition that limited interference occurs to primary networks. Objective: Multiple-antenna and adaptive rate can be utilized as a power-efficient technique for improving the data rate of the secondary link while satisfying the interference constraint of the primary link by allowing the secondary user to adapt its transmitting antenna, power, and rate according to the channel state information. Methods: Two adaptive schemes are proposed using multiple-antenna transmit diversity and adaptive modulation in order to increase the spectral-efficiency of the secondary link while maintaining minimum interference with the primary. Both the switching efficient scheme (SES) and bandwidth efficient scheme (BES) use the scan-and-wait combining antenna technique (SWC) where there is a secondary transmission only when a branch with an acceptable performance is found; else the data is buffered. Results: In both these schemes the constellation size and selected transmit branch are determined to minimized the average number of switches and achieve the highest spectral efficiency given a minimum bit-error-rate (BER), fading conditions, and peak interference constraint. For delayed sensitive applications, two schemes using power control are used: SES-PC and BES-PC. In these schemes the secondary transmitter sends data using a nominal power level, which is optimized to minimize the average delay. Several numerical examples show

  8. On the classification techniques in data mining for microarray data classification

    Science.gov (United States)

    Aydadenta, Husna; Adiwijaya

    2018-03-01

    Cancer is one of the deadly diseases, according to data from WHO by 2015 there are 8.8 million more deaths caused by cancer, and this will increase every year if not resolved earlier. Microarray data has become one of the most popular cancer-identification studies in the field of health, since microarray data can be used to look at levels of gene expression in certain cell samples that serve to analyze thousands of genes simultaneously. By using data mining technique, we can classify the sample of microarray data thus it can be identified with cancer or not. In this paper we will discuss some research using some data mining techniques using microarray data, such as Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5, and simulation of Random Forest algorithm with technique of reduction dimension using Relief. The result of this paper show performance measure (accuracy) from classification algorithm (SVM, ANN, Naive Bayes, kNN, C4.5, and Random Forets).The results in this paper show the accuracy of Random Forest algorithm higher than other classification algorithms (Support Vector Machine (SVM), Artificial Neural Network (ANN), Naive Bayes, k-Nearest Neighbor (kNN), and C4.5). It is hoped that this paper can provide some information about the speed, accuracy, performance and computational cost generated from each Data Mining Classification Technique based on microarray data.

  9. A dynamic approach merging network theory and credit risk techniques to assess systemic risk in financial networks.

    Science.gov (United States)

    Petrone, Daniele; Latora, Vito

    2018-04-03

    The interconnectedness of financial institutions affects instability and credit crises. To quantify systemic risk we introduce here the PD model, a dynamic model that combines credit risk techniques with a contagion mechanism on the network of exposures among banks. A potential loss distribution is obtained through a multi-period Monte Carlo simulation that considers the probability of default (PD) of the banks and their tendency of defaulting in the same time interval. A contagion process increases the PD of banks exposed toward distressed counterparties. The systemic risk is measured by statistics of the loss distribution, while the contribution of each node is quantified by the new measures PDRank and PDImpact. We illustrate how the model works on the network of the European Global Systemically Important Banks. For a certain range of the banks' capital and of their assets volatility, our results reveal the emergence of a strong contagion regime where lower default correlation between banks corresponds to higher losses. This is the opposite of the diversification benefits postulated by standard credit risk models used by banks and regulators who could therefore underestimate the capital needed to overcome a period of crisis, thereby contributing to the financial system instability.

  10. Quality-of-Service Routing Using Path and Power Aware Techniques in Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    R. Asokan

    2008-01-01

    Full Text Available Mobile ad hoc network (MANET is a collection of wireless mobile hosts dynamically forming a temporary network without the aid of any existing established infrastructure. Quality of service (QoS is a set of service requirements that needs to be met by the network while transporting a packet stream from a source to its destination. QoS support MANETs is a challenging task due to the dynamic topology and limited resources. The main objective of this paper is to enhance the QoS routing for MANET using temporally ordered routing algorithm (TORA with self-healing and optimized routing techniques (SHORT. SHORT improves routing optimality by monitoring routing paths continuously and redirecting the path whenever a shortcut path is available. In this paper, the performance comparison of TORA and TORA with SHORT has been analyzed using network simulator for various parameters. TORA with SHORT enhances performance of TORA in terms of throughput, packet loss, end-to-end delay, and energy.

  11. A Review of Anomaly Detection Techniques and Distributed Denial of Service (DDoS on Software Defined Network (SDN

    Directory of Open Access Journals (Sweden)

    M. H. H. Khairi

    2018-04-01

    Full Text Available Software defined network (SDN is a network architecture in which the network traffic may be operated and managed dynamically according to user requirements and demands. Issue of security is one of the big challenges of SDN because different attacks may affect performance and these attacks can be classified into different types. One of the famous attacks is distributed denial of service (DDoS. SDN is a new networking approach that is introduced with the goal to simplify the network management by separating the data and control planes. However, the separation leads to the emergence of new types of distributed denial-of-service (DDOS attacks on SDN networks. The centralized role of the controller in SDN makes it a perfect target for the attackers. Such attacks can easily bring down the entire network by bringing down the controller. This research explains DDoS attacks and the anomaly detection as one of the famous detection techniques for intelligent networks.

  12. The NN and NantiN interactions

    International Nuclear Information System (INIS)

    Vinh Mau, R.

    1981-01-01

    The present status of the low and medium energy NN interaction and of the low energy NantiN interaction is reviewed. Careful confrontation of theoretical predictions with the most recent results on experimental observables is emphasized. The question of the dibaryon resonances is discussed. For the NantiN interaction, in view of the problem of the existence of baryonium states as bound states or resonant states of the NantiN system and of their properties, tests of different types of annihilation potentials against the existing experimental data are examined. Implications for the future experimental program at LEAR are discussed

  13. When are network coding based dynamic multi-homing techniques beneficial?

    DEFF Research Database (Denmark)

    Pereira, Carlos; Aguiar, Ana; Roetter, Daniel Enrique Lucani

    2016-01-01

    Mechanisms that can cope with unreliable wireless channels in an efficient manner are required due to the increasing number of resource constrained devices. Concurrent use of multiple communications technologies can be instrumental towards improving services to mobile devices in heterogeneous...... networks. In our previous work, we developed an optimization framework to generate channel-aware transmission policies for multi-homed devices under different cost criteria. Our formulation considers network coding as a key technique that simplifies load allocation across multiple channels and provides...... high resiliency under time-varying channel conditions. This paper seeks to explore the parameter space and identify the operating regions where dynamic coded policies offer most improvement over static ones in terms of energy consumption and channel utilization. We leverage meta-heuristics to find...

  14. Achieving energy efficiency in LTE with joint D2D communications and green networking techniques

    KAUST Repository

    Yaacoub, Elias E.

    2013-07-01

    In this paper, the joint operation of cooperative device-to-device (D2D) communications and green cellular communications is investigated. An efficient approach for grouping mobile terminals (MTs) into cooperative clusters is described. In each cluster, MTs cooperate via D2D communications to share content of common interest. Furthermore, an energy-efficient technique for putting BSs in sleep mode in an LTE cellular network is presented. Finally, both methods are combined in order to ensure green communications for both the users\\' MTs and the operator\\'s BSs. The studied methods are investigated in the framework of OFDMA-based state-of-the-art LTE cellular networks, while taking into account intercell interference and resource allocation. © 2013 IEEE.

  15. Robust Automatic Modulation Classification Technique for Fading Channels via Deep Neural Network

    Directory of Open Access Journals (Sweden)

    Jung Hwan Lee

    2017-08-01

    Full Text Available In this paper, we propose a deep neural network (DNN-based automatic modulation classification (AMC for digital communications. While conventional AMC techniques perform well for additive white Gaussian noise (AWGN channels, classification accuracy degrades for fading channels where the amplitude and phase of channel gain change in time. The key contributions of this paper are in two phases. First, we analyze the effectiveness of a variety of statistical features for AMC task in fading channels. We reveal that the features that are shown to be effective for fading channels are different from those known to be good for AWGN channels. Second, we introduce a new enhanced AMC technique based on DNN method. We use the extensive and diverse set of statistical features found in our study for the DNN-based classifier. The fully connected feedforward network with four hidden layers are trained to classify the modulation class for several fading scenarios. Numerical evaluation shows that the proposed technique offers significant performance gain over the existing AMC methods in fading channels.

  16. A Method for Dynamically Selecting the Best Frequency Hopping Technique in Industrial Wireless Sensor Network Applications.

    Science.gov (United States)

    Fernández de Gorostiza, Erlantz; Berzosa, Jorge; Mabe, Jon; Cortiñas, Roberto

    2018-02-23

    Industrial wireless applications often share the communication channel with other wireless technologies and communication protocols. This coexistence produces interferences and transmission errors which require appropriate mechanisms to manage retransmissions. Nevertheless, these mechanisms increase the network latency and overhead due to the retransmissions. Thus, the loss of data packets and the measures to handle them produce an undesirable drop in the QoS and hinder the overall robustness and energy efficiency of the network. Interference avoidance mechanisms, such as frequency hopping techniques, reduce the need for retransmissions due to interferences but they are often tailored to specific scenarios and are not easily adapted to other use cases. On the other hand, the total absence of interference avoidance mechanisms introduces a security risk because the communication channel may be intentionally attacked and interfered with to hinder or totally block it. In this paper we propose a method for supporting the design of communication solutions under dynamic channel interference conditions and we implement dynamic management policies for frequency hopping technique and channel selection at runtime. The method considers several standard frequency hopping techniques and quality metrics, and the quality and status of the available frequency channels to propose the best combined solution to minimize the side effects of interferences. A simulation tool has been developed and used in this work to validate the method.

  17. A Wireless Sensor Network with Soft Computing Localization Techniques for Track Cycling Applications.

    Science.gov (United States)

    Gharghan, Sadik Kamel; Nordin, Rosdiadee; Ismail, Mahamod

    2016-08-06

    In this paper, we propose two soft computing localization techniques for wireless sensor networks (WSNs). The two techniques, Neural Fuzzy Inference System (ANFIS) and Artificial Neural Network (ANN), focus on a range-based localization method which relies on the measurement of the received signal strength indicator (RSSI) from the three ZigBee anchor nodes distributed throughout the track cycling field. The soft computing techniques aim to estimate the distance between bicycles moving on the cycle track for outdoor and indoor velodromes. In the first approach the ANFIS was considered, whereas in the second approach the ANN was hybridized individually with three optimization algorithms, namely Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA), and Backtracking Search Algorithm (BSA). The results revealed that the hybrid GSA-ANN outperforms the other methods adopted in this paper in terms of accuracy localization and distance estimation accuracy. The hybrid GSA-ANN achieves a mean absolute distance estimation error of 0.02 m and 0.2 m for outdoor and indoor velodromes, respectively.

  18. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks.

    Science.gov (United States)

    Garcia-Font, Victor; Garrigues, Carles; Rifà-Pous, Helena

    2016-06-13

    In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens' quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN) and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  19. Surface Casting Defects Inspection Using Vision System and Neural Network Techniques

    Directory of Open Access Journals (Sweden)

    Świłło S.J.

    2013-12-01

    Full Text Available The paper presents a vision based approach and neural network techniques in surface defects inspection and categorization. Depending on part design and processing techniques, castings may develop surface discontinuities such as cracks and pores that greatly influence the material’s properties Since the human visual inspection for the surface is slow and expensive, a computer vision system is an alternative solution for the online inspection. The authors present the developed vision system uses an advanced image processing algorithm based on modified Laplacian of Gaussian edge detection method and advanced lighting system. The defect inspection algorithm consists of several parameters that allow the user to specify the sensitivity level at which he can accept the defects in the casting. In addition to the developed image processing algorithm and vision system apparatus, an advanced learning process has been developed, based on neural network techniques. Finally, as an example three groups of defects were investigated demonstrates automatic selection and categorization of the measured defects, such as blowholes, shrinkage porosity and shrinkage cavity.

  20. A Comparative Study of Anomaly Detection Techniques for Smart City Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Victor Garcia-Font

    2016-06-01

    Full Text Available In many countries around the world, smart cities are becoming a reality. These cities contribute to improving citizens’ quality of life by providing services that are normally based on data extracted from wireless sensor networks (WSN and other elements of the Internet of Things. Additionally, public administration uses these smart city data to increase its efficiency, to reduce costs and to provide additional services. However, the information received at smart city data centers is not always accurate, because WSNs are sometimes prone to error and are exposed to physical and computer attacks. In this article, we use real data from the smart city of Barcelona to simulate WSNs and implement typical attacks. Then, we compare frequently used anomaly detection techniques to disclose these attacks. We evaluate the algorithms under different requirements on the available network status information. As a result of this study, we conclude that one-class Support Vector Machines is the most appropriate technique. We achieve a true positive rate at least 56% higher than the rates achieved with the other compared techniques in a scenario with a maximum false positive rate of 5% and a 26% higher in a scenario with a false positive rate of 15%.

  1. Improved NN-GM(1,1 for Postgraduates’ Employment Confidence Index Forecasting

    Directory of Open Access Journals (Sweden)

    Lu Wang

    2014-01-01

    Full Text Available Postgraduates’ employment confidence index (ECI forecasting can help the university to predict the future trend of postgraduates’ employment. However, the common forecast method based on the grey model (GM has unsatisfactory performance to a certain extent. In order to forecast postgraduates’ ECI efficiently, this paper discusses a novel hybrid forecast model using limited raw samples. Different from previous work, the residual modified GM(1,1 model is combined with the improved neural network (NN in this work. In particullar, the hybrid model reduces the residue of the standard GM(1,1 model as well as accelerating the convergence rate of the standard NN. After numerical studies, the illustrative results are provided to demonstrate the forecast performance of the proposed model. In addition, some strategies for improving the postgraduates’ employment confidence have been discussed.

  2. Network modeling and analysis technique for the evaluation of nuclear safeguards systems effectiveness

    International Nuclear Information System (INIS)

    Grant, F.H. III; Miner, R.J.; Engi, D.

    1978-01-01

    Nuclear safeguards systems are concerned with the physical protection and control of nuclear materials. The Safeguards Network Analysis Procedure (SNAP) provides a convenient and standard analysis methodology for the evaluation of safeguards system effectiveness. This is achieved through a standard set of symbols which characterize the various elements of safeguards systems and an analysis program to execute simulation models built using the SNAP symbology. The reports provided by the SNAP simulation program enable analysts to evaluate existing sites as well as alternative design possibilities. This paper describes the SNAP modeling technique and provides an example illustrating its use

  3. Modified multiphotodiode balanced detection technique for improving SAC-OCDMA networks

    Science.gov (United States)

    Tseng, Shin-Pin

    2015-06-01

    The aim of this study was to develop a feasible modified multiphotodiode balanced detection (MMBD) technique suitable for application in spectral-amplitude-coding optical code-division multiple-access networks, which could mitigate the effect of phase-induced intensity noise (PIIN). Because PIIN is related to the beating between optical signals with wavelengths arriving simultaneously at a balanced photodetector, combining spectral partitioning and an MMBD scheme facilitates reducing PIIN, which tends to dominate performance. Therefore, the system using the proposed scheme exhibited an enhanced performance.

  4. ZnO nanowall network grown by chemical vapor deposition technique

    Energy Technology Data Exchange (ETDEWEB)

    Mukherjee, Amrita, E-mail: but.then.perhaps@gmail.com; Dhar, Subhabrata [Department of Physics, Indian Institute of Technology Bombay, Powai, Mumbai-400076 (India)

    2015-06-24

    Network of wedge shaped ZnO nanowalls are grown on c-sapphire by Chemical Vapor Deposition (CVD) technique. Structural studies using x-ray diffraction show much better crystallinity in the nanowall sample as compared to the continuous film. Moreover, the defect related broad green luminescence is found to be suppressed in the nanowall sample. The low temperature photoluminescence study also suggests the quantum confinement of carriers in nanowall sample. Electrical studies performed on the nanowalls show higher conductivity, which has been explained in terms of the reduction of scattering cross-section as a result of 1D quantum confinement of carriers on the tip of the nanowalls.

  5. Network modeling and analysis technique for the evaluation of nuclear safeguards systems effectiveness

    International Nuclear Information System (INIS)

    Grant, F.H. III; Miner, R.J.; Engi, D.

    1979-02-01

    Nuclear safeguards systems are concerned with the physical protection and control of nuclear materials. The Safeguards Network Analysis Procedure (SNAP) provides a convenient and standard analysis methodology for the evaluation of safeguards system effectiveness. This is achieved through a standard set of symbols which characterize the various elements of safeguards systems and an analysis program to execute simulation models built using the SNAP symbology. The reports provided by the SNAP simulation program enable analysts to evaluate existing sites as well as alternative design possibilities. This paper describes the SNAP modeling technique and provides an example illustrating its use

  6. Different Apple Varieties Classification Using kNN and MLP Algorithms

    OpenAIRE

    Sabancı, Kadir

    2016-01-01

    In this study, three different apple varieties grown in Karaman provinceare classified using kNN and MLP algorithms. 90 apples in total, 30 GoldenDelicious, 30 Granny Smith and 30 Starking Delicious have been used in thestudy. DFK 23U445 USB 3.0 (with Fujinon C Mount Lens) industrial camera hasbeen used to capture apple images. 4 size properties (diameter, area, perimeterand fullness) and 3 color properties (red, green, blue) have been decided usingimage processing techniques through analyzin...

  7. A Comparison of Techniques for Camera Selection and Hand-Off in a Video Network

    Science.gov (United States)

    Li, Yiming; Bhanu, Bir

    Video networks are becoming increasingly important for solving many real-world problems. Multiple video sensors require collaboration when performing various tasks. One of the most basic tasks is the tracking of objects, which requires mechanisms to select a camera for a certain object and hand-off this object from one camera to another so as to accomplish seamless tracking. In this chapter, we provide a comprehensive comparison of current and emerging camera selection and hand-off techniques. We consider geometry-, statistics-, and game theory-based approaches and provide both theoretical and experimental comparison using centralized and distributed computational models. We provide simulation and experimental results using real data for various scenarios of a large number of cameras and objects for in-depth understanding of strengths and weaknesses of these techniques.

  8. Location estimation in wireless sensor networks using spring-relaxation technique.

    Science.gov (United States)

    Zhang, Qing; Foh, Chuan Heng; Seet, Boon-Chong; Fong, A C M

    2010-01-01

    Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN). Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS) is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  9. Location Estimation in Wireless Sensor Networks Using Spring-Relaxation Technique

    Directory of Open Access Journals (Sweden)

    Qing Zhang

    2010-05-01

    Full Text Available Accurate and low-cost autonomous self-localization is a critical requirement of various applications of a large-scale distributed wireless sensor network (WSN. Due to its massive deployment of sensors, explicit measurements based on specialized localization hardware such as the Global Positioning System (GPS is not practical. In this paper, we propose a low-cost WSN localization solution. Our design uses received signal strength indicators for ranging, light weight distributed algorithms based on the spring-relaxation technique for location computation, and the cooperative approach to achieve certain location estimation accuracy with a low number of nodes with known locations. We provide analysis to show the suitability of the spring-relaxation technique for WSN localization with cooperative approach, and perform simulation experiments to illustrate its accuracy in localization.

  10. Techniques for Improving the Accuracy of 802.11 WLAN-Based Networking Experimentation

    Directory of Open Access Journals (Sweden)

    Portoles-Comeras Marc

    2010-01-01

    Full Text Available Wireless networking experimentation research has become highly popular due to both the frequent mismatch between theory and practice and the widespread availability of low-cost WLAN cards. However, current WLAN solutions present a series of performance issues, sometimes difficult to predict in advance, that may compromise the validity of the results gathered. This paper surveys recent literature dealing with such issues and draws attention on the negative results of starting experimental research without properly understanding the tools that are going to be used. Furthermore, the paper details how a conscious assessment strategy can prevent placing wrong assumptions on the hardware. Indeed, there are numerous techniques that have been described throughout the literature that can be used to obtain a deeper understanding of the solutions that have been adopted. The paper surveys these techniques and classifies them in order to provide a handful reference for building experimental setups from which accurate measurements may be obtained.

  11. Measure of pore size in micro filtration polymeric membrane using ultrasonic technique and artificial neural networks

    International Nuclear Information System (INIS)

    Lucas, Carla de Souza

    2009-01-01

    This work presents a study of the pore size in micro filtration polymeric membranes, used in the nuclear area for the filtration of radioactive liquid effluent, in the residual water treatment of the petrochemical industry, in the electronic industry for the ultrapure water production for the manufacture of conductors and laundering of microcircuits and in many other processes of separation. Diverse processes for measures of pores sizes in membranes exist, amongst these, electronic microscopy, of bubble point and mercury intrusion porosimetry, however the majority of these uses destructive techniques, of high cost or great time of analysis. The proposal of this work is to measure so great of pore being used ultrasonic technique in the time domain of the frequency and artificial neural networks. A receiving/generator of ultrasonic pulses, a immersion transducer of 25 MHz was used, a tank of immersion and microporous membranes of pores sizes of 0,2 μm, 0,4 μm, 0,6 μm, 8 μm, 10 μm and 12 μm. The ultrasonic signals after to cover the membrane, come back to the transducer (emitting/receiving) bringing information of the interaction of the signal with the membranes. These signals had been used for the training of neural networks, and these had supplied the necessary precision the distinction of the same ones. Soon after, technique with the one of electronic microscopy of sweepings was made the comparison of this. The experiment showed very resulted next to the results gotten with the MEV, what it indicated that the studied technique is ideal for measure of pore size in membranes for being not destructive and of this form to be able to be used also on-line of production. (author)

  12. Performance Evaluation of 14 Neural Network Architectures Used for Predicting Heat Transfer Characteristics of Engine Oils

    Science.gov (United States)

    Al-Ajmi, R. M.; Abou-Ziyan, H. Z.; Mahmoud, M. A.

    2012-01-01

    This paper reports the results of a comprehensive study that aimed at identifying best neural network architecture and parameters to predict subcooled boiling characteristics of engine oils. A total of 57 different neural networks (NNs) that were derived from 14 different NN architectures were evaluated for four different prediction cases. The NNs were trained on experimental datasets performed on five engine oils of different chemical compositions. The performance of each NN was evaluated using a rigorous statistical analysis as well as careful examination of smoothness of predicted boiling curves. One NN, out of the 57 evaluated, correctly predicted the boiling curves for all cases considered either for individual oils or for all oils taken together. It was found that the pattern selection and weight update techniques strongly affect the performance of the NNs. It was also revealed that the use of descriptive statistical analysis such as R2, mean error, standard deviation, and T and slope tests, is a necessary but not sufficient condition for evaluating NN performance. The performance criteria should also include inspection of the smoothness of the predicted curves either visually or by plotting the slopes of these curves.

  13. Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

    Science.gov (United States)

    Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

    2018-07-01

    Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

  14. Prediction of Monthly Summer Monsoon Rainfall Using Global Climate Models Through Artificial Neural Network Technique

    Science.gov (United States)

    Nair, Archana; Singh, Gurjeet; Mohanty, U. C.

    2018-01-01

    The monthly prediction of summer monsoon rainfall is very challenging because of its complex and chaotic nature. In this study, a non-linear technique known as Artificial Neural Network (ANN) has been employed on the outputs of Global Climate Models (GCMs) to bring out the vagaries inherent in monthly rainfall prediction. The GCMs that are considered in the study are from the International Research Institute (IRI) (2-tier CCM3v6) and the National Centre for Environmental Prediction (Coupled-CFSv2). The ANN technique is applied on different ensemble members of the individual GCMs to obtain monthly scale prediction over India as a whole and over its spatial grid points. In the present study, a double-cross-validation and simple randomization technique was used to avoid the over-fitting during training process of the ANN model. The performance of the ANN-predicted rainfall from GCMs is judged by analysing the absolute error, box plots, percentile and difference in linear error in probability space. Results suggest that there is significant improvement in prediction skill of these GCMs after applying the ANN technique. The performance analysis reveals that the ANN model is able to capture the year to year variations in monsoon months with fairly good accuracy in extreme years as well. ANN model is also able to simulate the correct signs of rainfall anomalies over different spatial points of the Indian domain.

  15. Alternative method of highway traffic safety analysis for developing countries using delphi technique and Bayesian network.

    Science.gov (United States)

    Mbakwe, Anthony C; Saka, Anthony A; Choi, Keechoo; Lee, Young-Jae

    2016-08-01

    Highway traffic accidents all over the world result in more than 1.3 million fatalities annually. An alarming number of these fatalities occurs in developing countries. There are many risk factors that are associated with frequent accidents, heavy loss of lives, and property damage in developing countries. Unfortunately, poor record keeping practices are very difficult obstacle to overcome in striving to obtain a near accurate casualty and safety data. In light of the fact that there are numerous accident causes, any attempts to curb the escalating death and injury rates in developing countries must include the identification of the primary accident causes. This paper, therefore, seeks to show that the Delphi Technique is a suitable alternative method that can be exploited in generating highway traffic accident data through which the major accident causes can be identified. In order to authenticate the technique used, Korea, a country that underwent similar problems when it was in its early stages of development in addition to the availability of excellent highway safety records in its database, is chosen and utilized for this purpose. Validation of the methodology confirms the technique is suitable for application in developing countries. Furthermore, the Delphi Technique, in combination with the Bayesian Network Model, is utilized in modeling highway traffic accidents and forecasting accident rates in the countries of research. Copyright © 2016 Elsevier Ltd. All rights reserved.

  16. The application of neural network techniques to magnetic and optical inverse problems

    International Nuclear Information System (INIS)

    Jones, H.V.

    2000-12-01

    The processing power of the computer has increased at unimaginable rates over the last few decades. However, even today's fastest computer can take several hours to find solutions to some mathematical problems; and there are instances where a high powered supercomputer may be impractical, with the need for near instant solutions just as important (such as in an on-line testing system). This led us to believe that such complex problems could be solved using a novel approach, whereby the system would have prior knowledge about the expected solutions through a process of learning. One method of approaching this kind of problem is through the use of machine learning. Just as a human can be trained and is able to learn from past experiences, a machine is can do just the same. This is the concept of neural networks. The research which was conducted involves the investigation of various neural network techniques, and their applicability to solve some known complex inverse problems in the field of magnetic and optical recording. In some cases a comparison is also made to more conventional methods of solving the problems, from which it was possible to outline some key advantages of using a neural network approach. We initially investigated the application of neural networks to transverse susceptibility data in order to determine anisotropy distributions. This area of research is proving to be very important, as it gives us information about the switching field distribution, which then determines the minimum transition width achievable in a medium, and affects the overwrite characteristics of the media. Secondly, we investigated a similar situation, but applied to an optical problem. This involved the determination of important compact disc parameters from the diffraction pattern of a laser from a disc. This technique was then intended for use in an on-line testing system. Finally we investigated another area of neural networks with the analysis of magnetisation maps and

  17. Performance Comparison of Reputation Assessment Techniques Based on Self-Organizing Maps in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sabrina Sicari

    2017-01-01

    Full Text Available Many solutions based on machine learning techniques have been proposed in literature aimed at detecting and promptly counteracting various kinds of malicious attack (data violation, clone, sybil, neglect, greed, and DoS attacks, which frequently affect Wireless Sensor Networks (WSNs. Besides recognizing the corrupted or violated information, also the attackers should be identified, in order to activate the proper countermeasures for preserving network’s resources and to mitigate their malicious effects. To this end, techniques adopting Self-Organizing Maps (SOM for intrusion detection in WSN were revealed to represent a valuable and effective solution to the problem. In this paper, the mechanism, namely, Good Network (GoNe, which is based on SOM and is able to assess the reliability of the sensor nodes, is compared with another relevant and similar work existing in literature. Extensive performance simulations, in terms of nodes’ classification, attacks’ identification, data accuracy, energy consumption, and signalling overhead, have been carried out in order to demonstrate the better feasibility and efficiency of the proposed solution in WSN field.

  18. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-01-01

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy. PMID:29231868

  19. Cross-Layer Techniques for Adaptive Video Streaming over Wireless Networks

    Directory of Open Access Journals (Sweden)

    Yufeng Shan

    2005-02-01

    Full Text Available Real-time streaming media over wireless networks is a challenging proposition due to the characteristics of video data and wireless channels. In this paper, we propose a set of cross-layer techniques for adaptive real-time video streaming over wireless networks. The adaptation is done with respect to both channel and data. The proposed novel packetization scheme constructs the application layer packet in such a way that it is decomposed exactly into an integer number of equal-sized radio link protocol (RLP packets. FEC codes are applied within an application packet at the RLP packet level rather than across different application packets and thus reduce delay at the receiver. A priority-based ARQ, together with a scheduling algorithm, is applied at the application layer to retransmit only the corrupted RLP packets within an application layer packet. Our approach combines the flexibility and programmability of application layer adaptations, with low delay and bandwidth efficiency of link layer techniques. Socket-level simulations are presented to verify the effectiveness of our approach.

  20. Vehicle Assisted Data Delievery Technique To Control Data Dissemination In Vehicular AD - HOC Networks Vanets

    Directory of Open Access Journals (Sweden)

    Sandeep Kumar

    2015-08-01

    Full Text Available Abstract Multi-hop data delivery through vehicular ad hoc networks is complicated by the fact that vehicular networks are highly mobile and frequently disconnected. To address this issue the idea of helper node is opted where a moving vehicles carries the packet until a new vehicle moves into its vicinity and forwards the packet. Different from existing helper node solution use of the predicable vehicle mobility is made which is limited by the traffic pattern and the road layout. Based on the existing traffic pattern a vehicle can find the next road to forward packet a vehicle can find the next road to forward the packet to reduce the delay. Several vehicle-assisted date delievery VADD protocol is proposed to forward the packet to the best road with the road with the lowest data delivery delay. Experiment results are used to evaluate the proposed solutions. Results show that the proposed VADD protocol outperform existing solution in terms of packet delivery ratio data packet delay and protocol overhead. Among the proposed VADD protocols the Hybrid probe HVADD protocol has much better performance. In this Solution the helper node technique is provider with which the helper node will contain destination node path and the path in routine table continuously changes with the help of helper node technique.

  1. A Data-Driven Response Virtual Sensor Technique with Partial Vibration Measurements Using Convolutional Neural Network.

    Science.gov (United States)

    Sun, Shan-Bin; He, Yuan-Yuan; Zhou, Si-Da; Yue, Zhen-Jiang

    2017-12-12

    Measurement of dynamic responses plays an important role in structural health monitoring, damage detection and other fields of research. However, in aerospace engineering, the physical sensors are limited in the operational conditions of spacecraft, due to the severe environment in outer space. This paper proposes a virtual sensor model with partial vibration measurements using a convolutional neural network. The transmissibility function is employed as prior knowledge. A four-layer neural network with two convolutional layers, one fully connected layer, and an output layer is proposed as the predicting model. Numerical examples of two different structural dynamic systems demonstrate the performance of the proposed approach. The excellence of the novel technique is further indicated using a simply supported beam experiment comparing to a modal-model-based virtual sensor, which uses modal parameters, such as mode shapes, for estimating the responses of the faulty sensors. The results show that the presented data-driven response virtual sensor technique can predict structural response with high accuracy.

  2. Histopathological Breast Cancer Image Classification by Deep Neural Network Techniques Guided by Local Clustering.

    Science.gov (United States)

    Nahid, Abdullah-Al; Mehrabi, Mohamad Ali; Kong, Yinan

    2018-01-01

    Breast Cancer is a serious threat and one of the largest causes of death of women throughout the world. The identification of cancer largely depends on digital biomedical photography analysis such as histopathological images by doctors and physicians. Analyzing histopathological images is a nontrivial task, and decisions from investigation of these kinds of images always require specialised knowledge. However, Computer Aided Diagnosis (CAD) techniques can help the doctor make more reliable decisions. The state-of-the-art Deep Neural Network (DNN) has been recently introduced for biomedical image analysis. Normally each image contains structural and statistical information. This paper classifies a set of biomedical breast cancer images (BreakHis dataset) using novel DNN techniques guided by structural and statistical information derived from the images. Specifically a Convolutional Neural Network (CNN), a Long-Short-Term-Memory (LSTM), and a combination of CNN and LSTM are proposed for breast cancer image classification. Softmax and Support Vector Machine (SVM) layers have been used for the decision-making stage after extracting features utilising the proposed novel DNN models. In this experiment the best Accuracy value of 91.00% is achieved on the 200x dataset, the best Precision value 96.00% is achieved on the 40x dataset, and the best F -Measure value is achieved on both the 40x and 100x datasets.

  3. Comparison of Machine Learning Techniques for the Prediction of Compressive Strength of Concrete

    Directory of Open Access Journals (Sweden)

    Palika Chopra

    2018-01-01

    Full Text Available A comparative analysis for the prediction of compressive strength of concrete at the ages of 28, 56, and 91 days has been carried out using machine learning techniques via “R” software environment. R is digging out a strong foothold in the statistical realm and is becoming an indispensable tool for researchers. The dataset has been generated under controlled laboratory conditions. Using R miner, the most widely used data mining techniques decision tree (DT model, random forest (RF model, and neural network (NN model have been used and compared with the help of coefficient of determination (R2 and root-mean-square error (RMSE, and it is inferred that the NN model predicts with high accuracy for compressive strength of concrete.

  4. Neural network based photovoltaic electrical forecasting in south Algeria

    International Nuclear Information System (INIS)

    Hamid Oudjana, S.; Hellal, A.; Hadj Mahammed, I

    2014-01-01

    Photovoltaic electrical forecasting is significance for the optimal operation and power predication of grid-connected photovoltaic (PV) plants, and it is important task in renewable energy electrical system planning and operating. This paper explores the application of neural networks (NN) to study the design of photovoltaic electrical forecasting systems for one week ahead using weather databases include the global irradiance, and temperature of Ghardaia city (south of Algeria) for one year of 2013 using a data acquisition system. Simulations were run and the results are discussed showing that neural networks Technique is capable to decrease the photovoltaic electrical forecasting error. (author)

  5. The development of the cancer control service in Belarus (the 80th birthday of N.N. Alexandrov)

    International Nuclear Information System (INIS)

    Korotkevich, E.A.; Mashevskij, A.A.; Shitikov, B.D.

    1997-01-01

    History of the development of oncological service in Belarus is discussed in view of the 80-th birthday anniversary of N.N. Alexandrov being the founder of Belarus oncology and the first director of the Belarus Research Institue of Oncology and Medical Radiology died in 1981. Developed original techniques of radiotherapy, progress in radiotherapy and automation processes are considered

  6. Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

    Energy Technology Data Exchange (ETDEWEB)

    Salgado, Cesar M.; Brandao, Luis E.B., E-mail: otero@ien.gov.br, E-mail: brandao@ien.gov.br [Instituto de Engenharia Nuclear (IEN/CNEN-RJ), Rio de Janeiro, RJ (Brazil)

    2015-07-01

    The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ({sup 137}Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

  7. Volume fraction prediction in biphasic flow using nuclear technique and artificial neural network

    International Nuclear Information System (INIS)

    Salgado, Cesar M.; Brandao, Luis E.B.

    2015-01-01

    The volume fraction is one of the most important parameters used to characterize air-liquid two-phase flows. It is a physical value to determine other parameters, such as the phase's densities and to determine the flow rate of each phase. These parameters are important to predict the flow pattern and to determine a mathematical model for the system. To study, for example, heat transfer and pressure drop. This work presents a methodology for volume fractions prediction in water-gas stratified flow regime using the nuclear technique and artificial intelligence. The volume fractions calculate in biphasic flow systems is complex and the analysis by means of analytical equations becomes very difficult. The approach is based on gamma-ray pulse height distributions pattern recognition by means of the artificial neural network. The detection system uses appropriate broad beam geometry, comprised of a ( 137 Cs) energy gamma-ray source and a NaI(Tl) scintillation detector in order measure transmitted beam whose the counts rates are influenced by the phases composition. These distributions are directly used by the network without any parameterization of the measured signal. The ideal and static theoretical models for stratified regime have been developed using MCNP-X code, which was used to provide training, test and validation data for the network. The detector also was modeled with this code and the results were compared to experimental photopeak efficiency measurements of radiation sources. The proposed network could obtain with satisfactory prediction of the volume fraction in water-gas system, demonstrating to be a promising approach for this purpose. (author)

  8. Renormalization of NN scattering: Contact potential

    International Nuclear Information System (INIS)

    Yang Jifeng; Huang Jianhua

    2005-01-01

    The renormalization of the T matrix for NN scattering with a contact potential is re-examined in a nonperturbative regime through rigorous nonperturbative solutions. Based on the underlying theory, it is shown that the ultraviolet divergences in the nonperturbative solutions of the T matrix should be subtracted through 'endogenous' counterterms, which in turn leads to a nontrivial prescription dependence. Moreover, employing the effective range expansion, the importance of imposing physical boundary conditions to remove the nontrivial prescription dependence, especially before making any physical claims, is discussed and highlighted. As by-products, some relations between the effective range expansion parameters are derived. We also discuss the power counting of the couplings for the nucleon-nucleon interactions and other subtle points related to the EFT framework beyond perturbative treatment

  9. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Directory of Open Access Journals (Sweden)

    Hazlee Azil Illias

    Full Text Available It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN and particle swarm optimisation (PSO techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  10. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques.

    Science.gov (United States)

    Illias, Hazlee Azil; Chai, Xin Rui; Abu Bakar, Ab Halim; Mokhlis, Hazlie

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works.

  11. An intelligent signal processing and pattern recognition technique for defect identification using an active sensor network

    Science.gov (United States)

    Su, Zhongqing; Ye, Lin

    2004-08-01

    The practical utilization of elastic waves, e.g. Rayleigh-Lamb waves, in high-performance structural health monitoring techniques is somewhat impeded due to the complicated wave dispersion phenomena, the existence of multiple wave modes, the high susceptibility to diverse interferences, the bulky sampled data and the difficulty in signal interpretation. An intelligent signal processing and pattern recognition (ISPPR) approach using the wavelet transform and artificial neural network algorithms was developed; this was actualized in a signal processing package (SPP). The ISPPR technique comprehensively functions as signal filtration, data compression, characteristic extraction, information mapping and pattern recognition, capable of extracting essential yet concise features from acquired raw wave signals and further assisting in structural health evaluation. For validation, the SPP was applied to the prediction of crack growth in an alloy structural beam and construction of a damage parameter database for defect identification in CF/EP composite structures. It was clearly apparent that the elastic wave propagation-based damage assessment could be dramatically streamlined by introduction of the ISPPR technique.

