WorldWideScience

Sample records for network based diagnostic

  1. Symptom based diagnostic system using artificial neural networks

    International Nuclear Information System (INIS)

    Santosh; Vinod, Gopika; Saraf, R.K.

    2003-01-01

    Nuclear power plant experiences a number of transients during its operations. In case of such an undesired plant condition generally known as an initiating event, the operator has to carry out diagnostic and corrective actions. The operator's response may be too late to mitigate or minimize the negative consequences in such scenarios. The objective of this work is to develop an operator support system based on artificial neural networks that will assist the operator to identify the initiating events at the earliest stages of their developments. A symptom based diagnostic system has been developed to investigate the initiating events. Neutral networks are utilized for carrying out the event identification by continuously monitoring process parameters. Whenever an event is detected, the system will display the necessary operator actions along with the initiating event. The system will also show the graphical trend of process parameters that are relevant to the event. This paper describes the features of the software that is used to monitor the reactor. (author)

  2. First Steps Toward Incorporating Image Based Diagnostics Into Particle Accelerator Control Systems Using Convolutional Neural Networks

    Energy Technology Data Exchange (ETDEWEB)

    Edelen, A. L.; Biedron, S. G.; Milton, S. V.; Edelen, J. P.

    2016-12-16

    At present, a variety of image-based diagnostics are used in particle accelerator systems. Often times, these are viewed by a human operator who then makes appropriate adjustments to the machine. Given recent advances in using convolutional neural networks (CNNs) for image processing, it should be possible to use image diagnostics directly in control routines (NN-based or otherwise). This is especially appealing for non-intercepting diagnostics that could run continuously during beam operation. Here, we show results of a first step toward implementing such a controller: our trained CNN can predict multiple simulated downstream beam parameters at the Fermilab Accelerator Science and Technology (FAST) facility's low energy beamline using simulated virtual cathode laser images, gun phases, and solenoid strengths.

  3. Crack identification for reinforced concrete using PZT based smart rebar active sensing diagnostic network

    Science.gov (United States)

    Song, N. N.; Wu, F.

    2016-04-01

    An active sensing diagnostic system using PZT based smart rebar for SHM of RC structure has been currently under investigation. Previous test results showed that the system could detect the de-bond of concrete from reinforcement, and the diagnostic signals were increased exponentially with the de-bonding size. Previous study also showed that the smart rebar could function well like regular reinforcement to undertake tension stresses. In this study, a smart rebar network has been used to detect the crack damage of concrete based on guided waves. Experimental test has been carried out for the study. In the test, concrete beams with 2 reinforcements have been built. 8 sets of PZT elements were mounted onto the reinforcement bars in an optimized way to form an active sensing diagnostic system. A 90 kHz 5-cycle Hanning-windowed tone burst was used as input. Multiple cracks have been generated on the concrete structures. Through the guided bulk waves propagating in the structures from actuators and sensors mounted from different bars, crack damage could be detected clearly. Cases for both single and multiple cracks were tested. Different crack depths from the surface and different crack numbers have been studied. Test result shows that the amplitude of sensor output signals is deceased linearly with a propagating crack, and is decreased exponentially with increased crack numbers. From the study, the active sensing diagnostic system using PZT based smart rebar network shows a promising way to provide concrete crack damage information through the "talk" among sensors.

  4. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network.

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-08

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  5. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Directory of Open Access Journals (Sweden)

    Ke Li

    2016-01-01

    Full Text Available A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF and Diagnostic Bayesian Network (DBN is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO. To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA is proposed to evaluate the sensitiveness of symptom parameters (SPs for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method.

  6. Intelligent Condition Diagnosis Method Based on Adaptive Statistic Test Filter and Diagnostic Bayesian Network

    Science.gov (United States)

    Li, Ke; Zhang, Qiuju; Wang, Kun; Chen, Peng; Wang, Huaqing

    2016-01-01

    A new fault diagnosis method for rotating machinery based on adaptive statistic test filter (ASTF) and Diagnostic Bayesian Network (DBN) is presented in this paper. ASTF is proposed to obtain weak fault features under background noise, ASTF is based on statistic hypothesis testing in the frequency domain to evaluate similarity between reference signal (noise signal) and original signal, and remove the component of high similarity. The optimal level of significance α is obtained using particle swarm optimization (PSO). To evaluate the performance of the ASTF, evaluation factor Ipq is also defined. In addition, a simulation experiment is designed to verify the effectiveness and robustness of ASTF. A sensitive evaluation method using principal component analysis (PCA) is proposed to evaluate the sensitiveness of symptom parameters (SPs) for condition diagnosis. By this way, the good SPs that have high sensitiveness for condition diagnosis can be selected. A three-layer DBN is developed to identify condition of rotation machinery based on the Bayesian Belief Network (BBN) theory. Condition diagnosis experiment for rolling element bearings demonstrates the effectiveness of the proposed method. PMID:26761006

  7. Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

    Science.gov (United States)

    Levy, Roy

    2014-01-01

    Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

  8. Development of network based control and data acquisition systems for diagnostics using CCD detectors. Application to LHD experiments

    International Nuclear Information System (INIS)

    Kado, Shinichiro; Nakanishi, Hideya; Ida, Katsumi; Kojima, Mamoru

    2000-01-01

    The needs of CCD detectors as a plasma diagnostic tool have recently been increased. However, many CCD providers have developed their own controlling systems, and it is difficult to customized the usages in order to make them applicable to the network-based data acquisition clusters which consist of various sorts of diagnostics. This paper presents the development of systems in which CCD detectors are controlled and the data are acquired through networks. By making use of the Client/Server (C/S) model in the Windows NT operating system and block transfer method via shared memory relevant to the model, the dependence on the hardware is hidden by the server service, CCD list sequencer. The client program is designed for the LHD (Large Helical Device) discharge operation sequences and the data acquisition system. (author)

  9. Diagnostics of Nuclear Reactor Accidents Based on Particle Swarm Optimization Trained Neural Networks

    International Nuclear Information System (INIS)

    Abdel-Aal, M.M.Z.

    2004-01-01

    Automation in large, complex systems such as chemical plants, electrical power generation, aerospace and nuclear plants has been steadily increasing in the recent past. automated diagnosis and control forms a necessary part of these systems,this contains thousands of alarms processing in every component, subsystem and system. so the accurate and speed of diagnosis of faults is an important factors in operation and maintaining their health and continued operation and in reducing of repair and recovery time. using of artificial intelligence facilitates the alarm classifications and faults diagnosis to control any abnormal events during the operation cycle of the plant. thesis work uses the artificial neural network as a powerful classification tool. the work basically is has two components, the first is to effectively train the neural network using particle swarm optimization, which non-derivative based technique. to achieve proper training of the neural network to fault classification problem and comparing this technique to already existing techniques

  10. Comparison of Back propagation neural network and Back propagation neural network Based Particle Swarm intelligence in Diagnostic Breast Cancer

    Directory of Open Access Journals (Sweden)

    Farahnaz SADOUGHI

    2014-03-01

    Full Text Available Breast cancer is the most commonly diagnosed cancer and the most common cause of death in women all over the world. Use of computer technology supporting breast cancer diagnosing is now widespread and pervasive across a broad range of medical areas. Early diagnosis of this disease can greatly enhance the chances of long-term survival of breast cancer victims. Artificial Neural Networks (ANN as mainly method play important role in early diagnoses breast cancer. This paper studies Levenberg Marquardet Backpropagation (LMBP neural network and Levenberg Marquardet Backpropagation based Particle Swarm Optimization(LMBP-PSO for the diagnosis of breast cancer. The obtained results show that LMBP and LMBP based PSO system provides higher classification efficiency. But LMBP based PSO needs minimum training and testing time. It helps in developing Medical Decision System (MDS for breast cancer diagnosing. It can also be used as secondary observer in clinical decision making.

  11. Intelligent neural network diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  12. Systems Bioinformatics: increasing precision of computational diagnostics and therapeutics through network-based approaches.

    Science.gov (United States)

    Oulas, Anastasis; Minadakis, George; Zachariou, Margarita; Sokratous, Kleitos; Bourdakou, Marilena M; Spyrou, George M

    2017-11-27

    Systems Bioinformatics is a relatively new approach, which lies in the intersection of systems biology and classical bioinformatics. It focuses on integrating information across different levels using a bottom-up approach as in systems biology with a data-driven top-down approach as in bioinformatics. The advent of omics technologies has provided the stepping-stone for the emergence of Systems Bioinformatics. These technologies provide a spectrum of information ranging from genomics, transcriptomics and proteomics to epigenomics, pharmacogenomics, metagenomics and metabolomics. Systems Bioinformatics is the framework in which systems approaches are applied to such data, setting the level of resolution as well as the boundary of the system of interest and studying the emerging properties of the system as a whole rather than the sum of the properties derived from the system's individual components. A key approach in Systems Bioinformatics is the construction of multiple networks representing each level of the omics spectrum and their integration in a layered network that exchanges information within and between layers. Here, we provide evidence on how Systems Bioinformatics enhances computational therapeutics and diagnostics, hence paving the way to precision medicine. The aim of this review is to familiarize the reader with the emerging field of Systems Bioinformatics and to provide a comprehensive overview of its current state-of-the-art methods and technologies. Moreover, we provide examples of success stories and case studies that utilize such methods and tools to significantly advance research in the fields of systems biology and systems medicine. © The Author 2017. Published by Oxford University Press.

  13. ELECTRIC MOTOR DIAGNOSTICS OF SWITCHES BASED ON THE NEURAL NETWORK DATA MODELING THE SPECTRAL DECOMPOSITION OF THE CURRENTS

    Directory of Open Access Journals (Sweden)

    O. M. Shvets

    2009-07-01

    Full Text Available The method of automated diagnostics of electric motors is offered. It uses a neural network revealing the electric motor faults on the basis of analysis of frequency spectrum of current flowing through the motor.

  14. Neural network application to diesel generator diagnostics

    International Nuclear Information System (INIS)

    Logan, K.P.

    1990-01-01

    Diagnostic problems typically begin with the observation of some system behavior which is recognized as a deviation from the expected. The fundamental underlying process is one involving pattern matching cf observed symptoms to a set of compiled symptoms belonging to a fault-symptom mapping. Pattern recognition is often relied upon for initial fault detection and diagnosis. Parallel distributed processing (PDP) models employing neural network paradigms are known to be good pattern recognition devices. This paper describes the application of neural network processing techniques to the malfunction diagnosis of subsystems within a typical diesel generator configuration. Neural network models employing backpropagation learning were developed to correctly recognize fault conditions from the input diagnostic symptom patterns pertaining to various engine subsystems. The resulting network models proved to be excellent pattern recognizers for malfunction examples within the training set. The motivation for employing network models in lieu of a rule-based expert system, however, is related to the network's potential for generalizing malfunctions outside of the training set, as in the case of noisy or partial symptom patterns

  15. Diagnostics using different configurations of sensing networks

    Energy Technology Data Exchange (ETDEWEB)

    Wandowski, T; Malinowski, P; Ostachowicz, W, E-mail: tomaszw@imp.gda.pl [Institute of Fluid-Flow Machinery of Polish Academy of Sciences, Fiszera 14, 80-952 Gdansk (Poland)] [Gdynia Maritime University, Faculty of Navigation, Al. Jana Pawla II 3, 81-345, Gdynia (Poland)

    2011-07-19

    The aim of this work was the investigation and improvement of a Structural Health Monitoring method. This method was based on guided elastic waves propagation. These waves propagate in thin-walled structures guided by their walls. This particular technique has been considered in the literature as very promising. This research concentrated on diagnostics using attached piezoelectric transducers that excite guided elastic waves. Non-contact method of measurement was used. The laser vibrometer was utilized to measure the velocities of out of plane vibrations related to propagating elastic waves. Excited waves propagate and reflect from the discontinuities encountered on their way, therefore registering them one can obtain information about the structural health of the structure. Several sensor arrangements (networks) were investigated. The focus of the research was on possible area of application and limitations of the investigated networks. Presented research was based on laboratory experiments on prepared specimens. Signals gathered in the discrete network nodes were processed in order to obtain diagnostics information for the whole surface monitored. The voltage signals in the time domain were transferred into spatial domain to indicate the damage position. The signal processing oriented on structural diagnostics was realized in the MATLAB environment.

  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. Decentralized diagnostics based on a distributed micro-genetic algorithm for transducer networks monitoring large experimental systems.

    Science.gov (United States)

    Arpaia, P; Cimmino, P; Girone, M; La Commara, G; Maisto, D; Manna, C; Pezzetti, M

    2014-09-01

    Evolutionary approach to centralized multiple-faults diagnostics is extended to distributed transducer networks monitoring large experimental systems. Given a set of anomalies detected by the transducers, each instance of the multiple-fault problem is formulated as several parallel communicating sub-tasks running on different transducers, and thus solved one-by-one on spatially separated parallel processes. A micro-genetic algorithm merges evaluation time efficiency, arising from a small-size population distributed on parallel-synchronized processors, with the effectiveness of centralized evolutionary techniques due to optimal mix of exploitation and exploration. In this way, holistic view and effectiveness advantages of evolutionary global diagnostics are combined with reliability and efficiency benefits of distributed parallel architectures. The proposed approach was validated both (i) by simulation at CERN, on a case study of a cold box for enhancing the cryogeny diagnostics of the Large Hadron Collider, and (ii) by experiments, under the framework of the industrial research project MONDIEVOB (Building Remote Monitoring and Evolutionary Diagnostics), co-funded by EU and the company Del Bo srl, Napoli, Italy.

  18. Tune-Based Halo Diagnostics

    International Nuclear Information System (INIS)

    Cameron, Peter

    2003-01-01

    Tune-based halo diagnostics can be divided into two categories -- diagnostics for halo prevention, and diagnostics for halo measurement. Diagnostics for halo prevention are standard fare in accumulators, synchrotrons, and storage rings, and again can be divided into two categories -- diagnostics to measure the tune distribution (primarily to avoid resonances), and diagnostics to identify instabilities (which will not be discussed here). These diagnostic systems include kicked (coherent) tune measurement, phase-locked loop (PLL) tune measurement, Schottky tune measurement, beam transfer function (BTF) measurements, and measurement of transverse quadrupole mode envelope oscillations. We refer briefly to tune diagnostics used at RHIC and intended for the SNS, and then present experimental results. Tune-based diagnostics for halo measurement (as opposed to prevention) are considerably more difficult. We present one brief example of tune-based halo measurement

  19. Case-Based Fault Diagnostic System

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Nowadays, case-based fault diagnostic (CBFD) systems have become important and widely applied problem solving technologies. They are based on the assumption that “similar faults have similar diagnosis”. On the other hand, CBFD systems still suffer from some limitations. Common ones of them are: (1) failure of CBFD to have the needed diagnosis for the new faults that have no similar cases in the case library. (2) Limited memorization when increasing the number of stored cases in the library. The proposed research introduces incorporating the neural network into the case based system to enable the system to diagnose all the faults. Neural networks have proved their success in the classification and diagnosis problems. The suggested system uses the neural network to diagnose the new faults (cases) that cannot be diagnosed by the traditional CBR diagnostic system. Besides, the proposed system can use the another neural network to control adding and deleting the cases in the library to manage the size of the cases in the case library. However, the suggested system has improved the performance of the case based fault diagnostic system when applied for the motor rolling bearing as a case of study

  20. Hybrid case-neural network (CNN) diagnostic system

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

    recently, the mobile health care has a great attention for the researcher and people all over the world. Case based reasoning (CBR) systems have proved their performance as world wide web (WWW) medical diagnostic systems. They were preferred rather than different reasoning approaches due to their high performance and results' explanation. But, their operations require a complex knowledge acquisition and management processes. On the other hand, it is found that, artificial neural network (ANN) has a great acceptance as a classifier methodology using a little amount of knowledge. But, ANN lacks of an explanation capability .The present research introduces a new web-based hybrid diagnostic system that can use the ANN inside the CBR , cycle.It can provide higher performance for the web diagnostic systems. Besides, the proposed system can be used as a web diagnostic system. It can be applied for diagnosis different types of systems in several domains. It has been applied in diagnosis of the cancer diseases that has a great spreading in recent years as a case of study . However, the suggested system has proved its acceptance in the manner.

  1. Integrated Fault Diagnostics of Networks and IT Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — The lecture of the Stanford-IVHM lecture series will give an overview of the approaches in building diagnostic solutions for networks and complex systems. The...

  2. Artificial Neural Networks in Mammography Interpretation and Diagnostic Decision Making

    Directory of Open Access Journals (Sweden)

    Turgay Ayer

    2013-01-01

    Full Text Available Screening mammography is the most effective means for early detection of breast cancer. Although general rules for discriminating malignant and benign lesions exist, radiologists are unable to perfectly detect and classify all lesions as malignant and benign, for many reasons which include, but are not limited to, overlap of features that distinguish malignancy, difficulty in estimating disease risk, and variability in recommended management. When predictive variables are numerous and interact, ad hoc decision making strategies based on experience and memory may lead to systematic errors and variability in practice. The integration of computer models to help radiologists increase the accuracy of mammography examinations in diagnostic decision making has gained increasing attention in the last two decades. In this study, we provide an overview of one of the most commonly used models, artificial neural networks (ANNs, in mammography interpretation and diagnostic decision making and discuss important features in mammography interpretation. We conclude by discussing several common limitations of existing research on ANN-based detection and diagnostic models and provide possible future research directions.

  3. Computer-based diagnostic decisionmaking.

    Science.gov (United States)

    Miller, R A

    1987-12-01

    The three decisionmaking aids described by the authors attack the generic problem of "see no evil, hear no evil, speak no evil"--improving the detection, diagnosis, and therapy of psychiatric disorders in the primary care setting. The three systems represent interventions at different steps in the process of providing appropriate care to psychiatric patients. The DSPW system of Robins and Marcus offers the potential of increasing the recognition of psychiatric disease in the physician's office. Politser's IDS program is representative of the sort of sophisticated microcomputer-based decisionmaking support tools that will become available to physicians in the not-too-distant future. Erdman's study of the impact of explanation capabilities on the acceptability of therapy recommending systems points out the need for careful scientific evaluations of features added to diagnostic and therapeutic systems.

  4. Accident scenario diagnostics with neural networks

    International Nuclear Information System (INIS)

    Guo, Z.

    1992-01-01

    Nuclear power plants are very complex systems. The diagnoses of transients or accident conditions is very difficult because a large amount of information, which is often noisy, or intermittent, or even incomplete, need to be processed in real time. To demonstrate their potential application to nuclear power plants, neural networks axe used to monitor the accident scenarios simulated by the training simulator of TVA's Watts Bar Nuclear Power Plant. A self-organization network is used to compress original data to reduce the total number of training patterns. Different accident scenarios are closely related to different key parameters which distinguish one accident scenario from another. Therefore, the accident scenarios can be monitored by a set of small size neural networks, called modular networks, each one of which monitors only one assigned accident scenario, to obtain fast training and recall. Sensitivity analysis is applied to select proper input variables for modular networks

  5. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

    Neural Networks is one of the most important artificial intelligent approaches for solving the diagnostic processes. This research concerns with uses the neural networks for diagnosis of the electronic circuits. Modern electronic systems contain both the analog and digital circuits. But, diagnosis of the analog circuits suffers from great complexity due to their nonlinearity. To overcome this problem, the proposed system introduces a diagnostic system that uses the neural network to diagnose both the digital and analog circuits. So, it can face the new requirements for the modern electronic systems. A fault dictionary method was implemented in the system. Experimental results are presented on three electronic systems. They are: artificial kidney, wireless network and personal computer systems. The proposed system has improved the performance of the diagnostic systems when applied for these practical cases

  6. Constitution and application of reactor make-up system's fault diagnostic Bayesian networks

    International Nuclear Information System (INIS)

    Liang Jie; Cai Qi; Chu Zhuli; Wang Haiping

    2013-01-01

    A fault diagnostic Bayesian network of reactor make-up system was constituted. The system's structure characters, operation rules and experts' experience were combined and an initial net was built. As the fault date sets were learned with the particle swarm optimization based Bayesian network structure, the structure of diagnostic net was completed and used to inference case. The built net can analyze diagnostic probability of every node in the net and afford assistant decision to fault diagnosis. (authors)

  7. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ...) to increase competitive advantage, innovation, and mission effectiveness. Network-based effectiveness occurs due to the influence of various factors such as people, procedures, technology, and organizations...

  8. Nuclear power plant status diagnostics using artificial neural networks

    International Nuclear Information System (INIS)

    Bartlett, E.B.; Uhrig, R.E.

    1991-01-01

    In this work, the nuclear power plant operating status recognition issue is investigated using artificial neural networks (ANNs). The objective is to train an ANN to classify nuclear power plant accident conditions and to assess the potential of future work in the area of plant diagnostics with ANNS. To this end, an ANN was trained to recognize normal operating conditions as well as potentially unsafe conditions based on nuclear power plant training simulator generated accident scenarios. These scenarios include; hot and cold leg loss of coolant, control rod ejection, loss of offsite power, main steam line break, main feedwater line break and steam generator tube leak accidents. Findings show that ANNs can be used to diagnose and classify nuclear power plant conditions with good results

  9. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks | Center for Cancer Research

    Science.gov (United States)

    The purpose of this study was to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial neural networks (ANNs). We trained the ANNs using the small, round blue-cell tumors (SRBCTs) as a model. These cancers belong to four distinct diagnostic categories and often present diagnostic dilemmas in

  10. 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.

  11. NETWORK SERVICES FOR DIAGNOSTIC OPTODIGITAL COMPLEX FOR TELEMEDICINE

    Directory of Open Access Journals (Sweden)

    D. S. Kopylov

    2014-03-01

    Full Text Available The paper deals with a result of the network services development for the optodigital complex for telemedicine diagnostics. This complex is designed for laboratory and clinical tests in health care facilities. Composition of network services includes the following: a client application for database of diagnostic test, a web-service, a web interface, a video server and microimage processing server. Structure of these services makes it possible to combine set of software for transferring depersonalized medical data via the Internet and operating with optodigital devices included in the complex. Complex is consisted of three systems: micro-vision, endoscopic and network. The micro-vision system includes an automated digital microscope with two highly sensitive cameras which can be controlled remotely via the Internet. The endoscopic system gives the possibility to implement video broadcasting to remote users both during diagnostic tests and also off-line after tests. The network system is the core of the complex where network services and application software are functioning, intended for archiving, storage and providing access to the database of diagnostic tests. The following subjects are developed and tested for functional stability: states transfer protocol, commands transfer protocol and video-stream transfer protocol from automated digital microscope and video endoscope. These protocols can work in web browsers on modern mobile devices without additional software.