  12. Transformer Incipient Fault Prediction Using Combined Artificial Neural Network and Various Particle Swarm Optimisation Techniques

    Science.gov (United States)

    2015-01-01

    It is important to predict the incipient fault in transformer oil accurately so that the maintenance of transformer oil can be performed correctly, reducing the cost of maintenance and minimise the error. Dissolved gas analysis (DGA) has been widely used to predict the incipient fault in power transformers. However, sometimes the existing DGA methods yield inaccurate prediction of the incipient fault in transformer oil because each method is only suitable for certain conditions. Many previous works have reported on the use of intelligence methods to predict the transformer faults. However, it is believed that the accuracy of the previously proposed methods can still be improved. Since artificial neural network (ANN) and particle swarm optimisation (PSO) techniques have never been used in the previously reported work, this work proposes a combination of ANN and various PSO techniques to predict the transformer incipient fault. The advantages of PSO are simplicity and easy implementation. The effectiveness of various PSO techniques in combination with ANN is validated by comparison with the results from the actual fault diagnosis, an existing diagnosis method and ANN alone. Comparison of the results from the proposed methods with the previously reported work was also performed to show the improvement of the proposed methods. It was found that the proposed ANN-Evolutionary PSO method yields the highest percentage of correct identification for transformer fault type than the existing diagnosis method and previously reported works. PMID:26103634

  13. A unitary approach to the coupling between the NN and πNN channels

    International Nuclear Information System (INIS)

    Blankleider, B.

    1980-11-01

    Some basic properties of the πNN system, in particular its coupling to the NN channel, are investigated. A set of linear integral equations that couple the N-N to the π-d channel, and satisfy two- and three-body unitarity is derived. By including the π-N amplitude in the P 11 channel, and retaining certain disconnected diagrams, it is found that the propagators for the nucleons, and form factors for the vertices, become dressed without changing the basic structure of the equations. For the numerical solution relativistic kinematics for the pion and non-relativistic kinematics for the nucleons are used. There is uncertainty about the importance of real pion absorption in the π-d elastic scattering reaction. Although the effect of absorption can be very large, its influence is cancelled to a large extent by the further inclusion of P 11 rescattering. The inclusion of absorption signnificantly lowers the dips in the π-d differential cross sections at higher energies. The model is able to reproduce the sole experimental value of the tensor polarization t 20 at 180 deg. so far available. Numerical results for the reaction NN→πd are in excellent agreement with the differential cross sections at all but the very high energies

  14. Macrocell path loss prediction using artificial intelligence techniques

    Science.gov (United States)

    Usman, Abraham U.; Okereke, Okpo U.; Omizegba, Elijah E.

    2014-04-01

    The prediction of propagation loss is a practical non-linear function approximation problem which linear regression or auto-regression models are limited in their ability to handle. However, some computational Intelligence techniques such as artificial neural networks (ANNs) and adaptive neuro-fuzzy inference systems (ANFISs) have been shown to have great ability to handle non-linear function approximation and prediction problems. In this study, the multiple layer perceptron neural network (MLP-NN), radial basis function neural network (RBF-NN) and an ANFIS network were trained using actual signal strength measurement taken at certain suburban areas of Bauchi metropolis, Nigeria. The trained networks were then used to predict propagation losses at the stated areas under differing conditions. The predictions were compared with the prediction accuracy of the popular Hata model. It was observed that ANFIS model gave a better fit in all cases having higher R2 values in each case and on average is more robust than MLP and RBF models as it generalises better to a different data.

  15. Fundamental study of interpretation technique for 3-D magnetotelluric data using neural networks; Neural network wo mochiita sanjigen MT ho data kaishaku gijutsu no kisoteki kenkyu

    Energy Technology Data Exchange (ETDEWEB)

    Kobayashi, T; Fukuoka, K; Shima, H [Oyo Corp., Tokyo (Japan); Mogi, T [Kyushu University, Fukuoka (Japan). Faculty of Engineering; Spichak, V

    1997-05-27

    The research and development have been conducted to apply neural networks to interpretation technique for 3-D MT data. In this study, a data base of various data was made from the numerical modeling of 3-D fault model, and the data base management system was constructed. In addition, an unsupervised neural network for treating noise and a supervised neural network for estimating fault parameters such as dip, strike and specific resistance were made, and a basic neural network system was constructed. As a result of the application to the various data, basically sufficient performance for estimating the fault parameters was confirmed. Thus, the optimum MT data for this system were selected. In future, it is necessary to investigate the optimum model and the number of models for learning these neural networks. 3 refs., 5 figs., 2 tabs.

  16. Present status of the theory of πNN systems

    International Nuclear Information System (INIS)

    Mizutani, T.

    1987-01-01

    In the present discussion the existing data are compared with various model calculations within the πNN theory to assess the appropriateness of the latter. Globally the models in their present form reproduce the data quite well in view of the number of channels in which the comparison was made. From the quantitative point of view the models must be upgraded in several different ingredients. First, it is the parameters of the NN heavy meson exchange potentials when combined with the explicit pion and nucleon isobar degrees of freedom. A right choice of their values will lead to a better account of such observables like the beam asymmetries and vector analyzing power in a number of channels like NN in equilibrium πd, NN → πNN, etc. The next one may be the model for the Δ resonance (or the πN P 33 off-shell amplitude). A simple monopole or dipole form factor for the πNΔ vertex had difficulty in reproducing the major NN partial wave phase parameters. In view of the fact that dσ/dΩ for the deuteron photodisintegration in the Δ resonance region cannot be explained at all by the coupled NN-NΔ model, one should seriously consider a better model for the Δ. 39 refs., 10 figs

  17. Signal peptide discrimination and cleavage site identification using SVM and NN.

    Science.gov (United States)

    Kazemian, H B; Yusuf, S A; White, K

    2014-02-01

    About 15% of all proteins in a genome contain a signal peptide (SP) sequence, at the N-terminus, that targets the protein to intracellular secretory pathways. Once the protein is targeted correctly in the cell, the SP is cleaved, releasing the mature protein. Accurate prediction of the presence of these short amino-acid SP chains is crucial for modelling the topology of membrane proteins, since SP sequences can be confused with transmembrane domains due to similar composition of hydrophobic amino acids. This paper presents a cascaded Support Vector Machine (SVM)-Neural Network (NN) classification methodology for SP discrimination and cleavage site identification. The proposed method utilises a dual phase classification approach using SVM as a primary classifier to discriminate SP sequences from Non-SP. The methodology further employs NNs to predict the most suitable cleavage site candidates. In phase one, a SVM classification utilises hydrophobic propensities as a primary feature vector extraction using symmetric sliding window amino-acid sequence analysis for discrimination of SP and Non-SP. In phase two, a NN classification uses asymmetric sliding window sequence analysis for prediction of cleavage site identification. The proposed SVM-NN method was tested using Uni-Prot non-redundant datasets of eukaryotic and prokaryotic proteins with SP and Non-SP N-termini. Computer simulation results demonstrate an overall accuracy of 0.90 for SP and Non-SP discrimination based on Matthews Correlation Coefficient (MCC) tests using SVM. For SP cleavage site prediction, the overall accuracy is 91.5% based on cross-validation tests using the novel SVM-NN model. © 2013 Published by Elsevier Ltd.

  18. Novel Machine Learning-Based Techniques for Efficient Resource Allocation in Next Generation Wireless Networks

    KAUST Repository

    Alqerm, Ismail

    2018-01-01

    There is a large demand for applications of high data rates in wireless networks. These networks are becoming more complex and challenging to manage due to the heterogeneity of users and applications specifically in sophisticated networks

  19. An Alternative to Optimize the Indonesian’s Airport Network Design: An Application of Minimum Spanning Tree (MST Technique

    Directory of Open Access Journals (Sweden)

    Luluk Lusiantoro

    2012-09-01

    Full Text Available Using minimum spanning tree technique (MST, this exploratory research was done to optimize the interrelation and hierarchical network design of Indonesian’s airports. This research also identifies the position of the Indonesian’s airports regionally based on the ASEAN Open Sky Policy 2015. The secondary data containing distance between airports (both in Indonesia and in ASEAN, flight frequency, and correlation of Gross Domestic Regional Product (GDRP for each region in Indonesia are used as inputs to form MST networks. The result analysis is done by comparing the MST networks with the existing network in Indonesia. This research found that the existing airport network in Indonesia does not depict the optimal network connecting all airports with the shortest distance and maximizing the correlation of regional economic potential in the country. This research then suggests the optimal networks and identifies the airports and regions as hubs and spokes formed by the networks. Lastly, this research indicates that the Indonesian airports have no strategic position in the ASEAN Open Sky network, but they have an opportunity to get strategic positions if 33 airports in 33 regions in Indonesia are included in the network.

  20. Networks systems and operations. [wideband communication techniques for data links with satellites

    Science.gov (United States)

    1975-01-01

    The application of wideband communication techniques for data links with satellites is discussed. A diagram of the demand assigned voice communications system is provided. The development of prototype integrated spacecraft paramps at S- and C-bands is described and the performance of space-qualified paramps is tabulated. The characteristics of a dual parabolic cylinder monopulse zoom antenna for use with the tracking and data relay satellite system (TDRSS) are analyzed. The development of a universally applicable transponder at S-band is reported. A block diagram of the major subassemblies of the S-band transponder is included. The technology aspects of network timing and synchronization of communication systems are to show the use of the Omega navigation system. The telemetry data compression system used during the Skylab program is evaluated.

  1. Estimation of peak ground accelerations for Mexican subduction zone earthquakes using neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Garcia, Silvia R; Romo, Miguel P; Mayoral, Juan M [Instituto de Ingenieria, Universidad Nacional Autonoma de Mexico, Mexico D.F. (Mexico)

    2007-01-15

    An extensive analysis of the strong ground motion Mexican data base was conducted using Soft Computing (SC) techniques. A Neural Network NN is used to estimate both orthogonal components of the horizontal (PGAh) and vertical (PGAv) peak ground accelerations measured at rock sites during Mexican subduction zone earthquakes. The work discusses the development, training, and testing of this neural model. Attenuation phenomenon was characterized in terms of magnitude, epicentral distance and focal depth. Neural approximators were used instead of traditional regression techniques due to their flexibility to deal with uncertainty and noise. NN predictions follow closely measured responses exhibiting forecasting capabilities better than those of most established attenuation relations for the Mexican subduction zone. Assessment of the NN, was also applied to subduction zones in Japan and North America. For the database used in this paper the NN and the-better-fitted- regression approach residuals are compared. [Spanish] Un analisis exhaustivo de la base de datos mexicana de sismos fuertes se llevo a cabo utilizando tecnicas de computo aproximado, SC (soft computing). En particular, una red neuronal, NN, es utilizada para estimar ambos componentes ortogonales de la maxima aceleracion horizontal del terreno, PGAh, y la vertical, PGAv, medidas en sitios en roca durante terremotos generados en la zona de subduccion de la Republica Mexicana. El trabajo discute el desarrollo, entrenamiento, y prueba de este modelo neuronal. El fenomeno de atenuacion fue caracterizado en terminos de la magnitud, la distancia epicentral y la profundidad focal. Aproximaciones neuronales fueron utilizadas en lugar de tecnicas de regresion tradicionales por su flexibilidad para tratar con incertidumbre y ruido en los datos. La NN sigue de cerca la respuesta medida exhibiendo capacidades predictivas mejores que las mostradas por muchas de las relaciones de atenuacion establecidas para la zona de

  2. Detection of breast cancer using advanced techniques of data mining with neural networks

    International Nuclear Information System (INIS)

    Ortiz M, J. A.; Celaya P, J. M.; Martinez B, M. R.; Solis S, L. O.; Castaneda M, R.; Garza V, I.; Martinez F, M.; Lopez H, Y.; Ortiz R, J. M.

    2016-10-01

    The breast cancer is one of the biggest health problems worldwide, is the most diagnosed cancer in women and prevention seems impossible since its cause is unknown, due to this; the early detection has a key role in the patient prognosis. In developing countries such as Mexico, where access to specialized health services is minimal, the regular clinical review is infrequent and there are not enough radiologists; the most common form of detection of breast cancer is through self-exploration, but this is only detected in later stages, when is already palpable. For these reasons, the objective of the present work is the creation of a system of computer assisted diagnosis (CAD x) using information analysis techniques such as data mining and advanced techniques of artificial intelligence, seeking to offer a previous medical diagnosis or a second opinion, as if it was a second radiologist in order to reduce the rate of mortality from breast cancer. In this paper, advances in the design of computational algorithms using computer vision techniques for the extraction of features derived from mammograms are presented. Using data mining techniques of data mining is possible to identify patients with a high risk of breast cancer. With the information obtained from the mammography analysis, the objective in the next stage will be to establish a methodology for the generation of imaging bio-markers to establish a breast cancer risk index for Mexican patients. In this first stage we present results of the classification of patients with high and low risk of suffering from breast cancer using neural networks. (Author)

  3. Integrated approach to model decomposed flow hydrograph using artificial neural network and conceptual techniques

    Science.gov (United States)

    Jain, Ashu; Srinivasulu, Sanaga

    2006-02-01

    This paper presents the findings of a study aimed at decomposing a flow hydrograph into different segments based on physical concepts in a catchment, and modelling different segments using different technique viz. conceptual and artificial neural networks (ANNs). An integrated modelling framework is proposed capable of modelling infiltration, base flow, evapotranspiration, soil moisture accounting, and certain segments of the decomposed flow hydrograph using conceptual techniques and the complex, non-linear, and dynamic rainfall-runoff process using ANN technique. Specifically, five different multi-layer perceptron (MLP) and two self-organizing map (SOM) models have been developed. The rainfall and streamflow data derived from the Kentucky River catchment were employed to test the proposed methodology and develop all the models. The performance of all the models was evaluated using seven different standard statistical measures. The results obtained in this study indicate that (a) the rainfall-runoff relationship in a large catchment consists of at least three or four different mappings corresponding to different dynamics of the underlying physical processes, (b) an integrated approach that models the different segments of the decomposed flow hydrograph using different techniques is better than a single ANN in modelling the complex, dynamic, non-linear, and fragmented rainfall runoff process, (c) a simple model based on the concept of flow recession is better than an ANN to model the falling limb of a flow hydrograph, and (d) decomposing a flow hydrograph into the different segments corresponding to the different dynamics based on the physical concepts is better than using the soft decomposition employed using SOM.

  4. Localization in Wireless Sensor Networks: A Survey on Algorithms, Measurement Techniques, Applications and Challenges

    Directory of Open Access Journals (Sweden)

    Anup Kumar Paul

    2017-10-01

    Full Text Available Localization is an important aspect in the field of wireless sensor networks (WSNs that has developed significant research interest among academia and research community. Wireless sensor network is formed by a large number of tiny, low energy, limited processing capability and low-cost sensors that communicate with each other in ad-hoc fashion. The task of determining physical coordinates of sensor nodes in WSNs is known as localization or positioning and is a key factor in today’s communication systems to estimate the place of origin of events. As the requirement of the positioning accuracy for different applications varies, different localization methods are used in different applications and there are several challenges in some special scenarios such as forest fire detection. In this paper, we survey different measurement techniques and strategies for range based and range free localization with an emphasis on the latter. Further, we discuss different localization-based applications, where the estimation of the location information is crucial. Finally, a comprehensive discussion of the challenges such as accuracy, cost, complexity, and scalability are given.

  5. Pre-optimization of radiotherapy treatment planning: an artificial neural network classification aided technique

    International Nuclear Information System (INIS)

    Hosseini-Ashrafi, M.E.; Bagherebadian, H.; Yahaqi, E.

    1999-01-01

    A method has been developed which, by using the geometric information from treatment sample cases, selects from a given data set an initial treatment plan as a step for treatment plan optimization. The method uses an artificial neural network (ANN) classification technique to select a best matching plan from the 'optimized' ANN database. Separate back-propagation ANN classifiers were trained using 50, 60 and 77 examples for three groups of treatment case classes (up to 21 examples from each class were used). The performance of the classifiers in selecting the correct treatment class was tested using the leave-one-out method; the networks were optimized with respect their architecture. For the three groups used in this study, successful classification fractions of 0.83, 0.98 and 0.93 were achieved by the optimized ANN classifiers. The automated response of the ANN may be used to arrive at a pre-plan where many treatment parameters may be identified and therefore a significant reduction in the steps required to arrive at the optimum plan may be achieved. Treatment planning 'experience' and also results from lengthy calculations may be used for training the ANN. (author)

  6. Hydrological time series modeling: A comparison between adaptive neuro-fuzzy, neural network and autoregressive techniques

    Science.gov (United States)

    Lohani, A. K.; Kumar, Rakesh; Singh, R. D.

    2012-06-01

    SummaryTime series modeling is necessary for the planning and management of reservoirs. More recently, the soft computing techniques have been used in hydrological modeling and forecasting. In this study, the potential of artificial neural networks and neuro-fuzzy system in monthly reservoir inflow forecasting are examined by developing and comparing monthly reservoir inflow prediction models, based on autoregressive (AR), artificial neural networks (ANNs) and adaptive neural-based fuzzy inference system (ANFIS). To take care the effect of monthly periodicity in the flow data, cyclic terms are also included in the ANN and ANFIS models. Working with time series flow data of the Sutlej River at Bhakra Dam, India, several ANN and adaptive neuro-fuzzy models are trained with different input vectors. To evaluate the performance of the selected ANN and adaptive neural fuzzy inference system (ANFIS) models, comparison is made with the autoregressive (AR) models. The ANFIS model trained with the input data vector including previous inflows and cyclic terms of monthly periodicity has shown a significant improvement in the forecast accuracy in comparison with the ANFIS models trained with the input vectors considering only previous inflows. In all cases ANFIS gives more accurate forecast than the AR and ANN models. The proposed ANFIS model coupled with the cyclic terms is shown to provide better representation of the monthly inflow forecasting for planning and operation of reservoir.

  7. A Comparison of Alternative Distributed Dynamic Cluster Formation Techniques for Industrial Wireless Sensor Networks.

    Science.gov (United States)

    Gholami, Mohammad; Brennan, Robert W

    2016-01-06

    In this paper, we investigate alternative distributed clustering techniques for wireless sensor node tracking in an industrial environment. The research builds on extant work on wireless sensor node clustering by reporting on: (1) the development of a novel distributed management approach for tracking mobile nodes in an industrial wireless sensor network; and (2) an objective comparison of alternative cluster management approaches for wireless sensor networks. To perform this comparison, we focus on two main clustering approaches proposed in the literature: pre-defined clusters and ad hoc clusters. These approaches are compared in the context of their reconfigurability: more specifically, we investigate the trade-off between the cost and the effectiveness of competing strategies aimed at adapting to changes in the sensing environment. To support this work, we introduce three new metrics: a cost/efficiency measure, a performance measure, and a resource consumption measure. The results of our experiments show that ad hoc clusters adapt more readily to changes in the sensing environment, but this higher level of adaptability is at the cost of overall efficiency.

  8. Forecasting of electricity prices with neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Gareta, Raquel [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain); Romeo, Luis M. [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)]. E-mail: luismi@unizar.es; Gil, Antonia [Centro de Investigacion de Recursos y Consumos Energeticos (CIRCE), Universidad de Zaragoza, Centro Politecnico Superior, Maria de Luna, 3, 50018 Zaragoza (Spain)

    2006-08-15

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools.

  9. Forecasting of electricity prices with neural networks

    International Nuclear Information System (INIS)

    Gareta, Raquel; Romeo, Luis M.; Gil, Antonia

    2006-01-01

    During recent years, the electricity energy market deregulation has led to a new free competition situation in Europe and other countries worldwide. Generators, distributors and qualified clients have some uncertainties about the future evolution of electricity markets. In consequence, feasibility studies of new generation plants, design of new systems and energy management optimization are frequently postponed. The ability of forecasting energy prices, for instance the electricity prices, would be highly appreciated in order to improve the profitability of utility investments. The development of new simulation techniques, such as Artificial Intelligence (AI), has provided a good tool to forecast time series. In this paper, it is demonstrated that the Neural Network (NN) approach can be used to forecast short term hourly electricity pool prices (for the next day and two or three days after). The NN architecture and design for prices forecasting are described in this paper. The results are tested with extensive data sets, and good agreement is found between actual data and NN results. This methodology could help to improve power plant generation capacity management and, certainly, more profitable operation in daily energy pools

  10. A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

    Directory of Open Access Journals (Sweden)

    P.-Y. Chen

    2009-01-01

    Full Text Available This study proposes a neural network-family competition genetic algorithm (NN-FCGA for solving the electromagnetic (EM optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

  11. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz; Alouini, Mohamed-Slim; Hasna, Mazen Omar

    2010-01-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  12. A signal combining technique based on channel shortening for cooperative sensor networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2010-06-01

    The cooperative relaying process needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems, e.g. wireless sensor networks where the nodes are equipped with very basic communication hardware. In this paper, we consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination can capture the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. ©2010 IEEE.

  13. Northern Edge Navajo Casino, Fruitland, NM: NN0030343

    Science.gov (United States)

    NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030343) to the Navajo Tribal Utility Authority Northern Edge Navajo Casino Wastewater Treatment Facility, 2752 Indian Service Road 36, Fruitland, NM.

  14. Old Torreon Navajo Day School, Cuba, NM: NN0030341

    Science.gov (United States)

    NPDES Permit and Fact Sheet explaining EPA's action under the Clean Water Act to issue NPDES Permit No. NN0030341 to Bureau of Indian Affairs Old Torreon Navajo Day School Wastewater Treatment Lagoon.

  15. Modified GMDH-NN algorithm and its application for global sensitivity analysis

    International Nuclear Information System (INIS)

    Song, Shufang; Wang, Lu

    2017-01-01

    Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages. - Highlights: • The GMDH-NN is improved to construct the explicit polynomial model of optimal complexity by self-organization. • The paper aims at combining improved GMDH-NN with HDMR expansions and using it to compute Sobol' indices directly. • The method can be applied in uniform, normal and exponential distribution by using suitable orthogonal polynomials. • Engineering examples, e.g., electronic circuit models can be solved by the presented method.

  16. NN resonance and the corrections to Goldberger-Treiman relation

    International Nuclear Information System (INIS)

    Bhamathi, G.; Raghavan, S.

    1977-01-01

    The relevance of the recent experimental observation of possible bound and resonant states in NN scattering to the Goldberger-Treiman (GT) relation is examined. It is pointed out that an S-wave resonance in NN scattering goes a long way towards accounting for the corrections to the GT relations. Values of the mass and width of the resonance capable of giving a reasonable fit for the GT relation are presented. (author)

  17. NNNN π: the new frontier in nucleon-nucleon interactions

    International Nuclear Information System (INIS)

    Silbar, R.R.

    1986-01-01

    The torch in nucleon-nucleon scattering has been passed to experimental and theoretical studies of pion production. Comparing two unitary models shows that most of the structures predicted for spin observables in NN → NNπ are model independent and roughly in agreement with the data. The contribution of rho- exchange is small, indicating the reaction is largely ''peripheral''. The energy dependence of these isobar models is smooth. The largely unstudied reactions producing neutral and negatively-charged pions show richer structure than positively-charged pion production. 6 refs

  18. Estimation of Apple Volume and Its Shape Indentation Using Image Processing Technique and Neural Network

    Directory of Open Access Journals (Sweden)

    M Jafarlou

    2014-04-01

    Full Text Available Physical properties of agricultural products such as volume are the most important parameters influencing grading and packaging systems. They should be measured accurately as they are considered for any good system design. Image processing and neural network techniques are both non-destructive and useful methods which are recently used for such purpose. In this study, the images of apples were captured from a constant distance and then were processed in MATLAB software and the edges of apple images were extracted. The interior area of apple image was divided into some thin trapezoidal elements perpendicular to longitudinal axis. Total volume of apple was estimated by the summation of incremental volumes of these elements revolved around the apple’s longitudinal axis. The picture of half cut apple was also captured in order to obtain the apple shape’s indentation volume, which was subtracted from the previously estimated total volume of apple. The real volume of apples was measured using water displacement method and the relation between the real volume and estimated volume was obtained. The t-test and Bland-Altman indicated that the difference between the real volume and the estimated volume was not significantly different (p>0.05 i.e. the mean difference was 1.52 cm3 and the accuracy of measurement was 92%. Utilizing neural network with input variables of dimension and mass has increased the accuracy up to 97% and the difference between the mean of volumes decreased to 0.7 cm3.

  19. New approach to the theory of coupled πNN-NN system. III. A three-body limit

    International Nuclear Information System (INIS)

    Avishai, Y.; Mizutani, T.

    1980-01-01

    In the limit where the pion is restricted to be emitted only by the nucleon that first absorbed it, it is shown that the equations previously developed to describe the couple πNN (πd) - NN system reduce to conventional three-body equations. Specifically, it is found in this limit that the input πN p 11 amplitude which, put on-shell, is directly related to the experimental phase shift, contrary to the original equations where the direct (dressed) nucleon pole term and the non-pole part of this partial wave enter separately. The present study clarifies the limitation of pure three-body approach to the πNN-NN problems as well as suggests a rare opportunity of observing a possible resonance behavior in the non-pole part of the πN P 11 amplitude through πd experiments

  20. Portable Rule Extraction Method for Neural Network Decisions Reasoning

    Directory of Open Access Journals (Sweden)

    Darius PLIKYNAS

    2005-08-01

    Full Text Available Neural network (NN methods are sometimes useless in practical applications, because they are not properly tailored to the particular market's needs. We focus thereinafter specifically on financial market applications. NNs have not gained full acceptance here yet. One of the main reasons is the "Black Box" problem (lack of the NN decisions explanatory power. There are though some NN decisions rule extraction methods like decompositional, pedagogical or eclectic, but they suffer from low portability of the rule extraction technique across various neural net architectures, high level of granularity, algorithmic sophistication of the rule extraction technique etc. The authors propose to eliminate some known drawbacks using an innovative extension of the pedagogical approach. The idea is exposed by the use of a widespread MLP neural net (as a common tool in the financial problems' domain and SOM (input data space clusterization. The feedback of both nets' performance is related and targeted through the iteration cycle by achievement of the best matching between the decision space fragments and input data space clusters. Three sets of rules are generated algorithmically or by fuzzy membership functions. Empirical validation of the common financial benchmark problems is conducted with an appropriately prepared software solution.

  1. Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia

    Science.gov (United States)

    Karimi, Sepideh; Kisi, Ozgur; Shiri, Jalal; Makarynskyy, Oleg

    2013-03-01

    Accurate predictions of sea level with different forecast horizons are important for coastal and ocean engineering applications, as well as in land drainage and reclamation studies. The methodology of tidal harmonic analysis, which is generally used for obtaining a mathematical description of the tides, is data demanding requiring processing of tidal observation collected over several years. In the present study, hourly sea levels for Darwin Harbor, Australia were predicted using two different, data driven techniques, adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network (ANN). Multi linear regression (MLR) technique was used for selecting the optimal input combinations (lag times) of hourly sea level. The input combination comprises current sea level as well as five previous level values found to be optimal. For the ANFIS models, five different membership functions namely triangular, trapezoidal, generalized bell, Gaussian and two Gaussian membership function were tested and employed for predicting sea level for the next 1 h, 24 h, 48 h and 72 h. The used ANN models were trained using three different algorithms, namely, Levenberg-Marquardt, conjugate gradient and gradient descent. Predictions of optimal ANFIS and ANN models were compared with those of the optimal auto-regressive moving average (ARMA) models. The coefficient of determination, root mean square error and variance account statistics were used as comparison criteria. The obtained results indicated that triangular membership function was optimal for predictions with the ANFIS models while adaptive learning rate and Levenberg-Marquardt were most suitable for training the ANN models. Consequently, ANFIS and ANN models gave similar forecasts and performed better than the developed for the same purpose ARMA models for all the prediction intervals.

  2. Developing a secured social networking site using information security awareness techniques

    Directory of Open Access Journals (Sweden)

    Julius O. Okesola

    2014-11-01

    Full Text Available Background: Ever since social network sites (SNS became a global phenomenon in almost every industry, security has become a major concern to many SNS stakeholders. Several security techniques have been invented towards addressing SNS security, but information security awareness (ISA remains a critical point. Whilst very few users have used social circles and applications because of a lack of users’ awareness, the majority have found it difficult to determine the basis of categorising friends in a meaningful way for privacy and security policies settings. This has confirmed that technical control is just part of the security solutions and not necessarily a total solution. Changing human behaviour on SNSs is essential; hence the need for a privately enhanced ISA SNS. Objective: This article presented sOcialistOnline – a newly developed SNS, duly secured and platform independent with various ISA techniques fully implemented. Method: Following a detailed literature review of the related works, the SNS was developed on the basis of Object Oriented Programming (OOP approach, using PhP as the coding language with the MySQL database engine at the back end. Result: This study addressed the SNS requirements of privacy, security and services, and attributed them as the basis of architectural design for sOcialistOnline. SNS users are more aware of potential risk and the possible consequences of unsecured behaviours. Conclusion: ISA is focussed on the users who are often the greatest security risk on SNSs, regardless of technical securities implemented. Therefore SNSs are required to incorporate effective ISA into their platform and ensure users are motivated to embrace it.

  3. An investigation of scalable anomaly detection techniques for a large network of Wi-Fi hotspots

    CSIR Research Space (South Africa)

    Machaka, P

    2015-01-01

    Full Text Available . The Neural Networks, Bayesian Networks and Artificial Immune Systems were used for this experiment. Using a set of data extracted from a live network of Wi-Fi hotspots managed by an ISP; we integrated algorithms into a data collection system to detect...

  4. Compression and Combining Based on Channel Shortening and Rank Reduction Technique for Cooperative Wireless Sensor Networks

    KAUST Repository

    Ahmed, Qasim Zeeshan

    2013-12-18

    This paper investigates and compares the performance of wireless sensor networks where sensors operate on the principles of cooperative communications. We consider a scenario where the source transmits signals to the destination with the help of L sensors. As the destination has the capacity of processing only U out of these L signals, the strongest U signals are selected while the remaining (L?U) signals are suppressed. A preprocessing block similar to channel-shortening is proposed in this contribution. However, this preprocessing block employs a rank-reduction technique instead of channel-shortening. By employing this preprocessing, we are able to decrease the computational complexity of the system without affecting the bit error rate (BER) performance. From our simulations, it can be shown that these schemes outperform the channel-shortening schemes in terms of computational complexity. In addition, the proposed schemes have a superior BER performance as compared to channel-shortening schemes when sensors employ fixed gain amplification. However, for sensors which employ variable gain amplification, a tradeoff exists in terms of BER performance between the channel-shortening and these schemes. These schemes outperform channel-shortening scheme for lower signal-to-noise ratio.

  5. Sensorless Speed/Torque Control of DC Machine Using Artificial Neural Network Technique

    Directory of Open Access Journals (Sweden)

    Rakan Kh. Antar

    2017-12-01

    Full Text Available In this paper, Artificial Neural Network (ANN technique is implemented to improve speed and torque control of a separately excited DC machine drive. The speed and torque sensorless scheme based on ANN is estimated adaptively. The proposed controller is designed to estimate rotor speed and mechanical load torque as a Model Reference Adaptive System (MRAS method for DC machine. The DC drive system consists of four quadrant DC/DC chopper with MOSFET transistors, ANN, logic gates and routing circuits. The DC drive circuit is designed, evaluated and modeled by Matlab/Simulink in the forward and reverse operation modes as a motor and generator, respectively. The DC drive system is simulated at different speed values (±1200 rpm and mechanical torque (±7 N.m in steady state and dynamic conditions. The simulation results illustratethe effectiveness of the proposed controller without speed or torque sensors.

  6. Third-Order Spectral Techniques for the Diagnosis of Motor Bearing Condition Using Artificial Neural Networks

    Science.gov (United States)

    Yang, D.-M.; Stronach, A. F.; MacConnell, P.; Penman, J.

    2002-03-01

    This paper addresses the development of a novel condition monitoring procedure for rolling element bearings which involves a combination of signal processing, signal analysis and artificial intelligence methods. Seven approaches based on power spectrum, bispectral and bicoherence vibration analyses are investigated as signal pre-processing techniques for application in the diagnosis of a number of induction motor rolling element bearing conditions. The bearing conditions considered are a normal bearing and bearings with cage and inner and outer race faults. The vibration analysis methods investigated are based on the power spectrum, the bispectrum, the bicoherence, the bispectrum diagonal slice, the bicoherence diagonal slice, the summed bispectrum and the summed bicoherence. Selected features are extracted from the vibration signatures so obtained and these are used as inputs to an artificial neural network trained to identify the bearing conditions. Quadratic phase coupling (QPC), examined using the magnitude of bispectrum and bicoherence and biphase, is shown to be absent from the bearing system and it is therefore concluded that the structure of the bearing vibration signatures results from inter-modulation effects. In order to test the proposed procedure, experimental data from a bearing test rig are used to develop an example diagnostic system. Results show that the bearing conditions examined can be diagnosed with a high success rate, particularly when using the summed bispectrum signatures.

  7. A comparative performance evaluation of intrusion detection techniques for hierarchical wireless sensor networks

    Directory of Open Access Journals (Sweden)

    H.H. Soliman

    2012-11-01

    Full Text Available An explosive growth in the field of wireless sensor networks (WSNs has been achieved in the past few years. Due to its important wide range of applications especially military applications, environments monitoring, health care application, home automation, etc., they are exposed to security threats. Intrusion detection system (IDS is one of the major and efficient defensive methods against attacks in WSN. Therefore, developing IDS for WSN have attracted much attention recently and thus, there are many publications proposing new IDS techniques or enhancement to the existing ones. This paper evaluates and compares the most prominent anomaly-based IDS systems for hierarchical WSNs and identifying their strengths and weaknesses. For each IDS, the architecture and the related functionality are briefly introduced, discussed, and compared, focusing on both the operational strengths and weakness. In addition, a comparison of the studied IDSs is carried out using a set of critical evaluation metrics that are divided into two groups; the first one related to performance and the second related to security. Finally based on the carried evaluation and comparison, a set of design principles are concluded, which have to be addressed and satisfied in future research of designing and implementing IDS for WSNs.

  8. Soil temperature modeling at different depths using neuro-fuzzy, neural network, and genetic programming techniques

    Science.gov (United States)

    Kisi, Ozgur; Sanikhani, Hadi; Cobaner, Murat

    2017-08-01

    The applicability of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and genetic programming (GP) techniques in estimating soil temperatures (ST) at different depths is investigated in this study. Weather data from two stations, Mersin and Adana, Turkey, were used as inputs to the applied models in order to model monthly STs. The first part of the study focused on comparison of ANN, ANFIS, and GP models in modeling ST of two stations at the depths of 10, 50, and 100 cm. GP was found to perform better than the ANN and ANFIS-SC in estimating monthly ST. The effect of periodicity (month of the year) on models' accuracy was also investigated. Including periodicity component in models' inputs considerably increased their accuracies. The root mean square error (RMSE) of ANN models was respectively decreased by 34 and 27 % for the depths of 10 and 100 cm adding the periodicity input. In the second part of the study, the accuracies of the ANN, ANFIS, and GP models were compared in estimating ST of Mersin Station using the climatic data of Adana Station. The ANN models generally performed better than the ANFIS-SC and GP in modeling ST of Mersin Station without local climatic inputs.

  9. Projecting impacts of climate change on water availability using artificial neural network techniques

    Science.gov (United States)

    Swain, Eric D.; Gomez-Fragoso, Julieta; Torres-Gonzalez, Sigfredo

    2017-01-01

    Lago Loíza reservoir in east-central Puerto Rico is one of the primary sources of public water supply for the San Juan metropolitan area. To evaluate and predict the Lago Loíza water budget, an artificial neural network (ANN) technique is trained to predict river inflows. A method is developed to combine ANN-predicted daily flows with ANN-predicted 30-day cumulative flows to improve flow estimates. The ANN application trains well for representing 2007–2012 and the drier 1994–1997 periods. Rainfall data downscaled from global circulation model (GCM) simulations are used to predict 2050–2055 conditions. Evapotranspiration is estimated with the Hargreaves equation using minimum and maximum air temperatures from the downscaled GCM data. These simulated 2050–2055 river flows are input to a water budget formulation for the Lago Loíza reservoir for comparison with 2007–2012. The ANN scenarios require far less computational effort than a numerical model application, yet produce results with sufficient accuracy to evaluate and compare hydrologic scenarios. This hydrologic tool will be useful for future evaluations of the Lago Loíza reservoir and water supply to the San Juan metropolitan area.

  10. Propagation Measurements and Comparison with EM Techniques for In-Cabin Wireless Networks

    Directory of Open Access Journals (Sweden)

    Nektarios Moraitis

    2009-01-01

    Full Text Available This paper presents results of a narrowband measurement campaign conducted inside a Boeing 737–400 aircraft, the objective being the development of a propagation prediction model which can be used in the deployment of in-cabin wireless networks. The measurements were conducted at three different frequency bands: 1.8, 2.1, and 2.45 GHz, representative of several wireless services. Both a simple, empirical, inverse distance power law and a deterministic, site-specific model were investigated. Parameters for the empirical model were extracted from the measurements at different locations inside the cabin: aisle and seats. Additionally, a statistical characterization of the multipath scenario created by the transmitted signal and the various cabin elements is presented. The deterministic model, based on Physical Optics (PO techniques, provides a reasonable match with the empirical results. Finally, measurements and modeling results are provided for the penetration loss into the cabin (or out of the cabin, representative of interference scenarios.

  11. Advanced Integration Techniques on Broadband Millimeter-Wave Beam Steering for 5G Wireless Networks and Beyond

    NARCIS (Netherlands)

    Cao, Zizheng; Ma, Qian; Smolders, Bart; Jiao, Yuqing; Wale, Mike; Oh, Joanne; wu, hequan; Koonen, Ton

    2015-01-01

    Recently, the desired very high throughput of 5G wireless networks drives millimeter-wave (mm-wave) communication into practical applications. A phased array technique is required to increase the effective antenna aperture at mm-wave frequency. Integrated solutions of beamforming/beam steering are

  12. Improving Intrusion Detection System Based on Snort Rules for Network Probe Attacks Detection with Association Rules Technique of Data Mining

    Directory of Open Access Journals (Sweden)

    Nattawat Khamphakdee

    2015-07-01

    Full Text Available The intrusion detection system (IDS is an important network security tool for securing computer and network systems. It is able to detect and monitor network traffic data. Snort IDS is an open-source network security tool. It can search and match rules with network traffic data in order to detect attacks, and generate an alert. However, the Snort IDS  can detect only known attacks. Therefore, we have proposed a procedure for improving Snort IDS rules, based on the association rules data mining technique for detection of network probe attacks.  We employed the MIT-DARPA 1999 data set for the experimental evaluation. Since behavior pattern traffic data are both normal and abnormal, the abnormal behavior data is detected by way of the Snort IDS. The experimental results showed that the proposed Snort IDS rules, based on data mining detection of network probe attacks, proved more efficient than the original Snort IDS rules, as well as icmp.rules and icmp-info.rules of Snort IDS.  The suitable parameters for the proposed Snort IDS rules are defined as follows: Min_sup set to 10%, and Min_conf set to 100%, and through the application of eight variable attributes. As more suitable parameters are applied, higher accuracy is achieved.