  12. Artificial neural networks for processing fluorescence spectroscopy data in skin cancer diagnostics

    International Nuclear Information System (INIS)

    Lenhardt, L; Zeković, I; Dramićanin, T; Dramićanin, M D

    2013-01-01

    Over the years various optical spectroscopic techniques have been widely used as diagnostic tools in the discrimination of many types of malignant diseases. Recently, synchronous fluorescent spectroscopy (SFS) coupled with chemometrics has been applied in cancer diagnostics. The SFS method involves simultaneous scanning of both emission and excitation wavelengths while keeping the interval of wavelengths (constant-wavelength mode) or frequencies (constant-energy mode) between them constant. This method is fast, relatively inexpensive, sensitive and non-invasive. Total synchronous fluorescence spectra of normal skin, nevus and melanoma samples were used as input for training of artificial neural networks. Two different types of artificial neural networks were trained, the self-organizing map and the feed-forward neural network. Histopathology results of investigated skin samples were used as the gold standard for network output. Based on the obtained classification success rate of neural networks, we concluded that both networks provided high sensitivity with classification errors between 2 and 4%. (paper)

  13. Neural network analysis of vibration signals in the diagnostics of ...

    African Journals Online (AJOL)

    The article is devoted to the improvement of heat network calculation and diagnostics methods. Currently used instruments have many shortcomings for the diagnosis of pipelines, including low reliability of defect detection and subjective decision-making. The authors created an experimental stand, which allows to conduct ...

  14. Network-Based Effectiveness

    National Research Council Canada - National Science Library

    Friman, Henrik

    2006-01-01

    ... (extended from Leavitt, 1965). This text identifies aspects of network-based effectiveness that can benefit from a better understanding of leadership and management development of people, procedures, technology, and organizations...

  15. NEURAL NETWORK SYSTEM FOR DIAGNOSTICS OF AVIATION DESIGNATION PRODUCTS

    Directory of Open Access Journals (Sweden)

    В. Єременко

    2011-02-01

    Full Text Available In the article for solving the classification problem of the technical state of the  object, proposed to use a hybrid neural network with a Kohonen layer and multilayer perceptron. The information-measuring system can be used for standardless diagnostics, cluster analysis and to classify the products which made from composite materials. The advantage of this architecture is flexibility, high performance, ability to use different methods for collecting diagnostic information about unit under test, high reliability of information processing

  16. Field-based systems and advanced diagnostics

    International Nuclear Information System (INIS)

    Eryurek, E.

    1998-01-01

    Detection and characterization of anomalies in an industrial plant provide improved plant availability and plant efficiency thus yielding increased economic efficiency. Traditionally, detection of process anomalies is done at a high-level control system through various signal validation methods. These signal validation techniques rely on data from transmitters, which measure related process variables. Correlating these signals and deducing anomalies often is a very time consuming and a difficult task. Delays in detecting these anomalies can be costly during plant operation. Conventional centralized approaches also suffer from their dependence on detailed mathematical models of the processes. Smart field devices have the advantage of providing the necessary information directly to the control system as anomalies develop during operation of the processes enabling operators to take necessary steps to either prevent an unnecessary shut down before the problem becomes serious or schedule maintenance on the problematic loop. Fisher-Rosemount's PlantWeb TM architecture addresses 'Enhanced Measurement, Advanced Diagnostics and Control in the Field'. PlantWeb TM builds open process management systems by networking intelligent field devices, scalable control and systems platforms, and integrated modular software. A description of PlantWeb TM and how it improves various process conditions and reduces operating cost of a plant as well as a high level description of 'Enhanced Measurement, Advanced Diagnostics and Control in the Field', will be provided in this paper. PlantWeb TM is the trademark for Fisher-Rosemount's new field-based architecture that uses emerging technologies to utilize the power of intelligent field devices and deliver critical process and equipment information to improve plant performance. (author)

  17. Application of neural networks and neutron noise for diagnostics of reactor internals vibration

    International Nuclear Information System (INIS)

    Garis, N.S.; Pazsit, I.; Gloeckler, O.

    1995-01-01

    It has long been known that vibration of reactor internals, in particular excessive vibrations of control rods, can be detected via the neutron noise they induce. Noise measurements are actually suitable to determine important diagnostic parameters such as the location of the vibrating rod and the vibration amplitude. An algorithm was earlier elaborated for this purpose, which is based on inversion of the expression describing the neutron noise as a function of vibration parameters. This inversion procedure is nevertheless complicated and not always unique. It was investigated whether a properly trained neural network can perform the inversion more effectively. It was found that artificial neural networks can be trained effectively to perform vibration diagnostics from neutron noise data fast, effectively and reliably. The present paper gives a description of the development and use of the neural networks for purposes of vibration diagnostics

  18. Studies on neutron noise diagnostics of control rod vibrations by neural networks

    International Nuclear Information System (INIS)

    Roston, G.; Kozma, R.; Kitamura, M.; Garis, N.S.; Pazsit, I.

    1996-01-01

    This work is focussed on the study of a neutron noise based technique for the diagnostics of reactor core internal, in particular, excessively vibrating control rods. The use of a combination of physical models and neural networks offers an alternative way of performing the inversion procedure. The application of a neural network technique to determine the rod position from the detector spectra is much faster, more effective and simpler to use than the conventional method. (author). 5 refs., 1 fig., 1 tab

  19. Knowledge based diagnostics in nuclear power plants

    International Nuclear Information System (INIS)

    Baldeweg, F.; Fiedler, U.; Weiss, F.P.; Werner, M.

    1987-01-01

    In this paper a special process diagnostic system (PDS) is presented. It must be seen as the result of a long term work on computerized process surveillance and control; it includes a model based system for noise analysis of mechanical vibrations, which has recently been enhanced by using of knowledge based technique (expert systems). The paper discusses the process diagnostic frame concept and emphasize the vibration analysis expert system

  20. Bayesian networks applied to process diagnostics. Applications in energy industry

    Energy Technology Data Exchange (ETDEWEB)

    Widarsson, Bjoern (ed.); Karlsson, Christer; Dahlquist, Erik [Maelardalen Univ., Vaesteraas (Sweden); Nielsen, Thomas D.; Jensen, Finn V. [Aalborg Univ. (Denmark)

    2004-10-01

    Uncertainty in process operation occurs frequently in heat and power industry. This makes it hard to find the occurrence of an abnormal process state from a number of process signals (measurements) or find the correct cause to an abnormality. Among several other methods, Bayesian Networks (BN) is a method to build a model which can handle uncertainty in both process signals and the process itself. The purpose of this project is to investigate the possibilities to use BN for fault detection and diagnostics in combined heat and power industries through execution of two different applications. Participants from Aalborg University represent the knowledge of BN and participants from Maelardalen University have the experience from modelling heat and power applications. The co-operation also includes two energy companies; Elsam A/S (Nordjyllandsverket) and Maelarenergi AB (Vaesteraas CHP-plant), where the two applications are made with support from the plant personnel. The project ended out in two quite different applications. At Nordjyllandsverket, an application based (due to the lack of process knowledge) on pure operation data is build with capability to detect an abnormal process state in a coal mill. Detection is made through a conflict analysis when entering process signals into a model built by analysing the operation database. The application at Maelarenergi is built with a combination of process knowledge and operation data and can detect various faults caused by the fuel. The process knowledge is used to build a causal network structure and the structure is then trained by data from the operation database. Both applications are made as off-online applications, but they are ready for being run on-line. The performance of fault detection and diagnostics are good, but a lack of abnormal process states with known cause reduces the evaluation possibilities. Advantages with combining expert knowledge of the process with operation data are the possibility to represent

  1. User interface on networked workstations for MFTF plasma diagnostic instruments

    International Nuclear Information System (INIS)

    Renbarger, V.L.; Balch, T.R.

    1985-01-01

    A network of Sun-2/170 workstations is used to provide an interface to the MFTF-B Plasma Diagnostics System at Lawrence Livermore National Laboratory. The Plasma Diagnostics System (PDS) is responsible for control of MFTF-B plasma diagnostic instrumentation. An EtherNet Local Area Network links the workstations to a central multiprocessing system which furnishes data processing, data storage and control services for PDS. These workstations permit a physicist to command data acquisition, data processing, instrument control, and display of results. The interface is implemented as a metaphorical desktop, which helps the operator form a mental model of how the system works. As on a real desktop, functions are provided by sheets of paper (windows on a CRT screen) called worksheets. The worksheets may be invoked by pop-up menus and may be manipulated with a mouse. These worksheets are actually tasks that communicate with other tasks running in the central computer system. By making entries in the appropriate worksheet, a physicist may specify data acquisition or processing, control a diagnostic, or view a result

  2. Nanotechnology based diagnostics for neurological disorders

    Energy Technology Data Exchange (ETDEWEB)

    Kurek, Nicholas S; Chandra, Sathees B., E-mail: schandra@roosevelt.edu [Department of Biological, Chemical and Physical Sciences, Roosevelt University, Chicago, IL (United States)

    2012-07-01

    Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

  3. Nanotechnology based diagnostics for neurological disorders

    Energy Technology Data Exchange (ETDEWEB)

    Kurek, Nicholas S.; Chandra, Sathees B., E-mail: schandra@roosevelt.edu [Department of Biological, Chemical and Physical Sciences, Roosevelt University, Chicago, IL (United States)

    2012-07-01

    Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

  4. Nanotechnology based diagnostics for neurological disorders

    International Nuclear Information System (INIS)

    Kurek, Nicholas S.; Chandra, Sathees B.

    2012-01-01

    Nanotechnology involves probing and manipulating matter at the molecular level. Nanotechnology based molecular diagnostics have the potential to alleviate the suffering caused by many diseases, including neurological disorders, due to the unique properties of nanomaterials. Most neurological illnesses are multifactorial conditions and many of these are also classified as neurobehavioral disorders. Alzheimer's disease, Parkinson's disease, Huntington disease, cerebral ischemia, epilepsy, schizophrenia and autism spectrum disorders like Rett syndrome are some examples of neurological disorders that could be better treated, diagnosed, prevented and possibly cured using nanotechnology. In order to improve the quality of life for disease afflicted people, a wide range of nanomaterials that include gold and silica nanoparticles, quantum dots and DNA along with countless other forms of nanotechnology have been investigated regarding their usefulness in advancing molecular diagnostics. Other small scaled materials like viruses and proteins also have potential for use as molecular diagnostic tools. Information obtained from nanotechnology based diagnostics can be stored and manipulated using bioinformatics software. More advanced nanotechnology based diagnostic procedures for the acquisition of even greater proteomic and genomic knowledge can then be developed along with better ways to fight various diseases. Nanotechnology also has numerous applications besides those related to biotechnology and medicine. In this article, we will discuss and analyze many novel nanotechnology based diagnostic techniques at our disposal today. (author)

  5. Remote network control plasma diagnostic system for Tokamak T-10

    International Nuclear Information System (INIS)

    Troynov, V I; Zimin, A M; Krupin, V A; Notkin, G E; Nurgaliev, M R

    2016-01-01

    The parameters of molecular plasma in closed magnetic trap is studied in this paper. Using the system of molecular diagnostics, which was designed by the authors on the «Tokamak T-10» facility, the radiation of hydrogen isotopes at the plasma edge is investigated. The scheme of optical radiation registration within visible spectrum is described. For visualization, identification and processing of registered molecular spectra a new software is developed using MatLab environment. The software also includes electronic atlas of electronic-vibrational-rotational transitions for molecules of protium and deuterium. To register radiation from limiter cross-section a network control system is designed using the means of the Internet/Intranet. Remote control system diagram and methods are given. The examples of web-interfaces for working out equipment control scenarios and viewing of results are provided. After test run in Intranet, the remote diagnostic system will be accessible through Internet. (paper)

  6. Diagnostic tests based on human basophils

    DEFF Research Database (Denmark)

    Kleine-Tebbe, Jörg; Erdmann, Stephan; Knol, Edward F

    2006-01-01

    -maximal responses, termed 'intrinsic sensitivity'. These variables give rise to shifts in the dose-response curves which, in a diagnostic setting where only a single antigen concentration is employed, may produce false-negative data. Thus, in order to meaningfully utilize the current basophil activation tests....... Diagnostic studies using CD63 or CD203c in hymenoptera, food and drug allergy are critically discussed. Basophil-based tests are indicated for allergy testing in selected cases but should only be performed by experienced laboratories....

  7. Graphene-based nanoprobes for molecular diagnostics.

    Science.gov (United States)

    Chen, Shixing; Li, Fuwu; Fan, Chunhai; Song, Shiping

    2015-10-07

    In recent years, graphene has received widespread attention owing to its extraordinary electrical, chemical, optical, mechanical and structural properties. Lately, considerable interest has been focused on exploring the potential applications of graphene in life sciences, particularly in disease-related molecular diagnostics. In particular, the coupling of functional molecules with graphene as a nanoprobe offers an excellent platform to realize the detection of biomarkers, such as nucleic acids, proteins and other bioactive molecules, with high performance. This article reviews emerging graphene-based nanoprobes in electrical, optical and other assay methods and their application in various strategies of molecular diagnostics. In particular, this review focuses on the construction of graphene-based nanoprobes and their special advantages for the detection of various bioactive molecules. Properties of graphene-based materials and their functionalization are also comprehensively discussed in view of the development of nanoprobes. Finally, future challenges and perspectives of graphene-based nanoprobes are discussed.

  8. Delivering Diagnostic Quality Video over Mobile Wireless Networks for Telemedicine

    Directory of Open Access Journals (Sweden)

    Sira P. Rao

    2009-01-01

    Full Text Available In real-time remote diagnosis of emergency medical events, mobility can be enabled by wireless video communications. However, clinical use of this potential advance will depend on definitive and compelling demonstrations of the reliability of diagnostic quality video. Because the medical domain has its own fidelity criteria, it is important to incorporate diagnostic video quality criteria into any video compression system design. To this end, we used flexible algorithms for region-of-interest (ROI video compression and obtained feedback from medical experts to develop criteria for diagnostically lossless (DL quality. The design of the system occurred in three steps-measurement of bit rate at which DL quality is achieved through evaluation of videos by medical experts, incorporation of that information into a flexible video encoder through the notion of encoder states, and an encoder state update option based on a built-in quality criterion. Medical experts then evaluated our system for the diagnostic quality of the video, allowing us to verify that it is possible to realize DL quality in the ROI at practical communication data transfer rates, enabling mobile medical assessment over bit-rate limited wireless channels. This work lays the scientific foundation for additional validation through prototyped technology, field testing, and clinical trials.

  9. [Cutaneous lymphoproliferations: proposal for the use of diagnostic algorithms based on 2760 cases of cutaneous lymphoproliferations taken from the INCa networks (LYMPHOPATH and GFELC) over a two-year period].

    Science.gov (United States)

    Laban, Émilie; Beylot-Barry, Marie; Ortonne, Nicolas; Battistella, Maxime; Carlotti, Agnes; de Muret, Anne; Wechsler, Janine; Balme, Brigitte; Petrella, Tony; Lamant, Laurence; Frouin, Éric; Merlio, Jean-Philippe; Vergier, Béatrice

    2015-04-01

    Taking as a base our retrospective study of 2760 cases of cutaneous lymphoproliferations from the LYMPHOPATH and GFELC networks, we analyzed the doubtful and discordant cases between non-expert and expert pathologists, and the interest of clinicopathological confrontation. We defined the main diagnostic difficulties presented by cutaneous lymphoproliferations. We then designed and tested the algorithms on 20 random cases with 20 pathologists, in order to be used by any pathologist (not necessarily specialised in dermatopathology). The problematic differential diagnoses most frequently encountered are the following: MF or reactive dermatose; lymphoma without any other precision or reactive infiltrate; small B cell lymphoproliferation: lymphoma or reactive infiltrate; phenotyping of large B cell lymphoproliferation. We also analyzed less common problematic differential diagnoses, on the grounds that they are over- or under- diagnosed. Our test had a 72% success rate among the 20 randomly tested cases. The use of several algorithms for the same case is possible. Our study shows that an expert second-opinion is of interest in the area of cutaneous lymphoproliferations. A second opinion is useful for distinguishing a small B cell lymphoma from a HLR, and for defining a final diagnosis when the first pathologist doubts between lymphoma and reactive infiltrate. However, we demonstrate that for the problem MF or reactive dermatose, an initial clinicopathological confrontation produces more results than a second-opinion pathology review. This is the first study of cutaneous lymphoproliferations that, without excluding reactionary infiltrates, concentrates on doubtful and discordant diagnoses between non expert and expert pathologists, and which has produced tested diagnostic algorithms. Copyright © 2015 Elsevier Masson SAS. All rights reserved.

  10. Distributed processing and network of data acquisition and diagnostics control for Large Helical Device (LHD)

    International Nuclear Information System (INIS)

    Nakanishi, H.; Kojima, M.; Hidekuma, S.

    1997-11-01

    The LHD (Large Helical Device) data processing system has been designed in order to deal with the huge amount of diagnostics data of 600-900 MB per 10-second short-pulse experiment. It prepares the first plasma experiment in March 1998. The recent increase of the data volume obliged to adopt the fully distributed system structure which uses multiple data transfer paths in parallel and separates all of the computer functions into clients and servers. The fundamental element installed for every diagnostic device consists of two kinds of server computers; the data acquisition PC/Windows NT and the real-time diagnostics control VME/VxWorks. To cope with diversified kinds of both device control channels and diagnostics data, the object-oriented method are utilized wholly for the development of this system. It not only reduces the development burden, but also widen the software portability and flexibility. 100Mbps EDDI-based fast networks will re-integrate the distributed server computers so that they can behave as one virtual macro-machine for users. Network methods applied for the LHD data processing system are completely based on the TCP/IP internet technology, and it provides the same accessibility to the remote collaborators as local participants can operate. (author)

  11. Evidence-based diagnostics: adult septic arthritis.

    Science.gov (United States)

    Carpenter, Christopher R; Schuur, Jeremiah D; Everett, Worth W; Pines, Jesse M

    2011-08-01

    Acutely swollen or painful joints are common complaints in the emergency department (ED). Septic arthritis in adults is a challenging diagnosis, but prompt differentiation of a bacterial etiology is crucial to minimize morbidity and mortality. The objective was to perform a systematic review describing the diagnostic characteristics of history, physical examination, and bedside laboratory tests for nongonococcal septic arthritis. A secondary objective was to quantify test and treatment thresholds using derived estimates of sensitivity and specificity, as well as best-evidence diagnostic and treatment risks and anticipated benefits from appropriate therapy. Two electronic search engines (PUBMED and EMBASE) were used in conjunction with a selected bibliography and scientific abstract hand search. Inclusion criteria included adult trials of patients presenting with monoarticular complaints if they reported sufficient detail to reconstruct partial or complete 2 × 2 contingency tables for experimental diagnostic test characteristics using an acceptable criterion standard. Evidence was rated by two investigators using the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS). When more than one similarly designed trial existed for a diagnostic test, meta-analysis was conducted using a random effects model. Interval likelihood ratios (LRs) were computed when possible. To illustrate one method to quantify theoretical points in the probability of disease whereby clinicians might cease testing altogether and either withhold treatment (test threshold) or initiate definitive therapy in lieu of further diagnostics (treatment threshold), an interactive spreadsheet was designed and sample calculations were provided based on research estimates of diagnostic accuracy, diagnostic risk, and therapeutic risk/benefits. The prevalence of nongonococcal septic arthritis in ED patients with a single acutely painful joint is approximately 27% (95% confidence interval [CI] = 17

  12. Common faults in turbines and applying neural networks in order to fault diagnostic by vibration analysis

    International Nuclear Information System (INIS)

    Masoudifar, M.; AghaAmini, M.

    2001-01-01

    Today the fault diagnostic of the rotating machinery based on the vibration analysis is an effective method in designing predictive maintenance programs. In this method, vibration level of the turbines is monitored and if it is higher than the allowable limit, vibrational data will be analyzed and the growing faults will be detected. But because of the high complexity of the system monitoring, the interpretation of the measured data is more difficult. Therefore, design of the fault diagnostic expert systems by using the expert's technical experiences and knowledge; seem to be the best solution. In this paper,at first several common faults in turbines are studied and the how applying the neural networks to interpret the vibrational data for fault diagnostic is explained

  13. Learning Diagnostic Diagrams in Transport-Based Data-Collection Systems

    DEFF Research Database (Denmark)

    Tran, Vu The; Eklund, Peter; Cook, Chris

    2014-01-01

    Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural...

  14. Hybrid digital signal processing and neural networks for automated diagnostics using NDE methods

    International Nuclear Information System (INIS)

    Upadhyaya, B.R.; Yan, W.

    1993-11-01

    The primary purpose of the current research was to develop an integrated approach by combining information compression methods and artificial neural networks for the monitoring of plant components using nondestructive examination data. Specifically, data from eddy current inspection of heat exchanger tubing were utilized to evaluate this technology. The focus of the research was to develop and test various data compression methods (for eddy current data) and the performance of different neural network paradigms for defect classification and defect parameter estimation. Feedforward, fully-connected neural networks, that use the back-propagation algorithm for network training, were implemented for defect classification and defect parameter estimation using a modular network architecture. A large eddy current tube inspection database was acquired from the Metals and Ceramics Division of ORNL. These data were used to study the performance of artificial neural networks for defect type classification and for estimating defect parameters. A PC-based data preprocessing and display program was also developed as part of an expert system for data management and decision making. The results of the analysis showed that for effective (low-error) defect classification and estimation of parameters, it is necessary to identify proper feature vectors using different data representation methods. The integration of data compression and artificial neural networks for information processing was established as an effective technique for automation of diagnostics using nondestructive examination methods

  15. Divergence-based tests for model diagnostic

    Czech Academy of Sciences Publication Activity Database

    Hobza, Tomáš; Esteban, M. D.; Morales, D.; Marhuenda, Y.