  13. Multi-focus and multi-level techniques for visualization and analysis of networks with thematic data

    Science.gov (United States)

    Cossalter, Michele; Mengshoel, Ole J.; Selker, Ted

    2013-01-01

    Information-rich data sets bring several challenges in the areas of visualization and analysis, even when associated with node-link network visualizations. This paper presents an integration of multi-focus and multi-level techniques that enable interactive, multi-step comparisons in node-link networks. We describe NetEx, a visualization tool that enables users to simultaneously explore different parts of a network and its thematic data, such as time series or conditional probability tables. NetEx, implemented as a Cytoscape plug-in, has been applied to the analysis of electrical power networks, Bayesian networks, and the Enron e-mail repository. In this paper we briefly discuss visualization and analysis of the Enron social network, but focus on data from an electrical power network. Specifically, we demonstrate how NetEx supports the analytical task of electrical power system fault diagnosis. Results from a user study with 25 subjects suggest that NetEx enables more accurate isolation of complex faults compared to an especially designed software tool.

  14. Artificial neural network modelling in heavy ion collisions

    International Nuclear Information System (INIS)

    El-dahshan, E.; Radi, A.; El-Bakry, M.Y.; El Mashad, M.

    2008-01-01

    The neural network (NN) model and parton two fireball model (PTFM) have been used to study the pseudo-rapidity distribution of the shower particles for C 12, O 16, Si 28 and S 32 on nuclear emulsion. The trained NN shows a better fitting with experimental data than the PTFM calculations. The NN is then used to predict the distributions that are not present in the training set and matched them effectively. The NN simulation results prove a strong presence modeling in heavy ion collisions

  15. Modified GMDH-NN algorithm and its application for global sensitivity analysis

    Science.gov (United States)

    Song, Shufang; Wang, Lu

    2017-11-01

    Global sensitivity analysis (GSA) is a very useful tool to evaluate the influence of input variables in the whole distribution range. Sobol' method is the most commonly used among variance-based methods, which are efficient and popular GSA techniques. High dimensional model representation (HDMR) is a popular way to compute Sobol' indices, however, its drawbacks cannot be ignored. We show that modified GMDH-NN algorithm can calculate coefficients of metamodel efficiently, so this paper aims at combining it with HDMR and proposes GMDH-HDMR method. The new method shows higher precision and faster convergent rate. Several numerical and engineering examples are used to confirm its advantages.

  16. Classification in medical images using adaptive metric k-NN

    Science.gov (United States)

    Chen, C.; Chernoff, K.; Karemore, G.; Lo, P.; Nielsen, M.; Lauze, F.

    2010-03-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier with respect to different adaptive metrics in the context of medical imaging. We propose using adaptive metrics such that the structure of the data is better described, introducing some unsupervised learning knowledge in k-NN. We investigated four different metrics are estimated: a theoretical metric based on the assumption that images are drawn from Brownian Image Model (BIM), the normalized metric based on variance of the data, the empirical metric is based on the empirical covariance matrix of the unlabeled data, and an optimized metric obtained by minimizing the classification error. The spectral structure of the empirical covariance also leads to Principal Component Analysis (PCA) performed on it which results the subspace metrics. The metrics are evaluated on two data sets: lateral X-rays of the lumbar aortic/spine region, where we use k-NN for performing abdominal aorta calcification detection; and mammograms, where we use k-NN for breast cancer risk assessment. The results show that appropriate choice of metric can improve classification.

  17. Prediction of Classroom Reverberation Time using Neural Network

    Science.gov (United States)

    Liyana Zainudin, Fathin; Kadir Mahamad, Abd; Saon, Sharifah; Nizam Yahya, Musli

    2018-04-01

    In this paper, an alternative method for predicting the reverberation time (RT) using neural network (NN) for classroom was designed and explored. Classroom models were created using Google SketchUp software. The NN applied training dataset from the classroom models with RT values that were computed from ODEON 12.10 software. The NN was conducted separately for 500Hz, 1000Hz, and 2000Hz as absorption coefficient that is one of the prominent input variable is frequency dependent. Mean squared error (MSE) and regression (R) values were obtained to examine the NN efficiency. Overall, the NN shows a good result with MSE 0.9. The NN also managed to achieve a percentage of accuracy of 92.53% for 500Hz, 93.66% for 1000Hz, and 93.18% for 2000Hz and thus displays a good and efficient performance. Nevertheless, the optimum RT value is range between 0.75 – 0.9 seconds.

  18. Simulating GPS radio signal to synchronize network--a new technique for redundant timing.

    Science.gov (United States)

    Shan, Qingxiao; Jun, Yang; Le Floch, Jean-Michel; Fan, Yaohui; Ivanov, Eugene N; Tobar, Michael E

    2014-07-01

    Currently, many distributed systems such as 3G mobile communications and power systems are time synchronized with a Global Positioning System (GPS) signal. If there is a GPS failure, it is difficult to realize redundant timing, and thus time-synchronized devices may fail. In this work, we develop time transfer by simulating GPS signals, which promises no extra modification to original GPS-synchronized devices. This is achieved by applying a simplified GPS simulator for synchronization purposes only. Navigation data are calculated based on a pre-assigned time at a fixed position. Pseudo-range data which describes the distance change between the space vehicle (SV) and users are calculated. Because real-time simulation requires heavy-duty computations, we use self-developed software optimized on a PC to generate data, and save the data onto memory disks while the simulator is operating. The radio signal generation is similar to the SV at an initial position, and the frequency synthesis of the simulator is locked to a pre-assigned time. A filtering group technique is used to simulate the signal transmission delay corresponding to the SV displacement. Each SV generates a digital baseband signal, where a unique identifying code is added to the signal and up-converted to generate the output radio signal at the centered frequency of 1575.42 MHz (L1 band). A prototype with a field-programmable gate array (FPGA) has been built and experiments have been conducted to prove that we can realize time transfer. The prototype has been applied to the CDMA network for a three-month long experiment. Its precision has been verified and can meet the requirements of most telecommunication systems.

  19. Error Control Techniques for Efficient Multicast Streaming in UMTS Networks: Proposals andPerformance Evaluation

    Directory of Open Access Journals (Sweden)

    Michele Rossi

    2004-06-01

    Full Text Available In this paper we introduce techniques for efficient multicast video streaming in UMTS networks where a video content has to be conveyed to multiple users in the same cell. Efficient multicast data delivery in UMTS is still an open issue. In particular, suitable solutions have to be found to cope with wireless channel errors, while maintaining both an acceptable channel utilization and a controlled delivery delay over the wireless link between the serving base station and the mobile terminals. Here, we first highlight that standard solutions such as unequal error protection (UEP of the video flow are ineffective in the UMTS systems due to its inherent large feedback delay at the link layer (Radio Link Control, RLC. Subsequently, we propose a local approach to solve errors directly at the UMTS link layer while keeping a reasonably high channel efficiency and saving, as much as possible, system resources. The solution that we propose in this paper is based on the usage of the common channel to serve all the interested users in a cell. In this way, we can save resources with respect to the case where multiple dedicated channels are allocated for every user. In addition to that, we present a hybrid ARQ (HARQ proactive protocol that, at the cost of some redundancy (added to the link layer flow, is able to consistently improve the channel efficiency with respect to the plain ARQ case, by therefore making the use of a single common channel for multicast data delivery feasible. In the last part of the paper we give some hints for future research, by envisioning the usage of the aforementioned error control protocols with suitably encoded video streams.

  20. Satellite retrievals of Karenia brevis harmful algal blooms in the West Florida shelf using neural networks and impacts of temporal variabilities

    Science.gov (United States)

    El-Habashi, Ahmed; Duran, Claudia M.; Lovko, Vincent; Tomlinson, Michelle C.; Stumpf, Richard P.; Ahmed, Sam

    2017-07-01

    We apply a neural network (NN) technique to detect/track Karenia brevis harmful algal blooms (KB HABs) plaguing West Florida shelf (WFS) coasts from Visible-Infrared Imaging Radiometer Suite (VIIRS) satellite observations. Previously KB HABs detection primarily relied on the Moderate Resolution Imaging Spectroradiometer Aqua (MODIS-A) satellite, depending on its remote sensing reflectance signal at the 678-nm chlorophyll fluorescence band (Rrs678) needed for normalized fluorescence height and related red band difference retrieval algorithms. VIIRS, MODIS-A's successor, does not have a 678-nm channel. Instead, our NN uses Rrs at 486-, 551-, and 671-nm VIIRS channels to retrieve phytoplankton absorption at 443 nm (a). The retrieved a images are next filtered by applying limits, defined by (i) low Rrs551-nm backscatter and (ii) a minimum a value associated with KB HABs. The filtered residual images are then converted to show chlorophyll-a concentrations [Chla] and KB cell counts. VIIRS retrievals using our NN and five other retrieval algorithms were compared and evaluated against numerous in situ measurements made over the four-year 2012 to 2016 period, for which VIIRS data are available. These comparisons confirm the viability and higher retrieval accuracies of the NN technique, when combined with the filtering constraints, for effective detection of KB HABs. Analysis of these results as well as sequential satellite observations and recent field measurements underline the importance of short-term temporal variabilities on retrieval accuracies.

  1. A study on Optical Labelling Techniques for All-Optical Networks

    DEFF Research Database (Denmark)

    Holm-Nielsen, Pablo Villanueva

    2005-01-01

    Optical switching has been proposed as an effective solution to overcoming the potential electronic bottleneck in all-optical network nodes carrying IP over WDM. The solution builds on the use of optical labelling as a mean to route packets or bursts of packets through the network. In addition...... of an intermediate wavelength between label erasure and label insertion. The above mentioned functionalities are assembled in whole network systems experiments that validates the different labelling schemes with respect to transmission, wavelength conversion, label swapping and retransmission. Optical labelling...... and specially the orthogonal schemes for optical labelling, are thus shown to be an effective solution to all-optical networks....

  2. A Review of Fuzzy Logic and Neural Network Based Intelligent Control Design for Discrete-Time Systems

    Directory of Open Access Journals (Sweden)

    Yiming Jiang

    2016-01-01

    Full Text Available Over the last few decades, the intelligent control methods such as fuzzy logic control (FLC and neural network (NN control have been successfully used in various applications. The rapid development of digital computer based control systems requires control signals to be calculated in a digital or discrete-time form. In this background, the intelligent control methods developed for discrete-time systems have drawn great attentions. This survey aims to present a summary of the state of the art of the design of FLC and NN-based intelligent control for discrete-time systems. For discrete-time FLC systems, numerous remarkable design approaches are introduced and a series of efficient methods to deal with the robustness, stability, and time delay of FLC discrete-time systems are recommended. Techniques for NN-based intelligent control for discrete-time systems, such as adaptive methods and adaptive dynamic programming approaches, are also reviewed. Overall, this paper is devoted to make a brief summary for recent progresses in FLC and NN-based intelligent control design for discrete-time systems as well as to present our thoughts and considerations of recent trends and potential research directions in this area.

  3. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

    Energy Technology Data Exchange (ETDEWEB)

    Wijayasekara, Dumidu, E-mail: wija2589@vandals.uidaho.edu [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Manic, Milos [Department of Computer Science, University of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402 (United States); Sabharwall, Piyush [Idaho National Laboratory, Idaho Falls, ID (United States); Utgikar, Vivek [Department of Chemical Engineering, University of Idaho, Idaho Falls, ID 83402 (United States)

    2011-07-15

    Highlights: > Performance prediction of PCHE using artificial neural networks. > Evaluating artificial neural network performance for PCHE modeling. > Selection of over-training resilient artificial neural networks. > Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the testing

  4. Optimal artificial neural network architecture selection for performance prediction of compact heat exchanger with the EBaLM-OTR technique

    International Nuclear Information System (INIS)

    Wijayasekara, Dumidu; Manic, Milos; Sabharwall, Piyush; Utgikar, Vivek

    2011-01-01

    Highlights: → Performance prediction of PCHE using artificial neural networks. → Evaluating artificial neural network performance for PCHE modeling. → Selection of over-training resilient artificial neural networks. → Artificial neural network architecture selection for modeling problems with small data sets. - Abstract: Artificial Neural Networks (ANN) have been used in the past to predict the performance of printed circuit heat exchangers (PCHE) with satisfactory accuracy. Typically published literature has focused on optimizing ANN using a training dataset to train the network and a testing dataset to evaluate it. Although this may produce outputs that agree with experimental results, there is a risk of over-training or over-learning the network rather than generalizing it, which should be the ultimate goal. An over-trained network is able to produce good results with the training dataset but fails when new datasets with subtle changes are introduced. In this paper we present EBaLM-OTR (error back propagation and Levenberg-Marquardt algorithms for over training resilience) technique, which is based on a previously discussed method of selecting neural network architecture that uses a separate validation set to evaluate different network architectures based on mean square error (MSE), and standard deviation of MSE. The method uses k-fold cross validation. Therefore in order to select the optimal architecture for the problem, the dataset is divided into three parts which are used to train, validate and test each network architecture. Then each architecture is evaluated according to their generalization capability and capability to conform to original data. The method proved to be a comprehensive tool in identifying the weaknesses and advantages of different network architectures. The method also highlighted the fact that the architecture with the lowest training error is not always the most generalized and therefore not the optimal. Using the method the

  5. Why General Outlier Detection Techniques Do Not Suffice For Wireless Sensor Networks?

    NARCIS (Netherlands)

    Zhang, Y.; Meratnia, Nirvana; Havinga, Paul J.M.

    2009-01-01

    Raw data collected in wireless sensor networks are often unreliable and inaccurate due to noise, faulty sensors and harsh environmental effects. Sensor data that significantly deviate from normal pattern of sensed data are often called outliers. Outlier detection in wireless sensor networks aims at

  6. Practical network design techniques a complete guide for WANs and LANs

    CERN Document Server

    Held, Gilbert

    2004-01-01

    This new edition has two parts. The first part focuses on wide area networks; the second, which is entirely new, focuses on local area networks. Because Ethernet emerged victorious in the LAN war, the second section pays particular attention to Ethernet design and performance characteristics.

  7. Forecasting macroeconomic variables using neural network models and three automated model selection techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    2016-01-01

    When forecasting with neural network models one faces several problems, all of which influence the accuracy of the forecasts. First, neural networks are often hard to estimate due to their highly nonlinear structure. To alleviate the problem, White (2006) presented a solution (QuickNet) that conv...

  8. Accuracy assessment of the scalar network analyzer using sliding termination techniques

    DEFF Research Database (Denmark)

    Knudsen, Bent; Engen, Glenn F.; Guldbrandsen, Birthe

    1989-01-01

    In the absence of phase response the major, if not the primary, sources of error in the scalar network analyzer are the imperfect directivity, etc., associated with its internal and frequently inaccessible test set or measurement network. An explicit expression is obtained for this error in terms...

  9. Flow control and routing techniques for integrated voice and data networks

    Science.gov (United States)

    Ibe, O. C.

    1981-10-01

    We consider a model of integrated voice and data networks. In this model the network flow problem is formulated as a convex optimization problem. The objective function comprises two types of cost functions: the congestion cost functions, which limit the average input traffic to values compatible with the network conditions; and the rate limitation cost functions, which ensure that all conversations are fairly treated. A joint flow control and routing algorithm is constructed which determines the routes for each conversation, and effects flow control by setting voice packet lengths and data input rates in a manner that achieves optimal tradeoff between each user's satisfaction and the cost of network congestion. An additional congestion control protocol is specified which could be used in conjunction with the algorithm to make the latter respond more dynamically to network congestion.

  10. Character Recognition Using Genetically Trained Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Diniz, C.; Stantz, K.M.; Trahan, M.W.; Wagner, J.S.

    1998-10-01

    Computationally intelligent recognition of characters and symbols addresses a wide range of applications including foreign language translation and chemical formula identification. The combination of intelligent learning and optimization algorithms with layered neural structures offers powerful techniques for character recognition. These techniques were originally developed by Sandia National Laboratories for pattern and spectral analysis; however, their ability to optimize vast amounts of data make them ideal for character recognition. An adaptation of the Neural Network Designer soflsvare allows the user to create a neural network (NN_) trained by a genetic algorithm (GA) that correctly identifies multiple distinct characters. The initial successfid recognition of standard capital letters can be expanded to include chemical and mathematical symbols and alphabets of foreign languages, especially Arabic and Chinese. The FIN model constructed for this project uses a three layer feed-forward architecture. To facilitate the input of characters and symbols, a graphic user interface (GUI) has been developed to convert the traditional representation of each character or symbol to a bitmap. The 8 x 8 bitmap representations used for these tests are mapped onto the input nodes of the feed-forward neural network (FFNN) in a one-to-one correspondence. The input nodes feed forward into a hidden layer, and the hidden layer feeds into five output nodes correlated to possible character outcomes. During the training period the GA optimizes the weights of the NN until it can successfully recognize distinct characters. Systematic deviations from the base design test the network's range of applicability. Increasing capacity, the number of letters to be recognized, requires a nonlinear increase in the number of hidden layer neurodes. Optimal character recognition performance necessitates a minimum threshold for the number of cases when genetically training the net. And, the

  11. Modelling thermal comfort of visitors at urban squares in hot and arid climate using NN-ARX soft computing method

    Science.gov (United States)

    Kariminia, Shahab; Motamedi, Shervin; Shamshirband, Shahaboddin; Piri, Jamshid; Mohammadi, Kasra; Hashim, Roslan; Roy, Chandrabhushan; Petković, Dalibor; Bonakdari, Hossein

    2016-05-01

    Visitors utilize the urban space based on their thermal perception and thermal environment. The thermal adaptation engages the user's behavioural, physiological and psychological aspects. These aspects play critical roles in user's ability to assess the thermal environments. Previous studies have rarely addressed the effects of identified factors such as gender, age and locality on outdoor thermal comfort, particularly in hot, dry climate. This study investigated the thermal comfort of visitors at two city squares in Iran based on their demographics as well as the role of thermal environment. Assessing the thermal comfort required taking physical measurement and questionnaire survey. In this study, a non-linear model known as the neural network autoregressive with exogenous input (NN-ARX) was employed. Five indices of physiological equivalent temperature (PET), predicted mean vote (PMV), standard effective temperature (SET), thermal sensation votes (TSVs) and mean radiant temperature ( T mrt) were trained and tested using the NN-ARX. Then, the results were compared to the artificial neural network (ANN) and the adaptive neuro-fuzzy inference system (ANFIS). The findings showed the superiority of the NN-ARX over the ANN and the ANFIS. For the NN-ARX model, the statistical indicators of the root mean square error (RMSE) and the mean absolute error (MAE) were 0.53 and 0.36 for the PET, 1.28 and 0.71 for the PMV, 2.59 and 1.99 for the SET, 0.29 and 0.08 for the TSV and finally 0.19 and 0.04 for the T mrt.

  12. A new XML-aware compression technique for improving performance of healthcare information systems over hospital networks.

    Science.gov (United States)

    Al-Shammary, Dhiah; Khalil, Ibrahim

    2010-01-01

    Most organizations exchange, collect, store and process data over the Internet. Many hospital networks deploy Web services to send and receive patient information. SOAP (Simple Object Access Protocol) is the most usable communication protocol for Web services. XML is the standard encoding language of SOAP messages. However, the major drawback of XML messages is the high network traffic caused by large overheads. In this paper, two XML-aware compressors are suggested to compress patient messages stemming from any data transactions between Web clients and servers. The proposed compression techniques are based on the XML structure concepts and use both fixed-length and Huffman encoding methods for translating the XML message tree. Experiments show that they outperform all the conventional compression methods and can save tremendous amount of network bandwidth.

  13. Local TEC Modelling and Forecasting using Neural Networks

    Science.gov (United States)

    Tebabal, A.; Radicella, S. M.; Nigussie, M.; Damtie, B.; Nava, B.; Yizengaw, E.

    2017-12-01

    Abstract Modelling the Earth's ionospheric characteristics is the focal task for the ionospheric community to mitigate its effect on the radio communication, satellite navigation and technologies. However, several aspects of modelling are still challenging, for example, the storm time characteristics. This paper presents modelling efforts of TEC taking into account solar and geomagnetic activity, time of the day and day of the year using neural networks (NNs) modelling technique. The NNs have been designed with GPS-TEC measured data from low and mid-latitude GPS stations. The training was conducted using the data obtained for the period from 2011 to 2014. The model prediction accuracy was evaluated using data of year 2015. The model results show that diurnal and seasonal trend of the GPS-TEC is well reproduced by the model for the two stations. The seasonal characteristics of GPS-TEC is compared with NN and NeQuick 2 models prediction when the latter one is driven by the monthly average value of solar flux. It is found that NN model performs better than the corresponding NeQuick 2 model for low latitude region. For the mid-latitude both NN and NeQuick 2 models reproduce the average characteristics of TEC variability quite successfully. An attempt of one day ahead forecast of TEC at the two locations has been made by introducing as driver previous day solar flux and geomagnetic index values. The results show that a reasonable day ahead forecast of local TEC can be achieved.

  14. Determination of nn scattering length from data on nn final state interaction in nd-breakup reaction

    International Nuclear Information System (INIS)

    Konobeevski, E.S.; Mordovskoy, M.V.; Sergeev, V.A.; Potashev, S.I.; Zuev, S.V.

    2006-01-01

    Full text: An experiment is proposed for the high-precision determination of the neutron-neutron scattering length investigating the nn final state interaction in the nd breakup reaction. The singlet pp and nn scattering lengths are very sensitive probes of the NN-interaction, and their difference is a direct measure of charge-symmetry breaking (CSB) of the nuclear force. However CSB is a small effect, and accurate values of the scattering lengths are needed for a theoretical analysis. The proton-proton scattering length is well known from pp-scattering data (a pp = -17.3± 0.4 fm), and its uncertainty is mainly due to a model-dependent procedure of removing Coulomb effects. The neutron-neutron scattering length is determined from the following processes n+d→p+n+n, π - + d → γ +n+n, d+d→ 2 He+n+n by investigating the kinematic region of the nn final-state interaction (FSI) where two neutrons fly with low relative energy. The results obtained by now are characterized by a significant uncertainty in values of a nn ; they are grouped near -16 and -19 fm [1,2], so even the sign of the difference a nn - a pp is uncertain. In this experiment neutron-neutron scattering length is determined by measuring the yield of the nd breakup reaction as a function of the relative energy ε nn =(E 1 +E 2 -2(E 1 E 2 ) 1/2 cosθ)/2 of two neutrons in the FSI region (two neutrons fly in a narrow angular cone) where nn-interaction is strongly revealed. The theory of reactions in 3N system predicts the ε nn dependence of the FSI cross section being sensitive to the value of a nn . The measurements will be made using the neutron channel RADEX at Moscow meson factory of the Institute for Nuclear Research. The momenta and angles of the two emitted neutrons and the energy of the proton will be measured for each breakup event. The measured dependence of the reaction yield on the relative energy of the two neutrons will be compared to results of the Monte Carlo simulation that includes

  15. New approach to the theory of coupled πNN-NN systems. 4. Completion of formal development

    International Nuclear Information System (INIS)

    Avishai, Y.; Mizutani, T.

    1980-05-01

    We complete our discussion pertaining to the coupled πNN-NN system in succession of our previous works and approach the problem in two independent ways. The first one starts from a Hamiltonian formalism and coupled Schroedinger equations whereas the second one employs an off-mass-shell relativistic theory of classifying perturbation diagrams. Both ways lead to connected equations among transition operators in which πNN vertices as well as nucleon propagators are completely dressed and renormalized. Furthermore, the physical amplitudes obey two and three body unitarity relations. The resultant equations form a sound theoretical basis for subsequent numerical calculations leading to the evaluation of physical observables in the reactions π+d→π+d, π+d→N+N and N+N→N+N

  16. MIMO wireless networks channels, techniques and standards for multi-antenna, multi-user and multi-cell systems

    CERN Document Server

    Clerckx, Bruno

    2013-01-01

    This book is unique in presenting channels, techniques and standards for the next generation of MIMO wireless networks. Through a unified framework, it emphasizes how propagation mechanisms impact the system performance under realistic power constraints. Combining a solid mathematical analysis with a physical and intuitive approach to space-time signal processing, the book progressively derives innovative designs for space-time coding and precoding as well as multi-user and multi-cell techniques, taking into consideration that MIMO channels are often far from ideal. Reflecting developments

  17. Evolution of Positioning Techniques in Cellular Networks, from 2G to 4G

    Directory of Open Access Journals (Sweden)

    Rafael Saraiva Campos

    2017-01-01

    Full Text Available This review paper presents within a common framework the mobile station positioning methods applied in 2G, 3G, and 4G cellular networks, as well as the structure of the related 3GPP technical specifications. The evolution path through the generations is explored in three steps at each level: first, the new network elements supporting localization features are introduced; then, the standard localization methods are described; finally, the protocols providing specific support to mobile station positioning are studied. To allow a better understanding, this paper also brings a brief review of the cellular networks evolution paths.

  18. Two-stage neural-network-based technique for Urdu character two-dimensional shape representation, classification, and recognition

    Science.gov (United States)

    Megherbi, Dalila B.; Lodhi, S. M.; Boulenouar, A. J.

    2001-03-01

    This work is in the field of automated document processing. This work addresses the problem of representation and recognition of Urdu characters using Fourier representation and a Neural Network architecture. In particular, we show that a two-stage Neural Network scheme is used here to make classification of 36 Urdu characters into seven sub-classes namely subclasses characterized by seven proposed and defined fuzzy features specifically related to Urdu characters. We show that here Fourier Descriptors and Neural Network provide a remarkably simple way to draw definite conclusions from vague, ambiguous, noisy or imprecise information. In particular, we illustrate the concept of interest regions and describe a framing method that provides a way to make the proposed technique for Urdu characters recognition robust and invariant to scaling and translation. We also show that a given character rotation is dealt with by using the Hotelling transform. This transform is based upon the eigenvalue decomposition of the covariance matrix of an image, providing a method of determining the orientation of the major axis of an object within an image. Finally experimental results are presented to show the power and robustness of the proposed two-stage Neural Network based technique for Urdu character recognition, its fault tolerance, and high recognition accuracy.

  19. Intelligent Prediction of Soccer Technical Skill on Youth Soccer Player's Relative Performance Using Multivariate Analysis and Artificial Neural Network Techniques

    OpenAIRE

    Abdullah, M. R; Maliki, A. B. H. M; Musa, R. M; Kosni, N. A; Juahir, H

    2016-01-01

    This study aims to predict the potential pattern of soccer technical skill on Malaysia youth soccer players relative performance using multivariate analysis and artificial neural network techniques. 184 male youth soccer players were recruited in Malaysia soccer academy (average age = 15.2±2.0) underwent to, physical fitness test, anthropometric, maturity, motivation and the level of skill related soccer. Unsupervised pattern recognition of principal component analysis (PCA) was used to ident...

  20. Cool-season precipitation in the southwestern USA since AD 1000: comparison of linear and nonlinear techniques for reconstruction

    Science.gov (United States)

    Ni, Fenbiao; Cavazos, Tereza; Hughes, Malcolm K.; Comrie, Andrew C.; Funkhouser, Gary

    2002-11-01

    A 1000 year reconstruction of cool-season (November-April) precipitation was developed for each climate division in Arizona and New Mexico from a network of 19 tree-ring chronologies in the southwestern USA. Linear regression (LR) and artificial neural network (NN) models were used to identify the cool-season precipitation signal in tree rings. Using 1931-88 records, the stepwise LR model was cross-validated with a leave-one-out procedure and the NN was validated with a bootstrap technique. The final models were also independently validated using the 1896-1930 precipitation data. In most of the climate divisions, both techniques can successfully reconstruct dry and normal years, and the NN seems to capture large precipitation events and more variability better than the LR. In the 1000 year reconstructions the NN also produces more distinctive wet events and more variability, whereas the LR produces more distinctive dry events. The 1000 year reconstructed precipitation from the two models shows several sustained dry and wet periods comparable to the 1950s drought (e.g. 16th century mega drought) and to the post-1976 wet period (e.g. 1330s, 1610s). The impact of extreme periods on the environment may be stronger during sudden reversals from dry to wet, which were not uncommon throughout the millennium, such as the 1610s wet interval that followed the 16th century mega drought. The instrumental records suggest that strong dry to wet precipitation reversals in the past 1000 years might be linked to strong shifts from cold to warm El Niño-southern oscillation events and from a negative to positive Pacific decadal oscillation.

  1. Next-Generation Environment-Aware Cellular Networks: Modern Green Techniques and Implementation Challenges

    KAUST Repository

    Ghazzai, Hakim; Yaacoub, Elias; Kadri, Abdullah; Yanikomeroglu, Halim; Alouini, Mohamed-Slim

    2016-01-01

    achieve significant economic benefits and environmental savings: 1) the base station sleeping strategy; 2) the optimized energy procurement from the smart grid; 3) the base station energy sharing; and 4) the green networking collaboration between

  2. Improving Air Force Active Network Defense Systems through an Analysis of Intrusion Detection Techniques

    National Research Council Canada - National Science Library

    Dunklee, David R

    2007-01-01

    .... The research then presents four recommendations to improve DCC operations. These include: Transition or improve the current signature-based IDS systems to include the capability to query and visualize network flows to detect malicious traffic...

  3. Next generation passive optical networks based on orthogonal frequency division multiplexing techniques

    OpenAIRE

    Escayola Elias, Francesc Xavier

    2015-01-01

    In recent decades, the industry of communications has acquired huge significance, and nowadays constitutes an essential tool for the society information. Thus, the exponential growth in demand of broadband services and the increasing amount of information to be transmitted have spurred the evolution of the access network infrastructure to effectively meet the user needs in an effective way in terms of costs of both installation and maintenance. Passive optical networks (PON) are current...

  4. Breast Cancer Diagnosis using Artificial Neural Networks with Extreme Learning Techniques

    OpenAIRE

    Chandra Prasetyo Utomo; Aan Kardiana; Rika Yuliwulandari

    2014-01-01

    Breast cancer is the second cause of dead among women. Early detection followed by appropriate cancer treatment can reduce the deadly risk. Medical professionals can make mistakes while identifying a disease. The help of technology such as data mining and machine learning can substantially improve the diagnosis accuracy. Artificial Neural Networks (ANN) has been widely used in intelligent breast cancer diagnosis. However, the standard Gradient-Based Back Propagation Artificial Neural Networks...

  5. A kNN method that uses a non-natural evolutionary algorithm for ...

    African Journals Online (AJOL)

    We used this algorithm for component selection of a kNN (k Nearest Neighbor) method for breast cancer prognosis. Results with the UCI prognosis data set show that we can find components that help improve the accuracy of kNN by almost 3%, raising it above 79%. Keywords: kNN; classification; evolutionary algorithm; ...

  6. Performance improvement of optical CDMA networks with stochastic artificial bee colony optimization technique

    Science.gov (United States)

    Panda, Satyasen

    2018-05-01

    This paper proposes a modified artificial bee colony optimization (ABC) algorithm based on levy flight swarm intelligence referred as artificial bee colony levy flight stochastic walk (ABC-LFSW) optimization for optical code division multiple access (OCDMA) network. The ABC-LFSW algorithm is used to solve asset assignment problem based on signal to noise ratio (SNR) optimization in OCDM networks with quality of service constraints. The proposed optimization using ABC-LFSW algorithm provides methods for minimizing various noises and interferences, regulating the transmitted power and optimizing the network design for improving the power efficiency of the optical code path (OCP) from source node to destination node. In this regard, an optical system model is proposed for improving the network performance with optimized input parameters. The detailed discussion and simulation results based on transmitted power allocation and power efficiency of OCPs are included. The experimental results prove the superiority of the proposed network in terms of power efficiency and spectral efficiency in comparison to networks without any power allocation approach.

  7. Monitoring soil moisture patterns in alpine meadows using ground sensor networks and remote sensing techniques

    Science.gov (United States)

    Bertoldi, Giacomo; Brenner, Johannes; Notarnicola, Claudia; Greifeneder, Felix; Nicolini, Irene; Della Chiesa, Stefano; Niedrist, Georg; Tappeiner, Ulrike

    2015-04-01

    Soil moisture content (SMC) is a key factor for numerous processes, including runoff generation, groundwater recharge, evapotranspiration, soil respiration, and biological productivity. Understanding the controls on the spatial and temporal variability of SMC in mountain catchments is an essential step towards improving quantitative predictions of catchment hydrological processes and related ecosystem services. The interacting influences of precipitation, soil properties, vegetation, and topography on SMC and the influence of SMC patterns on runoff generation processes have been extensively investigated (Vereecken et al., 2014). However, in mountain areas, obtaining reliable SMC estimations is still challenging, because of the high variability in topography, soil and vegetation properties. In the last few years, there has been an increasing interest in the estimation of surface SMC at local scales. On the one hand, low cost wireless sensor networks provide high-resolution SMC time series. On the other hand, active remote sensing microwave techniques, such as Synthetic Aperture Radars (SARs), show promising results (Bertoldi et al. 2014). As these data provide continuous coverage of large spatial extents with high spatial resolution (10-20 m), they are particularly in demand for mountain areas. However, there are still limitations related to the fact that the SAR signal can penetrate only a few centimeters in the soil. Moreover, the signal is strongly influenced by vegetation, surface roughness and topography. In this contribution, we analyse the spatial and temporal dynamics of surface and root-zone SMC (2.5 - 5 - 25 cm depth) of alpine meadows and pastures in the Long Term Ecological Research (LTER) Area Mazia Valley (South Tyrol - Italy) with different techniques: (I) a network of 18 stations; (II) field campaigns with mobile ground sensors; (III) 20-m resolution RADARSAT2 SAR images; (IV) numerical simulations using the GEOtop hydrological model (Rigon et al

  8. Communication: Fitting potential energy surfaces with fundamental invariant neural network

    Energy Technology Data Exchange (ETDEWEB)

    Shao, Kejie; Chen, Jun; Zhao, Zhiqiang; Zhang, Dong H., E-mail: zhangdh@dicp.ac.cn [State Key Laboratory of Molecular Reaction Dynamics and Center for Theoretical Computational Chemistry, Dalian Institute of Chemical Physics, Chinese Academy of Sciences, Dalian 116023, People’s Republic of China and University of Chinese Academy of Sciences, Beijing 100049, People’s Republic of China. (China)

    2016-08-21

    A more flexible neural network (NN) method using the fundamental invariants (FIs) as the input vector is proposed in the construction of potential energy surfaces for molecular systems involving identical atoms. Mathematically, FIs finitely generate the permutation invariant polynomial (PIP) ring. In combination with NN, fundamental invariant neural network (FI-NN) can approximate any function to arbitrary accuracy. Because FI-NN minimizes the size of input permutation invariant polynomials, it can efficiently reduce the evaluation time of potential energy, in particular for polyatomic systems. In this work, we provide the FIs for all possible molecular systems up to five atoms. Potential energy surfaces for OH{sub 3} and CH{sub 4} were constructed with FI-NN, with the accuracy confirmed by full-dimensional quantum dynamic scattering and bound state calculations.

  9. Renormalization of NN Interaction with Relativistic Chiral Two Pion Exchange

    Energy Technology Data Exchange (ETDEWEB)

    Higa, R; Valderrama, M Pavon; Arriola, E Ruiz

    2007-06-14

    The renormalization of the NN interaction with the Chiral Two Pion Exchange Potential computed using relativistic baryon chiral perturbation theory is considered. The short distance singularity reduces the number of counter-terms to about a half as those in the heavy-baryon expansion. Phase shifts and deuteron properties are evaluated and a general overall agreement is observed.

  10. Stress Assignment in N+N Combinations in Arabic

    Directory of Open Access Journals (Sweden)

    Abdel Rahman Mitib Altakhaineh

    2017-09-01

    Full Text Available The validity of stress as a criterion to distinguish between compounds and phrases has been investigated in many languages, including English (see e.g. Lieber 2005: 376; Booij 2012: 84. However, the possibility of using stress as a criterion in this way has not been investigated for Arabic. Siloni (1997: 21 claims that in N+N combinations in Semitic languages, stress always falls on the second element. However, the results of a study using PRAAT reveal that, in Modern Standard Arabic (MSA and Jordanian Arabic (JA, stress plays no role in distinguishing between various N+N combinations, i.e. compounds and phrases, e.g.ˈmuʕallim lfiizyaaʔ ‘the physics teacher’ vs.ˈbayt lwalad ‘the boy’s house’, respectively. Analysis shows that the default position of stress in N+N combinations in MSA and JA is on the first element. There is only one systematic exception, which is phonetically conditioned: in N+N combinations with assimilated geminates on the word boundary, a secondary stress or perhaps double stress is assigned.

  11. The crossing phenomenon and power of the 1-NN rule

    Czech Academy of Sciences Publication Activity Database

    Jiřina, Marcel; Krayem, S.

    submitted 2017 (2018) ISSN 0176-4268 R&D Projects: GA MŠk(CZ) LG15047 Institutional support: RVO:67985807 Keywords : kNN rule * multivariate data * classification * distance * nearest neighbor * distribution mapping function Impact factor: 3.083, year: 2016

  12. Breakdown of NpNn scheme in very heavy nuclei

    International Nuclear Information System (INIS)

    Varshney, A.K.; Singh, M.; Kumar, Rajesh; Gupta, K.K.; Gupta, D.K.

    2016-01-01

    The proton neutron interaction has been considered the key ingredient in the development of configuration mixing, collectivity and ultimately deformation in atomic nuclei for over five decades. Phenomenologically, the correlation of the integrated valance p - n interaction with the onset of collectivity and deformation has been described in terms of NpNn scheme

  13. Prospects for N-N* Transitions from Lattice QCD

    International Nuclear Information System (INIS)

    Richards, David

    2008-01-01

    I describe the status of calculations of N-N* transition form factors in lattice QCD, and the prospects for future calculations. I emphasise the need to reliably delineate the states in the spectrum, and the progress that has been made by the Hadron Spectrum Collaboration in so doing.

  14. Narrow coherent effects in πNN-dynamics

    International Nuclear Information System (INIS)

    Kudryavtsev, A.E.; Obrant, G.Z.

    1990-01-01

    Coherent effect production is considered in πNN-dynamics with resonant pion-nucleon interaction via Brueckner theory and Faddev equations. It is shown that the narrow energy and final momentum dependence can arise in the inelastic S-wave πd-scattering. The energy dependence peculiarities can have a width an order magnitude less than πN-resonance one

  15. Remote sensing of nutrient deficiency in Lactuca sativa using neural networks for terrestrial and advanced life support applications

    Science.gov (United States)

    Sears, Edie Seldon

    2000-12-01

    A remote sensing study using reflectance and fluorescence spectra of hydroponically grown Lactuca sativa (lettuce) canopies was conducted. An optical receiver was designed and constructed to interface with a commercial fiber optic spectrometer for data acquisition. Optical parameters were varied to determine effects of field of view and distance to target on vegetation stress assessment over the test plant growth cycle. Feedforward backpropagation neural networks (NN) were implemented to predict the presence of canopy stress. Effects of spatial and spectral resolutions on stress predictions of the neural network were also examined. Visual inspection and fresh mass values failed to differentiate among controls, plants cultivated with 25% of the recommended concentration of phosphorous (P), and those cultivated with 25% nitrogen (N) based on fresh mass and visual inspection. The NN's were trained on input vectors created using reflectance and test day, fluorescence and test day, and reflectance, fluorescence, and test day. Four networks were created representing four levels of spectral resolution: 100-nm NN, 10-nm NN, 1-nm NN, and 0.1-nm NN. The 10-nm resolution was found to be sufficient for classifying extreme nitrogen deficiency in freestanding hydroponic lettuce. As a result of leaf angle and canopy structure broadband scattering intensity in the 700-nm to 1000-nm range was found to be the most useful portion of the spectrum in this study. More subtle effects of "greenness" and fluorescence emission were believed to be obscured by canopy structure and leaf orientation. As field of view was not as found to be as significant as originally believed, systems implementing higher repetitions over more uniformly oriented, i.e. smaller, flatter, target areas would provide for more discernible neural network input vectors. It is believed that this technique holds considerable promise for early detection of extreme nitrogen deficiency. Further research is recommended using

  16. Development of a neural network technique for KSTAR Thomson scattering diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Lee, Seung Hun, E-mail: leesh81@nfri.re.kr; Lee, J. H. [National Fusion Research Institute, 169-148 Gwahak-ro, Yuseong-gu, Daejeon 34133 (Korea, Republic of); Yamada, I. [National Institute Fusion Science, Toki, Gifu 509-5292 (Japan); Park, Jae Sun [Department of Physics, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141 (Korea, Republic of)

    2016-11-15

    Neural networks provide powerful approaches of dealing with nonlinear data and have been successfully applied to fusion plasma diagnostics and control systems. Controlling tokamak plasmas in real time is essential to measure the plasma parameters in situ. However, the χ{sup 2} method traditionally used in Thomson scattering diagnostics hampers real-time measurement due to the complexity of the calculations involved. In this study, we applied a neural network approach to Thomson scattering diagnostics in order to calculate the electron temperature, comparing the results to those obtained with the χ{sup 2} method. The best results were obtained for 10{sup 3} training cycles and eight nodes in the hidden layer. Our neural network approach shows good agreement with the χ{sup 2} method and performs the calculation twenty times faster.

  17. Applying Fuzzy Logic and Data Mining Techniques in Wireless Sensor Network for Determination Residential Fire Confidence

    Directory of Open Access Journals (Sweden)

    Mirjana Maksimović

    2014-09-01

    Full Text Available The main goal of soft computing technologies (fuzzy logic, neural networks, fuzzy rule-based systems, data mining techniques… is to find and describe the structural patterns in the data in order to try to explain connections between data and on their basis create predictive or descriptive models. Integration of these technologies in sensor nodes seems to be a good idea because it can significantly lead to network performances improvements, above all to reduce the energy consumption and enhance the lifetime of the network. The purpose of this paper is to analyze different algorithms in the case of fire confidence determination in order to see which of the methods and parameter values work best for the given problem. Hence, an analysis between different classification algorithms in a case of nominal and numerical d

  18. Measuring the Influence of Information Networks on Transaction Costs Using a Non-parametric Regression Technique

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Henning, Christian

    2011-01-01

    All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs (TAC). One of the major factors in TAC theory is information. Information networks can catalyse the interpersonal information exchange and hence, increase the access...... to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...

  19. Simple techniques for improving deep neural network outcomes on commodity hardware

    Science.gov (United States)

    Colina, Nicholas Christopher A.; Perez, Carlos E.; Paraan, Francis N. C.

    2017-08-01

    We benchmark improvements in the performance of deep neural networks (DNN) on the MNIST data test upon imple-menting two simple modifications to the algorithm that have little overhead computational cost. First is GPU parallelization on a commodity graphics card, and second is initializing the DNN with random orthogonal weight matrices prior to optimization. Eigenspectra analysis of the weight matrices reveal that the initially orthogonal matrices remain nearly orthogonal after training. The probability distributions from which these orthogonal matrices are drawn are also shown to significantly affect the performance of these deep neural networks.

  20. Model-based monitoring techniques for leakage localization in distribution water networks

    OpenAIRE

    Meseguer Amela, Jordi; Mirats Tur, Josep Maria; Cembrano Gennari, Gabriela; Puig Cayuela, Vicenç

    2015-01-01

    This is an open access article under the CC BY-NC-ND license This paper describes an integrated model-based monitoring framework for leakage localization in district-metered areas (DMA) of water distribution networks, which takes advantage of the availability of a hydraulic model of the network. The leakage localization methodology is based on the use of flow and pressure sensors at the DMA inlets and a limited number of pressure sensors deployed inside the DMA. The placement of these sens...

  1. A Low Power 2.4 GHz CMOS Mixer Using Forward Body Bias Technique for Wireless Sensor Network

    Science.gov (United States)

    Yin, C. J.; Murad, S. A. Z.; Harun, A.; Ramli, M. M.; Zulkifli, T. Z. A.; Karim, J.

    2018-03-01

    Wireless sensor network (WSN) is a highly-demanded application since the evolution of wireless generation which is often used in recent communication technology. A radio frequency (RF) transceiver in WSN should have a low power consumption to support long operating times of mobile devices. A down-conversion mixer is responsible for frequency translation in a receiver. By operating a down-conversion mixer at a low supply voltage, the power consumed by WSN receiver can be greatly reduced. This paper presents a development of low power CMOS mixer using forward body bias technique for wireless sensor network. The proposed mixer is implemented using CMOS 0.13 μm Silterra technology. The forward body bias technique is adopted to obtain low power consumption. The simulation results indicate that a low power consumption of 0.91 mW is achieved at 1.6 V supply voltage. Moreover, the conversion gain (CG) of 21.83 dB, the noise figure (NF) of 16.51 dB and the input-referred third-order intercept point (IIP3) of 8.0 dB at 2.4 GHz are obtained. The proposed mixer is suitable for wireless sensor network.

  2. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Directory of Open Access Journals (Sweden)

    A. Elshorbagy

    2010-10-01

    Full Text Available In this second part of the two-part paper, the data driven modeling (DDM experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs, genetic programming (GP, evolutionary polynomial regression (EPR, Support vector machines (SVM, M5 model trees (M5, K-nearest neighbors (K-nn, and multiple linear regression (MLR techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it

  3. Experimental investigation of the predictive capabilities of data driven modeling techniques in hydrology - Part 2: Application

    Science.gov (United States)

    Elshorbagy, A.; Corzo, G.; Srinivasulu, S.; Solomatine, D. P.

    2010-10-01

    In this second part of the two-part paper, the data driven modeling (DDM) experiment, presented and explained in the first part, is implemented. Inputs for the five case studies (half-hourly actual evapotranspiration, daily peat soil moisture, daily till soil moisture, and two daily rainfall-runoff datasets) are identified, either based on previous studies or using the mutual information content. Twelve groups (realizations) were randomly generated from each dataset by randomly sampling without replacement from the original dataset. Neural networks (ANNs), genetic programming (GP), evolutionary polynomial regression (EPR), Support vector machines (SVM), M5 model trees (M5), K-nearest neighbors (K-nn), and multiple linear regression (MLR) techniques are implemented and applied to each of the 12 realizations of each case study. The predictive accuracy and uncertainties of the various techniques are assessed using multiple average overall error measures, scatter plots, frequency distribution of model residuals, and the deterioration rate of prediction performance during the testing phase. Gamma test is used as a guide to assist in selecting the appropriate modeling technique. Unlike two nonlinear soil moisture case studies, the results of the experiment conducted in this research study show that ANNs were a sub-optimal choice for the actual evapotranspiration and the two rainfall-runoff case studies. GP is the most successful technique due to its ability to adapt the model complexity to the modeled data. EPR performance could be close to GP with datasets that are more linear than nonlinear. SVM is sensitive to the kernel choice and if appropriately selected, the performance of SVM can improve. M5 performs very well with linear and semi linear data, which cover wide range of hydrological situations. In highly nonlinear case studies, ANNs, K-nn, and GP could be more successful than other modeling techniques. K-nn is also successful in linear situations, and it should

  4. Energy saving techniques applied over a nation-wide mobile network

    DEFF Research Database (Denmark)

    Perez, Eva; Frank, Philipp; Micallef, Gilbert

    2014-01-01

    Traffic carried over wireless networks has grown significantly in recent years and actual forecasts show that this trend is expected to continue. However, the rapid mobile data explosion and the need for higher data rates comes at a cost of increased complexity and energy consumption of the mobil...

  5. Accurate localization technique for smart fiber-wireless in-house networks

    NARCIS (Netherlands)

    Tangdiongga, E.; Abraha, S.T.; Crivellaro, A.; Okonkwo, C.M.; Gaudino, R.; Koonen, A.M.J.

    2012-01-01

    The use of a RoF scheme for localization purposes of mobile stations for in-house networks is presented. Using impulse radio UWB over SMF and time-of-arrival localization method, mobile stations can be localized within centimeters accuracy.

  6. Application of an artificial neural network and morphing techniques in the redesign of dysplastic trochlea.

    Science.gov (United States)

    Cho, Kyung Jin; Müller, Jacobus H; Erasmus, Pieter J; DeJour, David; Scheffer, Cornie

    2014-01-01

    Segmentation and computer assisted design tools have the potential to test the validity of simulated surgical procedures, e.g., trochleoplasty. A repeatable measurement method for three dimensional femur models that enables quantification of knee parameters of the distal femur is presented. Fifteen healthy knees are analysed using the method to provide a training set for an artificial neural network. The aim is to use this artificial neural network for the prediction of parameter values that describe the shape of a normal trochlear groove geometry. This is achieved by feeding the artificial neural network with the unaffected parameters of a dysplastic knee. Four dysplastic knees (Type A through D) are virtually redesigned by way of morphing the groove geometries based on the suggested shape from the artificial neural network. Each of the four resulting shapes is analysed and compared to its initial dysplastic shape in terms of three anteroposterior dimensions: lateral, central and medial. For the four knees the trochlear depth is increased, the ventral trochlear prominence reduced and the sulcus angle corrected to within published normal ranges. The results show a lateral facet elevation inadequate, with a sulcus deepening or a depression trochleoplasty more beneficial to correct trochlear dysplasia.

  7. Forecasting Macroeconomic Variables using Neural Network Models and Three Automated Model Selection Techniques

    DEFF Research Database (Denmark)

    Kock, Anders Bredahl; Teräsvirta, Timo

    such as the neural network model is not appropriate if the data is generated by a linear mechanism. Hence, it might be appropriate to test the null of linearity prior to building a nonlinear model. We investigate whether this kind of pretesting improves the forecast accuracy compared to the case where...

  8. Measuring the Influence of Information Networks on Transaction Costs Using a Non-parametric Regression Technique

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Henning, Christian

    2011-01-01

    to nonpublic information. Our analysis shows that information networks have an impact on the level of TAC. Many resources that are sacrificed for TAC are inputs that also enter the technical production process. As most production data do not separate between these two usages of inputs, high transaction costs...

  9. Microarray Data Processing Techniques for Genome-Scale Network Inference from Large Public Repositories.

    Science.gov (United States)

    Chockalingam, Sriram; Aluru, Maneesha; Aluru, Srinivas

    2016-09-19

    Pre-processing of microarray data is a well-studied problem. Furthermore, all popular platforms come with their own recommended best practices for differential analysis of genes. However, for genome-scale network inference using microarray data collected from large public repositories, these methods filter out a considerable number of genes. This is primarily due to the effects of aggregating a diverse array of experiments with different technical and biological scenarios. Here we introduce a pre-processing pipeline suitable for inferring genome-scale gene networks from large microarray datasets. We show that partitioning of the available microarray datasets according to biological relevance into tissue- and process-specific categories significantly extends the limits of downstream network construction. We demonstrate the effectiveness of our pre-processing pipeline by inferring genome-scale networks for the model plant Arabidopsis thaliana using two different construction methods and a collection of 11,760 Affymetrix ATH1 microarray chips. Our pre-processing pipeline and the datasets used in this paper are made available at http://alurulab.cc.gatech.edu/microarray-pp.

  10. Analysis of Drug Design for a Selection of G Protein-Coupled Neuro-Receptors Using Neural Network Techniques

    DEFF Research Database (Denmark)

    Agerskov, Claus; Mortensen, Rasmus M.; Bohr, Henrik G.

    2015-01-01

    A study is presented on how well possible drug-molecules can be predicted with respect to their function and binding to a selection of neuro-receptors by the use of artificial neural networks. The ligands investigated in this study are chosen to be corresponding to the G protein-coupled receptors...... computational tools, able to aid in drug-design in a fast and cheap fashion, compared to conventional pharmacological techniques....... mu-opioid, serotonin 2B (5-HT2B) and metabotropic glutamate D5. They are selected due to the availability of pharmacological drug-molecule binding data for these receptors. Feedback and deep belief artificial neural network architectures (NNs) were chosen to perform the task of aiding drug-design.......925. The performance of 8 category networks (8 output classes for binding strength) obtained a prediction accuracy of above 60 %. After training the networks, tests were done on how well the systems could be used as an aid in designing candidate drug molecules. Specifically, it was shown how a selection of chemical...

  11. Deriving frequency-dependent spatial patterns in MEG-derived resting state sensorimotor network: A novel multiband ICA technique.

    Science.gov (United States)

    Nugent, Allison C; Luber, Bruce; Carver, Frederick W; Robinson, Stephen E; Coppola, Richard; Zarate, Carlos A

    2017-02-01

    Recently, independent components analysis (ICA) of resting state magnetoencephalography (MEG) recordings has revealed resting state networks (RSNs) that exhibit fluctuations of band-limited power envelopes. Most of the work in this area has concentrated on networks derived from the power envelope of beta bandpass-filtered data. Although research has demonstrated that most networks show maximal correlation in the beta band, little is known about how spatial patterns of correlations may differ across frequencies. This study analyzed MEG data from 18 healthy subjects to determine if the spatial patterns of RSNs differed between delta, theta, alpha, beta, gamma, and high gamma frequency bands. To validate our method, we focused on the sensorimotor network, which is well-characterized and robust in both MEG and functional magnetic resonance imaging (fMRI) resting state data. Synthetic aperture magnetometry (SAM) was used to project signals into anatomical source space separately in each band before a group temporal ICA was performed over all subjects and bands. This method preserved the inherent correlation structure of the data and reflected connectivity derived from single-band ICA, but also allowed identification of spatial spectral modes that are consistent across subjects. The implications of these results on our understanding of sensorimotor function are discussed, as are the potential applications of this technique. Hum Brain Mapp 38:779-791, 2017. © 2016 Wiley Periodicals, Inc. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

  12. Skyrme-model πNN form factor and nucleon-nucleon interaction

    International Nuclear Information System (INIS)

    Holzwarth, G.; Machleidt, R.

    1997-01-01

    We apply the strong πNN form factor, which emerges from the Skyrme model, in the two-nucleon system using a one-boson-exchange (OBE) model for the nucleon-nucleon (NN) interaction. Deuteron properties and phase parameters of NN scattering are reproduced well. In contrast to the form factor of monopole shape that is traditionally used in OBE models, the Skyrme form factor leaves low-momentum transfers essentially unaffected while it suppresses the high-momentum region strongly. It turns out that this behavior is very appropriate for models of the NN interaction and makes it possible to use a soft pion form factor in the NN system. As a consequence, the πN and the NN systems can be described using the same πNN form factor, which is impossible with the monopole. copyright 1997 The American Physical Society

  13. Forwarding Techniques for IP Fragmented Packets in a Real 6LoWPAN Network

    Directory of Open Access Journals (Sweden)

    Jordi Casademont

    2011-01-01

    Full Text Available Wireless Sensor Networks (WSNs are attracting more and more interest since they offer a low-cost solution to the problem of providing a means to deploy large sensor networks in a number of application domains. We believe that a crucial aspect to facilitate WSN diffusion is to make them interoperable with external IP networks. This can be achieved by using the 6LoWPAN protocol stack. 6LoWPAN enables the transmission of IPv6 packets over WSNs based on the IEEE 802.15.4 standard. IPv6 packet size is considerably larger than that of IEEE 802.15.4 data frame. To overcome this problem, 6LoWPAN introduces an adaptation layer between the network and data link layers, allowing IPv6 packets to be adapted to the lower layer constraints. This adaptation layer provides fragmentation and header compression of IP packets. Furthermore, it also can be involved in routing decisions. Depending on which layer is responsible for routing decisions, 6LoWPAN divides routing in two categories: mesh under if the layer concerned is the adaptation layer and route over if it is the network layer. In this paper we analyze different routing solutions (route over, mesh under and enhanced route over focusing on how they forward fragments. We evaluate their performance in terms of latency and energy consumption when transmitting IP fragmented packets. All the tests have been performed in a real 6LoWPAN implementation. After consideration of the main problems in forwarding of mesh frames in WSN, we propose and analyze a new alternative scheme based on mesh under, which we call controlled mesh under.

  14. Forwarding techniques for IP fragmented packets in a real 6LoWPAN network.

    Science.gov (United States)

    Ludovici, Alessandro; Calveras, Anna; Casademont, Jordi

    2011-01-01

    Wireless Sensor Networks (WSNs) are attracting more and more interest since they offer a low-cost solution to the problem of providing a means to deploy large sensor networks in a number of application domains. We believe that a crucial aspect to facilitate WSN diffusion is to make them interoperable with external IP networks. This can be achieved by using the 6LoWPAN protocol stack. 6LoWPAN enables the transmission of IPv6 packets over WSNs based on the IEEE 802.15.4 standard. IPv6 packet size is considerably larger than that of IEEE 802.15.4 data frame. To overcome this problem, 6LoWPAN introduces an adaptation layer between the network and data link layers, allowing IPv6 packets to be adapted to the lower layer constraints. This adaptation layer provides fragmentation and header compression of IP packets. Furthermore, it also can be involved in routing decisions. Depending on which layer is responsible for routing decisions, 6LoWPAN divides routing in two categories: mesh under if the layer concerned is the adaptation layer and route over if it is the network layer. In this paper we analyze different routing solutions (route over, mesh under and enhanced route over) focusing on how they forward fragments. We evaluate their performance in terms of latency and energy consumption when transmitting IP fragmented packets. All the tests have been performed in a real 6LoWPAN implementation. After consideration of the main problems in forwarding of mesh frames in WSN, we propose and analyze a new alternative scheme based on mesh under, which we call controlled mesh under.

  15. Automatic target classification of man-made objects in synthetic aperture radar images using Gabor wavelet and neural network

    Science.gov (United States)

    Vasuki, Perumal; Roomi, S. Mohamed Mansoor

    2013-01-01

    Processing of synthetic aperture radar (SAR) images has led to the development of automatic target classification approaches. These approaches help to classify individual and mass military ground vehicles. This work aims to develop an automatic target classification technique to classify military targets like truck/tank/armored car/cannon/bulldozer. The proposed method consists of three stages via preprocessing, feature extraction, and neural network (NN). The first stage removes speckle noise in a SAR image by the identified frost filter and enhances the image by histogram equalization. The second stage uses a Gabor wavelet to extract the image features. The third stage classifies the target by an NN classifier using image features. The proposed work performs better than its counterparts, like K-nearest neighbor (KNN). The proposed work performs better on databases like moving and stationary target acquisition and recognition against the earlier methods by KNN.

  16. Analysis of tribological behaviour of zirconia reinforced Al-SiC hybrid composites using statistical and artificial neural network technique

    Science.gov (United States)

    Arif, Sajjad; Tanwir Alam, Md; Ansari, Akhter H.; Bilal Naim Shaikh, Mohd; Arif Siddiqui, M.

    2018-05-01

    The tribological performance of aluminium hybrid composites reinforced with micro SiC (5 wt%) and nano zirconia (0, 3, 6 and 9 wt%) fabricated through powder metallurgy technique were investigated using statistical and artificial neural network (ANN) approach. The influence of zirconia reinforcement, sliding distance and applied load were analyzed with test based on full factorial design of experiments. Analysis of variance (ANOVA) was used to evaluate the percentage contribution of each process parameters on wear loss. ANOVA approach suggested that wear loss be mainly influenced by sliding distance followed by zirconia reinforcement and applied load. Further, a feed forward back propagation neural network was applied on input/output date for predicting and analyzing the wear behaviour of fabricated composite. A very close correlation between experimental and ANN output were achieved by implementing the model. Finally, ANN model was effectively used to find the influence of various control factors on wear behaviour of hybrid composites.

  17. Day-ahead price forecasting of electricity markets by a new feature selection algorithm and cascaded neural network technique

    International Nuclear Information System (INIS)

    Amjady, Nima; Keynia, Farshid

    2009-01-01

    With the introduction of restructuring into the electric power industry, the price of electricity has become the focus of all activities in the power market. Electricity price forecast is key information for electricity market managers and participants. However, electricity price is a complex signal due to its non-linear, non-stationary, and time variant behavior. In spite of performed research in this area, more accurate and robust price forecast methods are still required. In this paper, a new forecast strategy is proposed for day-ahead price forecasting of electricity markets. Our forecast strategy is composed of a new two stage feature selection technique and cascaded neural networks. The proposed feature selection technique comprises modified Relief algorithm for the first stage and correlation analysis for the second stage. The modified Relief algorithm selects candidate inputs with maximum relevancy with the target variable. Then among the selected candidates, the correlation analysis eliminates redundant inputs. Selected features by the two stage feature selection technique are used for the forecast engine, which is composed of 24 consecutive forecasters. Each of these 24 forecasters is a neural network allocated to predict the price of 1 h of the next day. The whole proposed forecast strategy is examined on the Spanish and Australia's National Electricity Markets Management Company (NEMMCO) and compared with some of the most recent price forecast methods.

  18. Wave forecasting in near real time basis by neural network

    Digital Repository Service at National Institute of Oceanography (India)

    Rao, S.; Mandal, S.; Prabaharan, N.

    ., forecasting of waves become an important aspect of marine environment. This paper presents application of the neural network (NN) with better update algorithms, namely rprop, quickprop and superSAB for wave forecasting. Measured waves off Marmagoa, Goa, India...

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

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  20. Repellent Activity of TRIG (N-N Diethyl Benzamide against Man-Biting Mosquitoes

    Directory of Open Access Journals (Sweden)

    Shandala Msangi

    2018-01-01

    Full Text Available A study was conducted to assess efficacy of a new repellent brand TRIG (15% N-N Diethyl Benzamide when compared to DEET (20% N-N Methyl Toluamide. The repellents were tested in laboratory and field. In the laboratory, the repellence was tested on human volunteers, by exposing their repellent-treated arms on starved mosquitoes in cages for 3 minutes at hourly intervals, while counting the landing and probing attempts. Anopheles gambiae and Aedes aegypti mosquitoes were used. Field evaluation was conducted by Human Landing Catch technique. During the night, the repellents were applied on arms and legs and mosquitoes landing on these areas were collected. In laboratory tests, TRIG provided complete protection (100% against Anopheles gambiae when applied at 1.25 g, while DEET provided this at 0.75 g. When tested on Aedes aegypti, TRIG provided complete protection when applied at 1 g, compared to 0.5 g for DEET. In the field, when applied at a recommended dose, both TRIG and DEET achieved above 90% protection against both An. arabiensis and Culex quinquefasciatus and a Complete Protection Time of about 6 hrs against both species of mosquitoes. The performances of the two products were found to be comparable and TRIG was recommended for use as repellent against mosquito bites.

  1. Repellent Activity of TRIG (N-N Diethyl Benzamide) against Man-Biting Mosquitoes.

    Science.gov (United States)

    Msangi, Shandala; Kweka, Eliningaya; Mahande, Aneth

    2018-01-01

    A study was conducted to assess efficacy of a new repellent brand TRIG (15% N-N Diethyl Benzamide) when compared to DEET (20% N-N Methyl Toluamide). The repellents were tested in laboratory and field. In the laboratory, the repellence was tested on human volunteers, by exposing their repellent-treated arms on starved mosquitoes in cages for 3 minutes at hourly intervals, while counting the landing and probing attempts. Anopheles gambiae and Aedes aegypti mosquitoes were used. Field evaluation was conducted by Human Landing Catch technique. During the night, the repellents were applied on arms and legs and mosquitoes landing on these areas were collected. In laboratory tests, TRIG provided complete protection (100%) against Anopheles gambiae when applied at 1.25 g, while DEET provided this at 0.75 g. When tested on Aedes aegypti, TRIG provided complete protection when applied at 1 g, compared to 0.5 g for DEET. In the field, when applied at a recommended dose, both TRIG and DEET achieved above 90% protection against both An. arabiensis and Culex quinquefasciatus and a Complete Protection Time of about 6 hrs against both species of mosquitoes. The performances of the two products were found to be comparable and TRIG was recommended for use as repellent against mosquito bites.

  2. A new jamming technique for secrecy in multi-antenna wireless networks

    KAUST Repository

    Bakr, Omar; Mudumbai, Raghuraman

    2010-01-01

    . The main contribution of this paper is to show that the Gaussian signaling model has important limitations and propose an alternative "induced fading" jamming technique that takes some of these limitations into account. Specifically we show that under

  3. Evaluation of normalization methods for cDNA microarray data by k-NN classification

    Energy Technology Data Exchange (ETDEWEB)

    Wu, Wei; Xing, Eric P; Myers, Connie; Mian, Saira; Bissell, Mina J

    2004-12-17

    Non-biological factors give rise to unwanted variations in cDNA microarray data. There are many normalization methods designed to remove such variations. However, to date there have been few published systematic evaluations of these techniques for removing variations arising from dye biases in the context of downstream, higher-order analytical tasks such as classification. Ten location normalization methods that adjust spatial- and/or intensity-dependent dye biases, and three scale methods that adjust scale differences were applied, individually and in combination, to five distinct, published, cancer biology-related cDNA microarray data sets. Leave-one-out cross-validation (LOOCV) classification error was employed as the quantitative end-point for assessing the effectiveness of a normalization method. In particular, a known classifier, k-nearest neighbor (k-NN), was estimated from data normalized using a given technique, and the LOOCV error rate of the ensuing model was computed. We found that k-NN classifiers are sensitive to dye biases in the data. Using NONRM and GMEDIAN as baseline methods, our results show that single-bias-removal techniques which remove either spatial-dependent dye bias (referred later as spatial effect) or intensity-dependent dye bias (referred later as intensity effect) moderately reduce LOOCV classification errors; whereas double-bias-removal techniques which remove both spatial- and intensity effect reduce LOOCV classification errors even further. Of the 41 different strategies examined, three two-step processes, IGLOESS-SLFILTERW7, ISTSPLINE-SLLOESS and IGLOESS-SLLOESS, all of which removed intensity effect globally and spatial effect locally, appear to reduce LOOCV classification errors most consistently and effectively across all data sets. We also found that the investigated scale normalization methods do not reduce LOOCV classification error. Using LOOCV error of k-NNs as the evaluation criterion, three double

  4. Development of a technique for level measurement in pressure vessels using thermal probes and artificial neural networks

    International Nuclear Information System (INIS)

    Torres, Walmir Maximo

    2008-01-01

    A technique for level measurement in pressure vessels was developed using thermal probes with internal cooling and artificial neural networks (ANN's). This new concept of thermal probes was experimentally tested in an experimental facility (BETSNI) with two test sections, ST1 and ST2. Two different thermal probes were designed and constructed: concentric tubes probe and U tube probe. A data acquisition system (DAS) was assembled to record the experimental data during the tests. Steady state and transient level tests were carried out and the experimental data obtained were used as learning and recall data sets in the ANN's program RETRO-05 that simulate a multilayer perceptron with backpropagation. The results of the analysis show that the technique can be applied for level measurements in pressure vessel. The technique is applied for a less input temperature data than the initially designed to the probes. The technique is robust and can be used in case of lack of some temperature data. Experimental data available in literature from electrically heated thermal probe were also used in the ANN's analysis producing good results. The results of the ANN's analysis show that the technique can be improved and applied to level measurements in pressure vessels. (author)

  5. Fault-tolerance techniques for high-speed fiber-optic networks

    Science.gov (United States)

    Deruiter, John

    1991-01-01

    Four fiber optic network topologies (linear bus, ring, central star, and distributed star) are discussed relative to their application to high data throughput, fault tolerant networks. The topologies are also examined in terms of redundancy and the need to provide for single point, failure free (or better) system operation. Linear bus topology, although traditionally the method of choice for wire systems, presents implementation problems when larger fiber optic systems are considered. Ring topology works well for high speed systems when coupled with a token passing protocol, but it requires a significant increase in protocol complexity to manage system reconfiguration due to ring and node failures. Star topologies offer a natural fault tolerance, without added protocol complexity, while still providing high data throughput capability.

  6. Neural network technique for orbit correction in accelerators/storage rings

    International Nuclear Information System (INIS)

    Bozoki, E.; Friedman, A.

    1995-01-01

    The authors are exploring the use of Neural Networks, using the SNNS simulator, for orbit control in accelerators (primarily circular accelerators) and storage rings. The orbit of the beam in those machines are measured by orbit monitors (input nodes) and controlled by orbit corrector magnets (output nodes). The physical behavior of an accelerator is changing slowly in time. Thus, an adoptive algorithm is necessary. The goal is to have a trained net which will predict the exact corrector strengths which will minimize a measured orbit. The relationship between open-quotes kickclose quotes from the correctors and open-quotes responseclose quotes from the monitors is in general non-linear and may slowly change during long-term operation of the machine. In the study, several network architectures are examined as well as various training methods for each architecture

  7. Techniques for Efficient Tracking of Road-Network-Based Moving Objects

    DEFF Research Database (Denmark)

    Civilis, Alminas; Jensen, Christian Søndergaard; Saltenis, Simonas

    With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

  8. Techniques for efficient road-network-based tracking of moving objects

    DEFF Research Database (Denmark)

    Civilis, A.; Jensen, Christian Søndergaard; Pakalnis, Stardas

    2005-01-01

    With the continued advances in wireless communications, geo-positioning, and consumer electronics, an infrastructure is emerging that enables location-based services that rely on the tracking of the continuously changing positions of entire populations of service users, termed moving objects....... The main issue considered is how to represent the location of a moving object in a database so that tracking can be done with as few updates as possible. The paper proposes to use the road network within which the objects are assumed to move for predicting their future positions. The paper presents...... algorithms that modify an initial road-network representation, so that it works better as a basis for predicting an object's position; it proposes to use known movement patterns of the object, in the form of routes; and it proposes to use acceleration profiles together with the routes. Using real GPS...

  9. Sensitivity analysis in Gaussian Bayesian networks using a symbolic-numerical technique

    International Nuclear Information System (INIS)

    Castillo, Enrique; Kjaerulff, Uffe

    2003-01-01

    The paper discusses the problem of sensitivity analysis in Gaussian Bayesian networks. The algebraic structure of the conditional means and variances, as rational functions involving linear and quadratic functions of the parameters, are used to simplify the sensitivity analysis. In particular the probabilities of conditional variables exceeding given values and related probabilities are analyzed. Two examples of application are used to illustrate all the concepts and methods

  10. Airborne particle monitoring with urban closed-circuit television camera networks and a chromatic technique

    International Nuclear Information System (INIS)

    Kolupula, Y R; Jones, G R; Deakin, A G; Spencer, J W; Aceves-Fernandez, M A

    2010-01-01

    An economic approach for the preliminary assessment of 2–10 µm sized (PM10) airborne particle levels in urban areas is described. It uses existing urban closed-circuit television (CCTV) surveillance camera networks in combination with particle accumulating units and chromatic quantification of polychromatic light scattered by the captured particles. Methods for accommodating extraneous light effects are discussed and test results obtained from real urban sites are presented to illustrate the potential of the approach

  11. Preliminary results of neural networks and zernike polynomials for classification of videokeratography maps.

    Science.gov (United States)

    Carvalho, Luis Alberto

    2005-02-01

    Our main goal in this work was to develop an artificial neural network (NN) that could classify specific types of corneal shapes using Zernike coefficients as input. Other authors have implemented successful NN systems in the past and have demonstrated their efficiency using different parameters. Our claim is that, given the increasing popularity of Zernike polynomials among the eye care community, this may be an interesting choice to add complementing value and precision to existing methods. By using a simple and well-documented corneal surface representation scheme, which relies on corneal elevation information, one can generate simple NN input parameters that are independent of curvature definition and that are also efficient. We have used the Matlab Neural Network Toolbox (MathWorks, Natick, MA) to implement a three-layer feed-forward NN with 15 inputs and 5 outputs. A database from an EyeSys System 2000 (EyeSys Vision, Houston, TX) videokeratograph installed at the Escola Paulista de Medicina-Sao Paulo was used. This database contained an unknown number of corneal types. From this database, two specialists selected 80 corneas that could be clearly classified into five distinct categories: (1) normal, (2) with-the-rule astigmatism, (3) against-the-rule astigmatism, (4) keratoconus, and (5) post-laser-assisted in situ keratomileusis. The corneal height (SAG) information of the 80 data files was fit with the first 15 Vision Science and it Applications (VSIA) standard Zernike coefficients, which were individually used to feed the 15 neurons of the input layer. The five output neurons were associated with the five typical corneal shapes. A group of 40 cases was randomly selected from the larger group of 80 corneas and used as the training set. The NN responses were statistically analyzed in terms of sensitivity [true positive/(true positive + false negative)], specificity [true negative/(true negative + false positive)], and precision [(true positive + true

  12. Estimation of dew point temperature using neuro-fuzzy and neural network techniques

    Science.gov (United States)

    Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

    2013-11-01

    This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.

  13. Dynamic Regulatory Network Reconstruction for Alzheimer’s Disease Based on Matrix Decomposition Techniques

    Directory of Open Access Journals (Sweden)

    Wei Kong

    2014-01-01

    Full Text Available Alzheimer’s disease (AD is the most common form of dementia and leads to irreversible neurodegenerative damage of the brain. Finding the dynamic responses of genes, signaling proteins, transcription factor (TF activities, and regulatory networks of the progressively deteriorative progress of AD would represent a significant advance in discovering the pathogenesis of AD. However, the high throughput technologies of measuring TF activities are not yet available on a genome-wide scale. In this study, based on DNA microarray gene expression data and a priori information of TFs, network component analysis (NCA algorithm is applied to determining the TF activities and regulatory influences on TGs of incipient, moderate, and severe AD. Based on that, the dynamical gene regulatory networks of the deteriorative courses of AD were reconstructed. To select significant genes which are differentially expressed in different courses of AD, independent component analysis (ICA, which is better than the traditional clustering methods and can successfully group one gene in different meaningful biological processes, was used. The molecular biological analysis showed that the changes of TF activities and interactions of signaling proteins in mitosis, cell cycle, immune response, and inflammation play an important role in the deterioration of AD.

  14. WDM Optical Access Network for Full-Duplex and Reconfigurable Capacity Assignment Based on PolMUX Technique

    Directory of Open Access Journals (Sweden)

    Jose Mora

    2014-12-01

    Full Text Available We present a novel bidirectional WDM-based optical access network featuring reconfigurable capacity assignment. The architecture relies on the PolMUX technique allowing a compact, flexible, and bandwidth-efficient router in addition to source-free ONUs and color-less ONUs for cost/complexity minimization. Moreover, the centralized architecture contemplates remote management and control of polarization. High-quality transmission of digital signals is demonstrated through different routing scenarios where all channels are dynamically assigned in both downlink and uplink directions.

  15. A Partnership Training Program in Breast Cancer Diagnosis: Concept Development of the Next Generation Diagnostic Breast Imaging Using Digital Image Library and Networking Techniques

    National Research Council Canada - National Science Library

    Chouikha, Mohamed F

    2004-01-01

    ...); and Georgetown University (Image Science and Information Systems, ISIS). In this partnership training program, we will train faculty and students in breast cancer imaging, digital image database library techniques and network communication strategy...

  16. Mobility Based Key Management Technique for Multicast Security in Mobile Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    B. Madhusudhanan

    2015-01-01

    Full Text Available In MANET multicasting, forward and backward secrecy result in increased packet drop rate owing to mobility. Frequent rekeying causes large message overhead which increases energy consumption and end-to-end delay. Particularly, the prevailing group key management techniques cause frequent mobility and disconnections. So there is a need to design a multicast key management technique to overcome these problems. In this paper, we propose the mobility based key management technique for multicast security in MANET. Initially, the nodes are categorized according to their stability index which is estimated based on the link availability and mobility. A multicast tree is constructed such that for every weak node, there is a strong parent node. A session key-based encryption technique is utilized to transmit a multicast data. The rekeying process is performed periodically by the initiator node. The rekeying interval is fixed depending on the node category so that this technique greatly minimizes the rekeying overhead. By simulation results, we show that our proposed approach reduces the packet drop rate and improves the data confidentiality.

  17. Quantitative comparison of performance analysis techniques for modular and generic network-on-chip

    Directory of Open Access Journals (Sweden)

    M. C. Neuenhahn

    2009-05-01

    Full Text Available NoC-specific parameters feature a huge impact on performance and implementation costs of NoC. Hence, performance and cost evaluation of these parameter-dependent NoC is crucial in different design-stages but the requirements on performance analysis differ from stage to stage. In an early design-stage an analysis technique featuring reduced complexity and limited accuracy can be applied, whereas in subsequent design-stages more accurate techniques are required.

    In this work several performance analysis techniques at different levels of abstraction are presented and quantitatively compared. These techniques include a static performance analysis using timing-models, a Colored Petri Net-based approach, VHDL- and SystemC-based simulators and an FPGA-based emulator. Conducting NoC-experiments with NoC-sizes from 9 to 36 functional units and various traffic patterns, characteristics of these experiments concerning accuracy, complexity and effort are derived.

    The performance analysis techniques discussed here are quantitatively evaluated and finally assigned to the appropriate design-stages in an automated NoC-design-flow.

  18. Metals pollution tracing in the sewerage network using the diffusive gradients in thin films technique.

    Science.gov (United States)

    Thomas, P

    2009-01-01

    Diffusive Gradients in Thin-films (DGT) is a quantitative, passive monitoring technique that can be used to measure concentrations of trace species in situ in solutions. Its potential for tracing metals pollution in the sewer system has been investigated by placing the DGT devices into sewage pumping stations and into manholes, to measure the concentration of certain metals in the catchment of a sewage treatment works with a known metals problem. In addition the methodology and procedure of using the DGT technique in sewers was investigated. Parameters such as temperature and pH were measured to ensure they were within the limits required by the DGT devices, and the optimum deployment time was examined. It was found that although the results given by the DGT technique could not be considered to be fully quantitative, they could be used to identify locations that were showing an excess concentration of metals, and hence trace pollution back to its source. The DGT technique is 'user friendly' and requires no complicated equipment for deployment or collection, and minimal training for use. It is thought that this is the first time that the DGT technique has been used in situ in sewers for metals pollution tracing.