    2008-01-01

    Roč. 78, č. 13 (2008), s. 1702-1710 ISSN 0167-7152 R&D Projects: GA MŠk 1M0572 Grant - others:Instituto Nacional de Estadistica (ES) MTM2006-05693 Institutional research plan: CEZ:AV0Z10750506 Keywords : goodness of fit * devergence statistics * GLM * model checking * bootstrap Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 0.445, year: 2008 http://library.utia.cas.cz/separaty/2008/SI/hobza-divergence-based%20tests%20for%20model%20diagnostic.pdf

  16. Influence of the Different Primary Cancers and Different Types of Bone Metastasis on the Lesion-based Artificial Neural Network Value Calculated by a Computer-aided Diagnostic System,BONENAVI, on Bone Scintigraphy Images

    Directory of Open Access Journals (Sweden)

    TAKURO ISODA

    2017-01-01

    Full Text Available Objective(s: BONENAVI, a computer-aided diagnostic system, is used in bone scintigraphy. This system provides the artificial neural network (ANN and bone scan index (BSI values. ANN is associated with the possibility of bone metastasis, while BSI is related to the amount of bone metastasis. The degree of uptake on bone scintigraphy can be affected by the type of bone metastasis. Therefore, the ANN value provided by BONENAVI may be influenced by the characteristics of bone metastasis. In this study, we aimed to assess the relationship between ANN value and characteristics of bone metastasis. Methods: We analyzed 50 patients (36 males, 14 females; age range: 42–87 yrs, median age: 72.5 yrs with prostate, breast, or lung cancer who had undergone bone scintigraphy and were diagnosed with bone metastasis (32 cases of prostate cancer, nine cases of breast cancer, and nine cases of lung cancer. Those who had received systematic therapy over the past years were excluded. Bone metastases were diagnosed clinically, and the type of bone metastasis (osteoblastic, mildly osteoblastic,osteolytic, and mixed components was decided visually by the agreement of two radiologists. We compared the ANN values (case-based and lesion-based among the three primary cancers and four types of bone metastasis.Results: There was no significant difference in case-based ANN values among prostate, breast, and lung cancers. However, the lesion-based ANN values were the highest in cases with prostate cancer and the lowest in cases of lung cancer (median values: prostate cancer, 0.980; breast cancer, 0.909; and lung cancer, 0.864. Mildly osteoblastic lesions showed significantly lower ANN values than the other three types of bone metastasis (median values: osteoblastic, 0.939; mildly osteoblastic, 0.788; mixed type, 0.991; and osteolytic, 0.969. The possibility of a lesion-based ANN value below 0.5 was 10.9% for bone metastasis in prostate cancer, 12.9% for breast cancer, and 37

  17. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    selection in Wi-Fi networks and predictive handover optimization in heterogeneous wireless networks. The investigations in this work have indicated that location based network optimizations are beneficial compared to typical link measurement based approaches. Especially the knowledge of geographical...

  18. Diagnostic reliability of MMPI-2 computer-based test interpretations.

    Science.gov (United States)

    Pant, Hina; McCabe, Brian J; Deskovitz, Mark A; Weed, Nathan C; Williams, John E

    2014-09-01

    Reflecting the common use of the MMPI-2 to provide diagnostic considerations, computer-based test interpretations (CBTIs) also typically offer diagnostic suggestions. However, these diagnostic suggestions can sometimes be shown to vary widely across different CBTI programs even for identical MMPI-2 profiles. The present study evaluated the diagnostic reliability of 6 commercially available CBTIs using a 20-item Q-sort task developed for this study. Four raters each sorted diagnostic classifications based on these 6 CBTI reports for 20 MMPI-2 profiles. Two questions were addressed. First, do users of CBTIs understand the diagnostic information contained within the reports similarly? Overall, diagnostic sorts of the CBTIs showed moderate inter-interpreter diagnostic reliability (mean r = .56), with sorts for the 1/2/3 profile showing the highest inter-interpreter diagnostic reliability (mean r = .67). Second, do different CBTIs programs vary with respect to diagnostic suggestions? It was found that diagnostic sorts of the CBTIs had a mean inter-CBTI diagnostic reliability of r = .56, indicating moderate but not strong agreement across CBTIs in terms of diagnostic suggestions. The strongest inter-CBTI diagnostic agreement was found for sorts of the 1/2/3 profile CBTIs (mean r = .71). Limitations and future directions are discussed. PsycINFO Database Record (c) 2014 APA, all rights reserved.

  19. Cloud networking understanding cloud-based data center networks

    CERN Document Server

    Lee, Gary

    2014-01-01

    Cloud Networking: Understanding Cloud-Based Data Center Networks explains the evolution of established networking technologies into distributed, cloud-based networks. Starting with an overview of cloud technologies, the book explains how cloud data center networks leverage distributed systems for network virtualization, storage networking, and software-defined networking. The author offers insider perspective to key components that make a cloud network possible such as switch fabric technology and data center networking standards. The final chapters look ahead to developments in architectures

  20. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1998-01-01

    The purpose of this paper is to describe a neural network (SNN), that is based on Shannons ideas of reconstruction of a real continuous function from its samples. The basic function, used in this network, is the Sinc-function. Two learning algorithms are described. A simple one called IM...

  1. Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses

    OpenAIRE

    Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik

    2016-01-01

    Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflect...

  2. Implementing artificial neural networks in nuclear power plants diagnostic systems: issues and challenges

    International Nuclear Information System (INIS)

    Boger, Z.

    1998-01-01

    A recent review of artificial intelligence applications in nuclear power plants (NPP) diagnostics and fault detection finds that mostly expert systems (ES) and artificial neural networks (ANN) techniques were researched and proposed, but the number of actual implementations in NPP diagnostics systems is very small. It lists the perceived obstacles to the ANN-based system acceptance and implementation. This paper analyses this list. Some of ANN limitations relate to 'quantitative' difficulties of designing and training large-scale ANNs. The availability of an efficient large-scale ANN training algorithm may alleviate most of these concerns. Other perceived drawbacks refer to the 'qualitative' aspects of ANN acceptance - how and when can we rely on the quality of the advice given by the ANN model. Several techniques are available that help to brighten the 'black box' image of the ANN. Analysis of the trained ANN can identify the significant inputs. Calculation of the Causal Indices may reveal the magnitude and sign of the influence of each input on each output. Both these techniques increase the confidence of the users when they conform to known knowledge, or point to plausible relationships. Analysis of the behavior of the neurons in the hidden layer can identify false ANN classification when presented with noisy or corrupt data. Auto-associative NN can identify faulty sensors or data. Two examples of the ANN capabilities as possible diagnostic tools are given, using NPP data, one classifying internal reactor disturbances by neutron noise spectra analysis, the other identifying the faults causes of several transients. To use these techniques the ANN developers need large amount of training data of as many transients as possible. Such data is routinely generated in NPP simulators during the periodic qualification of NPP operators. The IAEA can help by encouraging the saving and distributing the transient data to developers of ANN diagnostic system, to serve as

  3. Network-based functional enrichment

    Directory of Open Access Journals (Sweden)

    Poirel Christopher L

    2011-11-01

    Full Text Available Abstract Background Many methods have been developed to infer and reason about molecular interaction networks. These approaches often yield networks with hundreds or thousands of nodes and up to an order of magnitude more edges. It is often desirable to summarize the biological information in such networks. A very common approach is to use gene function enrichment analysis for this task. A major drawback of this method is that it ignores information about the edges in the network being analyzed, i.e., it treats the network simply as a set of genes. In this paper, we introduce a novel method for functional enrichment that explicitly takes network interactions into account. Results Our approach naturally generalizes Fisher’s exact test, a gene set-based technique. Given a function of interest, we compute the subgraph of the network induced by genes annotated to this function. We use the sequence of sizes of the connected components of this sub-network to estimate its connectivity. We estimate the statistical significance of the connectivity empirically by a permutation test. We present three applications of our method: i determine which functions are enriched in a given network, ii given a network and an interesting sub-network of genes within that network, determine which functions are enriched in the sub-network, and iii given two networks, determine the functions for which the connectivity improves when we merge the second network into the first. Through these applications, we show that our approach is a natural alternative to network clustering algorithms. Conclusions We presented a novel approach to functional enrichment that takes into account the pairwise relationships among genes annotated by a particular function. Each of the three applications discovers highly relevant functions. We used our methods to study biological data from three different organisms. Our results demonstrate the wide applicability of our methods. Our algorithms are

  4. Experimental Investigation on Admittance-Based Piezoelectric Sensor Diagnostic Process

    Energy Technology Data Exchange (ETDEWEB)

    Jo, Hyejin; Park, Tongil; Park, Gyuhae [Chonnam National University, Gwangju (Korea, Republic of)

    2015-01-15

    Structural health monitoring (SHM) techniques based on the use of active-sensing piezoelectric (PZT) materials have received considerable attention. The validation of the PZT functionality during SHM operation is critical to successfully implementing a reliable SHM system. In this study, we investigated several parameters that affect the admittance-based sensor diagnostic process. We experimentally identified the temperature dependency of the active-sensor diagnostic process. We found that the admittance-based sensor diagnostic process can differentiate the adhesion conditions of bonding materials that are used to install a PZT on a structure, which is important when designing a sensor diagnostic process for an SHM system.

  5. Diagnostics for generalized linear hierarchical models in network meta-analysis.

    Science.gov (United States)

    Zhao, Hong; Hodges, James S; Carlin, Bradley P

    2017-09-01

    Network meta-analysis (NMA) combines direct and indirect evidence comparing more than 2 treatments. Inconsistency arises when these 2 information sources differ. Previous work focuses on inconsistency detection, but little has been done on how to proceed after identifying inconsistency. The key issue is whether inconsistency changes an NMA's substantive conclusions. In this paper, we examine such discrepancies from a diagnostic point of view. Our methods seek to detect influential and outlying observations in NMA at a trial-by-arm level. These observations may have a large effect on the parameter estimates in NMA, or they may deviate markedly from other observations. We develop formal diagnostics for a Bayesian hierarchical model to check the effect of deleting any observation. Diagnostics are specified for generalized linear hierarchical NMA models and investigated for both published and simulated datasets. Results from our example dataset using either contrast- or arm-based models and from the simulated datasets indicate that the sources of inconsistency in NMA tend not to be influential, though results from the example dataset suggest that they are likely to be outliers. This mimics a familiar result from linear model theory, in which outliers with low leverage are not influential. Future extensions include incorporating baseline covariates and individual-level patient data. Copyright © 2017 John Wiley & Sons, Ltd.

  6. Diagnostic Classifiers: Revealing how Neural Networks Process Hierarchical Structure

    NARCIS (Netherlands)

    Veldhoen, S.; Hupkes, D.; Zuidema, W.

    2016-01-01

    We investigate how neural networks can be used for hierarchical, compositional semantics. To this end, we define the simple but nontrivial artificial task of processing nested arithmetic expressions and study whether different types of neural networks can learn to add and subtract. We find that

  7. COGITA network has constructed a glossary of diagnostic reasoning terms.

    Science.gov (United States)

    Barais, Marie; Hauswaldt, Johannes; Dinant, Geert-Jan; van de Wiel, Margje; Stolper, C F Erik; Van Royen, Paul

    2017-12-01

    The role of gut feelings in diagnostic reasoning is recognized by most GPs throughout Europe, and probably throughout the world. Studies on this topic have emerged from different countries but there is the risk that authors will use different terms for similar concepts. The European Expert Group on Cognitive and Interactive Processes in Diagnosis and Management in General Practice, COGITA for short, was founded in 2008 to conduct cross-border research in the area of non-analytical diagnostic reasoning. Academic GPs, PhD students, psychologists, linguists and students meet once a year to share their experiences, exchange results and initiate new studies on the topic. A milestone in their research is this publication of a short glossary of diagnostic reasoning terms relating to the gut feelings research topic. It was constructed by the COGITA group members following a literature review, which aimed to define salient terms used in their publications. They described the terms, cross-reviewed the wording and reached consensus within the group. Two sections were created: (1) a diagnostic reasoning section that describes concepts such as analytical and non-analytical reasoning, clinical mind lines, and intuition, and (2) a research methods section describing concepts such as linguistic validity and saturation. The glossary, including relevant literature, has been published on the website http://www.gutfeelingsingeneralpractice.eu . In the future, the glossary will be modified if necessary and completed by members of the COGITA group. [Box: see text].

  8. The role of neural networks in reactor diagnostics and control

    International Nuclear Information System (INIS)

    Pazsit, I.; Kitamura, M.

    1997-01-01

    Reactor diagnostics and core diagnostics in particular, is an inverse task, just as most other diagnostics. One measures some physical parameter at some position, or the fluctuation thereof, which is given rise by the fluctuation of another parameter, presumably at a different position. In neutron noise diagnostics, the measured quantity is the neutron noise, whereas the cause, fluctuations of the core material, is called the open-quotes noise sourceclose quotes. The relationship between the cause (noise source) and the induced noise (effect) is determined by the physics of the process and can usually be described by a theory. This means that the direct task, calculation of the noise from the noise source, can always be achieved. In diagnostics, however, the process starts from the back end, i.e. one observes the effect of some cause. The task is to infer the cause (noise source) from the effect (induced noise), which is an inverse task (sometimes also called unfolding). The situation is very much similar even regarding control problems. The system state is described by a vector in the parameter space. When it deviates from the desired one, the control parameters need to be changed to bring the system into the desired state. The computation of the change in the state vector, due to a change in the control vector, can be calculated with no difficulty in principle. The control requires however the inverse task to be performed, i.e. to determine which changes of the control vector would bring the state vector into the desired state

  9. The Application of Cognitive Diagnostic Approaches via Neural Network Analysis of Serious Educational Games

    Science.gov (United States)

    Lamb, Richard L.

    Serious Educational Games (SEGs) have been a topic of increased popularity within the educational realm since the early millennia. SEGs are generalized form of Serious Games to mean games for purposes other than entertainment but, that also specifically include training, educational purpose and pedagogy within their design. This rise in popularity (for SEGs) has occurred at a time when school systems have increased the type, number, and presentations of student achievement tests for decision-making purposes. These tests often task the form of end of course (year) tests and periodic benchmark testing. As the use of these tests, has increased policymakers have suggested their use as a measure for teacher accountability. The change in testing resulted from a push by school districts and policy makers at various component levels for a data-driven decision-making (D3M) approach. With the data-driven decision making approaches by school districts, there has been an increased focus on the measurement and assessment of student content knowledge with little focus on the contributing factors and cognitive attributes within learning that cross multiple-content areas. One-way to increase the focus on these aspects of learning (factors and attributes) that are additional to content learning is through assessments based in cognitive diagnostics. Cognitive diagnostics are a family of methodological approaches in which tasks tie to specific cognitive attributes for analytical purposes. This study explores data derived from computer data logging (n=158,000) in an observational design, using traditional statistical techniques such as clustering (exploratory and confirmatory), item response theory and through data mining techniques such as artificial neural network analysis. From these analyses, a model of student learning emerges illustrating student thinking and learning while engaged in SEG Design. This study seeks to use cognitive diagnostic type approaches to measure student

  10. 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.

  11. Towards smart service networks : An interdisciplinary diagnostic framework

    NARCIS (Netherlands)

    Wang, Yan; Taher, Yehia; van den Heuvel, Willem-Jan

    2015-01-01

    Service Networks (SNs) are open systems accommodating the co-production of new knowledge and services through organic peer-to-peer interactions. Key to broad success of SNs in practice is their ability to foster and ensure a high performance. By performance we mean the joint effort of tremendous

  12. Feasibility of streamlining an interactive Bayesian-based diagnostic support tool designed for clinical practice

    Science.gov (United States)

    Chen, Po-Hao; Botzolakis, Emmanuel; Mohan, Suyash; Bryan, R. N.; Cook, Tessa

    2016-03-01

    In radiology, diagnostic errors occur either through the failure of detection or incorrect interpretation. Errors are estimated to occur in 30-35% of all exams and contribute to 40-54% of medical malpractice litigations. In this work, we focus on reducing incorrect interpretation of known imaging features. Existing literature categorizes cognitive bias leading a radiologist to an incorrect diagnosis despite having correctly recognized the abnormal imaging features: anchoring bias, framing effect, availability bias, and premature closure. Computational methods make a unique contribution, as they do not exhibit the same cognitive biases as a human. Bayesian networks formalize the diagnostic process. They modify pre-test diagnostic probabilities using clinical and imaging features, arriving at a post-test probability for each possible diagnosis. To translate Bayesian networks to clinical practice, we implemented an entirely web-based open-source software tool. In this tool, the radiologist first selects a network of choice (e.g. basal ganglia). Then, large, clearly labeled buttons displaying salient imaging features are displayed on the screen serving both as a checklist and for input. As the radiologist inputs the value of an extracted imaging feature, the conditional probabilities of each possible diagnosis are updated. The software presents its level of diagnostic discrimination using a Pareto distribution chart, updated with each additional imaging feature. Active collaboration with the clinical radiologist is a feasible approach to software design and leads to design decisions closely coupling the complex mathematics of conditional probability in Bayesian networks with practice.

  13. Host Event Based Network Monitoring

    Energy Technology Data Exchange (ETDEWEB)

    Jonathan Chugg

    2013-01-01

    The purpose of INL’s research on this project is to demonstrate the feasibility of a host event based network monitoring tool and the effects on host performance. Current host based network monitoring tools work on polling which can miss activity if it occurs between polls. Instead of polling, a tool could be developed that makes use of event APIs in the operating system to receive asynchronous notifications of network activity. Analysis and logging of these events will allow the tool to construct the complete real-time and historical network configuration of the host while the tool is running. This research focused on three major operating systems commonly used by SCADA systems: Linux, WindowsXP, and Windows7. Windows 7 offers two paths that have minimal impact on the system and should be seriously considered. First is the new Windows Event Logging API, and, second, Windows 7 offers the ALE API within WFP. Any future work should focus on these methods.

  14. Diagnostics

    DEFF Research Database (Denmark)

    Donné, A.J.H.; Costley, A.E.; Barnsley, R.

    2007-01-01

    of the measurements—time and spatial resolutions, etc—will in some cases be more stringent. Many of the measurements will be used in the real time control of the plasma driving a requirement for very high reliability in the systems (diagnostics) that provide the measurements. The implementation of diagnostic systems...... on ITER is a substantial challenge. Because of the harsh environment (high levels of neutron and gamma fluxes, neutron heating, particle bombardment) diagnostic system selection and design has to cope with a range of phenomena not previously encountered in diagnostic design. Extensive design and R......&D is needed to prepare the systems. In some cases the environmental difficulties are so severe that new diagnostic techniques are required. The starting point in the development of diagnostics for ITER is to define the measurement requirements and develop their justification. It is necessary to include all...

  15. Pilot study of dynamic Bayesian networks approach for fault diagnostics and accident progression prediction in HTR-PM

    Energy Technology Data Exchange (ETDEWEB)

    Zhao, Yunfei; Tong, Jiejuan; Zhang, Liguo, E-mail: lgzhang@tsinghua.edu.cn; Zhang, Qin

    2015-09-15

    Highlights: • Dynamic Bayesian network is used to diagnose and predict accident progress in HTR-PM. • Dynamic Bayesian network model of HTR-PM is built based on detailed system analysis. • LOCA Simulations validate the above model even if part monitors are lost or false. - Abstract: The first high-temperature-reactor pebble-bed demonstration module (HTR-PM) is under construction currently in China. At the same time, development of a system that is used to support nuclear emergency response is in progress. The supporting system is expected to complete two tasks. The first one is diagnostics of the fault in the reactor based on abnormal sensor measurements obtained. The second one is prognostic of the accident progression based on sensor measurements obtained and operator actions. Both tasks will provide valuable guidance for emergency staff to take appropriate protective actions. Traditional method for the two tasks relies heavily on expert judgment, and has been proven to be inappropriate in some cases, such as Three Mile Island accident. To better perform the two tasks, dynamic Bayesian networks (DBN) is introduced in this paper and a pilot study based on the approach is carried out. DBN is advantageous in representing complex dynamic systems and taking full consideration of evidences obtained to perform diagnostics and prognostics. Pearl's loopy belief propagation (LBP) algorithm is recommended for diagnostics and prognostics in DBN. The DBN model of HTR-PM is created based on detailed system analysis and accident progression analysis. A small break loss of coolant accident (SBLOCA) is selected to illustrate the application of the DBN model of HTR-PM in fault diagnostics (FD) and accident progression prognostics (APP). Several advantages of DBN approach compared with other techniques are discussed. The pilot study lays the foundation for developing the nuclear emergency response supporting system (NERSS) for HTR-PM.

  16. The local area network for the plasma Diagnostics System of MFTF-B

    International Nuclear Information System (INIS)

    Lau, N.H.; Minor, E.G.

    1983-01-01

    The MFTF-B Plasma Diagnostics System will be implemented in stages, beginning with a start-up set of diagnostics and evolving toward a basic set. The start-up set contains 12 diagnostics which will acquire a total of about 800 Kbytes of data per machine pulse; the basic set contains 23 diagnostics which will acquire a total of about 8 Mbytes of data per pulse. Each diagnostic is controlled by a ''Foundation System'' consisting of a DEC LSI-11/23 microcomputer connected to CAMAC via a 5 Mbits/second serial fiber-optic link and connected to a supervisory computer (Perkin-Elmer 3250) via a 9600 baud RS232 link. The Foundation System is a building block used throughout MFTF-B for control and status monitoring. However, its 9600 baud link to the supervisor presents a bottleneck for the large data transfers required by diagnostics. To overcome this bottleneck the diagnostics Foundation Systems will be connected together with an additional LSI-11/23 called the ''master'' to form a Local Area Network (LAN) for data acquisition. The Diagnostics LAN has a ring architecture with token passing arbitration

  17. Statistical Classification for Cognitive Diagnostic Assessment: An Artificial Neural Network Approach

    Science.gov (United States)

    Cui, Ying; Gierl, Mark; Guo, Qi

    2016-01-01

    The purpose of the current investigation was to describe how the artificial neural networks (ANNs) can be used to interpret student performance on cognitive diagnostic assessments (CDAs) and evaluate the performances of ANNs using simulation results. CDAs are designed to measure student performance on problem-solving tasks and provide useful…

  18. Paper-based synthetic gene networks.

    Science.gov (United States)

    Pardee, Keith; Green, Alexander A; Ferrante, Tom; Cameron, D Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J

    2014-11-06

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides an alternate, versatile venue for synthetic biologists to operate and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze dried onto paper, enabling the inexpensive, sterile, and abiotic distribution of synthetic-biology-based technologies for the clinic, global health, industry, research, and education. For field use, we create circuits with colorimetric outputs for detection by eye and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small-molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors.