  19. A comprehensive performance analysis of EEMD-BLMS and DWT-NN hybrid algorithms for ECG denoising

    DEFF Research Database (Denmark)

    Kærgaard, Kevin; Jensen, Søren Hjøllund; Puthusserypady, Sadasivan

    2016-01-01

    Electrocardiogram (ECG) is a widely used non-invasive method to study the rhythmic activity of theheart. These signals, however, are often obscured by artifacts/noises from various sources and mini-mization of these artifacts is of paramount importance for detecting anomalies. This paper presents...... athorough analysis of the performance of two hybrid signal processing schemes ((i) Ensemble EmpiricalMode Decomposition (EEMD) based method in conjunction with the Block Least Mean Square (BLMS)adaptive algorithm (EEMD-BLMS), and (ii) Discrete Wavelet Transform (DWT) combined with the Neu-ral Network (NN...

  20. Proposal of a congestion control technique in LAN networks using an econometric model ARIMA

    Directory of Open Access Journals (Sweden)

    Joaquín F Sánchez

    2017-01-01

    Full Text Available Hasty software development can produce immediate implementations with source code unnecessarily complex and hardly readable. These small kinds of software decay generate a technical debt that could be big enough to seriously affect future maintenance activities. This work presents an analysis technique for identifying architectural technical debt related to non-uniformity of naming patterns; the technique is based on term frequency over package hierarchies. The proposal has been evaluated on projects of two popular organizations, Apache and Eclipse. The results have shown that most of the projects have frequent occurrences of the proposed naming patterns, and using a graph model and aggregated data could enable the elaboration of simple queries for debt identification. The technique has features that favor its applicability on emergent architectures and agile software development.

  1. Hybrid Recurrent Laguerre-Orthogonal-Polynomial NN Control System Applied in V-Belt Continuously Variable Transmission System Using Particle Swarm Optimization

    Directory of Open Access Journals (Sweden)

    Chih-Hong Lin

    2015-01-01

    Full Text Available Because the V-belt continuously variable transmission (CVT system driven by permanent magnet synchronous motor (PMSM has much unknown nonlinear and time-varying characteristics, the better control performance design for the linear control design is a time consuming procedure. In order to overcome difficulties for design of the linear controllers, the hybrid recurrent Laguerre-orthogonal-polynomial neural network (NN control system which has online learning ability to respond to the system’s nonlinear and time-varying behaviors is proposed to control PMSM servo-driven V-belt CVT system under the occurrence of the lumped nonlinear load disturbances. The hybrid recurrent Laguerre-orthogonal-polynomial NN control system consists of an inspector control, a recurrent Laguerre-orthogonal-polynomial NN control with adaptive law, and a recouped control with estimated law. Moreover, the adaptive law of online parameters in the recurrent Laguerre-orthogonal-polynomial NN is derived using the Lyapunov stability theorem. Furthermore, the optimal learning rate of the parameters by means of modified particle swarm optimization (PSO is proposed to achieve fast convergence. Finally, to show the effectiveness of the proposed control scheme, comparative studies are demonstrated by experimental results.

  2. Acupuncture-Related Techniques for Psoriasis: A Systematic Review with Pairwise and Network Meta-Analyses of Randomized Controlled Trials.

    Science.gov (United States)

    Yeh, Mei-Ling; Ko, Shu-Hua; Wang, Mei-Hua; Chi, Ching-Chi; Chung, Yu-Chu

    2017-12-01

    There has be a large body of evidence on the pharmacological treatments for psoriasis, but whether nonpharmacological interventions are effective in managing psoriasis remains largely unclear. This systematic review conducted pairwise and network meta-analyses to determine the effects of acupuncture-related techniques on acupoint stimulation for the treatment of psoriasis and to determine the order of effectiveness of these remedies. This study searched the following databases from inception to March 15, 2016: Medline, PubMed, Cochrane Central Register of Controlled Trials, EBSCO (including Academic Search Premier, American Doctoral Dissertations, and CINAHL), Airiti Library, and China National Knowledge Infrastructure. Randomized controlled trials (RCTs) on the effects of acupuncture-related techniques on acupoint stimulation as intervention for psoriasis were independently reviewed by two researchers. A total of 13 RCTs with 1,060 participants were included. The methodological quality of included studies was not rigorous. Acupoint stimulation, compared with nonacupoint stimulation, had a significant treatment for psoriasis. However, the most common adverse events were thirst and dry mouth. Subgroup analysis was further done to confirm that the short-term treatment effect was superior to that of the long-term effect in treating psoriasis. Network meta-analysis identified acupressure or acupoint catgut embedding, compared with medication, and had a significant effect for improving psoriasis. It was noted that acupressure was the most effective treatment. Acupuncture-related techniques could be considered as an alternative or adjuvant therapy for psoriasis in short term, especially of acupressure and acupoint catgut embedding. This study recommends further well-designed, methodologically rigorous, and more head-to-head randomized trials to explore the effects of acupuncture-related techniques for treating psoriasis.

  3. DSP implementation of a PV system with GA-MLP-NN based MPPT controller supplying BLDC motor drive

    International Nuclear Information System (INIS)

    Akkaya, R.; Kulaksiz, A.A.; Aydogdu, O.

    2007-01-01

    This paper presents a brushless dc motor drive for heating, ventilating and air conditioning fans, which is utilized as the load of a photovoltaic system with a maximum power point tracking (MPPT) controller. The MPPT controller is based on a genetic assisted, multi-layer perceptron neural network (GA-MLP-NN) structure and includes a DC-DC boost converter. Genetic assistance in the neural network is used to optimize the size of the hidden layer. Also, for training the network, a genetic assisted, Levenberg-Marquardt (GA-LM) algorithm is utilized. The off line GA-MLP-NN, trained by this hybrid algorithm, is utilized for online estimation of the voltage and current values in the maximum power point. A brushless dc (BLDC) motor drive system that incorporates a motor controller with proportional integral (PI) speed control loop is successfully implemented to operate the fans. The digital signal processor (DSP) based unit provides rapid achievement of the MPPT and current control of the BLDC motor drive. The performance results of the system are given, and experimental results are presented for a laboratory prototype of 120 W

  4. Precise strength of the πNN coupling constant

    International Nuclear Information System (INIS)

    Ericson, T.E.O.; Loiseau, B.; Rahm, J.; Blomgren, J.; Olsson, N.; Thomas, A. W.

    1999-01-01

    We report here a preliminary value for the πNN coupling constant deduced from the Goldberger-Miyazawa-Oehme sum rule for forward πN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives g 2 c =13.99(24)

  5. Derivation of the parity-violating NN potential

    International Nuclear Information System (INIS)

    Zenkin, S.

    1981-01-01

    The parity-violating NN potential due to vector-meson exchange is considered in the framework of current algebra. A method to calculate the effective P-odd NNV vertex, free of uncertainties due to the existence of the Schwinger and seagull terms, is presented. The final result coincides with the result of the factorization approximation and may be considered as a justification of the approximation

  6. Can the Σ-nn system be bound?

    International Nuclear Information System (INIS)

    Stadler, A.; Gibson, B.F.

    1994-01-01

    Motivated by the Σ-hypernuclear states reported in (K - ,π ± ) experiments, we have explored the possibility that there exists a particle-stable Σ - nn bound state. For the Juelich A hyperon-nucleon, realistic-force model, our calculations yield little reason to expect a positive-parity bound state or resonance in either the J=1/2 or the J=3/2 channels

  7. Direct determinations of the πNN coupling constants

    International Nuclear Information System (INIS)

    Ericson, T.E.O.; ); Loiseau, B.

    1998-01-01

    A novel extrapolation method has been used to deduce directly the charged πNN coupling constant from backward np differential scattering cross sections. The extracted value, g c 2 = 14.52(026)is higher than the indirectly deduced values obtained in nucleon-nucleon energy-dependent partial-wave analyses. Our preliminary direct value from a reanalysis of the GMO sum-rule points to an intermediate value of g c 2 about 13.97(30). (author)

  8. Precise strength of the $\\pi$NN coupling constant

    CERN Document Server

    Ericson, Torleif Eric Oskar; Rahm, J; Blomgren, J; Olsson, N; Thomas, A W

    1998-01-01

    We report here a preliminary value for the piNN coupling constant deduced from the GMO sumrule for forward piN scattering. As in our previous determination from np backward differential scattering cross sections we give a critical discussion of the analysis with careful attention not only to the statistical, but also to the systematic uncertainties. Our preliminary evaluation gives $g^2_c$(GMO) = 13.99(24).

  9. Measuring the Influence of Networks on Transaction Costs Using a Nonparametric Regression Technique

    DEFF Research Database (Denmark)

    Henningsen, Geraldine; Henningsen, Arne; Henning, Christian H.C.A.

    All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to non......-public information so that transaction costs are reduced.Many resources that are sacrificed for transaction costs are inputs that also enter the technical production process. As most production data do not distinguish between these two usages of inputs, high transaction costs result in reduced observed productivity...

  10. Measuring the influence of networks on transaction costs using a non-parametric regression technique

    DEFF Research Database (Denmark)

    Henningsen, Géraldine; Henningsen, Arne; Henning, Christian H.C.A.

    All business transactions as well as achieving innovations take up resources, subsumed under the concept of transaction costs. One of the major factors in transaction costs theory is information. Firm networks can catalyse the interpersonal information exchange and hence, increase the access to non......-public information so that transaction costs are reduced. Many resources that are sacrificed for transaction costs are inputs that also enter the technical production process. As most production data do not distinguish between these two usages of inputs, high transaction costs result in reduced observed productivity...

  11. Fuzzy-Based Adaptive Hybrid Burst Assembly Technique for Optical Burst Switched Networks

    Directory of Open Access Journals (Sweden)

    Abubakar Muhammad Umaru

    2014-01-01

    Full Text Available The optical burst switching (OBS paradigm is perceived as an intermediate switching technology for future all-optical networks. Burst assembly that is the first process in OBS is the focus of this paper. In this paper, an intelligent hybrid burst assembly algorithm that is based on fuzzy logic is proposed. The new algorithm is evaluated against the traditional hybrid burst assembly algorithm and the fuzzy adaptive threshold (FAT burst assembly algorithm via simulation. Simulation results show that the proposed algorithm outperforms the hybrid and the FAT algorithms in terms of burst end-to-end delay, packet end-to-end delay, and packet loss ratio.

  12. Optical transmission testing based on asynchronous sampling techniques: images analysis containing chromatic dispersion using convolutional neural network

    Science.gov (United States)

    Mrozek, T.; Perlicki, K.; Tajmajer, T.; Wasilewski, P.

    2017-08-01

    The article presents an image analysis method, obtained from an asynchronous delay tap sampling (ADTS) technique, which is used for simultaneous monitoring of various impairments occurring in the physical layer of the optical network. The ADTS method enables the visualization of the optical signal in the form of characteristics (so called phase portraits) that change their shape under the influence of impairments such as chromatic dispersion, polarization mode dispersion and ASE noise. Using this method, a simulation model was built with OptSim 4.0. After the simulation study, data were obtained in the form of images that were further analyzed using the convolutional neural network algorithm. The main goal of the study was to train a convolutional neural network to recognize the selected impairment (distortion); then to test its accuracy and estimate the impairment for the selected set of test images. The input data consisted of processed binary images in the form of two-dimensional matrices, with the position of the pixel. This article focuses only on the analysis of images containing chromatic dispersion.

  13. Toward Bulk Synchronous Parallel-Based Machine Learning Techniques for Anomaly Detection in High-Speed Big Data Networks

    Directory of Open Access Journals (Sweden)

    Kamran Siddique

    2017-09-01

    Full Text Available Anomaly detection systems, also known as intrusion detection systems (IDSs, continuously monitor network traffic aiming to identify malicious actions. Extensive research has been conducted to build efficient IDSs emphasizing two essential characteristics. The first is concerned with finding optimal feature selection, while another deals with employing robust classification schemes. However, the advent of big data concepts in anomaly detection domain and the appearance of sophisticated network attacks in the modern era require some fundamental methodological revisions to develop IDSs. Therefore, we first identify two more significant characteristics in addition to the ones mentioned above. These refer to the need for employing specialized big data processing frameworks and utilizing appropriate datasets for validating system’s performance, which is largely overlooked in existing studies. Afterwards, we set out to develop an anomaly detection system that comprehensively follows these four identified characteristics, i.e., the proposed system (i performs feature ranking and selection using information gain and automated branch-and-bound algorithms respectively; (ii employs logistic regression and extreme gradient boosting techniques for classification; (iii introduces bulk synchronous parallel processing to cater computational requirements of high-speed big data networks; and; (iv uses the Infromation Security Centre of Excellence, of the University of Brunswick real-time contemporary dataset for performance evaluation. We present experimental results that verify the efficacy of the proposed system.

  14. Pithy Review on Routing Protocols in Wireless Sensor Networks and Least Routing Time Opportunistic Technique in WSN

    Science.gov (United States)

    Salman Arafath, Mohammed; Rahman Khan, Khaleel Ur; Sunitha, K. V. N.

    2018-01-01

    Nowadays due to most of the telecommunication standard development organizations focusing on using device-to-device communication so that they can provide proximity-based services and add-on services on top of the available cellular infrastructure. An Oppnets and wireless sensor network play a prominent role here. Routing in these networks plays a significant role in fields such as traffic management, packet delivery etc. Routing is a prodigious research area with diverse unresolved issues. This paper firstly focuses on the importance of Opportunistic routing and its concept then focus is shifted to prime aspect i.e. on packet reception ratio which is one of the highest QoS Awareness parameters. This paper discusses the two important functions of routing in wireless sensor networks (WSN) namely route selection using least routing time algorithm (LRTA) and data forwarding using clustering technique. Finally, the simulation result reveals that LRTA performs relatively better than the existing system in terms of average packet reception ratio and connectivity.

  15. Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

    Science.gov (United States)

    Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

    2013-09-01

    In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

  16. An Experimental Technique for Structural Diagnostic Based on Laser Vibrometry and Neural Networks

    Directory of Open Access Journals (Sweden)

    Paolo Castellini

    2000-01-01

    Full Text Available In recent years damage detection techniques based on vibration data have been largely investigated with promising results for many applications. In particular, several attempts have been made to determine which kind of data should be extracted for damage monitoring.

  17. Information System Design Methodology Based on PERT/CPM Networking and Optimization Techniques.

    Science.gov (United States)

    Bose, Anindya

    The dissertation attempts to demonstrate that the program evaluation and review technique (PERT)/Critical Path Method (CPM) or some modified version thereof can be developed into an information system design methodology. The methodology utilizes PERT/CPM which isolates the basic functional units of a system and sets them in a dynamic time/cost…

  18. Hydraulic Parameter Generation Technique Using a Discrete Fracture Network with Bedrock Heterogeneity in Korea

    Directory of Open Access Journals (Sweden)

    Jae-Yeol Cheong

    2017-12-01

    Full Text Available In instances of damage to engineered barriers containing nuclear waste material, surrounding bedrock is a natural barrier that retards radionuclide movement by way of adsorption and delay due to groundwater flow through highly tortuous fractured rock pathways. At the Gyeongju nuclear waste disposal site, groundwater mainly flows through granitic and sedimentary rock fractures. Therefore, to understand the nuclide migration path, it is necessary to understand discrete fracture networks based on heterogeneous fracture orientations, densities, and size characteristics. In this study, detailed heterogeneous fracture distribution, including the density and orientation of the fractures, was considered for a region that has undergone long periods of change from various geological activities at and around the Gyeongju site. A site-scale discrete fracture network (DFN model was constructed taking into account: (i regional fracture heterogeneity constrained by a multiple linear regression analysis of fracture intensity on faults and electrical resistivity; and (ii the connectivity of conductive fractures having fracture hydraulic parameters, using transient flow simulation. Geometric and hydraulic heterogeneity of the DFN was upscaled into equivalent porous media for flow and transport simulation for a large-scale model.

  19. Using Web 2.0 Techniques in NASA's Ares Engineering Operations Network (AEON) Environment - First Impressions

    Science.gov (United States)

    Scott, David W.

    2010-01-01

    The Mission Operations Laboratory (MOL) at Marshall Space Flight Center (MSFC) is responsible for Engineering Support capability for NASA s Ares rocket development and operations. In pursuit of this, MOL is building the Ares Engineering and Operations Network (AEON), a web-based portal to support and simplify two critical activities: Access and analyze Ares manufacturing, test, and flight performance data, with access to Shuttle data for comparison Establish and maintain collaborative communities within the Ares teams/subteams and with other projects, e.g., Space Shuttle, International Space Station (ISS). AEON seeks to provide a seamless interface to a) locally developed engineering applications and b) a Commercial-Off-The-Shelf (COTS) collaborative environment that includes Web 2.0 capabilities, e.g., blogging, wikis, and social networking. This paper discusses how Web 2.0 might be applied to the typically conservative engineering support arena, based on feedback from Integration, Verification, and Validation (IV&V) testing and on searching for their use in similar environments.

  20. Inductive coupling between overhead power lines and nearby metallic pipelines. A neural network approach

    Directory of Open Access Journals (Sweden)

    Levente Czumbil

    2015-12-01

    Full Text Available The current paper presents an artificial intelligence based technique applied in the investigation of electromagnetic interference problems between high voltage power lines (HVPL and nearby underground metallic pipelines (MP. An artificial neural network (NN solution has been implemented by the authors to evaluate the inductive coupling between HVPL and MP for different constructive geometries of an electromagnetic interference problem considering a multi-layer soil structure. Obtained results are compared to solutions provided by a finite element method (FEM based analysis and considered as reference. The advantage of the proposed method yields in a simplified computation model compared to FEM, and implicitly a lower computational time.

  1. Stability and Hopf bifurcation in a simplified BAM neural network with two time delays.

    Science.gov (United States)

    Cao, Jinde; Xiao, Min

    2007-03-01

    Various local periodic solutions may represent different classes of storage patterns or memory patterns, and arise from the different equilibrium points of neural networks (NNs) by applying Hopf bifurcation technique. In this paper, a bidirectional associative memory NN with four neurons and multiple delays is considered. By applying the normal form theory and the center manifold theorem, analysis of its linear stability and Hopf bifurcation is performed. An algorithm is worked out for determining the direction and stability of the bifurcated periodic solutions. Numerical simulation results supporting the theoretical analysis are also given.

  2. Meson-exchange N-N potential

    International Nuclear Information System (INIS)

    Nutt, W.T.

    1976-01-01

    A meson-theoretic model of the intermediate range nucleon-nucleon potential is presented with emphasis placed on the two-pion exchange contribution. The Bethe-Salpeter equation is reduced, by the Blankenbecler-Sugar technique, to a Lippmann-Schwinger equation, from which an approximate nonlocal, energy-dependent potential is obtained. The nucleon-antinucleon pair contribution, which plagues meson-theoretical two-pion calculations, is suppressed by the complex poles of the one-nucleon Green's function. The importance of the retention of the explicit energy dependence of the potential is demonstrated by calculating the off-shell scattering matrices. The potential is presented in a linearized (in energy) form with the core region adjusted to produce a fit to low energy data

  3. Land Cover Change Detection using Neural Network and Grid Cells Techniques

    Science.gov (United States)

    Bagan, H.; Li, Z.; Tangud, T.; Yamagata, Y.

    2017-12-01

    In recent years, many advanced neural network methods have been applied in land cover classification, each of which has both strengths and limitations. In which, the self-organizing map (SOM) neural network method have been used to solve remote sensing data classification problems and have shown potential for efficient classification of remote sensing data. In SOM, both the distribution and the topology of features of the input layer are identified by using an unsupervised, competitive, neighborhood learning method. The high-dimensional data are then projected onto a low-dimensional map (competitive layer), usually as a two-dimensional map. The neurons (nodes) in the competitive layer are arranged by topological order in the input space. Spatio-temporal analyses of land cover change based on grid cells have demonstrated that gridded data are useful for obtaining spatial and temporal information about areas that are smaller than municipal scale and are uniform in size. Analysis based on grid cells has many advantages: grid cells all have the same size allowing for easy comparison; grids integrate easily with other scientific data; grids are stable over time and thus facilitate the modelling and analysis of very large multivariate spatial data sets. This study chose time-series MODIS and Landsat images as data sources, applied SOM neural network method to identify the land utilization in Inner Mongolia Autonomous Region of China. Then the results were integrated into grid cell to get the dynamic change maps. Land cover change using MODIS data in Inner Mongolia showed that urban area increased more than fivefold in recent 15 years, along with the growth of mining area. In terms of geographical distribution, the most obvious place of urban expansion is Ordos in southwest Inner Mongolia. The results using Landsat images from 1986 to 2014 in northeastern part of the Inner Mongolia show degradation in grassland from 1986 to 2014. Grid-cell-based spatial correlation

  4. Multimodal imaging of vascular network and blood microcirculation by optical diagnostic techniques

    International Nuclear Information System (INIS)

    Kuznetsov, Yu L; Kalchenko, V V; Meglinski, I V

    2011-01-01

    We present a multimodal optical diagnostic approach for simultaneous non-invasive in vivo imaging of blood and lymphatic microvessels, utilising a combined use of fluorescence intravital microscopy and a method of dynamic light scattering. This approach makes it possible to renounce the use of fluorescent markers for visualisation of blood vessels and, therefore, significantly (tenfold) reduce the toxicity of the technique and minimise side effects caused by the use of contrast fluorescent markers. We demonstrate that along with the ability to obtain images of lymph and blood microvessels with a high spatial resolution, current multimodal approach allows one to observe in real time permeability of blood vessels. This technique appears to be promising in physiology studies of blood vessels, and especially in the study of peripheral cardiovascular system in vivo. (optical technologies in biophysics and medicine)

  5. A new jamming technique for secrecy in multi-antenna wireless networks

    KAUST Repository

    Bakr, Omar

    2010-06-01

    We consider the problem of secure wireless communication in the presence of an eavesdropper when the transmitter has multiple antennas, using a variation of the recently proposed artificial noise technique. Under this technique, the transmitter sends a pseudo-noise jamming signal to selectively degrade the link to the eavesdropper without affecting the desired receiver. The previous work in the literature focuses on ideal Gaussian signaling for both the desired signal and the noise signal. The main contribution of this paper is to show that the Gaussian signaling model has important limitations and propose an alternative "induced fading" jamming technique that takes some of these limitations into account. Specifically we show that under the Gaussian noise scheme, the eavesdropper is able to recover the desired signal with very low bit error rates when the transmitter is constrained to use constant envelope signaling. Furthermore, we show that an eavesdropper with multiple antennas is able to use simple, blind constant-envelope algorithms to completely remove the Gaussian artificial noise signal and thus defeat the secrecy scheme. We propose an alternative scheme that induces artificial fading in the channel to the eavesdropper, and show that it outperforms the Gaussian noise scheme in the sense of causing higher bit error rates at the eavesdropper and is also more resistant to constant modulus-type algorithms. © 2010 IEEE.

  6. Radiation dose rate map interpolation in nuclear plants using neural networks and virtual reality techniques

    Energy Technology Data Exchange (ETDEWEB)

    Mol, Antonio Carlos A., E-mail: mol@ien.gov.br [Comissao Nacional de Energia Nuclear, Instituto de Engenharia Nuclear Rua Helio de Almeida, 75, Ilha do Fundao, P.O. Box 68550, 21941-906 Rio de Janeiro, RJ (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores/CNPq (Brazil); Pereira, Claudio Marcio N.A., E-mail: cmnap@ien.gov.br [Comissao Nacional de Energia Nuclear, Instituto de Engenharia Nuclear Rua Helio de Almeida, 75, Ilha do Fundao, P.O. Box 68550, 21941-906 Rio de Janeiro, RJ (Brazil); Instituto Nacional de Ciencia e Tecnologia de Reatores Nucleares Inovadores/CNPq (Brazil); Freitas, Victor Goncalves G. [Universidade Federal do Rio de Janeiro, Programa de Engenharia Nuclear, Rio de Janeiro, RJ (Brazil); Jorge, Carlos Alexandre F., E-mail: calexandre@ien.gov.br [Comissao Nacional de Energia Nuclear, Instituto de Engenharia Nuclear Rua Helio de Almeida, 75, Ilha do Fundao, P.O. Box 68550, 21941-906 Rio de Janeiro, RJ (Brazil)

    2011-02-15

    This paper reports the most recent development results of a simulation tool for assessment of radiation dose exposition by nuclear plant's personnel, using artificial intelligence and virtual reality technologies. The main purpose of this tool is to support training of nuclear plants' personnel, to optimize working tasks for minimisation of received dose. A finer grid of measurement points was considered within the nuclear plant's room, for different power operating conditions. Further, an intelligent system was developed, based on neural networks, to interpolate dose rate values among measured points. The intelligent dose prediction system is thus able to improve the simulation of dose received by personnel. This work describes the improvements implemented in this simulation tool.

  7. Radiation dose rate map interpolation in nuclear plants using neural networks and virtual reality techniques

    International Nuclear Information System (INIS)

    Mol, Antonio Carlos A.; Pereira, Claudio Marcio N.A.; Freitas, Victor Goncalves G.; Jorge, Carlos Alexandre F.

    2011-01-01

    This paper reports the most recent development results of a simulation tool for assessment of radiation dose exposition by nuclear plant's personnel, using artificial intelligence and virtual reality technologies. The main purpose of this tool is to support training of nuclear plants' personnel, to optimize working tasks for minimisation of received dose. A finer grid of measurement points was considered within the nuclear plant's room, for different power operating conditions. Further, an intelligent system was developed, based on neural networks, to interpolate dose rate values among measured points. The intelligent dose prediction system is thus able to improve the simulation of dose received by personnel. This work describes the improvements implemented in this simulation tool.

  8. A NEW RECOGNITION TECHNIQUE NAMED SOMP BASED ON PALMPRINT USING NEURAL NETWORK BASED SELF ORGANIZING MAPS

    Directory of Open Access Journals (Sweden)

    A. S. Raja

    2012-08-01

    Full Text Available The word biometrics refers to the use of physiological or biological characteristics of human to recognize and verify the identity of an individual. Palmprint has become a new class of human biometrics for passive identification with uniqueness and stability. This is considered to be reliable due to the lack of expressions and the lesser effect of aging. In this manuscript a new Palmprint based biometric system based on neural networks self organizing maps (SOM is presented. The method is named as SOMP. The paper shows that the proposed SOMP method improves the performance and robustness of recognition. The proposed method is applied to a variety of datasets and the results are shown.

  9. A discrimination technique for extensive air showers based on multiscale, lacunarity and neural network analysis

    International Nuclear Information System (INIS)

    Pagliaro, Antonio; D'Ali Staiti, G.; D'Anna, F.

    2011-01-01

    We present a new method for the identification of extensive air showers initiated by different primaries. The method uses the multiscale concept and is based on the analysis of multifractal behaviour and lacunarity of secondary particle distributions together with a properly designed and trained artificial neural network. In the present work the method is discussed and applied to a set of fully simulated vertical showers, in the experimental framework of ARGO-YBJ, to obtain hadron to gamma primary separation. We show that the presented approach gives very good results, leading, in the 1-10 TeV energy range, to a clear improvement of the discrimination power with respect to the existing figures for extended shower detectors.

  10. The nuclear fuel rod character recognition system based on neural network technique

    International Nuclear Information System (INIS)

    Kim, Woong-Ki; Park, Soon-Yong; Lee, Yong-Bum; Kim, Seung-Ho; Lee, Jong-Min; Chien, Sung-Il.

    1994-01-01

    The nuclear fuel rods should be discriminated and managed systematically by numeric characters which are printed at the end part of each rod in the process of producing fuel assembly. The characters are used to examine manufacturing process of the fuel rods in the inspection process of irradiated fuel rod. Therefore automatic character recognition is one of the most important technologies to establish automatic manufacturing process of fuel assembly. In the developed character recognition system, mesh feature set extracted from each character written in the fuel rod is employed to train a neural network based on back-propagation algorithm as a classifier for character recognition system. Performance evaluation has been achieved on a test set which is not included in a training character set. (author)

  11. Multimodality Inferring of Human Cognitive States Based on Integration of Neuro-Fuzzy Network and Information Fusion Techniques

    Directory of Open Access Journals (Sweden)

    P. Bhattacharya

    2007-11-01

    Full Text Available To achieve an effective and safe operation on the machine system where the human interacts with the machine mutually, there is a need for the machine to understand the human state, especially cognitive state, when the human's operation task demands an intensive cognitive activity. Due to a well-known fact with the human being, a highly uncertain cognitive state and behavior as well as expressions or cues, the recent trend to infer the human state is to consider multimodality features of the human operator. In this paper, we present a method for multimodality inferring of human cognitive states by integrating neuro-fuzzy network and information fusion techniques. To demonstrate the effectiveness of this method, we take the driver fatigue detection as an example. The proposed method has, in particular, the following new features. First, human expressions are classified into four categories: (i casual or contextual feature, (ii contact feature, (iii contactless feature, and (iv performance feature. Second, the fuzzy neural network technique, in particular Takagi-Sugeno-Kang (TSK model, is employed to cope with uncertain behaviors. Third, the sensor fusion technique, in particular ordered weighted aggregation (OWA, is integrated with the TSK model in such a way that cues are taken as inputs to the TSK model, and then the outputs of the TSK are fused by the OWA which gives outputs corresponding to particular cognitive states under interest (e.g., fatigue. We call this method TSK-OWA. Validation of the TSK-OWA, performed in the Northeastern University vehicle drive simulator, has shown that the proposed method is promising to be a general tool for human cognitive state inferring and a special tool for the driver fatigue detection.

  12. Artificial Astrocytes Improve Neural Network Performance

    Science.gov (United States)

    Porto-Pazos, Ana B.; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-01-01

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function. PMID:21526157

  13. Artificial astrocytes improve neural network performance.

    Directory of Open Access Journals (Sweden)

    Ana B Porto-Pazos

    Full Text Available Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN and artificial neuron-glia networks (NGN to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  14. Artificial astrocytes improve neural network performance.

    Science.gov (United States)

    Porto-Pazos, Ana B; Veiguela, Noha; Mesejo, Pablo; Navarrete, Marta; Alvarellos, Alberto; Ibáñez, Oscar; Pazos, Alejandro; Araque, Alfonso

    2011-04-19

    Compelling evidence indicates the existence of bidirectional communication between astrocytes and neurons. Astrocytes, a type of glial cells classically considered to be passive supportive cells, have been recently demonstrated to be actively involved in the processing and regulation of synaptic information, suggesting that brain function arises from the activity of neuron-glia networks. However, the actual impact of astrocytes in neural network function is largely unknown and its application in artificial intelligence remains untested. We have investigated the consequences of including artificial astrocytes, which present the biologically defined properties involved in astrocyte-neuron communication, on artificial neural network performance. Using connectionist systems and evolutionary algorithms, we have compared the performance of artificial neural networks (NN) and artificial neuron-glia networks (NGN) to solve classification problems. We show that the degree of success of NGN is superior to NN. Analysis of performances of NN with different number of neurons or different architectures indicate that the effects of NGN cannot be accounted for an increased number of network elements, but rather they are specifically due to astrocytes. Furthermore, the relative efficacy of NGN vs. NN increases as the complexity of the network increases. These results indicate that artificial astrocytes improve neural network performance, and established the concept of Artificial Neuron-Glia Networks, which represents a novel concept in Artificial Intelligence with implications in computational science as well as in the understanding of brain function.

  15. Comparison of Pattern Recognition, Artificial Neural Network and Pedotransfer Functions for Estimation of Soil Water Parameters

    Directory of Open Access Journals (Sweden)

    Amir LAKZIAN

    2010-09-01

    Full Text Available This paper presents the comparison of three different approaches to estimate soil water content at defined values of soil water potential based on selected parameters of soil solid phase. Forty different sampling locations in northeast of Iran were selected and undisturbed samples were taken to measure the water content at field capacity (FC, -33 kPa, and permanent wilting point (PWP, -1500 kPa. At each location solid particle of each sample including the percentage of sand, silt and clay were measured. Organic carbon percentage and soil texture were also determined for each soil sample at each location. Three different techniques including pattern recognition approach (k nearest neighbour, k-NN, Artificial Neural Network (ANN and pedotransfer functions (PTF were used to predict the soil water at each sampling location. Mean square deviation (MSD and its components, index of agreement (d, root mean square difference (RMSD and normalized RMSD (RMSDr were used to evaluate the performance of all the three approaches. Our results showed that k-NN and PTF performed better than ANN in prediction of water content at both FC and PWP matric potential. Various statistics criteria for simulation performance also indicated that between kNN and PTF, the former, predicted water content at PWP more accurate than PTF, however both approach showed a similar accuracy to predict water content at FC.

  16. A new approach to the analysis of alpha spectra based on neural network techniques

    Energy Technology Data Exchange (ETDEWEB)

    Baeza, A.; Miranda, J. [LARUEX, Environmental Radioactivity Laboratory, Dept. Applied Physics, Faculty of Veterinary Science, University of Extremadura, Avda. Universidad s/n, 10003 Caceres (Spain); Guillen, J., E-mail: fguillen@unex.es [LARUEX, Environmental Radioactivity Laboratory, Dept. Applied Physics, Faculty of Veterinary Science, University of Extremadura, Avda. Universidad s/n, 10003 Caceres (Spain); Corbacho, J.A. [LARUEX, Environmental Radioactivity Laboratory, Dept. Applied Physics, Faculty of Veterinary Science, University of Extremadura, Avda. Universidad s/n, 10003 Caceres (Spain); Perez, R. [Dept. Technology of Computers and Communications, Polytechnics School, University of Extremadura, Avda. Universidad s/n, 10003 Caceres (Spain)

    2011-10-01

    The analysis of alpha spectra requires good radiochemical procedures in order to obtain well differentiated alpha peaks in the spectrum, and the easiest way to analyze them is by directly summing the counts obtained in the Regions of Interest (ROIs). However, the low-energy tails of the alpha peaks frequently make this simple approach unworkable because some peaks partially overlap. Many fitting procedures have been proposed to solve this problem, most of them based on semi-empirical mathematical functions that emulate the shape of a theoretical alpha peak. The main drawback of these methods is that the great number of fitting parameters used means that their physical meaning is obscure or completely lacking. We propose another approach-the application of an artificial neural network. Instead of fitting the experimental data to a mathematical function, the fit is carried out by an artificial neural network (ANN) that has previously been trained to model the shape of an alpha peak using as training patterns several polonium spectra obtained from actual samples analyzed in our laboratory. In this sense, the ANN is able to learn the shape of an actual alpha peak. We have designed such an ANN as a feed-forward multi-layer perceptron with supervised training based on a back-propagation algorithm. The fitting procedure is based on the experimental observables that are characteristic of alpha peaks-the number of counts of the maximum and several peak widths at different heights. Polonium isotope spectra were selected because the alpha peaks corresponding to {sup 208}Po, {sup 209}Po, and {sup 210}Po are monoenergetic and well separated. The uncertainties introduced by this fitting procedure were less than the counting uncertainties. This new approach was applied to the problem of resolving overlapping peaks. Firstly, a theoretical study was carried out by artificially overlapping alpha peaks from actual samples in order to test the ability of the ANN to resolve each peak

  17. A new approach to the analysis of alpha spectra based on neural network techniques

    International Nuclear Information System (INIS)

    Baeza, A.; Miranda, J.; Guillen, J.; Corbacho, J.A.; Perez, R.

    2011-01-01

    The analysis of alpha spectra requires good radiochemical procedures in order to obtain well differentiated alpha peaks in the spectrum, and the easiest way to analyze them is by directly summing the counts obtained in the Regions of Interest (ROIs). However, the low-energy tails of the alpha peaks frequently make this simple approach unworkable because some peaks partially overlap. Many fitting procedures have been proposed to solve this problem, most of them based on semi-empirical mathematical functions that emulate the shape of a theoretical alpha peak. The main drawback of these methods is that the great number of fitting parameters used means that their physical meaning is obscure or completely lacking. We propose another approach-the application of an artificial neural network. Instead of fitting the experimental data to a mathematical function, the fit is carried out by an artificial neural network (ANN) that has previously been trained to model the shape of an alpha peak using as training patterns several polonium spectra obtained from actual samples analyzed in our laboratory. In this sense, the ANN is able to learn the shape of an actual alpha peak. We have designed such an ANN as a feed-forward multi-layer perceptron with supervised training based on a back-propagation algorithm. The fitting procedure is based on the experimental observables that are characteristic of alpha peaks-the number of counts of the maximum and several peak widths at different heights. Polonium isotope spectra were selected because the alpha peaks corresponding to 208 Po, 209 Po, and 210 Po are monoenergetic and well separated. The uncertainties introduced by this fitting procedure were less than the counting uncertainties. This new approach was applied to the problem of resolving overlapping peaks. Firstly, a theoretical study was carried out by artificially overlapping alpha peaks from actual samples in order to test the ability of the ANN to resolve each peak. Then, the ANN

  18. A new approach to the analysis of alpha spectra based on neural network techniques

    Science.gov (United States)

    Baeza, A.; Miranda, J.; Guillén, J.; Corbacho, J. A.; Pérez, R.

    2011-10-01

    The analysis of alpha spectra requires good radiochemical procedures in order to obtain well differentiated alpha peaks in the spectrum, and the easiest way to analyze them is by directly summing the counts obtained in the Regions of Interest (ROIs). However, the low-energy tails of the alpha peaks frequently make this simple approach unworkable because some peaks partially overlap. Many fitting procedures have been proposed to solve this problem, most of them based on semi-empirical mathematical functions that emulate the shape of a theoretical alpha peak. The main drawback of these methods is that the great number of fitting parameters used means that their physical meaning is obscure or completely lacking. We propose another approach—the application of an artificial neural network. Instead of fitting the experimental data to a mathematical function, the fit is carried out by an artificial neural network (ANN) that has previously been trained to model the shape of an alpha peak using as training patterns several polonium spectra obtained from actual samples analyzed in our laboratory. In this sense, the ANN is able to learn the shape of an actual alpha peak. We have designed such an ANN as a feed-forward multi-layer perceptron with supervised training based on a back-propagation algorithm. The fitting procedure is based on the experimental observables that are characteristic of alpha peaks—the number of counts of the maximum and several peak widths at different heights. Polonium isotope spectra were selected because the alpha peaks corresponding to 208Po, 209Po, and 210Po are monoenergetic and well separated. The uncertainties introduced by this fitting procedure were less than the counting uncertainties. This new approach was applied to the problem of resolving overlapping peaks. Firstly, a theoretical study was carried out by artificially overlapping alpha peaks from actual samples in order to test the ability of the ANN to resolve each peak. Then, the ANN

  19. What quark theory gives for the potential description of the parity violation in NN interactions

    International Nuclear Information System (INIS)

    Dubovik, V.M.; Zenkin, S.V.