  19. Paper-based Synthetic Gene Networks

    Science.gov (United States)

    Pardee, Keith; Green, Alexander A.; Ferrante, Tom; Cameron, D. Ewen; DaleyKeyser, Ajay; Yin, Peng; Collins, James J.

    2014-01-01

    Synthetic gene networks have wide-ranging uses in reprogramming and rewiring organisms. To date, there has not been a way to harness the vast potential of these networks beyond the constraints of a laboratory or in vivo environment. Here, we present an in vitro paper-based platform that provides a new venue for synthetic biologists to operate, and a much-needed medium for the safe deployment of engineered gene circuits beyond the lab. Commercially available cell-free systems are freeze-dried onto paper, enabling the inexpensive, sterile and abiotic distribution of synthetic biology-based technologies for the clinic, global health, industry, research and education. For field use, we create circuits with colorimetric outputs for detection by eye, and fabricate a low-cost, electronic optical interface. We demonstrate this technology with small molecule and RNA actuation of genetic switches, rapid prototyping of complex gene circuits, and programmable in vitro diagnostics, including glucose sensors and strain-specific Ebola virus sensors. PMID:25417167

  20. Automatic Decision Support for Clinical Diagnostic Literature Using Link Analysis in a Weighted Keyword Network.

    Science.gov (United States)

    Li, Shuqing; Sun, Ying; Soergel, Dagobert

    2017-12-23

    We present a novel approach to recommending articles from the medical literature that support clinical diagnostic decision-making, giving detailed descriptions of the associated ideas and principles. The specific goal is to retrieve biomedical articles that help answer questions of a specified type about a particular case. Based on the filtered keywords, MeSH(Medical Subject Headings) lexicon and the automatically extracted acronyms, the relationship between keywords and articles was built. The paper gives a detailed description of the process of by which keywords were measured and relevant articles identified based on link analysis in a weighted keywords network. Some important challenges identified in this study include the extraction of diagnosis-related keywords and a collection of valid sentences based on the keyword co-occurrence analysis and existing descriptions of symptoms. All data were taken from medical articles provided in the TREC (Text Retrieval Conference) clinical decision support track 2015. Ten standard topics and one demonstration topic were tested. In each case, a maximum of five articles with the highest relevance were returned. The total user satisfaction of 3.98 was 33% higher than average. The results also suggested that the smaller the number of results, the higher the average satisfaction. However, a few shortcomings were also revealed since medical literature recommendation for clinical diagnostic decision support is so complex a topic that it cannot be fully addressed through the semantic information carried solely by keywords in existing descriptions of symptoms. Nevertheless, the fact that these articles are actually relevant will no doubt inspire future research.

  1. PC based diagnostic system for nitrogen production unit of HWP

    International Nuclear Information System (INIS)

    Lamba, D.S.; Rao, V.C.; Krishnan, S.; Kamaraj, T.; Krishnaswamy, C.

    1992-01-01

    The plant diagnostic system monitors the input data from local processing unit and tries to diagnose the cause of the failure. The system is a rule based application program that can perform tasks itself using fault tree model which displays the logical relationships between critical events and their possible ways occurrence, i.e. hardware failure, process faults and human error etc. Unit 37 Nitrogen Plant is taken as a prototype model for trying the plant diagnostics system. (author). 3 refs., 2 figs

  2. On Tree-Based Phylogenetic Networks.

    Science.gov (United States)

    Zhang, Louxin

    2016-07-01

    A large class of phylogenetic networks can be obtained from trees by the addition of horizontal edges between the tree edges. These networks are called tree-based networks. We present a simple necessary and sufficient condition for tree-based networks and prove that a universal tree-based network exists for any number of taxa that contains as its base every phylogenetic tree on the same set of taxa. This answers two problems posted by Francis and Steel recently. A byproduct is a computer program for generating random binary phylogenetic networks under the uniform distribution model.

  3. Latent class analysis of diagnostic science assessment data using Bayesian networks

    Science.gov (United States)

    Steedle, Jeffrey Thomas

    2008-10-01

    Diagnostic science assessments seek to draw inferences about student understanding by eliciting evidence about the mental models that underlie students' reasoning about physical systems. Measurement techniques for analyzing data from such assessments embody one of two contrasting assessment programs: learning progressions and facet-based assessments. Learning progressions assume that students have coherent theories that they apply systematically across different problem contexts. In contrast, the facet approach makes no such assumption, so students should not be expected to reason systematically across different problem contexts. A systematic comparison of these two approaches is of great practical value to assessment programs such as the National Assessment of Educational Progress as they seek to incorporate small clusters of related items in their tests for the purpose of measuring depth of understanding. This dissertation describes an investigation comparing learning progression and facet models. Data comprised student responses to small clusters of multiple-choice diagnostic science items focusing on narrow aspects of understanding of Newtonian mechanics. Latent class analysis was employed using Bayesian networks in order to model the relationship between students' science understanding and item responses. Separate models reflecting the assumptions of the learning progression and facet approaches were fit to the data. The technical qualities of inferences about student understanding resulting from the two models were compared in order to determine if either modeling approach was more appropriate. Specifically, models were compared on model-data fit, diagnostic reliability, diagnostic certainty, and predictive accuracy. In addition, the effects of test length were evaluated for both models in order to inform the number of items required to obtain adequately reliable latent class diagnoses. Lastly, changes in student understanding over time were studied with a

  4. Nonbinary Tree-Based Phylogenetic Networks.

    Science.gov (United States)

    Jetten, Laura; van Iersel, Leo

    2018-01-01

    Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can, for example, represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and strictly-tree-based nonbinary phylogenetic networks. We give simple graph-theoretic characterizations of tree-based and strictly-tree-based nonbinary phylogenetic networks. Moreover, we show for each of these two classes that it can be decided in polynomial time whether a given network is contained in the class. Our approach also provides a new view on tree-based binary phylogenetic networks. Finally, we discuss two examples of nonbinary phylogenetic networks in biology and show how our results can be applied to them.

  5. Global Accelerator Network, Control Systems And Beam Diagnostics

    CERN Document Server

    Raich, U

    2003-01-01

    Falling funds force all accelerator centers to look for new sources of financing and for the most efficient way of implementing new projects. This very often leads to collaborations between institutes scattered around the globe, a problem well known to big high energy physics experiments. The collaborations working on big detectors e.g. for LHC started thinking about detector acquisition and control systems which can be remotely used from their respective home institutes with minimal support on the spot. This idea was taken up by A. Wagner from DESY for the TESLA machine, who proposed the “Global Accelerator Network” (GAN) enabling users from around the world to run an accelerator remotely. Questions around this subject that immediately come to mind Is the GAN only relevant to big labs ? Or is it reasonable e.g. for operators or engineers in charge to do certain manipulations from home? Are our instruments ready for the GAN? Does the fact of being “GAN ready” increa...

  6. Local area network for the plasma diagnostics system of MFTF-B

    International Nuclear Information System (INIS)

    Lau, N.H.; Minor, E.G.

    1983-01-01

    The MFTF-B Plasma Diagnostics System will be implemented in stages, beginning with a start-up set of diagnostics and evolving toward a basic set. The start-up set contains 12 diagnostics which will acquire a total of about 800 Kbytes of data per machine pulse; the basic set contains 23 diagnostics which will acquire a total of about 8 Mbytes of data per pulse. Each diagnostic is controlled by a Foundation System consisting of a DEC LSI-11/23 microcomputer connected to CAMAC via a 5 Mbits/second serial fiber-optic link and connected to a supervisory computer (Perkin-Elmer 3250) via a 9600 baud RS232 link. The Foundation System is a building block used throughout MFTF-B for control and status monitoring. However, its 9600 baud link to the supervisor presents a bottleneck for the large data transfers required by diagnostics. To overcome this bottleneck the diagnostics Foundation Systems will be connected together with an additional LSI-11/23 called the master to form a Local Area Network (LAN) for data acquisition

  7. Density-based and transport-based core-periphery structures in networks.

    Science.gov (United States)

    Lee, Sang Hoon; Cucuringu, Mihai; Porter, Mason A

    2014-03-01

    Networks often possess mesoscale structures, and studying them can yield insights into both structure and function. It is most common to study community structure, but numerous other types of mesoscale structures also exist. In this paper, we examine core-periphery structures based on both density and transport. In such structures, core network components are well-connected both among themselves and to peripheral components, which are not well-connected to anything. We examine core-periphery structures in a wide range of examples of transportation, social, and financial networks-including road networks in large urban areas, a rabbit warren, a dolphin social network, a European interbank network, and a migration network between counties in the United States. We illustrate that a recently developed transport-based notion of node coreness is very useful for characterizing transportation networks. We also generalize this notion to examine core versus peripheral edges, and we show that the resulting diagnostic is also useful for transportation networks. To examine the properties of transportation networks further, we develop a family of generative models of roadlike networks. We illustrate the effect of the dimensionality of the embedding space on transportation networks, and we demonstrate that the correlations between different measures of coreness can be very different for different types of networks.

  8. An acoustical model based monitoring network

    NARCIS (Netherlands)

    Wessels, P.W.; Basten, T.G.H.; Eerden, F.J.M. van der

    2010-01-01

    In this paper the approach for an acoustical model based monitoring network is demonstrated. This network is capable of reconstructing a noise map, based on the combination of measured sound levels and an acoustic model of the area. By pre-calculating the sound attenuation within the network the

  9. Advances in nucleic acid-based diagnostics of bacterial infections

    DEFF Research Database (Denmark)

    Barken, Kim Bundvig; Haagensen, Janus Anders Juul; Tolker-Nielsen, Tim

    2007-01-01

    Methods for rapid detection of infectious bacteria and antimicrobial-resistant pathogens have evolved significantly over the last decade. Many of the new procedures are nucleic acid-based and replace conventional diagnostic methods like culturing which is time consuming especially with fastidious...... of these pathogens is important to isolate patients and prevent further spreading of the diseases. Newly developed diagnostic procedures are superior with respect to turnaround time, sensitivity and specificity. Methods like multiplex real time PCR and different array-based technologies offer the possibility...

  10. Optical burst switching based satellite backbone network

    Science.gov (United States)

    Li, Tingting; Guo, Hongxiang; Wang, Cen; Wu, Jian

    2018-02-01

    We propose a novel time slot based optical burst switching (OBS) architecture for GEO/LEO based satellite backbone network. This architecture can provide high speed data transmission rate and high switching capacity . Furthermore, we design the control plane of this optical satellite backbone network. The software defined network (SDN) and network slice (NS) technologies are introduced. Under the properly designed control mechanism, this backbone network is flexible to support various services with diverse transmission requirements. Additionally, the LEO access and handoff management in this network is also discussed.