    1982-01-01

    The constants of the parity violating (PV) πNN, rhoNN and #betta#NN interactions are calculated in the framework of quark picture based on the standard SU(2)sub(L)xU(1) electroweak model with account for the QCD corrections. The constants are close to the well-known ''best values'', which provide a successful fit to the low-energy PV experimental data

  20. Weak ωNN coupling in the non-linear chiral model

    International Nuclear Information System (INIS)

    Shmatikov, M.

    1988-01-01

    In the non-linear chiral model with the soliton solution stabilized by the ω-meson field the weak ωNN coupling constants are calculated. Applying the vector dominance model for the isoscalar current the constant of the isoscalar P-odd ωNN interaction h ω (0) =0 is obtained while the constant of the isovector (of the Lagrangian of the ωNN interaction proves to be h ω (1) ≅ 1.0x10 -7

  1. Deep attractive NN potential as a potential for the N(1440)-N system

    International Nuclear Information System (INIS)

    Glozman, L.Y.; Kukulin, V.I.; Pomerantsev, V.N.

    1992-01-01

    This work demonstrates that the model of a deep attractive NN potential (the Moscow NN potential) with a deep lying extra state may be interpreted as a potential in the N(1440)-N system. Under such an interpretation, the deep-lying level forbidden for the NN system describes the N(1440)-N component in deuteron. This conclusion is used to obtain the momentum distribution for the N(1440)-N component in deuteron

  2. A diversity compression and combining technique based on channel shortening for cooperative networks

    KAUST Repository

    Hussain, Syed Imtiaz

    2012-02-01

    The cooperative relaying process with multiple relays needs proper coordination among the communicating and the relaying nodes. This coordination and the required capabilities may not be available in some wireless systems where the nodes are equipped with very basic communication hardware. We consider a scenario where the source node transmits its signal to the destination through multiple relays in an uncoordinated fashion. The destination captures the multiple copies of the transmitted signal through a Rake receiver. We analyze a situation where the number of Rake fingers N is less than that of the relaying nodes L. In this case, the receiver can combine N strongest signals out of L. The remaining signals will be lost and act as interference to the desired signal components. To tackle this problem, we develop a novel signal combining technique based on channel shortening principles. This technique proposes a processing block before the Rake reception which compresses the energy of L signal components over N branches while keeping the noise level at its minimum. The proposed scheme saves the system resources and makes the received signal compatible to the available hardware. Simulation results show that it outperforms the selection combining scheme. © 2012 IEEE.

  3. A neutral network based technique for short-term forecasting of anomalous load periods

    Energy Technology Data Exchange (ETDEWEB)

    Sforna, M [ENEL, s.p.a, Italian Power Company (Italy); Lamedica, R; Prudenzi, A [Rome Univ. ` La Sapienza` , Rome (Italy); Caciotta, M; Orsolini Cencelli, V [Rome Univ. III, Rome (Italy)

    1995-01-01

    The paper illustrates a part of the research activity conducted by authors in the field of electric Short Term Load Forecasting (STLF) based on Artificial Neural Network (ANN) architectures. Previous experiences with basic ANN architectures have shown that, even though these architecture provide results comparable with those obtained by human operators for most normal days, they evidence some accuracy deficiencies when applied to `anomalous` load conditions occurring during holidays and long weekends. For these periods a specific procedure based upon a combined (unsupervised/supervised) approach has been proposed. The unsupervised stage provides a preventive classification of the historical load data by means of a Kohonen`s Self Organizing Map (SOM). The supervised stage, performing the proper forecasting activity, is obtained by using a multi-layer percept ron with a back propagation learning algorithm similar to the ones above mentioned. The unconventional use of information deriving from the classification stage permits the proposed procedure to obtain a relevant enhancement of the forecast accuracy for anomalous load situations.

  4. Performance Analysis of Network-RTK Techniques for Drone Navigation considering Ionospheric Conditions

    Directory of Open Access Journals (Sweden)

    Tae-Suk Bae

    2018-01-01

    Full Text Available Recently, an accurate positioning has become the kernel of autonomous navigation with the rapid growth of drones including mapping purpose. The Network-based Real-time Kinematic (NRTK system was predominantly used for precision positioning in many fields such as surveying and agriculture, mostly in static mode or low-speed operation. The NRTK positioning, in general, shows much better performance with the fixed integer ambiguities. However, the success rate of the ambiguity resolution is highly dependent on the ionospheric condition and the surrounding environment of Global Navigation Satellite System (GNSS positioning, which particularly corresponds to the low-cost GNSS receivers. We analyzed the effects of the ionospheric conditions on the GNSS NRTK, as well as the possibility of applying the mobile NRTK to drone navigation for mapping. Two NRTK systems in operation were analyzed during a period of high ionospheric conditions, and the accuracy and the performance were compared for several operational cases. The test results show that a submeter accuracy is available even with float ambiguity under a favorable condition (i.e., visibility of the satellites as well as stable ionosphere. We still need to consider how to deal with ionospheric disturbances which may prevent NRTK positioning.

  5. The Bjorken sum rule with Monte Carlo and Neural Network techniques

    International Nuclear Information System (INIS)

    Debbio, L. Del; Guffanti, A.; Piccione, A.

    2009-01-01

    Determinations of structure functions and parton distribution functions have been recently obtained using Monte Carlo methods and neural networks as universal, unbiased interpolants for the unknown functional dependence. In this work the same methods are applied to obtain a parametrization of polarized Deep Inelastic Scattering (DIS) structure functions. The Monte Carlo approach provides a bias-free determination of the probability measure in the space of structure functions, while retaining all the information on experimental errors and correlations. In particular the error on the data is propagated into an error on the structure functions that has a clear statistical meaning. We present the application of this method to the parametrization from polarized DIS data of the photon asymmetries A 1 p and A 1 d from which we determine the structure functions g 1 p (x,Q 2 ) and g 1 d (x,Q 2 ), and discuss the possibility to extract physical parameters from these parametrizations. This work can be used as a starting point for the determination of polarized parton distributions.

  6. Neural network classification technique and machine vision for bread crumb grain evaluation

    Science.gov (United States)

    Zayas, Inna Y.; Chung, O. K.; Caley, M.

    1995-10-01

    Bread crumb grain was studied to develop a model for pattern recognition of bread baked at Hard Winter Wheat Quality Laboratory (HWWQL), Grain Marketing and Production Research Center (GMPRC). Images of bread slices were acquired with a scanner in a 512 multiplied by 512 format. Subimages in the central part of the slices were evaluated by several features such as mean, determinant, eigen values, shape of a slice and other crumb features. Derived features were used to describe slices and loaves. Neural network programs of MATLAB package were used for data analysis. Learning vector quantization method and multivariate discriminant analysis were applied to bread slices from what of different sources. A training and test sets of different bread crumb texture classes were obtained. The ranking of subimages was well correlated with visual judgement. The performance of different models on slice recognition rate was studied to choose the best model. The recognition of classes created according to human judgement with image features was low. Recognition of arbitrarily created classes, according to porosity patterns, with several feature patterns was approximately 90%. Correlation coefficient was approximately 0.7 between slice shape features and loaf volume.

  7. Traffic Regulation on Wireless 802.11 Networks Using Multiple Queue Technique

    Science.gov (United States)

    Dhanal, Radhika J.; Patil, G. A.

    2010-11-01

    WLAN technologies are becoming increasingly popular and are platform for many future applications. IEEE 802.11 Wireless LAN (WLAN) is an excellent solution for the broadband wireless networking. This paper presents a simple approach to enhance the performance of real time (RT) and non-real time (NRT) services over the 802.11 WLAN by using some special queues. This requires the system to first identify the type of service and then use the appropriate scheduling algorithm. The admission control algorithm is used first to determine the admission of particular station. Deficit round robin algorithm is used to set the priorities to RT and NRT packets in order to increase the QoS of WLAN. So we can combine both these algorithms by implementing them one after another. The proposed scheme can improve Voice/Data/Video services through simple software upgrades by reducing the delay, jitter and increasing the throughput. Through simulation, we show that the proposed scheme can give better QoS than existing schemes.

  8. Resource Optimization Techniques and Security Levels for Wireless Sensor Networks Based on the ARSy Framework

    Science.gov (United States)

    Kitagawa, Akio

    2018-01-01

    Wireless Sensor Networks (WSNs) with limited battery, central processing units (CPUs), and memory resources are a widely implemented technology for early warning detection systems. The main advantage of WSNs is their ability to be deployed in areas that are difficult to access by humans. In such areas, regular maintenance may be impossible; therefore, WSN devices must utilize their limited resources to operate for as long as possible, but longer operations require maintenance. One method of maintenance is to apply a resource adaptation policy when a system reaches a critical threshold. This study discusses the application of a security level adaptation model, such as an ARSy Framework, for using resources more efficiently. A single node comprising a Raspberry Pi 3 Model B and a DS18B20 temperature sensor were tested in a laboratory under normal and stressful conditions. The result shows that under normal conditions, the system operates approximately three times longer than under stressful conditions. Maintaining the stability of the resources also enables the security level of a network’s data output to stay at a high or medium level. PMID:29772773

  9. Performance Analysis of Control Signal Transmission Technique for Cognitive Radios in Dynamic Spectrum Access Networks

    Science.gov (United States)

    Sakata, Ren; Tomioka, Tazuko; Kobayashi, Takahiro

    When cognitive radio (CR) systems dynamically use the frequency band, a control signal is necessary to indicate which carrier frequencies are currently available in the network. In order to keep efficient spectrum utilization, this control signal also should be transmitted based on the channel conditions. If transmitters dynamically select carrier frequencies, receivers have to receive control signals without knowledge of their carrier frequencies. To enable such transmission and reception, this paper proposes a novel scheme called DCPT (Differential Code Parallel Transmission). With DCPT, receivers can receive low-rate information with no knowledge of the carrier frequencies. The transmitter transmits two signals whose carrier frequencies are spaced by a predefined value. The absolute values of the carrier frequencies can be varied. When the receiver acquires the DCPT signal, it multiplies the signal by a frequency-shifted version of the signal; this yields a DC component that represents the data signal which is then demodulated. The performance was evaluated by means of numerical analysis and computer simulation. We confirmed that DCPT operates successfully even under severe interference if its parameters are appropriately configured.

  10. Sum rules for the ed - NN scattering reactions and microscopic potential field-theoretical approach

    International Nuclear Information System (INIS)

    Machivariani, A.I.

    1996-01-01

    The connections between the equal-time commutators of nucleon and photon field-operators and relativistic potential approach of ed - NN scattering equations is established. Namely, it is demonstrated that: 1) equal-time commutator between nucleon field operators generated completeness condition for NN interaction functions, 2) the off-mass shell contributions in γd - NN exchange currents or in microscopic NN potential are determined by equal time commutator between nucleon field operator and photon or nucleon source operators, and 3) equal-time commutators between source operators produce sum rules for same vertex functions and effective potentials [ru

  11. Investigation of the charge exchange process π-d→π0nn

    International Nuclear Information System (INIS)

    Il-Tong Cheon.

    1978-02-01

    The transition rate has been calculated for the charge exchange of stopped π - in the deuteron. The present result is hω(π - d→π 0 nn)=0.695x10 -4 eV. Making use of the value hω(π - d→nn)=0.682 eV, which was previously obtained, estimated the branching ratio ω(π - d→π 0 nn)|ω(π - d→nn)=1.02x10 -4 has been estimated

  12. A Learning Method for Neural Networks Based on a Pseudoinverse Technique

    Directory of Open Access Journals (Sweden)

    Chinmoy Pal

    1996-01-01

    Full Text Available A theoretical formulation of a fast learning method based on a pseudoinverse technique is presented. The efficiency and robustness of the method are verified with the help of an Exclusive OR problem and a dynamic system identification of a linear single degree of freedom mass–spring problem. It is observed that, compared with the conventional backpropagation method, the proposed method has a better convergence rate and a higher degree of learning accuracy with a lower equivalent learning coefficient. It is also found that unlike the steepest descent method, the learning capability of which is dependent on the value of the learning coefficient ν, the proposed pseudoinverse based backpropagation algorithm is comparatively robust with respect to its equivalent variable learning coefficient. A combination of the pseudoinverse method and the steepest descent method is proposed for a faster, more accurate learning capability.

  13. Code division multiple-access techniques in optical fiber networks. II - Systems performance analysis

    Science.gov (United States)

    Salehi, Jawad A.; Brackett, Charles A.

    1989-08-01

    A technique based on optical orthogonal codes was presented by Salehi (1989) to establish a fiber-optic code-division multiple-access (FO-CDMA) communications system. The results are used to derive the bit error rate of the proposed FO-CDMA system as a function of data rate, code length, code weight, number of users, and receiver threshold. The performance characteristics for a variety of system parameters are discussed. A means of reducing the effective multiple-access interference signal by placing an optical hard-limiter at the front end of the desired optical correlator is presented. Performance calculations are shown for the FO-CDMA with an ideal optical hard-limiter, and it is shown that using a optical hard-limiter would, in general, improve system performance.

  14. Novel all-optical dispersion monitoring technique for ultra-high-speed WDM networks

    Energy Technology Data Exchange (ETDEWEB)

    Cui Sheng; Li Li; Liu Deming, E-mail: cuisheng@mail.hust.edu.cn [Wuhan National Laboratory for Optoelectronics, No.1037, Luoyu Road, Wuhan, Hubei, 430074 (China)

    2011-02-01

    This paper represents a novel all-optical dispersion monitoring technique based on fiber parametric amplifiers (FOPAs). The monitoring method is truly bit-rate transparent because it is enabled by the exponential power transfer function (PTF) provided by the FOPA gain. The slope of the PTF is increased from 2 to 3 by choosing appropriate phase-matching conditions. Due to the steeper PTF the monitoring sensitivity is greatly improved compared to the other PTF-based methods proposed before. The PTF obtained by numerical simulations agrees very well with the experimental results. Numerical simulations are then used to demonstrate that our method can be used to monitor signals in various modulation formats.

  15. Development and assessment of compression technique for medical images using neural network. I. Assessment of lossless compression

    International Nuclear Information System (INIS)

    Fukatsu, Hiroshi

    2007-01-01

    This paper describes assessment of the lossless compression of a new efficient compression technique (JIS system) using neural network that the author and co-workers have recently developed. At first, theory is explained for encoding and decoding the data. Assessment is done on 55 images each of chest digital roentgenography, digital mammography, 64-row multi-slice CT, 1.5 Tesla MRI, positron emission tomography (PET) and digital subtraction angiography, which are lossless-compressed by the present JIS system to see the compression rate and loss. For comparison, those data are also JPEG lossless-compressed. Personal computer (PC) is an Apple MacBook Pro with configuration of Boot Camp for Windows environment. The present JIS system is found to have a more than 4 times higher efficiency than the usual compressions which compressing the file volume to only 1/11 in average, and thus to be importantly responsible to the increasing medical imaging data. (R.T.)

  16. Visualization and Analysis of Wireless Sensor Network Data for Smart Civil Structure Applications Based On Spatial Correlation Technique

    Science.gov (United States)

    Chowdhry, Bhawani Shankar; White, Neil M.; Jeswani, Jai Kumar; Dayo, Khalil; Rathi, Manorma

    2009-07-01

    Disasters affecting infrastructure, such as the 2001 earthquakes in India, 2005 in Pakistan, 2008 in China and the 2004 tsunami in Asia, provide a common need for intelligent buildings and smart civil structures. Now, imagine massive reductions in time to get the infrastructure working again, realtime information on damage to buildings, massive reductions in cost and time to certify that structures are undamaged and can still be operated, reductions in the number of structures to be rebuilt (if they are known not to be damaged). Achieving these ideas would lead to huge, quantifiable, long-term savings to government and industry. Wireless sensor networks (WSNs) can be deployed in buildings to make any civil structure both smart and intelligent. WSNs have recently gained much attention in both public and research communities because they are expected to bring a new paradigm to the interaction between humans, environment, and machines. This paper presents the deployment of WSN nodes in the Top Quality Centralized Instrumentation Centre (TQCIC). We created an ad hoc networking application to collect real-time data sensed from the nodes that were randomly distributed throughout the building. If the sensors are relocated, then the application automatically reconfigures itself in the light of the new routing topology. WSNs are event-based systems that rely on the collective effort of several micro-sensor nodes, which are continuously observing a physical phenomenon. WSN applications require spatially dense sensor deployment in order to achieve satisfactory coverage. The degree of spatial correlation increases with the decreasing inter-node separation. Energy consumption is reduced dramatically by having only those sensor nodes with unique readings transmit their data. We report on an algorithm based on a spatial correlation technique that assures high QoS (in terms of SNR) of the network as well as proper utilization of energy, by suppressing redundant data transmission

  17. Using GNSS-R techniques to investigate the near sub-surface of Mars with the Deep Space Network

    Science.gov (United States)

    Elliott, H. M.; Bell, D. J.; Jin, C.; Decrossas, E.; Asmar, S.; Lazio, J.; Preston, R. A.; Ruf, C. S.; Renno, N. O.

    2017-12-01

    Global Navigation Satellite Systems Reflectometry (GNSS-R) has shown that passive measurements using separate active sources can infer the soil moisture, snow pack depth and other quantities of scientific interest. Here, we expand upon this method and propose that a passive measurement of the sub-surface dielectric profile of Mars can be made by using multipath interference between reflections off the surface and subsurface dielectric discontinuities. This measurement has the ability to reveal changes in the soil water content, the depth of a layer of sand, thickness of a layer of ice, and even identify centimeter-scale layering which may indicate the presence of a sedimentary bed. We have created a numerical ray tracing model to understand the potential of using multipath interference techniques to investigate the sub-surface dielectric properties and structure of Mars. We have further verified this model using layered beds of sand and concrete in laboratory experiments and then used the model to extrapolate how this technique may be applied to future Mars missions. We will present new results demonstrating how to characterize a multipath interference patterns as a function of frequency and/or incidence angle to measure the thickness of a dielectric layer of sand or ice. Our results demonstrate that dielectric discontinuities in the subsurface can be measured using this passive sensing technique and it could be used to effectively measure the thickness of a dielectric layer in the proximity of a landed spacecraft. In the case of an orbiter, we believe this technique would be effective at measuring the seasonal thickness of CO2 ice in the Polar Regions. This is exciting because our method can produce similar results to traditional ground penetrating radars without the need to have an active radar transmitter in-situ. Therefore, it is possible that future telecommunications systems can serve as both a radio and a scientific instrument when used in conjunction with

  18. Energy-efficient optical network units for OFDM PON based on time-domain interleaved OFDM technique.

    Science.gov (United States)

    Hu, Xiaofeng; Cao, Pan; Zhang, Liang; Jiang, Lipeng; Su, Yikai

    2014-06-02

    We propose and experimentally demonstrate a new scheme to reduce the energy consumption of optical network units (ONUs) in orthogonal frequency division multiplexing passive optical networks (OFDM PONs) by using time-domain interleaved OFDM (TI-OFDM) technique. In a conventional OFDM PON, each ONU has to process the complete downstream broadcast OFDM signal with a high sampling rate and a large FFT size to retrieve its required data, even if it employs a portion of OFDM subcarriers. However, in our scheme, the ONU only needs to sample and process one data group from the downlink TI-OFDM signal, effectively reducing the sampling rate and the FFT size of the ONU. Thus, the energy efficiency of ONUs in OFDM PONs can be greatly improved. A proof-of-concept experiment is conducted to verify the feasibility of the proposed scheme. Compared to the conventional OFDM PON, our proposal can save 17.1% and 26.7% energy consumption of ONUs by halving and quartering the sampling rate and the FFT size of ONUs with the use of the TI-OFDM technology.

  19. Hidden neural networks: application to speech recognition

    DEFF Research Database (Denmark)

    Riis, Søren Kamaric

    1998-01-01

    We evaluate the hidden neural network HMM/NN hybrid on two speech recognition benchmark tasks; (1) task independent isolated word recognition on the Phonebook database, and (2) recognition of broad phoneme classes in continuous speech from the TIMIT database. It is shown how hidden neural networks...

  20. Projection of the six-quark wave function onto the NN channel and the problem of the repulsive core in the NN interaction

    International Nuclear Information System (INIS)

    Kusainov, A.M.; Neudatchin, V.G.; Obukhovsky, I.T.

    1991-01-01

    A modification of the resonating-group method (RGM) is proposed which includes the multiquark shell-model configurations in the nucleon overlap region. The instanton, gluon, and π,σ exchange is taken into account, the interaction constants being consistent with the baryon spectrum. This enables one to cover a wide interval of NN scattering energies up to E lab =2 GeV. The projection of the six-quark wave function onto the NN and other baryon channels is discussed in detail in our approach and in other RGM versions as well, and in this context the problem of repulsive core in the NN forces is discussed

  1. DE-IDENTIFICATION TECHNIQUE FOR IOT WIRELESS SENSOR NETWORK PRIVACY PROTECTION

    Directory of Open Access Journals (Sweden)

    Yennun Huang

    2017-02-01

    Full Text Available As the IoT ecosystem becoming more and more mature, hardware and software vendors are trying create new value by connecting all kinds of devices together via IoT. IoT devices are usually equipped with sensors to collect data, and the data collected are transmitted over the air via different kinds of wireless connection. To extract the value of the data collected, the data owner may choose to seek for third-party help on data analysis, or even of the data to the public for more insight. In this scenario it is important to protect the released data from privacy leakage. Here we propose that differential privacy, as a de-identification technique, can be a useful approach to add privacy protection to the data released, as well as to prevent the collected from intercepted and decoded during over-the-air transmission. A way to increase the accuracy of the count queries performed on the edge cases in a synthetic database is also presented in this research.

  2. Further investigations of the NN interaction in the Skyrme model

    International Nuclear Information System (INIS)

    Kaelbermann, G.; Eisenberg, J.M.

    1989-01-01

    We examine the influence of the coupling to NΔ and ΔΔ degrees of freedom for the NN interaction as derived in the Skyrme model, carrying out an extensive search for parameters in the basic Lagrangian that will yield both reasonable single-baryon results and appreciable attraction. Separately the free one-body skyrmeon solution and an improved two-body solution are inserted in the product ansatz for the two-body system both with and without time-dependent dynamical terms. No appreciable central attraction between nucleons is found with either of these approaches. (author)

  3. Metrology in CNEN NN 3.05/13 standard

    International Nuclear Information System (INIS)

    Mello, Marina Santiago de

    2014-01-01

    The nuclear medicine exams are widely used tools in health services for a reliable clinical and functional diagnosis of a disease. In Brazil, the National Nuclear Energy Commission, through the norm CNEN-NN 3:05/13, provides for the requirements of safety and radiological protection in nuclear medicine services. The objective of this review article was to emphasize the importance of metrology in compliance with this norm. We observed that metrology plays a vital role as it ensures the quality, accuracy, reproducibility and consistency of the measurements in the field of nuclear medicine. (author)

  4. Measurements of NN → dπ near threshold

    International Nuclear Information System (INIS)

    Hutcheon, D.A.

    1990-09-01

    New, precise measurements of the differential cross sections for np → dπ 0 and π + d → pp and of analyzing powers for pp → dπ + have been made at energies within 10 MeV (c.m.) of threshold. They allow the pion s-wave and p-wave parts of the production strength to be distinguished unambiguously, yielding an s-wave strength at threshold which is significantly smaller than the previously accepted value. There is no evidence for charge independence breaking nor for πNN resonances near threshold. (Author) (17 refs., 17 figs., tab.)

  5. Direct nn-Scattering Measurement With the Pulsed Reactor YAGUAR.

    Science.gov (United States)

    Mitchell, G E; Furman, W I; Lychagin, E V; Muzichka, A Yu; Nekhaev, G V; Strelkov, A V; Sharapov, E I; Shvetsov, V N; Chernuhin, Yu I; Levakov, B G; Litvin, V I; Lyzhin, A E; Magda, E P; Crawford, B E; Stephenson, S L; Howell, C R; Tornow, W

    2005-01-01

    Although crucial for resolving the issue of charge symmetry in the nuclear force, direct measurement of nn-scattering by colliding free neutrons has never been performed. At present the Russian pulsed reactor YAGUAR is the best neutron source for performing such a measurement. It has a through channel where the neutron moderator is installed. The neutrons are counted by a neutron detector located 12 m from the reactor. In preliminary experiments an instantaneous value of 1.1 × 10(18)/cm(2)s was obtained for the thermal neutron flux density. The experiment will be performed by the DIANNA Collaboration as International Science & Technology Center (ISTC) project No. 2286.

  6. Imaging Fracture Networks Using Angled Crosshole Seismic Logging and Change Detection Techniques

    Science.gov (United States)

    Knox, H. A.; Grubelich, M. C.; Preston, L. A.; Knox, J. M.; King, D. K.

    2015-12-01

    We present results from a SubTER funded series of cross borehole geophysical imaging efforts designed to characterize fracture zones generated with an alternative stimulation method, which is being developed for Enhanced Geothermal Systems (EGS). One important characteristic of this stimulation method is that each detonation will produce multiple fractures without damaging the wellbore. To date, we have collected six full data sets with ~30k source-receiver pairs each for the purposes of high-resolution cross borehole seismic tomographic imaging. The first set of data serves as the baseline measurement (i.e. un-stimulated), three sets evaluate material changes after fracture emplacement and/or enhancement, and two sets are used for evaluation of pick error and seismic velocity changes attributable to changing environmental factors (i.e. saturation due to rain/snowfall in the shallow subsurface). Each of the six datasets has been evaluated for data quality and first arrivals have been picked on nearly 200k waveforms in the target area. Each set of data is then inverted using a Vidale-Hole finite-difference 3-D eikonal solver in two ways: 1) allowing for iterative ray tracing and 2) with fixed ray paths determined from the test performed before the fracture stimulation of interest. Utilizing these two methods allows us to compare and contrast the results from two commonly used change detection techniques. Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000.

  7. Predicting Diameter Distributions of Longleaf Pine Plantations: A Comparison Between Artificial Neural Networks and Other Accepted Methodologies

    Science.gov (United States)

    Daniel J. Leduc; Thomas G. Matney; Keith L. Belli; V. Clark Baldwin

    2001-01-01

    Artificial neural networks (NN) are becoming a popular estimation tool. Because they require no assumptions about the form of a fitting function, they can free the modeler from reliance on parametric approximating functions that may or may not satisfactorily fit the observed data. To date there have been few applications in forestry science, but as better NN software...

  8. Establishing structure-property correlations and classification of base oils using statistical techniques and artificial neural networks

    International Nuclear Information System (INIS)

    Kapur, G.S.; Sastry, M.I.S.; Jaiswal, A.K.; Sarpal, A.S.

    2004-01-01

    The present paper describes various classification techniques like cluster analysis, principal component (PC)/factor analysis to classify different types of base stocks. The API classification of base oils (Group I-III) has been compared to a more detailed NMR derived chemical compositional and molecular structural parameters based classification in order to point out the similarities of the base oils in the same group and the differences between the oils placed in different groups. The detailed compositional parameters have been generated using 1 H and 13 C nuclear magnetic resonance (NMR) spectroscopic methods. Further, oxidation stability, measured in terms of rotating bomb oxidation test (RBOT) life, of non-conventional base stocks and their blends with conventional base stocks, has been quantitatively correlated with their 1 H NMR and elemental (sulphur and nitrogen) data with the help of multiple linear regression (MLR) and artificial neural networks (ANN) techniques. The MLR based model developed using NMR and elemental data showed a high correlation between the 'measured' and 'estimated' RBOT values for both training (R=0.859) and validation (R=0.880) data sets. The ANN based model, developed using fewer number of input variables (only 1 H NMR data) also showed high correlation between the 'measured' and 'estimated' RBOT values for training (R=0.881), validation (R=0.860) and test (R=0.955) data sets

  9. Data Analysis and Neuro-Fuzzy Technique for EOR Screening: Application in Angolan Oilfields

    Directory of Open Access Journals (Sweden)

    Geraldo A. R. Ramos

    2017-06-01

    Full Text Available In this work, a neuro-fuzzy (NF simulation study was conducted in order to screen candidate reservoirs for enhanced oil recovery (EOR projects in Angolan oilfields. First, a knowledge pattern is extracted by combining both the searching potential of fuzzy-logic (FL and the learning capability of neural network (NN to make a priori decisions. The extracted knowledge pattern is validated against rock and fluid data trained from successful EOR projects around the world. Then, data from Block K offshore Angolan oilfields are then mined and analysed using box-plot technique for the investigation of the degree of suitability for EOR projects. The trained and validated model is then tested on the Angolan field data (Block K where EOR application is yet to be fully established. The results from the NF simulation technique applied in this investigation show that polymer, hydrocarbon gas, and combustion are the suitable EOR techniques.

  10. Use of neural network techniques to identify cosmic ray electrons and positrons during the 1993 balloon flight of the NMSU/Wizard-TS93 instrument

    Energy Technology Data Exchange (ETDEWEB)

    Bellotti, R.; Castellano, M. [Bari Univ. (Italy)]|[INFN, Bari (Italy); Candusso, M.; Casolino, M.; Morselli, A.; Picozza, P. [Rome Univ. `Tor Vergata` (Italy)]|[INFN, Rome (Italy); Aversa, F.; Boezio, M. [Trieste Univ. (Italy)]|[INFN, Trieste (Italy); Barbiellini, G. [Trieste Univ. (Italy)]|[INFN, Trieste (Italy); Basini, G. [INFN, Laboratori Nazionali di Frascati, Rome (Italy)

    1995-09-01

    The detectors used in the TS93 balloon flight produced a large volume of information for each cosmic ray trigger. Some of the data was visual in nature, other portions contained energy deposition and timing information. The data sets are amenable to conventional analysis techniques but there is no assurance that conventional techniques make full use of subtle correlations and relations amongst the detector responses. With the advent of neural network technologies, particularly adept at classification of complex phenomena, it would seem appropriate to explore the utility of neural network techniques to classify particles observed with the instruments. In this paper neural network based methodology for signal/background discrimination in a cosmic ray space experiment is discussed. Results are presented for electron and positron classification in the TS93 flight data set and will be compared to conventional analyses.

  11. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  12. Spectroscopy of light nuclei with realistic NN interaction JISP

    International Nuclear Information System (INIS)

    Shirokov, A. M.; Vary, J. P.; Mazur, A. I.; Weber, T. A.

    2008-01-01

    Recent results of our systematic ab initio studies of the spectroscopy of s- and p-shell nuclei in fully microscopic large-scale (up to a few hundred million basis functions) no-core shell-model calculations are presented. A new high-quality realistic nonlocal NN interaction JISP is used. This interaction is obtained in the J-matrix inverse-scattering approach (JISP stands for the J-matrix inverse-scattering potential) and is of the form of a small-rank matrix in the oscillator basis in each of the NN partial waves, providing a very fast convergence in shell-model studies. The current purely two-body JISP model of the nucleon-nucleon interaction JISP16 provides not only an excellent description of two-nucleon data (deuteron properties and np scattering) with χ 2 /datum = 1.05 but also a better description of a wide range of observables (binding energies, spectra, rms radii, quadrupole moments, electromagnetic-transition probabilities, etc.) in all s-and p-shell nuclei than the best modern interaction models combining realistic nucleon-nucleon and three-nucleon interactions.

  13. Classification in medical image analysis using adaptive metric k-NN

    DEFF Research Database (Denmark)

    Chen, Chen; Chernoff, Konstantin; Karemore, Gopal

    2010-01-01

    The performance of the k-nearest neighborhoods (k-NN) classifier is highly dependent on the distance metric used to identify the k nearest neighbors of the query points. The standard Euclidean distance is commonly used in practice. This paper investigates the performance of k-NN classifier...

  14. Bag-model motivated NN potentials and the three-nucleon system

    International Nuclear Information System (INIS)

    Grach, I.L.; Narodetskij, I.M.

    1986-01-01

    Few examples are presented of the short-range energy-dependent NN potentials derived in the quark compound bag model which satisfy the classical causality condition and show that for the radii of the NN interactions b=1.35-1.40 fm these potentials reproduce the trinucleon binding energy

  15. Distance-Constraint k-Nearest Neighbor Searching in Mobile Sensor Networks.

    Science.gov (United States)

    Han, Yongkoo; Park, Kisung; Hong, Jihye; Ulamin, Noor; Lee, Young-Koo

    2015-07-27

    The κ-Nearest Neighbors ( κNN) query is an important spatial query in mobile sensor networks. In this work we extend κNN to include a distance constraint, calling it a l-distant κ-nearest-neighbors (l-κNN) query, which finds the κ sensor nodes nearest to a query point that are also at or greater distance from each other. The query results indicate the objects nearest to the area of interest that are scattered from each other by at least distance l. The l-κNN query can be used in most κNN applications for the case of well distributed query results. To process an l-κNN query, we must discover all sets of κNN sensor nodes and then find all pairs of sensor nodes in each set that are separated by at least a distance l. Given the limited battery and computing power of sensor nodes, this l-κNN query processing is problematically expensive in terms of energy consumption. In this paper, we propose a greedy approach for l-κNN query processing in mobile sensor networks. The key idea of the proposed approach is to divide the search space into subspaces whose all sides are l. By selecting κ sensor nodes from the other subspaces near the query point, we guarantee accurate query results for l-κNN. In our experiments, we show that the proposed method exhibits superior performance compared with a post-processing based method using the κNN query in terms of energy efficiency, query latency, and accuracy.

  16. Study of production and cold nuclear matter effects in pPb collisions at s NN √sNN = 5 TeV

    NARCIS (Netherlands)

    Aaij, R.; Adeva, B.; Adinolfi, M.; Affolder, A.; Ajaltouni, Z.; Albrecht, J.; Alessio, F.; Alexander, M.; Ali, S.; Alkhazov, G.; Cartelle, P. Alvarez; Alves, A. A.; Amato, S.; Amerio, S.; Amhis, Y.; Everse, LA; Anderlini, L.; Anderson, J.; Andreassen, P.R.; Andreotti, M.; Andrews, J.E.; Appleby, R. B.; Gutierrez, O. Aquines; Archilli, F.; Artamonov, A.; Artuso, M.; Aslanides, E.; Auriemma, G.; Baalouch, M.; Bachmann, S.; Back, J. J.; Badalov, A.; Balagura, V.; Baldini, W.; Barlow, R. J.; Barschel, C.; Barsuk, S.; Barter, W.; Batozskaya, V.; Bay, A.; Beaucourt, L.; Beddow, J.; Bedeschi, F.; Bediaga, I.; Belogurov, S.; Belous, K.; Belyaev, I.; Ben-Haim, E.; Bencivenni, G.; Benson, S.; Benton, J.; Berezhnoy, A.; Bernet, R.; Bettler, M-O.; Van Beuzekom, Martin; Bien, A.; Bifani, S.; Bird, T.D.; Bizzeti, A.; Bjørnstad, P. M.; Blake, T.; Blanc, F.; Blouw, J.; Blusk, S.; Bocci, V.; Bondar, A.; Bondar, N.; Bonivento, W.; Borghi, S.; Borgia, A.; Borsato, M.; Bowcock, T. J. V.; Bowen, E.; Bozzi, C.; Brambach, T.; Van Den Brand, J.; Bressieux, J.; Brett, D.; Britsch, M.; Britton, T.; Brodzicka, J.; Brook, N. H.; Brown, H.; Bursche, A.; Busetto, G.; Buytaert, J.; Cadeddu, S.; Calabrese, R.; Calvi, M.; Gomez, M. Calvo; Camboni, A.; Campana, P.; Perez, D. H. Campora; Carbone, A.; Carboni, G.; Cardinale, R.; Cardini, A.; Carranza-Mejia, H.; Carson, L.; Akiba, K. Carvalho; Casse, G.; Cassina, L.; Garcia, L. Castillo; Cattaneo, M.; Cauet, Ch; Cenci, R.; Charles, M.; Charpentier, Ph; Chen, S.; Cheung, S-F.; Chiapolini, N.; Chrzaszcz, M.; Ciba, K.; Vidal, X. Cid; Ciezarek, G.; Clarke, P. E. L.; Clemencic, M.; Cliff, H. V.; Closier, J.; Coco, V.; Cogan, J.; Cogneras, E.; Collins, P.; Comerma-Montells, A.; Contu, A.; Cook, A.; Coombes, M.; Coquereau, S.; Corti, G.; Corvo, M.; Counts, I.; Couturier, B.; Cowan, G. A.; Craik, D. C.; Torres, M. Cruz; Cunliffe, S.; Currie, C.R.; D'Ambrosio, C.; Dalseno, J.; David, P.; Davis, A.; De Bruyn, K.; De Capua, S.; De Cian, M.; de Miranda, J. M.; Paula, L.E.; da-Silva, W.S.; De Simone, P.; Decamp, D.; Deckenhoff, M.; Del Buono, L.; Déléage, N.; Derkach, D.; Deschamps, O.; Dettori, F.; Di Canto, A.; Dijkstra, H.; Donleavy, S.; Dordei, F.; Dorigo, M.; Suárez, A. Dosil; Dossett, D.; Dovbnya, A.; Dujany, G.; Dupertuis, F.; Durante, P.; Dzhelyadin, R.; Dziurda, A.; Dzyuba, A.; Easo, S.; Egede, U.; Egorychev, V.; Eidelman, S.; Eisenhardt, S.; Eitschberger, U.; Ekelhof, R.; Eklund, L.; El Rifai, I.; Elsasser, Ch.; Ely, S.; Esen, S.; Evans, T. M.; Falabella, A.; Färber, C.; Farinelli, C.; Farley, N.; Farry, S.; Ferguson, D.; Albor, V. Fernandez; Rodrigues, F. Ferreira; Ferro-Luzzi, M.; Filippov, S.; Fiore, M.; Fiorini, M.; Firlej, M.; Fitzpatrick, C.; Fiutowski, T.; Fontana, Mark; Fontanelli, F.; Forty, R.; De Aguiar Francisco, O.; Frank, M.; Frei, C.; Frosini, M.; Fu, J.; Furfaro, E.; Torreira, A. Gallas; Galli, D.; Gallorini, S.; Gambetta, S.; Gandelman, M.; Gandini, P.; Gao, Y.; Garofoli, J.; Tico, J. Garra; Garrido, L.; Carvalho-Gaspar, M.; Gauld, Rhorry; Gavardi, L.; Gersabeck, E.; Gersabeck, M.; Gershon, T. J.; Ghez, Ph; Gianelle, A.; Giani, S.; Gibson, V.; Giubega, L.; Gligorov, V. V.; Göbel, C.; Golubkov, D.; Golutvin, A.; Gomes, A.Q.; Head-Gordon, Teresa; Gotti, C.; Gándara, M. Grabalosa; Diaz, R. Graciani; Cardoso, L. A.Granado; Graugés, E.; Graziani, G.; Grecu, A.; Greening, E.; Gregson, S.; Griffith, P.; Grillo, L.; Grünberg, O.; Gui, B.; Gushchin, E.; Guz, Yu; Gys, T.; Hadjivasiliou, C.; Haefeli, G.; Haen, C.; Haines, S. C.; Hall, S.; Hamilton, B.; Hampson, T.; Han, X.; Hansmann-Menzemer, S.; Harnew, N.; Harnew, S. T.; Harrison, J.; Hartmann, T.; He, J.; Head, T.; Heijne, V.; Hennessy, K.; Henrard, P.; Henry, L.; Morata, J. A.Hernando; van Herwijnen, E.; Heß, M.; Hicheur, A.; Hill, D.; Hoballah, M.; Hombach, C.; Hulsbergen, W.; Hunt, P.; Hussain, N.; Hutchcroft, D. E.; Hynds, D.; Idzik, M.; Ilten, P.; Jacobsson, R.; Jaeger, A.; Jalocha, J.; Jans, E.; Jaton, P.; Jawahery, A.; Jezabek, M.; Jing, F.; John, M.; Johnson, D.; Jones, C. R.; Joram, C.; Jost, B.; Jurik, N.; Kaballo, M.; Kandybei, S.; Kanso, W.; Karacson, M.; Karbach, T. M.; Kelsey, M. H.; Kenyon, I. R.; Ketel, T.; Khanji, B.; Khurewathanakul, C.; Klaver, S.M.; Kochebina, O.; Kolpin, M.; Komarov, I.; Koopman, R. F.; Koppenburg, P.; Korolev, M.; Kozlinskiy, A.; Kravchuk, L.; Kreplin, K.; Kreps, M.; Krocker, G.; Krokovny, P.; Kruse, F.; Kucharczyk, M.; Kudryavtsev, V.; Kurek, K.; Kvaratskheliya, T.; La Thi, V. N.; Lacarrere, D.; Lafferty, G. D.; Lai, A.; Lambert, D.M.; Lambert, R. W.; Lanciotti, E.; Lanfranchi, G.; Langenbruch, C.; Langhans, B.; Latham, T. E.; Lazzeroni, C.; Le Gac, R.; Van Leerdam, J.; Lees, J. P.; Lefèvre, R.; Leflat, A.; Lefrançois, J.; Di Leo, S.; Leroy, O.; Lesiak, T.; Leverington, B.; Li, Y.; Liles, M.; Lindner, R.; Linn, S.C.; Lionetto, F.; Liu, B.; Liu, G.; Lohn, S.; Longstaff, I.; Lopes, J. H.; Lopez-March, N.; Lowdon, P.; Lu, H.; Lucchesi, D.; Luo, H.; Lupato, A.; Luppi, E.; Lupton, O.; Machefert, F.; Machikhiliyan, I. V.; Maciuc, F.; Maev, O.; Malde, S.; Manca, G.; Mancinelli, G.; Manzali, M.; Maratas, J.; Marchand, J. F.; Marconi, U.; Benito, C. Marin; Marino, P.; Märki, R.; Marks, J.; Martellotti, G.; Martens, A.; Sánchez, A. Martín; Martinelli-Boneschi, F.; Santos, D. Martinez; Vidal, F. Martinez; Tostes, D. Martins; Massafferri, A.; Matev, R.; Mathe, Z.; Matteuzzi, C.; Mazurov, A.; McCann, M.; McCarthy, J.; Mcnab, A.; McNulty, R.; McSkelly, B.; Meadows, B. T.; Meier, F.; Meissner, M.; Merk, M.; Milanes, D. A.; Minard, M. N.; Moggi, N.; Rodriguez, J. Molina; Monteil, S.; Moran-Zenteno, D.; Morandin, M.; Morawski, P.; Mordà, A.; Morello, M. J.; Moron, J.; Morris, A. B.; Mountain, R.; Muheim, F.; Müller, Karl; Muresan, R.; Mussini, M.; Muster, B.; Naik, P.; Nakada, T.; Nandakumar, R.; Nasteva, I.; Needham, M.; Neri, N.; Neubert, S.; Neufeld, N.; Neuner, M.; Nguyen, A. D.; Nguyen, T. D.; Nguyen-Mau, C.; Nicol, M.; Niess, V.; Niet, R.; Nikitin, N.; Nikodem, T.; Novoselov, A.; Oblakowska-Mucha, A.; Obraztsov, V.; Oggero, S.; Ogilvy, S.; Okhrimenko, O.; Oldeman, R.; Onderwater, G.; Orlandea, M.; Goicochea, J. M.Otalora; Owen, R.P.; Oyanguren, A.; Pal, B. K.; Palano, A.; Palombo, F.; Palutan, M.; Panman, J.; Papanestis, A.; Pappagallo, M.; Parkes, C.; Parkinson, C. J.; Passaleva, G.; Patel, G. D.; Patel, M.; Patrignani, C.; Alvarez, A. Pazos; Pearce, D.A.; Pellegrino, A.; Altarelli, M. Pepe; Perazzini, S.; Trigo, E. Perez; Perret, P.; Perrin-Terrin, M.; Pescatore, L.; Pesen, E.; Petridis, K.; Petrolini, A.; Olloqui, E. Picatoste; Pietrzyk, B.; Pilař, T.; Pinci, D.; Pistone, A.; Playfer, S.; Casasus, M. Plo; Polci, F.; Poluektov, A.; Polycarpo, E.; Popov, A.; Popov, D.; Popovici, B.; Potterat, C.; Powell, A.; Prisciandaro, J.; Pritchard, C.A.; Prouve, C.; Pugatch, V.; Navarro, A. Puig; Punzi, G.; Qian, Y.W.; Rachwal, B.; Rademacker, J. H.; Rakotomiaramanana, B.; Rama, M.; Rangel, M. S.; Raniuk, I.; Rauschmayr, N.; Raven, G.; Reichert, S.; Reid, M.; dos Reis, A. C.; Ricciardi, S.; Richards, Al.; Rihl, M.; Rinnert, K.; Molina, V. Rives; Romero, D. A.Roa; Robbe, P.; Rodrigues, A. B.; Rodrigues, L.E.T.; Perez, P. Rodriguez; Roiser, S.; Romanovsky, V.; Vidal, A. Romero; Rotondo, M.; Rouvinet, J.; Ruf, T.; Ruffini, F.; Ruiz, van Hapere; Valls, P. Ruiz; Sabatino, G.; Silva, J. J.Saborido; Sagidova, N.; Sail, P.; Saitta, B.; Guimaraes, V. Salustino; Mayordomo, C. Sanchez; Sedes, B. Sanmartin; Santacesaria, R.; Rios, C. Santamarina; Santovetti, E.; Sapunov, M.; Sarti, A.; Satriano, C.; Satta, A.; Savrie, M.; Savrina, D.; Schiller, M.; Schindler, R. H.; Schlupp, M.; Schmelling, M.; Schmidt, B.; Schneider, O.; Schopper, A.; Schune, M. H.; Schwemmer, R.; Sciascia, B.; Sciubba, A.; Seco, M.; Semennikov, A.; Senderowska, K.; Sepp, I.; Serra, N.; Serrano, J.; Sestini, L.; Seyfert, P.; Shapkin, M.; Shapoval, I.; Shcheglov, Y.; Shears, T.; Shekhtman, L.; Shevchenko, V.; Shires, A.; Coutinho, R. Silva; Simi, G.; Sirendi, M.; Skidmore, N.; Skwarnicki, T.; Smith, N. A.; Smith, E.; Smith, E.; Smith, J; Smith, M.; Snoek, H.; Sokoloff, M. D.; Soler, F. J. P.; Soomro, F.; de Souza, D.K.; De Paula, B. Souza; Spaan, B.; Sparkes, A.; Spinella, F.; Spradlin, P.; Stagni, F.; Stahl, S.; Steinkamp, O.; Stenyakin, O.; Stevenson-Moore, P.; Stoica, S.; Stone, S.; Storaci, B.; Stracka, S.; Straticiuc, M.; Straumann, U.; Stroili, R.; Subbiah, V. K.; Sun, L.; Sutcliffe, W.; Swientek, K.; Swientek, S.; Syropoulos, V.; Szczekowski, M.; Szczypka, P.; Szilard, D.; Szumlak, T.; T'Jampens, S.; Teklishyn, M.; Tellarini, G.; Teubert, F.; Thomas, C.; Thomas, E.; Van Tilburg, J.; Tisserand, V.; Tobin, M. N.; Tolk, S.; Tomassetti, L.; Tonelli, D.; Topp-Joergensen, S.; Torr, N.; Tournefier, E.; Tourneur, S.; Tran, N.T.M.T.; Tresch, M.; Tsaregorodtsev, A.; Tsopelas, P.; Tuning, N.; Garcia, M. Ubeda; Ukleja, A.; Ustyuzhanin, A.; Uwer, U.; Vagnoni, V.; Valenti, G.; Vallier, A.; Gomez, R. Vazquez; Regueiro, P. Vazquez; Sierra, C. Vázquez; Vecchi, S.; Velthuis, M.J.; Veltri, M.; Veneziano, G.; Vesterinen, M.; Viaud, B.; Vieira, D.; Diaz, M. Vieites; Vilasis-Cardona, X.; Vollhardt, A.; Volyanskyy, D.; Voong, D.; Vorobyev, A.; Vorobyev, V.; Voß, C.; Voss, H.; De Vries, J. A.; Waldi, R.; Wallace, C.; Wallace, R.; Walsh, John; Wandernoth, S.; Wang, J.; Ward, D. R.; Watson, N. K.; Websdale, D.; Whitehead, M.; Wicht, J.; Wiedner, D.; Wilkinson, G.; Williams, M.P.; Williams, M.; Wilson, James F; Wimberley, J.; Wishahi, J.; Wislicki, W.; Witek, M.; Wormser, G.; Wotton, S. A.; Wright, S.J.; Wu, S.; Wyllie, K.; Xie, Y.; Xing, Z.; Xu, Z.; Yang, Z.; Yuan, X.; Yushchenko, O.; Zangoli, M.; Zavertyaev, M.; Zhang, F.; Zhang, L.; Zhang, W. C.; Zhang, Y.; Zhelezov, A.; Zhokhov, A.; Zhong, L.; Zvyagin, A.

    2014-01-01

    Production of mesons in proton-lead collisions at a nucleon-nucleon centre-of-mass energy s NN √sNN = 5 TeV is studied with the LHCb detector. The analysis is based on a data sample corresponding to an integrated luminosity of 1.6 nb-1. The mesons of transverse momenta up to 15 GeV/c are

  17. Neural network based online simultaneous policy update algorithm for solving the HJI equation in nonlinear H∞ control.

    Science.gov (United States)

    Wu, Huai-Ning; Luo, Biao

    2012-12-01

    It is well known that the nonlinear H∞ state feedback control problem relies on the solution of the Hamilton-Jacobi-Isaacs (HJI) equation, which is a nonlinear partial differential equation that has proven to be impossible to solve analytically. In this paper, a neural network (NN)-based online simultaneous policy update algorithm (SPUA) is developed to solve the HJI equation, in which knowledge of internal system dynamics is not required. First, we propose an online SPUA which can be viewed as a reinforcement learning technique for two players to learn their optimal actions in an unknown environment. The proposed online SPUA updates control and disturbance policies simultaneously; thus, only one iterative loop is needed. Second, the convergence of the online SPUA is established by proving that it is mathematically equivalent to Newton's method for finding a fixed point in a Banach space. Third, we develop an actor-critic structure for the implementation of the online SPUA, in which only one critic NN is needed for approximating the cost function, and a least-square method is given for estimating the NN weight parameters. Finally, simulation studies are provided to demonstrate the effectiveness of the proposed algorithm.

  18. Three-body ΛNN force due to Λ-Σ coupling

    International Nuclear Information System (INIS)

    Myint, Khin Swe; Akaishi, Yoshinori

    2003-01-01

    The ΛNN three - body force due to coherent Λ - Σ Coupling effect was derived from realistic Nijmegen model D potential. Repulsive and attractive three - body ΛNN forces were reconcilably accounted. For 5 He, within one - channel description, ΛNN force is largely repulsive and its origin comes from Pauli forbidden terms. Within two - channel description, attractive Pauli allowed terms exist and resulting three - body force is always attractive. Large attractive ΛNN force effect due to coherent Λ - Σ coupling effect is predicted in neutron - rich nuclei. The attractive coherent Λ - Σ coupling effect is largely enhanced at high density neutron matter. The attractive three - body ΛNN force effect is essential dynamics of Λ - Σ coupling while the repulsive Nogami three - body effect arises from Pauli forbidden diagrams. (Y. Kazumata)

  19. A Formal Method for Verification and Validation of Neural Network High Assurance Systems, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — Our proposed innovation is to develop neural network (NN) rule extraction technology to a level where it can be incorporated into a software tool, we are calling...

  20. Assessment of surface solar irradiance derived from real-time modelling techniques and verification with ground-based measurements

    Science.gov (United States)

    Kosmopoulos, Panagiotis G.; Kazadzis, Stelios; Taylor, Michael; Raptis, Panagiotis I.; Keramitsoglou, Iphigenia; Kiranoudis, Chris; Bais, Alkiviadis F.

    2018-02-01

    This study focuses on the assessment of surface solar radiation (SSR) based on operational neural network (NN) and multi-regression function (MRF) modelling techniques that produce instantaneous (in less than 1 min) outputs. Using real-time cloud and aerosol optical properties inputs from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the Meteosat Second Generation (MSG) satellite and the Copernicus Atmosphere Monitoring Service (CAMS), respectively, these models are capable of calculating SSR in high resolution (1 nm, 0.05°, 15 min) that can be used for spectrally integrated irradiance maps, databases and various applications related to energy exploitation. The real-time models are validated against ground-based measurements of the Baseline Surface Radiation Network (BSRN) in a temporal range varying from 15 min to monthly means, while a sensitivity analysis of the cloud and aerosol effects on SSR is performed to ensure reliability under different sky and climatological conditions. The simulated outputs, compared to their common training dataset created by the radiative transfer model (RTM) libRadtran, showed median error values in the range -15 to 15 % for the NN that produces spectral irradiances (NNS), 5-6 % underestimation for the integrated NN and close to zero errors for the MRF technique. The verification against BSRN revealed that the real-time calculation uncertainty ranges from -100 to 40 and -20 to 20 W m-2, for the 15 min and monthly mean global horizontal irradiance (GHI) averages, respectively, while the accuracy of the input parameters, in terms of aerosol and cloud optical thickness (AOD and COT), and their impact on GHI, was of the order of 10 % as compared to the ground-based measurements. The proposed system aims to be utilized through studies and real-time applications which are related to solar energy production planning and use.

  1. Prediction of Marshall Parameters of Modified Bituminous Mixtures Using Artificial Intelligence Techniques

    Directory of Open Access Journals (Sweden)

    Sunil Khuntia

    2014-09-01

    Full Text Available This study presents the application of artificial neural networks (ANN and least square support vector machine (LS-SVM for prediction of Marshall parameters obtained from Marshall tests for waste polyethylene (PE modified bituminous mixtures. Waste polyethylene in the form of fibres processed from utilized milk packets has been used to modify the bituminous mixes in order to improve their engineering properties. Marshall tests were carried out on mix specimens with variations in polyethylene and bitumen contents. It has been observed that the addition of waste polyethylene results in the improvement of Marshall characteristics such as stability, flow value and air voids, used to evaluate a bituminous mix. The proposed neural network (NN model uses the quantities of ingredients used for preparation of Marshall specimens such as polyethylene, bitumen and aggregate in order to predict the Marshall stability, flow value and air voids obtained from the tests. Out of two techniques used, the NN based model is found to be compact, reliable and predictable when compared with LS-SVM model. A sensitivity analysis has been performed to identify the importance of the parameters considered.

  2. Nuclear power plant monitoring and fault diagnosis methods based on the artificial intelligence technique

    International Nuclear Information System (INIS)

    Yoshikawa, S.; Saiki, A.; Ugolini, D.; Ozawa, K.

    1996-01-01

    The main objective of this paper is to develop an advanced diagnosis system based on the artificial intelligence technique to monitor the operation and to improve the operational safety of nuclear power plants. Three different methods have been elaborated in this study: an artificial neural network local diagnosis (NN ds ) scheme that acting at the component level discriminates between normal and abnormal transients, a model-based diagnostic reasoning mechanism that combines a physical causal network model-based knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge compiler (KC) that generates applicable diagnostic rules from widely accepted physical knowledge. Although the three methods have been developed and verified independently, they are highly correlated and, when connected together, form a effective and robust diagnosis and monitoring tool. (authors)

  3. Identification and control of plasma vertical position using neural network in Damavand tokamak

    International Nuclear Information System (INIS)

    Rasouli, H.; Rasouli, C.; Koohi, A.

    2013-01-01

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg–Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  4. Identification and control of plasma vertical position using neural network in Damavand tokamak

    Energy Technology Data Exchange (ETDEWEB)

    Rasouli, H. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of); Advanced Process Automation and Control (APAC) Research Group, Faculty of Electrical Engineering, K.N. Toosi University of Technology, P.O. Box 16315-1355, Tehran (Iran, Islamic Republic of); Rasouli, C.; Koohi, A. [School of Plasma Physics and Nuclear Fusion, Institute of Nuclear Science and Technology, AEOI, P.O. Box 14155-1339, Tehran (Iran, Islamic Republic of)

    2013-02-15

    In this work, a nonlinear model is introduced to determine the vertical position of the plasma column in Damavand tokamak. Using this model as a simulator, a nonlinear neural network controller has been designed. In the first stage, the electronic drive and sensory circuits of Damavand tokamak are modified. These circuits can control the vertical position of the plasma column inside the vacuum vessel. Since the vertical position of plasma is an unstable parameter, a direct closed loop system identification algorithm is performed. In the second stage, a nonlinear model is identified for plasma vertical position, based on the multilayer perceptron (MLP) neural network (NN) structure. Estimation of simulator parameters has been performed by back-propagation error algorithm using Levenberg-Marquardt gradient descent optimization technique. The model is verified through simulation of the whole closed loop system using both simulator and actual plant in similar conditions. As the final stage, a MLP neural network controller is designed for simulator model. In the last step, online training is performed to tune the controller parameters. Simulation results justify using of the NN controller for the actual plant.

  5. A hybrid method based on a new clustering technique and multilayer perceptron neural networks for hourly solar radiation forecasting

    International Nuclear Information System (INIS)

    Azimi, R.; Ghayekhloo, M.; Ghofrani, M.

    2016-01-01

    Highlights: • A novel clustering approach is proposed based on the data transformation approach. • A novel cluster selection method based on correlation analysis is presented. • The proposed hybrid clustering approach leads to deep learning for MLPNN. • A hybrid forecasting method is developed to predict solar radiations. • The evaluation results show superior performance of the proposed forecasting model. - Abstract: Accurate forecasting of renewable energy sources plays a key role in their integration into the grid. This paper proposes a hybrid solar irradiance forecasting framework using a Transformation based K-means algorithm, named TB K-means, to increase the forecast accuracy. The proposed clustering method is a combination of a new initialization technique, K-means algorithm and a new gradual data transformation approach. Unlike the other K-means based clustering methods which are not capable of providing a fixed and definitive answer due to the selection of different cluster centroids for each run, the proposed clustering provides constant results for different runs of the algorithm. The proposed clustering is combined with a time-series analysis, a novel cluster selection algorithm and a multilayer perceptron neural network (MLPNN) to develop the hybrid solar radiation forecasting method for different time horizons (1 h ahead, 2 h ahead, …, 48 h ahead). The performance of the proposed TB K-means clustering is evaluated using several different datasets and compared with different variants of K-means algorithm. Solar datasets with different solar radiation characteristics are also used to determine the accuracy and processing speed of the developed forecasting method with the proposed TB K-means and other clustering techniques. The results of direct comparison with other well-established forecasting models demonstrate the superior performance of the proposed hybrid forecasting method. Furthermore, a comparative analysis with the benchmark solar

  6. Performance of svm, k-nn and nbc classifiers for text-independent speaker identification with and without modelling through merging models

    Directory of Open Access Journals (Sweden)

    Yussouf Nahayo

    2016-04-01

    Full Text Available This paper proposes some methods of robust text-independent speaker identification based on Gaussian Mixture Model (GMM. We implemented a combination of GMM model with a set of classifiers such as Support Vector Machine (SVM, K-Nearest Neighbour (K-NN, and Naive Bayes Classifier (NBC. In order to improve the identification rate, we developed a combination of hybrid systems by using validation technique. The experiments were performed on the dialect DR1 of the TIMIT corpus. The results have showed a better performance for the developed technique compared to the individual techniques.

  7. Neural network analysis in pharmacogenetics of mood disorders

    Directory of Open Access Journals (Sweden)

    Serretti Alessandro

    2004-12-01

    Full Text Available Abstract Background The increasing number of available genotypes for genetic studies in humans requires more advanced techniques of analysis. We previously reported significant univariate associations between gene polymorphisms and antidepressant response in mood disorders. However the combined analysis of multiple gene polymorphisms and clinical variables requires the use of non linear methods. Methods In the present study we tested a neural network strategy for a combined analysis of two gene polymorphisms. A Multi Layer Perceptron model showed the best performance and was therefore selected over the other networks. One hundred and twenty one depressed inpatients treated with fluvoxamine in the context of previously reported pharmacogenetic studies were included. The polymorphism in the transcriptional control region upstream of the 5HTT coding sequence (SERTPR and in the Tryptophan Hydroxylase (TPH gene were analysed simultaneously. Results A multi layer perceptron network composed by 1 hidden layer with 7 nodes was chosen. 77.5 % of responders and 51.2% of non responders were correctly classified (ROC area = 0.731 – empirical p value = 0.0082. Finally, we performed a comparison with traditional techniques. A discriminant function analysis correctly classified 34.1 % of responders and 68.1 % of non responders (F = 8.16 p = 0.0005. Conclusions Overall, our findings suggest that neural networks may be a valid technique for the analysis of gene polymorphisms in pharmacogenetic studies. The complex interactions modelled through NN may be eventually applied at the clinical level for the individualized therapy.

  8. Medium energy measurements of N-N parameters

    International Nuclear Information System (INIS)

    Ambrose, D.; Bachman, M.; Coffey, P.; Glass, G.; Jobst, B.; McNaughton, K.H.; Nguyen, C.; Riley, P.J.

    1993-01-01

    The authors report here progress made during the three year period January 1, 1990, to December 31, 1993, for the Department of Energy Three-Year Grant No. DE-FG05-88ER40446, third year. A major part of the work has been associated with nucleon-nucleon (N-N) research carried out at the Nucleon Physics Laboratory (NPL) at the Los Alamos Meson Physics Facility (LAMPF). During this period they also completed data acquisition and analyses of a TRIUMF experiment, but they have no further plans for experimental work at TRIUMF. Other research has been and will be continued to be carried out at BNL, and involves two rare kaon decay experiments, BNL E791, now completed, and a second generation rare kaon decay experiment, E871, which has just this summer completed an engineering test run. The authors are now also members of a proposed experiment, STAR, (Solenoidal Tracker at RHIC) to be carried out at the Relativistic Heavy Ion Collider facility, RHIC, at BNL. The past three years have been a time of rapid change in the focus of the experimental program. A LAMPF experiment, E1097, in which they spent a large amount of effort during the past three years, was terminated due to funding shortages after they had fabricated the detector, but before data acquisition, and consequently they increased their participation in the rare kaon experiment at BNL, E871. It now appears that there will be no LAMPF N-N program after 1993, so that the research efforts will concentrate on the BNL rare kaon decay measurement, E871, and on STAR. The authors expect that STAR, which requires the fabrication of a large colliding beam detector facility, will use an increasing amount of their research efforts during the next few years. In what follows they describe recent progress on the LAMPF and TRIUMF N-N measurements, on the BNL rare kaon decay work, and on the initial work with the STAR group

  9. Power-Management Techniques for Wireless Sensor Networks and Similar Low-Power Communication Devices Based on Nonrechargeable Batteries

    Directory of Open Access Journals (Sweden)

    Agnelo Silva

    2012-01-01

    Full Text Available Despite the well-known advantages of communication solutions based on energy harvesting, there are scenarios where the absence of batteries (supercapacitor only or the use of rechargeable batteries is not a realistic option. Therefore, the alternative is to extend as much as possible the lifetime of primary cells (nonrechargeable batteries. By assuming low duty-cycle applications, three power-management techniques are combined in a novel way to provide an efficient energy solution for wireless sensor networks nodes or similar communication devices powered by primary cells. Accordingly, a customized node is designed and long-term experiments in laboratory and outdoors are realized. Simulated and empirical results show that the battery lifetime can be drastically enhanced. However, two trade-offs are identified: a significant increase of both data latency and hardware/software complexity. Unattended nodes deployed in outdoors under extreme temperatures, buried sensors (underground communication, and nodes embedded in the structure of buildings, bridges, and roads are some of the target scenarios for this work. Part of the provided guidelines can be used to extend the battery lifetime of communication devices in general.

  10. Trade-Offs between Energy Saving and Reliability in Low Duty Cycle Wireless Sensor Networks Using a Packet Splitting Forwarding Technique

    Directory of Open Access Journals (Sweden)

    Leonardi Alessandro

    2010-01-01

    Full Text Available One of the challenging topics and design constraints in Wireless Sensor Networks (WSNs is the reduction of energy consumption because, in most application scenarios, replacement of power resources in sensor devices might be unfeasible. In order to minimize the power consumption, some nodes can be put to sleep during idle times and wake up only when needed. Although it seems the best way to limit the consumption of energy, other performance parameters such as network reliability have to be considered. In a recent paper, we introduced a new forwarding algorithm for WSNs based on a simple splitting procedure able to increase the network lifetime. The forwarding technique is based on the Chinese Remainder Theorem and exhibits very good results in terms of energy efficiency and complexity. In this paper, we intend to investigate a trade-off between energy efficiency and reliability of the proposed forwarding scheme when duty-cycling techniques are considered too.

  11. Neural network based adaptive control for nonlinear dynamic regimes

    Science.gov (United States)

    Shin, Yoonghyun

    Adaptive control designs using neural networks (NNs) based on dynamic inversion are investigated for aerospace vehicles which are operated at highly nonlinear dynamic regimes. NNs play a key role as the principal element of adaptation to approximately cancel the effect of inversion error, which subsequently improves robustness to parametric uncertainty and unmodeled dynamics in nonlinear regimes. An adaptive control scheme previously named 'composite model reference adaptive control' is further developed so that it can be applied to multi-input multi-output output feedback dynamic inversion. It can have adaptive elements in both the dynamic compensator (linear controller) part and/or in the conventional adaptive controller part, also utilizing state estimation information for NN adaptation. This methodology has more flexibility and thus hopefully greater potential than conventional adaptive designs for adaptive flight control in highly nonlinear flight regimes. The stability of the control system is proved through Lyapunov theorems, and validated with simulations. The control designs in this thesis also include the use of 'pseudo-control hedging' techniques which are introduced to prevent the NNs from attempting to adapt to various actuation nonlinearities such as actuator position and rate saturations. Control allocation is introduced for the case of redundant control effectors including thrust vectoring nozzles. A thorough comparison study of conventional and NN-based adaptive designs for a system under a limit cycle, wing-rock, is included in this research, and the NN-based adaptive control designs demonstrate their performances for two highly maneuverable aerial vehicles, NASA F-15 ACTIVE and FQM-117B unmanned aerial vehicle (UAV), operated under various nonlinearities and uncertainties.

  12. Recovery and Resource Allocation Strategies to Maximize Mobile Network Survivability by Using Game Theories and Optimization Techniques

    Directory of Open Access Journals (Sweden)

    Pei-Yu Chen

    2013-01-01

    Full Text Available With more and more mobile device users, an increasingly important and critical issue is how to efficiently evaluate mobile network survivability. In this paper, a novel metric called Average Degree of Disconnectivity (Average DOD is proposed, in which the concept of probability is calculated by the contest success function. The DOD metric is used to evaluate the damage degree of the network, where the larger the value of the Average DOD, the more the damage degree of the network. A multiround network attack-defense scenario as a mathematical model is used to support network operators to predict all the strategies both cyber attacker and network defender would likely take. In addition, the Average DOD would be used to evaluate the damage degree of the network. In each round, the attacker could use the attack resources to launch attacks on the nodes of the target network. Meanwhile, the network defender could reallocate its existing resources to recover compromised nodes and allocate defense resources to protect the survival nodes of the network. In the approach to solving this problem, the “gradient method” and “game theory” are adopted to find the optimal resource allocation strategies for both the cyber attacker and mobile network defender.

  13. The GMO sumrule and the πNN coupling constant

    International Nuclear Information System (INIS)

    Ericson, T.E.O.; Loiseau, B.; Thomas, A.W.

    2000-01-01

    The isovector GMO sumrule for forward πN scattering is critically evaluated using the precise π - p and π - d scattering lengths obtained recently from pionic atom measurements. The charged πNN coupling constant is then deduced with careful analysis of systematic and statistical sources of uncertainties. This determination gives directly from data g c 2 (GMO)/4π = 14.17±0.09 (statistic) ±0.17 (systematic) or f c 2 / 4π=0.078(11). This value is half-way between that of indirect methods (phase-shift analyses) and the direct evaluation from from backward np differential scattering cross sections (extrapolation to pion pole). From the π - p and π - d scattering lengths our analysis leads also to accurate values for (1/2)(a π - p +a π - n ) and (1/2) (a π - p -a π - n ). (orig.)

  14. The GMO Sumrule and the πNN Coupling Constant

    Science.gov (United States)

    Ericson, T. E. O.; Loiseau, B.; Thomas, A. W.

    The isovector GMO sumrule for forward πN scattering is critically evaluated using the precise π-p and π-d scattering lengths obtained recently from pionic atom measurements. The charged πNN coupling constant is then deduced with careful analysis of systematic and statistical sources of uncertainties. This determination gives directly from data gc2(GMO)/4π = 14.17±0.09 (statistic) ±0.17 (systematic) or fc2/ 4π=0.078(11). This value is half-way between that of indirect methods (phase-shift analyses) and the direct evaluation from from backward np differential scattering cross sections (extrapolation to pion pole). From the π-p and π-d scattering lengths our analysis leads also to accurate values for (1/2)(aπ-p+aπ-n) and (1/2) (aπ-p-aπ-n).

  15. Construction of high-quality NN potential models

    International Nuclear Information System (INIS)

    Stoks, V.G.J.; Klomp, R.A.M.; Terheggen, C.P.F.; de Swart, J.J.

    1994-01-01

    We present an updated version (Nijm93) of the Nijmegen soft-core potential, which gives a much better description of the np data than the older version (Nijm78). The χ 2 per datum is 1.87. The configuration-space and momentum-space versions of this potential are exactly equivalent, a unique feature among meson-theoretical potentials. We also present three new NN potential models: a nonlocal Reid-like Nijmegen potential (Nijm I), a local version (Nijm II), and an updated regularized version (Reid 93) of the Reid soft-core potential. These three potentials all have a nearly optimal χ 2 per datum and can therefore be considered as alternative partial-wave analyses. All potentials contain the proper charge-dependent one-pion-exchange tail

  16. Fast clustering algorithm for large ECG data sets based on CS theory in combination with PCA and K-NN methods.

    Science.gov (United States)

    Balouchestani, Mohammadreza; Krishnan, Sridhar

    2014-01-01

    Long-term recording of Electrocardiogram (ECG) signals plays an important role in health care systems for diagnostic and treatment purposes of heart diseases. Clustering and classification of collecting data are essential parts for detecting concealed information of P-QRS-T waves in the long-term ECG recording. Currently used algorithms do have their share of drawbacks: 1) clustering and classification cannot be done in real time; 2) they suffer from huge energy consumption and load of sampling. These drawbacks motivated us in developing novel optimized clustering algorithm which could easily scan large ECG datasets for establishing low power long-term ECG recording. In this paper, we present an advanced K-means clustering algorithm based on Compressed Sensing (CS) theory as a random sampling procedure. Then, two dimensionality reduction methods: Principal Component Analysis (PCA) and Linear Correlation Coefficient (LCC) followed by sorting the data using the K-Nearest Neighbours (K-NN) and Probabilistic Neural Network (PNN) classifiers are applied to the proposed algorithm. We show our algorithm based on PCA features in combination with K-NN classifier shows better performance than other methods. The proposed algorithm outperforms existing algorithms by increasing 11% classification accuracy. In addition, the proposed algorithm illustrates classification accuracy for K-NN and PNN classifiers, and a Receiver Operating Characteristics (ROC) area of 99.98%, 99.83%, and 99.75% respectively.

  17. CONSTRUCTION COST PREDICTION USING NEURAL NETWORKS

    Directory of Open Access Journals (Sweden)

    Smita K Magdum

    2017-10-01

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

  18. NN-Based Implicit Stochastic Optimization of Multi-Reservoir Systems Management

    Directory of Open Access Journals (Sweden)

    Matteo Sangiorgio

    2018-03-01

    Full Text Available Multi-reservoir systems management is complex because of the uncertainty on future events and the variety of purposes, usually conflicting, of the involved actors. An efficient management of these systems can help improving resource allocation, preventing political crisis and reducing the conflicts between the stakeholders. Bellman stochastic dynamic programming (SDP is the most famous among the many proposed approaches to solve this optimal control problem. Unfortunately, SDP is affected by the curse of dimensionality: computational effort increases exponentially with the complexity of the considered system (i.e., number of reservoirs, and the problem rapidly becomes intractable. This paper proposes an implicit stochastic optimization approach for the solution of the reservoir management problem. The core idea is using extremely flexible functions, such as artificial neural networks (NN, for designing release rules which approximate the optimal policies obtained by an open-loop approach. These trained NNs can then be used to take decisions in real time. The approach thus requires a sufficiently long series of historical or synthetic inflows, and the definition of a compromise solution to be approximated. This work analyzes with particular emphasis the importance of the information which represents the input of the control laws, investigating the effects of different degrees of completeness. The methodology is applied to the Nile River basin considering the main management objectives (minimization of the irrigation water deficit and maximization of the hydropower production, but can be easily adopted also in other cases.

  19. Application of Meta-Heuristic Techniques for Optimal Load Shedding in Islanded Distribution Network with High Penetration of Solar PV Generation

    Directory of Open Access Journals (Sweden)

    Mohammad Dreidy

    2017-01-01

    Full Text Available Recently, several environmental problems are beginning to affect all aspects of life. For this reason, many governments and international agencies have expressed great interest in using more renewable energy sources (RESs. However, integrating more RESs with distribution networks resulted in several critical problems vis-à-vis the frequency stability, which might lead to a complete blackout if not properly treated. Therefore, this paper proposed a new Under Frequency Load Shedding (UFLS scheme for islanding distribution network. This scheme uses three meta-heuristics techniques, binary evolutionary programming (BEP, Binary genetic algorithm (BGA, and Binary particle swarm optimization (BPSO, to determine the optimal combination of loads that needs to be shed from the islanded distribution network. Compared with existing UFLS schemes using fixed priority loads, the proposed scheme has the ability to restore the network frequency without any overshooting. Furthermore, in terms of execution time, the simulation results show that the BEP technique is fast enough to shed the optimal combination of loads compared with BGA and BPSO techniques.

  20. LITERATURE SURVEY ON EXISTING POWER SAVING ROUTING METHODS AND TECHNIQUES FOR INCREASING NETWORK LIFE TIME IN MANET

    Directory of Open Access Journals (Sweden)

    K Mariyappan

    2017-06-01

    Full Text Available Mobile ad hoc network (MANET is a special type of wireless network in which a collection of wireless mobile devices (called also nodes dynamically forming a temporary network without the need of any pre-existing network infrastructure or centralized administration. Currently, Mobile ad hoc networks (MANETs play a significant role in university campus, advertisement, emergency response, disaster recovery, military use in battle fields, disaster management scenarios, in sensor network, and so on. However, wireless network devices, especially in ad hoc networks, are typically battery-powered. Thus, energy efficiency is a critical issue for battery-powered mobile devices in ad hoc networks. This is due to the fact that failure of node or link allows re-routing and establishing a new path from source to destination which creates extra energy consumption of nodes and sparse network connectivity, leading to a more likelihood occurrences of network partition. Routing based on energy related parameters is one of the important solutions to extend the lifetime of the node and reduce energy consumption of the network. In this paper detail literature survey on existing energy efficient routing method are studied and compared for their performance under different condition. The result has shown that both the broadcast schemes and energy aware metrics have great potential in overcoming the broadcast storm problem associated with flooding. However, the performances of these approaches rely on either the appropriate selection of the broadcast decision parameter or an energy efficient path. In the earlier proposed broadcast methods, the forwarding probability is selected based on fixed probability or number of neighbors regardless of nodes battery capacity whereas in energy aware schemes energy inefficient node could be part of an established path. Therefore, in an attempt to remedy the paucity of research and to address the gaps identified in this area, a study

  1. An effective Load shedding technique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system

    Directory of Open Access Journals (Sweden)

    Foday Conteh

    2017-09-01

    Full Text Available In recent years, the use of renewable energy sources in micro-grids has become an effectivemeans of power decentralization especially in remote areas where the extension of the main power gridis an impediment. Despite the huge deposit of natural resources in Africa, the continent still remains inenergy poverty. Majority of the African countries could not meet the electricity demand of their people.Therefore, the power system is prone to frequent black out as a result of either excess load to the systemor generation failure. The imbalance of power generation and load demand has been a major factor inmaintaining the stability of the power systems and is usually responsible for the under frequency andunder voltage in power systems. Currently, load shedding is the most widely used method to balancebetween load and demand in order to prevent the system from collapsing. But the conventional methodof under frequency or under voltage load shedding faces many challenges and may not perform asexpected. This may lead to over shedding or under shedding, causing system blackout or equipmentdamage. To prevent system cascade or equipment damage, appropriate amount of load must beintentionally and automatically curtailed during instability. In this paper, an effective load sheddingtechnique for micro-grids using artificial neural network and adaptive neuro-fuzzy inference system isproposed. The combined techniques take into account the actual system state and the exact amount ofload needs to be curtailed at a faster rate as compared to the conventional method. Also, this methodis able to carry out optimal load shedding for any input range other than the trained data. Simulationresults obtained from this work, corroborate the merit of this algorithm.

  2. An analytic Study of the Key Factors In uencing the Design and Routing Techniques of a Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Yogita Bahuguna

    2017-03-01

    Full Text Available A wireless sensor network contains various nodes having certain sensing, processing and communication capabilities. Actually they are multifunctional battery operated nodes called motes. These motes are small in size and battery constrained. They are operated by a power source. A wireless sensor network consists of a huge number of tiny sensor nodes which are deployed either randomly or according to some predefined distribution. The sensors nodes in a sensor network are cooperative among themselves having self-organizing ability. This ensures that a wireless network serves a wide variety of applications. Few of them are weather monitoring, health, security and military etc. As their applications are wide, this requires that sensors in a sensor network must play their role very efficiently. But, as discussed above, the sensor nodes have energy limitation. This limitation leads failure of nodes after certain round of communication. So, a sensor network suffers with sensors having energy limitations. Beside this, sensor nodes in a sensor network must fulfill connectivity and coverage requirements. In this paper, we have discussed various issues affecting the design of a wireless sensor network. This provides the readers various research issues in designing a wireless sensor network.

  3. Neural-network hybrid control for antilock braking systems.

    Science.gov (United States)

    Lin, Chih-Min; Hsu, C F

    2003-01-01

    The antilock braking systems are designed to maximize wheel traction by preventing the wheels from locking during braking, while also maintaining adequate vehicle steerability; however, the performance is often degraded under harsh road conditions. In this paper, a hybrid control system with a recurrent neural network (RNN) observer is developed for antilock braking systems. This hybrid control system is comprised of an ideal controller and a compensation controller. The ideal controller, containing an RNN uncertainty observer, is the principal controller; and the compensation controller is a compensator for the difference between the system uncertainty and the estimated uncertainty. Since for dynamic response the RNN has capabilities superior to the feedforward NN, it is utilized for the uncertainty observer. The Taylor linearization technique is employed to increase the learning ability of the RNN. In addition, the on-line parameter adaptation laws are derived based on a Lyapunov function, so the stability of the system can be guaranteed. Simulations are performed to demonstrate the effectiveness of the proposed NN hybrid control system for antilock braking control under various road conditions.

  4. A comparative study of breast cancer diagnosis based on neural network ensemble via improved training algorithms.

    Science.gov (United States)

    Azami, Hamed; Escudero, Javier

    2015-08-01

    Breast cancer is one of the most common types of cancer in women all over the world. Early diagnosis of this kind of cancer can significantly increase the chances of long-term survival. Since diagnosis of breast cancer is a complex problem, neural network (NN) approaches have been used as a promising solution. Considering the low speed of the back-propagation (BP) algorithm to train a feed-forward NN, we consider a number of improved NN trainings for the Wisconsin breast cancer dataset: BP with momentum, BP with adaptive learning rate, BP with adaptive learning rate and momentum, Polak-Ribikre conjugate gradient algorithm (CGA), Fletcher-Reeves CGA, Powell-Beale CGA, scaled CGA, resilient BP (RBP), one-step secant and quasi-Newton methods. An NN ensemble, which is a learning paradigm to combine a number of NN outputs, is used to improve the accuracy of the classification task. Results demonstrate that NN ensemble-based classification methods have better performance than NN-based algorithms. The highest overall average accuracy is 97.68% obtained by NN ensemble trained by RBP for 50%-50% training-test evaluation method.

  5. Computational intelligence techniques for biological data mining: An overview

    Science.gov (United States)

    Faye, Ibrahima; Iqbal, Muhammad Javed; Said, Abas Md; Samir, Brahim Belhaouari

    2014-10-01

    Computational techniques have been successfully utilized for a highly accurate analysis and modeling of multifaceted and raw biological data gathered from various genome sequencing projects. These techniques are proving much more effective to overcome the limitations of the traditional in-vitro experiments on the constantly increasing sequence data. However, most critical problems that caught the attention of the researchers may include, but not limited to these: accurate structure and function prediction of unknown proteins, protein subcellular localization prediction, finding protein-protein interactions, protein fold recognition, analysis of microarray gene expression data, etc. To solve these problems, various classification and clustering techniques using machine learning have been extensively used in the published literature. These techniques include neural network algorithms, genetic algorithms, fuzzy ARTMAP, K-Means, K-NN, SVM, Rough set classifiers, decision tree and HMM based algorithms. Major difficulties in applying the above algorithms include the limitations found in the previous feature encoding and selection methods while extracting the best features, increasing classification accuracy and decreasing the running time overheads of the learning algorithms. The application of this research would be potentially useful in the drug design and in the diagnosis of some diseases. This paper presents a concise overview of the well-known protein classification techniques.

  6. Hierarchical modeling of molecular energies using a deep neural network

    Science.gov (United States)

    Lubbers, Nicholas; Smith, Justin S.; Barros, Kipton

    2018-06-01

    We introduce the Hierarchically Interacting Particle Neural Network (HIP-NN) to model molecular properties from datasets of quantum calculations. Inspired by a many-body expansion, HIP-NN decomposes properties, such as energy, as a sum over hierarchical terms. These terms are generated from a neural network—a composition of many nonlinear transformations—acting on a representation of the molecule. HIP-NN achieves the state-of-the-art performance on a dataset of 131k ground state organic molecules and predicts energies with 0.26 kcal/mol mean absolute error. With minimal tuning, our model is also competitive on a dataset of molecular dynamics trajectories. In addition to enabling accurate energy predictions, the hierarchical structure of HIP-NN helps to identify regions of model uncertainty.

  7. CONTROLLED CONDENSATION IN K-NN AND ITS APPLICATION FOR REAL TIME COLOR IDENTIFICATION

    Directory of Open Access Journals (Sweden)

    Carmen Villar Patiño

    2017-04-01

    Full Text Available k-NN algorithms are frequently used in statistical classification. They are accurate and distribution free. Despite these advantages, k-NN algorithms imply a high computational cost. To find efficient ways to implement them is an important challenge in pattern recognition. In this article, an improved version of the k-NN Controlled Condensation algorithm is introduced. Its potential for instantaneous color identification in real time is also analyzed. This algorithm is based on the representation of data in terms of a reduced set of informative prototypes. It includes two parameters to control the balance between speed and precision. This gives us the opportunity to achieve a convenient percentage of condensation without incurring in an important loss of accuracy. We test our proposal in an instantaneous color identification exercise in video images. We achieve the real time identification by using k-NN Controlled Condensation executed through multi-threading programming methods. The results are encouraging.

  8. A Pade-Aided Analysis of Nonperturbative NN Scattering in 1S0 Channel

    International Nuclear Information System (INIS)

    Yang Jifeng; Huang Jianhua

    2007-01-01

    We carried out a Pade approximant analysis on a compact factor of the T-matrix for NN scattering to explore the nonperturbative renormalization prescription in a universal manner. The utilities and virtues for this Pade analysis are discussed.

  9. Repellent Activity of TRIG (N-N Diethyl Benzamide) against Man-Biting Mosquitoes

    OpenAIRE

    Msangi, Shandala; Kweka, Eliningaya; Mahande, Aneth

    2018-01-01

    A study was conducted to assess efficacy of a new repellent brand TRIG (15% N-N Diethyl Benzamide) when compared to DEET (20% N-N Methyl Toluamide). The repellents were tested in laboratory and field. In the laboratory, the repellence was tested on human volunteers, by exposing their repellent-treated arms on starved mosquitoes in cages for 3 minutes at hourly intervals, while counting the landing and probing attempts. Anopheles gambiae and Aedes aegypti mosquitoes were used. Field evaluation...

  10. On a New Variance Reduction Technique: Neural Network Biasing-a Study of Two Test Cases with the Monte Carlo Code Tripoli4

    International Nuclear Information System (INIS)

    Dumonteil, E.

    2009-01-01

    Various variance-reduction techniques are used in Monte Carlo particle transport. Most of them rely either on a hypothesis made by the user (parameters of the exponential biasing, mesh and weight bounds for weight windows, etc.) or on a previous calculation of the system with, for example, a deterministic solver. This paper deals with a new acceleration technique, namely, auto-adaptative neural network biasing. Indeed, instead of using any a priori knowledge of the system, it is possible, at a given point in a simulation, to use the Monte Carlo histories previously simulated to train a neural network, which, in return, should be able to provide an estimation of the adjoint flux, used then for biasing the simulation. We will describe this method, detail its implementation in the Monte Carlo code Tripoli4, and discuss its results on two test cases. (author)

  11. A comparative study of the SVM and K-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals.

    Science.gov (United States)

    Palaniappan, Rajkumar; Sundaraj, Kenneth; Sundaraj, Sebastian

    2014-06-27

    Pulmonary acoustic parameters extracted from recorded respiratory sounds provide valuable information for the detection of respiratory pathologies. The automated analysis of pulmonary acoustic signals can serve as a differential diagnosis tool for medical professionals, a learning tool for medical students, and a self-management tool for patients. In this context, we intend to evaluate and compare the performance of the support vector machine (SVM) and K-nearest neighbour (K-nn) classifiers in diagnosis respiratory pathologies using respiratory sounds from R.A.L.E database. The pulmonary acoustic signals used in this study were obtained from the R.A.L.E lung sound database. The pulmonary acoustic signals were manually categorised into three different groups, namely normal, airway obstruction pathology, and parenchymal pathology. The mel-frequency cepstral coefficient (MFCC) features were extracted from the pre-processed pulmonary acoustic signals. The MFCC features were analysed by one-way ANOVA and then fed separately into the SVM and K-nn classifiers. The performances of the classifiers were analysed using the confusion matrix technique. The statistical analysis of the MFCC features using one-way ANOVA showed that the extracted MFCC features are significantly different (p < 0.001). The classification accuracies of the SVM and K-nn classifiers were found to be 92.19% and 98.26%, respectively. Although the data used to train and test the classifiers are limited, the classification accuracies found are satisfactory. The K-nn classifier was better than the SVM classifier for the discrimination of pulmonary acoustic signals from pathological and normal subjects obtained from the RALE database.

  12. Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

    Science.gov (United States)

    Vitela, Javier E.; Martinell, Julio J.

    1998-02-01

    In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN.

  13. Stabilization of burn conditions in a thermonuclear reactor using artificial neural networks

    International Nuclear Information System (INIS)

    Vitela, J.E.; Martinell, J.J.

    1998-01-01

    In this work we develop an artificial neural network (ANN) for the feedback stabilization of a thermonuclear reactor at nearly ignited burn conditions. A volume-averaged zero-dimensional nonlinear model is used to represent the time evolution of the electron density, the relative density of alpha particles and the temperature of the plasma, where a particular scaling law for the energy confinement time previously used by other authors, was adopted. The control actions include the concurrent modulation of the D-T refuelling rate, the injection of a neutral He-4 beam and an auxiliary heating power modulation, which are constrained to take values within a maximum and minimum levels. For this purpose a feedforward multilayer artificial neural network with sigmoidal activation function is trained using a back-propagation through-time technique. Numerical examples are used to illustrate the behaviour of the resulting ANN-dynamical system configuration. It is concluded that the resulting ANN can successfully stabilize the nonlinear model of the thermonuclear reactor at nearly ignited conditions for temperature and density departures significantly far from their nominal operating values. The NN-dynamical system configuration is shown to be robust with respect to the thermalization time of the alpha particles for perturbations within the region used to train the NN. (author)

  14. Robust/optimal temperature profile control of a high-speed aerospace vehicle using neural networks.

    Science.gov (United States)

    Yadav, Vivek; Padhi, Radhakant; Balakrishnan, S N

    2007-07-01

    An approximate dynamic programming (ADP)-based suboptimal neurocontroller to obtain desired temperature for a high-speed aerospace vehicle is synthesized in this paper. A 1-D distributed parameter model of a fin is developed from basic thermal physics principles. "Snapshot" solutions of the dynamics are generated with a simple dynamic inversion-based feedback controller. Empirical basis functions are designed using the "proper orthogonal decomposition" (POD) technique and the snapshot solutions. A low-order nonlinear lumped parameter system to characterize the infinite dimensional system is obtained by carrying out a Galerkin projection. An ADP-based neurocontroller with a dual heuristic programming (DHP) formulation is obtained with a single-network-adaptive-critic (SNAC) controller for this approximate nonlinear model. Actual control in the original domain is calculated with the same POD basis functions through a reverse mapping. Further contribution of this paper includes development of an online robust neurocontroller to account for unmodeled dynamics and parametric uncertainties inherent in such a complex dynamic system. A neural network (NN) weight update rule that guarantees boundedness of the weights and relaxes the need for persistence of excitation (PE) condition is presented. Simulation studies show that in a fairly extensive but compact domain, any desired temperature profile can be achieved starting from any initial temperature profile. Therefore, the ADP and NN-based controllers appear to have the potential to become controller synthesis tools for nonlinear distributed parameter systems.

  15. Adaptive optimal control of unknown constrained-input systems using policy iteration and neural networks.

    Science.gov (United States)

    Modares, Hamidreza; Lewis, Frank L; Naghibi-Sistani, Mohammad-Bagher

    2013-10-01

    This paper presents an online policy iteration (PI) algorithm to learn the continuous-time optimal control solution for unknown constrained-input systems. The proposed PI algorithm is implemented on an actor-critic structure where two neural networks (NNs) are tuned online and simultaneously to generate the optimal bounded control policy. The requirement of complete knowledge of the system dynamics is obviated by employing a novel NN identifier in conjunction with the actor and critic NNs. It is shown how the identifier weights estimation error affects the convergence of the critic NN. A novel learning rule is developed to guarantee that the identifier weights converge to small neighborhoods of their ideal values exponentially fast. To provide an easy-to-check persistence of excitation condition, the experience replay technique is used. That is, recorded past experiences are used simultaneously with current data for the adaptation of the identifier weights. Stability of the whole system consisting of the actor, critic, system state, and system identifier is guaranteed while all three networks undergo adaptation. Convergence to a near-optimal control law is also shown. The effectiveness of the proposed method is illustrated with a simulation example.

  16. Short term and medium term power distribution load forecasting by neural networks

    International Nuclear Information System (INIS)

    Yalcinoz, T.; Eminoglu, U.

    2005-01-01

    Load forecasting is an important subject for power distribution systems and has been studied from different points of view. In general, load forecasts should be performed over a broad spectrum of time intervals, which could be classified into short term, medium term and long term forecasts. Several research groups have proposed various techniques for either short term load forecasting or medium term load forecasting or long term load forecasting. This paper presents a neural network (NN) model for short term peak load forecasting, short term total load forecasting and medium term monthly load forecasting in power distribution systems. The NN is used to learn the relationships among past, current and future temperatures and loads. The neural network was trained to recognize the peak load of the day, total load of the day and monthly electricity consumption. The suitability of the proposed approach is illustrated through an application to real load shapes from the Turkish Electricity Distribution Corporation (TEDAS) in Nigde. The data represents the daily and monthly electricity consumption in Nigde, Turkey

  17. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Directory of Open Access Journals (Sweden)

    Jingli Yang

    2016-12-01

    Full Text Available The k-nearest neighbour (kNN rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays.

  18. Influence of six-quark bags on the NN interaction in a resonating group scattering calculation

    International Nuclear Information System (INIS)

    Zhang Zongye; Braeuer, K.; Faessler, A.; Shimizu, K.

    1985-01-01

    The influence of six-quark bags oin the nucleon-nucleon (NN) interaction is studied in a dynamical calculation of the NN scattering process. The NN interaction is described by the exchange of gluons and pions between quarks and a phenomenological sigma-meson exchange between nucleons. The quark wave functions are harmonic oscillators and the relative wave function between the two nucleons is determined by the resonating group method. At short distances the NN system is allowed to fuse to a six-quark bag where all six quarks are in a ground state or where two quarks are in excited Op states. The sizes of these six-quark bags are dynamical parameters in the resonating group calculation allowing for spatial polarisation effects during the interaction. The S-wave NN scattering data can be reproduced by adjusting the sigma-coupling strength. The main result is that the six-quark bags with an increased radius have a large influence on the NN scattering process. (orig.)

  19. Fault Detection Using the Clustering-kNN Rule for Gas Sensor Arrays

    Science.gov (United States)

    Yang, Jingli; Sun, Zhen; Chen, Yinsheng

    2016-01-01

    The k-nearest neighbour (kNN) rule, which naturally handles the possible non-linearity of data, is introduced to solve the fault detection problem of gas sensor arrays. In traditional fault detection methods based on the kNN rule, the detection process of each new test sample involves all samples in the entire training sample set. Therefore, these methods can be computation intensive in monitoring processes with a large volume of variables and training samples and may be impossible for real-time monitoring. To address this problem, a novel clustering-kNN rule is presented. The landmark-based spectral clustering (LSC) algorithm, which has low computational complexity, is employed to divide the entire training sample set into several clusters. Further, the kNN rule is only conducted in the cluster that is nearest to the test sample; thus, the efficiency of the fault detection methods can be enhanced by reducing the number of training samples involved in the detection process of each test sample. The performance of the proposed clustering-kNN rule is fully verified in numerical simulations with both linear and non-linear models and a real gas sensor array experimental system with different kinds of faults. The results of simulations and experiments demonstrate that the clustering-kNN rule can greatly enhance both the accuracy and efficiency of fault detection methods and provide an excellent solution to reliable and real-time monitoring of gas sensor arrays. PMID:27929412

  20. Social Networks for Surveillance and Security: ‘Using Online Techniques to make something happen in the real or cyber world’

    OpenAIRE

    Harbisher, Ben

    2017-01-01

    This chapter examines the use of Social Networks for Surveillance and Security in relation to the deployment of intelligence resources in the UK. The chapter documents the rise of Military Intelligence agencies during both World Wars (such as GCHQ and MI5), and the subsequent use of these institutions to maintain order during peacetime. In addition to the way in which military organisations have used clandestine techniques such as double agents, spies, and various programmes designed for cond...

  1. 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.

  2. Application of neural network technique to determine a corrector surface for global geopotential model using GPS/levelling measurements in Egypt

    Science.gov (United States)

    Elshambaky, Hossam Talaat

    2018-01-01

    Owing to the appearance of many global geopotential models, it is necessary to determine the most appropriate model for use in Egyptian territory. In this study, we aim to investigate three global models, namely EGM2008, EIGEN-6c4, and GECO. We use five mathematical transformation techniques, i.e., polynomial expression, exponential regression, least-squares collocation, multilayer feed forward neural network, and radial basis neural networks to make the conversion from regional geometrical geoid to global geoid models and vice versa. From a statistical comparison study based on quality indexes between previous transformation techniques, we confirm that the multilayer feed forward neural network with two neurons is the most accurate of the examined transformation technique, and based on the mean tide condition, EGM2008 represents the most suitable global geopotential model for use in Egyptian territory to date. The final product gained from this study was the corrector surface that was used to facilitate the transformation process between regional geometrical geoid model and the global geoid model.

  3. Validation Techniques of network harmonic models based on switching of a series linear component and measuring resultant harmonic increments

    DEFF Research Database (Denmark)

    Wiechowski, Wojciech Tomasz; Lykkegaard, Jan; Bak, Claus Leth

    2007-01-01

    In this paper two methods of validation of transmission network harmonic models are introduced. The methods were developed as a result of the work presented in [1]. The first method allows calculating the transfer harmonic impedance between two nodes of a network. Switching a linear, series network......, as for example a transmission line. Both methods require that harmonic measurements performed at two ends of the disconnected element are precisely synchronized....... are used for calculation of the transfer harmonic impedance between the nodes. The determined transfer harmonic impedance can be used to validate a computer model of the network. The second method is an extension of the fist one. It allows switching a series element that contains a shunt branch...

  4. Intelligent Energy Management Control for Extended Range Electric Vehicles Based on Dynamic Programming and Neural Network

    Directory of Open Access Journals (Sweden)

    Lihe Xi

    2017-11-01

    Full Text Available The extended range electric vehicle (EREV can store much clean energy from the electric grid when it arrives at the charging station with lower battery energy. Consuming minimum gasoline during the trip is a common goal for most energy management controllers. To achieve these objectives, an intelligent energy management controller for EREV based on dynamic programming and neural networks (IEMC_NN is proposed. The power demand split ratio between the extender and battery are optimized by DP, and the control objectives are presented as a cost function. The online controller is trained by neural networks. Three trained controllers, constructing the controller library in IEMC_NN, are obtained from training three typical lengths of the driving cycle. To determine an appropriate NN controller for different driving distance purposes, the selection module in IEMC_NN is developed based on the remaining battery energy and the driving distance to the charging station. Three simulation conditions are adopted to validate the performance of IEMC_NN. They are target driving distance information, known and unknown, changing the destination during the trip. Simulation results using these simulation conditions show that the IEMC_NN had better fuel economy than the charging deplete/charging sustain (CD/CS algorithm. More significantly, with known driving distance information, the battery SOC controlled by IEMC_NN can just reach the lower bound as the EREV arrives at the charging station, which was also feasible when the driver changed the destination during the trip.

  5. A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

    OpenAIRE

    Parisa Bazmi; Manijeh Keshtgary

    2016-01-01

    Named Data Networking (NDN) is a new Internet architecture which has been proposed to eliminate TCP/IP Internet architecture restrictions. This architecture is abstracting away the notion of host and working based on naming datagrams. However, one of the major challenges of NDN is supporting QoS-aware forwarding strategy so as to forward Interest packets intelligently over multiple paths based on the current network condition. In this paper, Neural Network (NN) Based Traffic-aware Forwarding ...

  6. A Novel Approach for Transmission of 56 Gbit/s NRZ Signal in Access Network Using Spectrum Slicing Technique

    DEFF Research Database (Denmark)

    Spolitis, S.; Vegas Olmos, Juan José; Bobrovs, V.

    2013-01-01

    We present the spectrum slicing and stitching concept for high-capacity low optics complexity optical access networks. Spectrum slicing and stitching of a 56 Gbit/s NRZ electrical signal is experimentally demonstrated for the first time.......We present the spectrum slicing and stitching concept for high-capacity low optics complexity optical access networks. Spectrum slicing and stitching of a 56 Gbit/s NRZ electrical signal is experimentally demonstrated for the first time....

  7. Optimal and robust control of a class of nonlinear systems using dynamically re-optimised single network adaptive critic design

    Science.gov (United States)

    Tiwari, Shivendra N.; Padhi, Radhakant

    2018-01-01

    Following the philosophy of adaptive optimal control, a neural network-based state feedback optimal control synthesis approach is presented in this paper. First, accounting for a nominal system model, a single network adaptive critic (SNAC) based multi-layered neural network (called as NN1) is synthesised offline. However, another linear-in-weight neural network (called as NN2) is trained online and augmented to NN1 in such a manner that their combined output represent the desired optimal costate for the actual plant. To do this, the nominal model needs to be updated online to adapt to the actual plant, which is done by synthesising yet another linear-in-weight neural network (called as NN3) online. Training of NN3 is done by utilising the error information between the nominal and actual states and carrying out the necessary Lyapunov stability analysis using a Sobolev norm based Lyapunov function. This helps in training NN2 successfully to capture the required optimal relationship. The overall architecture is named as 'Dynamically Re-optimised single network adaptive critic (DR-SNAC)'. Numerical results for two motivating illustrative problems are presented, including comparison studies with closed form solution for one problem, which clearly demonstrate the effectiveness and benefit of the proposed approach.

  8. Validation of a scale for network therapy: a technique for systematic use of peer and family support in addition treatment.

    Science.gov (United States)

    Keller, D S; Galanter, M; Weinberg, S

    1997-02-01

    Substance abuse treatments are increasingly employing standardized formats. This is especially the case for approaches that utilize an individual psychotherapy format but less so for family-based approaches. Network therapy, an approach that involves family members and peers in the patient's relapse prevention efforts, is theoretically and clinically differentiated in this paper from family systems therapy for addiction. Based on these conceptual differences, a Network Therapy Rating Scale (NTRS) was developed to measure the integrity and differentiability of network therapy from other family-based approaches to addiction treatment. Seven addictions faculty and 10 third- and fourth-year psychiatry residents recently trained in the network approach used the NTRS to rate excerpts of network and family systems therapy sessions. Data revealed the NTRS had high internal consistency reliability when utilized by both groups of raters. In addition, network and nonnetwork subscales within the NTRS rated congruent therapy excerpts significantly higher than noncongruent therapy excerpts, indicating that the NTRS subscales measure what they are designed to measure. Implications for research and training are discussed.

  9. Hermite Functional Link Neural Network for Solving the Van der Pol-Duffing Oscillator Equation.

    Science.gov (United States)

    Mall, Susmita; Chakraverty, S

    2016-08-01

    Hermite polynomial-based functional link artificial neural network (FLANN) is proposed here to solve the Van der Pol-Duffing oscillator equation. A single-layer hermite neural network (HeNN) model is used, where a hidden layer is replaced by expansion block of input pattern using Hermite orthogonal polynomials. A feedforward neural network model with the unsupervised error backpropagation principle is used for modifying the network parameters and minimizing the computed error function. The Van der Pol-Duffing and Duffing oscillator equations may not be solved exactly. Here, approximate solutions of these types of equations have been obtained by applying the HeNN model for the first time. Three mathematical example problems and two real-life application problems of Van der Pol-Duffing oscillator equation, extracting the features of early mechanical failure signal and weak signal detection problems, are solved using the proposed HeNN method. HeNN approximate solutions have been compared with results obtained by the well known Runge-Kutta method. Computed results are depicted in term of graphs. After training the HeNN model, we may use it as a black box to get numerical results at any arbitrary point in the domain. Thus, the proposed HeNN method is efficient. The results reveal that this method is reliable and can be applied to other nonlinear problems too.

  10. Biological engineering applications of feedforward neural networks designed and parameterized by genetic algorithms.

    Science.gov (United States)

    Ferentinos, Konstantinos P

    2005-09-01

    Two neural network (NN) applications in the field of biological engineering are developed, designed and parameterized by an evolutionary method based on the evolutionary process of genetic algorithms. The developed systems are a fault detection NN model and a predictive modeling NN system. An indirect or 'weak specification' representation was used for the encoding of NN topologies and training parameters into genes of the genetic algorithm (GA). Some a priori knowledge of the demands in network topology for specific application cases is required by this approach, so that the infinite search space of the problem is limited to some reasonable degree. Both one-hidden-layer and two-hidden-layer network architectures were explored by the GA. Except for the network architecture, each gene of the GA also encoded the type of activation functions in both hidden and output nodes of the NN and the type of minimization algorithm that was used by the backpropagation algorithm for the training of the NN. Both models achieved satisfactory performance, while the GA system proved to be a powerful tool that can successfully replace the problematic trial-and-error approach that is usually used for these tasks.

  11. A Quantum Hybrid PSO Combined with Fuzzy k-NN Approach to Feature Selection and Cell Classification in Cervical Cancer Detection

    Directory of Open Access Journals (Sweden)

    Abdullah M. Iliyasu

    2017-12-01

    Full Text Available A quantum hybrid (QH intelligent approach that blends the adaptive search capability of the quantum-behaved particle swarm optimisation (QPSO method with the intuitionistic rationality of traditional fuzzy k-nearest neighbours (Fuzzy k-NN algorithm (known simply as the Q-Fuzzy approach is proposed for efficient feature selection and classification of cells in cervical smeared (CS images. From an initial multitude of 17 features describing the geometry, colour, and texture of the CS images, the QPSO stage of our proposed technique is used to select the best subset features (i.e., global best particles that represent a pruned down collection of seven features. Using a dataset of almost 1000 images, performance evaluation of our proposed Q-Fuzzy approach assesses the impact of our feature selection on classification accuracy by way of three experimental scenarios that are compared alongside two other approaches: the All-features (i.e., classification without prior feature selection and another hybrid technique combining the standard PSO algorithm with the Fuzzy k-NN technique (P-Fuzzy approach. In the first and second scenarios, we further divided the assessment criteria in terms of classification accuracy based on the choice of best features and those in terms of the different categories of the cervical cells. In the third scenario, we introduced new QH hybrid techniques, i.e., QPSO combined with other supervised learning methods, and compared the classification accuracy alongside our proposed Q-Fuzzy approach. Furthermore, we employed statistical approaches to establish qualitative agreement with regards to the feature selection in the experimental scenarios 1 and 3. The synergy between the QPSO and Fuzzy k-NN in the proposed Q-Fuzzy approach improves classification accuracy as manifest in the reduction in number cell features, which is crucial for effective cervical cancer detection and diagnosis.

  12. Discrimination of plant root zone water status in greenhouse production based on phenotyping and machine learning techniques.

    Science.gov (United States)

    Guo, Doudou; Juan, Jiaxiang; Chang, Liying; Zhang, Jingjin; Huang, Danfeng

    2017-08-15

    Plant-based sensing on water stress can provide sensitive and direct reference for precision irrigation system in greenhouse. However, plant information acquisition, interpretation, and systematical application remain insufficient. This study developed a discrimination method for plant root zone water status in greenhouse by integrating phenotyping and machine learning techniques. Pakchoi plants were used and treated by three root zone moisture levels, 40%, 60%, and 80% relative water content. Three classification models, Random Forest (RF), Neural Network (NN), and Support Vector Machine (SVM) were developed and validated in different scenarios with overall accuracy over 90% for all. SVM model had the highest value, but it required the longest training time. All models had accuracy over 85% in all scenarios, and more stable performance was observed in RF model. Simplified SVM model developed by the top five most contributing traits had the largest accuracy reduction as 29.5%, while simplified RF and NN model still maintained approximately 80%. For real case application, factors such as operation cost, precision requirement, and system reaction time should be synthetically considered in model selection. Our work shows it is promising to discriminate plant root zone water status by implementing phenotyping and machine learning techniques for precision irrigation management.

  13. Using neural networks to represent potential surfaces as sums of products.

    Science.gov (United States)

    Manzhos, Sergei; Carrington, Tucker

    2006-11-21

    By using exponential activation functions with a neural network (NN) method we show that it is possible to fit potentials to a sum-of-products form. The sum-of-products form is desirable because it reduces the cost of doing the quadratures required for quantum dynamics calculations. It also greatly facilitates the use of the multiconfiguration time dependent Hartree method. Unlike potfit product representation algorithm, the new NN approach does not require using a grid of points. It also produces sum-of-products potentials with fewer terms. As the number of dimensions is increased, we expect the advantages of the exponential NN idea to become more significant.

  14. φ meson production in Au + Au and p + p collisions at √sNN=200 GeV

    International Nuclear Information System (INIS)

    Adams, J.; Adler, C.; Aggarwal, M.M.; Ahammed, Z.; Amonett, J.; Anderson, B.D.; Arkhipkin, D.; Averichev, G.S.; Badyal, S.K.; Balewski, J.; Barannikova, O.; Barnby, L.S.; Baudot, J.; Bekele, S.; Belaga, V.V.; Bellwied, R.; Berger, J.; Bezverkhny, B.I.; Bhardwaj, S.; Bhati, A.K.; Bichsel, H.; Billmeier, A.; Bland, L.C.; Blyth, C.O.; Bonner, B.E.; Botje, M.; Boucham, A.; Brandin, A.; Bravar, A.; Cadman, R.V.; Cai, X.Z.; Caines, H.; Calderon de la Barca Sanchez, M.; Carroll, J.; Castillo, J.; Cebra, D.; Chaloupka, P.; Chattopadhyay, S.; Chen, H.F.; Chen, Y.; Chernenko, S.P.; Cherney, M.; Chikanian, A.; Christie, W.; Coffin, J.P.; Cormier, T.M.; Cramer, J.G.; Crawford, H.J.; Das, D.; Das, S.; Derevschikov, A.A.; Didenko, L.; Dietel, T.; Dong, W.J.; Dong, X.; Draper, J.E.; Du, F.; Dubey, A.K.; Dunin, V.B.; Dunlop, J.C.; Dutta Majumdar, M.R.; Eckardt, V.; Efimov, L.G.; Emelianov, V.; Engelage, J.; Eppley, G.; Erazmus, B.; Estienne, M.; Fachini, P.; Faine, V.; Faivre, J.; Fatemi, R.; Filimonov, K.; Filip, P.; Finch, E.; Fisyak, Y.; Flierl, D.; Foley, K.J.; Fu, J.; Gagliardi, C.A.; Gagunashvili, N.; Gans, J.; Ganti, M.S.; Gaudichet, L.; Germain, M.; Geurts, F.; Ghazikhanian, V.; Ghosh, P.; Gonzalez, J.E.; Grachov, O.; Grebenyuk, O.; Gronstal, S.; Grosnick, D.; Guedon, M.; Guertin, S.M.; Gupta, A.; Gutierrez, T.D.; Hallman, T.J.; Hamed, A.; Hardtke, D.; Harris, J.W.; Heinz, M.; Henry, T.W.; Heppelmann, S.; Hippolyte, B.; Hirsch, A.; Hjort, E.; Hoffmann, G.W.; Horsley, M.; Huang, H.Z.; Huang, S.L.; Hughes, E.; Humanic, T.J.; Igo, G.; Ishihara, A.; Jacobs, P.; Jacobs, W.W.; Janik, M.; Johnson, I.; Jones, P.G.; Judd, E.G.; Kabana, S.; Kaplan, M.; Keane, D.; Khodyrev; Kiryluk, J.; Kisiel, A.; Klay, J.; Klein, S.R.; Klyachko, A.; Koetke, D.D.; Kollegger, T.; Kopytine, S.M.; Kotchenda, L.; Kovalenko, A.D.; Kramer, M.; Kravtsov, P.; Kravstov, V.I.; Krueger, K.; Kuhn, C.; Kulikov, A.I.; Kumar, A.; Kunde, G.J.; Kunz, C.L.; Kutuev, R.Kh.

    2004-01-01

    We report the STAR measurement of ψ meson production in Au + Au and p + p collisions at √s NN = 200 GeV. Using the event mixing technique, the ψ spectra and yields are obtained at midrapidity for five centrality bins in Au+Au collisions and for non-singly-diffractive p+p collisions. It is found that the ψ transverse momentum distributions from Au+Au collisions are better fitted with a single-exponential while the p+p spectrum is better described by a double-exponential distribution. The measured nuclear modification factors indicate that ψ production in central Au+Au collisions is suppressed relative to peripheral collisions when scaled by the number of binary collisions ( bin >). The systematics of T > versus centrality and the constant ψ/K - ratio versus beam species, centrality, and collision energy rule out kaon coalescence as the dominant mechanism for ψ production

  15. Cross section measurement for the reaction /sup 103/Rh (n,n') /sup 103m/Rh

    International Nuclear Information System (INIS)

    Paulsen, A.; Liskien, H.; Vaninbroukx, R.; Widera, R.

    1980-01-01

    The excitation function for the reaction /sup 103/Rh (n,n') /sup 103m/Rh was measured by the activation technique from 0.2 to 6.1 MeV in 0.1-MeV steps and from 13.0 to 16.7 MeV in 1-MeV steps. This excitation function is normalized through an absolute measurement at 1.8 MeV. This measurement is based on n-p scattering for neutron flux determination and on liquid scintillation counting of /sup 103m/Rh separated from /sup 103/Pd solutions for the activity determination. The total uncertainty of the cross-section results is typically + or -5% above 0.5 MeV (about + or -10% above 13 MeV). Concurrence with existing data is good except below 0.35 MeV, where the present results are considerably higher

  16. Extraction of Built-Up Areas Using Convolutional Neural Networks and Transfer Learning from SENTINEL-2 Satellite Images

    Science.gov (United States)

    Bramhe, V. S.; Ghosh, S. K.; Garg, P. K.

    2018-04-01

    With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL) techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets) i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM) and Back-Propagation Neural Network (BP-NN) are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

  17. EXTRACTION OF BUILT-UP AREAS USING CONVOLUTIONAL NEURAL NETWORKS AND TRANSFER LEARNING FROM SENTINEL-2 SATELLITE IMAGES

    Directory of Open Access Journals (Sweden)

    V. S. Bramhe

    2018-04-01

    Full Text Available With rapid globalization, the extent of built-up areas is continuously increasing. Extraction of features for classifying built-up areas that are more robust and abstract is a leading research topic from past many years. Although, various studies have been carried out where spatial information along with spectral features has been utilized to enhance the accuracy of classification. Still, these feature extraction techniques require a large number of user-specific parameters and generally application specific. On the other hand, recently introduced Deep Learning (DL techniques requires less number of parameters to represent more abstract aspects of the data without any manual effort. Since, it is difficult to acquire high-resolution datasets for applications that require large scale monitoring of areas. Therefore, in this study Sentinel-2 image has been used for built-up areas extraction. In this work, pre-trained Convolutional Neural Networks (ConvNets i.e. Inception v3 and VGGNet are employed for transfer learning. Since these networks are trained on generic images of ImageNet dataset which are having very different characteristics from satellite images. Therefore, weights of networks are fine-tuned using data derived from Sentinel-2 images. To compare the accuracies with existing shallow networks, two state of art classifiers i.e. Gaussian Support Vector Machine (SVM and Back-Propagation Neural Network (BP-NN are also implemented. Both SVM and BP-NN gives 84.31 % and 82.86 % overall accuracies respectively. Inception-v3 and VGGNet gives 89.43 % of overall accuracy using fine-tuned VGGNet and 92.10 % when using Inception-v3. The results indicate high accuracy of proposed fine-tuned ConvNets on a 4-channel Sentinel-2 dataset for built-up area extraction.

  18. Classification Technique for Ultrasonic Weld Inspection Signals using a Neural Network based on 2-dimensional fourier Transform and Principle Component Analysis

    International Nuclear Information System (INIS)

    Kim, Jae Joon

    2004-01-01

    Neural network-based signal classification systems are increasingly used in the analysis of large volumes of data obtained in NDE applications. Ultrasonic inspection methods on the other hand are commonly used in the nondestructive evaluation of welds to detect flaws. An important characteristic of ultrasonic inspection is the ability to identify the type of discontinuity that gives rise to a peculiar signal. Standard techniques rely on differences in individual A-scans to classify the signals. This paper proposes an ultrasonic signal classification technique based on the information tying in the neighboring signals. The approach is based on a 2-dimensional Fourier transform and the principal component analysis to generate a reduced dimensional feature vector for classification. Results of applying the technique to data obtained from the inspection of actual steel welds are presented

  19. Fibre-tree network for water-surface ranging using an optical time-domain reflectometry technique

    Directory of Open Access Journals (Sweden)

    Yoshiaki Yamabayashi

    2014-10-01

    Full Text Available To monitor water level at long distance, a fibre-based time-domain reflectometry network is proposed. A collimator at each fibre end of a tree-type network retrieves 1.55 μm wavelength pulses that are reflected back from remote surfaces. Since this enables a power-supply-free sensor network with non-metal media, this system is expected to be less susceptible to lightning strikes and power cuts than conventional systems that use electrically powered sensors and metal cables. In the present Letter, a successful simultaneous monitoring experiment of two water levels in the laboratory, as well as a trial for detecting a disturbed surface by beam-expanding is reported.

  20. Datum maintenance of the main Egyptian geodetic control networks by utilizing Precise Point Positioning “PPP” technique

    Directory of Open Access Journals (Sweden)

    Mostafa Rabah

    2016-06-01

    To see how non-performing maintenance degrading the values of the HARN and NACN, the available HARN and NACN stations in the Nile Delta were observed. The Processing of the tested part was done by CSRS-PPP Service based on utilizing Precise Point Positioning “PPP” and Trimble Business Center “TBC”. The study shows the feasibility of Precise Point Positioning in updating the absolute positioning of the HARN network and its role in updating the reference frame (ITRF. The study also confirmed the necessity of the absent role of datum maintenance of Egypt networks.