  11. Validation of a Novel Traditional Chinese Medicine Pulse Diagnostic Model Using an Artificial Neural Network

    Directory of Open Access Journals (Sweden)

    Anson Chui Yan Tang

    2012-01-01

    Full Text Available In view of lacking a quantifiable traditional Chinese medicine (TCM pulse diagnostic model, a novel TCM pulse diagnostic model was introduced to quantify the pulse diagnosis. Content validation was performed with a panel of TCM doctors. Criterion validation was tested with essential hypertension. The gold standard was brachial blood pressure measured by a sphygmomanometer. Two hundred and sixty subjects were recruited (139 in the normotensive group and 121 in the hypertensive group. A TCM doctor palpated pulses at left and right cun, guan, and chi points, and quantified pulse qualities according to eight elements (depth, rate, regularity, width, length, smoothness, stiffness, and strength on a visual analog scale. An artificial neural network was used to develop a pulse diagnostic model differentiating essential hypertension from normotension. Accuracy, specificity, and sensitivity were compared among various diagnostic models. About 80% accuracy was attained among all models. Their specificity and sensitivity varied, ranging from 70% to nearly 90%. It suggested that the novel TCM pulse diagnostic model was valid in terms of its content and diagnostic ability.

  12. Cognitive diagnostic assessment based on knowledge structure

    Directory of Open Access Journals (Sweden)

    Huang Sue-Fen

    2018-01-01

    Full Text Available The purpose of this study is to provide an integrated method of fuzzy theory basis for individualized concept structure analysis. In order to insight the misconception of learning basic mathematics and progress teaching. This method integrates Fuzzy Logic Model of Perception (FLMP and Interpretive Structural Modelling (ISM. The combined algorithm could analyze individualized concepts structure based on the comparisons with concept structure of expert. In this paper, some well-known knowledge structure assessment methods will be discussed. For item connection, Bart et al ordering theory and Takeya’s item relational structure provided ordering coefficient to construct item relationships and hierarchies. For concepts or skills connection, Warfield’s ISM and Lin et al Concept Advanced Interpretive Structural Modelling (CAISM provided to construct graphic relationship among elements and display the individualized concept hierarchy structure by numeric and picture. Samples contain 427 which come from Min-Hwei Junior College. Subjects were analyzed by CAISM. It shows the traditional assessment is not the only criteria; it must be combined with other assessment tools. The result shows that CAISM gives meaningful learning and lacks of learners.

  13. A Quantum Cryptography Communication Network Based on Software Defined Network

    Directory of Open Access Journals (Sweden)

    Zhang Hongliang

    2018-01-01

    Full Text Available With the development of the Internet, information security has attracted great attention in today’s society, and quantum cryptography communication network based on quantum key distribution (QKD is a very important part of this field, since the quantum key distribution combined with one-time-pad encryption scheme can guarantee the unconditional security of the information. The secret key generated by quantum key distribution protocols is a very valuable resource, so making full use of key resources is particularly important. Software definition network (SDN is a new type of network architecture, and it separates the control plane and the data plane of network devices through OpenFlow technology, thus it realizes the flexible control of the network resources. In this paper, a quantum cryptography communication network model based on SDN is proposed to realize the flexible control of quantum key resources in the whole cryptography communication network. Moreover, we propose a routing algorithm which takes into account both the hops and the end-to-end availible keys, so that the secret key generated by QKD can be used effectively. We also simulate this quantum cryptography communication network, and the result shows that based on SDN and the proposed routing algorithm the performance of this network is improved since the effective use of the quantum key resources.

  14. Development of a diagnostic system for Klystron modulators using a neural network

    International Nuclear Information System (INIS)

    Mutoh, M.; Oonuma, T.; Shibasaki, Y.; Abe, I.; Nakahara, K.

    1992-01-01

    The diagnostic system for klystron modulators using a neural network has been developed. Large changes in the voltage and current of the main circuit in a klystron modulator were observed just several ten milli-seconds before the modulator experienced trouble. These changes formed a peculiar pattern that depended on the parts with problems. Diagnosis was possible by means of pattern recognition. The recognition test of patterns using a neutral network has shown good results. This system, which is built in a linac control system, is presently being operated so as to collect new trouble patterns and to carry out tests for practical use. (author)

  15. ENERGY AWARE NETWORK: BAYESIAN BELIEF NETWORKS BASED DECISION MANAGEMENT SYSTEM

    Directory of Open Access Journals (Sweden)

    Santosh Kumar Chaudhari

    2011-06-01

    Full Text Available A Network Management System (NMS plays a very important role in managing an ever-evolving telecommunication network. Generally an NMS monitors & maintains the health of network elements. The growing size of the network warrants extra functionalities from the NMS. An NMS provides all kinds of information about networks which can be used for other purposes apart from monitoring & maintaining networks like improving QoS & saving energy in the network. In this paper, we add another dimension to NMS services, namely, making an NMS energy aware. We propose a Decision Management System (DMS framework which uses a machine learning technique called Bayesian Belief Networks (BBN, to make the NMS energy aware. The DMS is capable of analysing and making control decisions based on network traffic. We factor in the cost of rerouting and power saving per port. Simulations are performed on standard network topologies, namely, ARPANet and IndiaNet. It is found that ~2.5-6.5% power can be saved.

  16. GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

    Energy Technology Data Exchange (ETDEWEB)

    Patel, Kiran, E-mail: kkpatel@ipr.res.in; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

    2016-11-15

    The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

  17. GPIB based instrumentation and control system for ADITYA Thomson Scattering Diagnostic

    International Nuclear Information System (INIS)

    Patel, Kiran; Pillai, Vishal; Singh, Neha; Chaudhary, Vishnu; Thomas, Jinto; Kumar, Ajai

    2016-01-01

    The ADITYA Thomson Scattering Diagnostic is a single point Ruby laser based system with a spectrometer for spectral dispersion and photomultiplier tubes for the detection of scattered light. The system uses CAMAC (Computer Automated Measurement And Control) based control and data acquisition system, which synchronizes the Ruby laser, detectors and the digitizer. Previously used serial based CAMAC controller is upgraded to GPIB (General Purpose Interface Bus) based CAMAC controller for configuration and data transfer. The communication protocols for different instruments are converted to a single GPIB based for better interface. The entire control and data acquisition program is developed on LabVIEW platform for versatile operation of diagnostics with improved user friendly GUI (Graphical User Interfaces) and allows user to remotely update the laser firing time with respect to the plasma shot. The software is in handshake with the Tokamak main control program through network to minimize manual interventions for the operation of the diagnostics. The upgraded system improved the performance of the diagnostics in comparison to earlier in terms of better data transmission rate, easy to maintain and program is upgradable.

  18. A user interface on networked workstations for MFTF-B plasma diagnostic instruments

    International Nuclear Information System (INIS)

    Balch, T.R.; Renbarger, V.L.

    1986-01-01

    A network of Sun-2/170 workstations is used to provide an interface to the MFTF-B Plasma Diagnostics System at Lawrence Livermore National Laboratory. The Plasma Diagnostics System (PDS) is responsible for control of MFTF-B plasma diagnostic instrumentation. An EtherNet Local Area Network links the workstations to a central multiprocessing system which furnishes data processing, data storage and control services for PDS. These workstations permit a physicist to command data acquisition, data processing, instrument control, and display of results. The interface is implemented as a metaphorical desktop, which helps the operator form a mental model of how the system works. As on a real desktop, functions are provided by sheets of paper (windows on a CRT screen) called worksheets. The worksheets may be invoked by pop-up menus and may be manipulated with a mouse. These worksheets are actually tasks that communicate with other tasks running in the central computer system. By making entries in the appropriate worksheet, a physicist may specify data acquisition or processing, control a diagnostic, or view a result

  19. Rapid development of paper-based fluidic diagnostic devices

    CSIR Research Space (South Africa)

    Smith, S

    2014-11-01

    Full Text Available We present a method for rapid and low-cost development of microfluidic diagnostic devices using paper-based techniques. Specifically, the implementation of fluidic flow paths and electronics on paper are demonstrated, with the goal of producing...

  20. MRI-based diagnostic imaging of the intratemporal facial nerve

    International Nuclear Information System (INIS)

    Kress, B.; Baehren, W.

    2001-01-01

    Detailed imaging of the five sections of the full intratemporal course of the facial nerve can be achieved by MRI and using thin tomographic section techniques and surface coils. Contrast media are required for tomographic imaging of pathological processes. Established methods are available for diagnostic evaluation of cerebellopontine angle tumors and chronic Bell's palsy, as well as hemifacial spasms. A method still under discussion is MRI for diagnostic evaluation of Bell's palsy in the presence of fractures of the petrous bone, when blood volumes in the petrous bone make evaluation even more difficult. MRI-based diagnostic evaluation of the idiopatic facial paralysis currently is subject to change. Its usual application cannot be recommended for routine evaluation at present. However, a quantitative analysis of contrast medium uptake of the nerve may be an approach to improve the prognostic value of MRI in acute phases of Bell's palsy. (orig./CB) [de

  1. CBDS: Constraint-based diagnostic system for malfunction identification in the nuclear power plant

    International Nuclear Information System (INIS)

    Ha, J.

    1992-01-01

    Traditional rule-based diagnostic expert systems use the experience of experts in the form of rules that associate symptoms with underlying faults. A commonly recognized failing of such systems is their narrow range of expertise and their inability to recognize problems outside this range of expertise. A model base diagnostic system isolating malfunctioning components-CBDS, the Constraint based Diagnostic System-has been developed. Since the intended behavior of a device is more predictable than unintended behaviors (faults), a model based system using the intended behavior has a potential to diagnose unexpected malfunctions by considering faults as open-quotes anything other than the intended behavior.close quotes As a knowledge base, the CBDS generates and decomposes a constraint network based on the structure and behavior model, which are represented symbolically in algebraic equations. Behaviors of generic components are organized in a component model library. Once the library is available, actual domain knowledge can be represented by declaring component types and their connections. To capture various plant knowledge, the mixed model was developed which allow the use of different parameter types in one equation by defining various operators. The CBDS uses the general idea of model based diagnosis. It detects a discrepancy between observation and prediction using constraint propagation, which carriers and accumulates the assumptions when parameter values are deduced. When measured plant parameters are asserted into a constraint network and are propagated through the network, a discrepancy will be detected if there exists any malfunctioning component. The CBDS was tested in the Recirculation Flow Control System of a BWR, and has been shown to be able to diagnose unexpected events

  2. Prodiag--a hybrid artificial intelligence based reactor diagnostic system for process faults

    International Nuclear Information System (INIS)

    Reifman, J.; Wei, T.Y.C.; Vitela, J.E.; Applequist, C. A.; Chasensky, T.M.

    1996-01-01

    Commonwealth Research Corporation (CRC) and Argonne National Laboratory (ANL) are collaborating on a DOE-sponsored Cooperative Research and Development Agreement (CRADA), project to perform feasibility studies on a novel approach to Artificial Intelligence (Al) based diagnostics for component faults in nuclear power plants. Investigations are being performed in the construction of a first-principles physics-based plant level process diagnostic expert system (ES) and the identification of component-level fault patterns through operating component characteristics using artificial neural networks (ANNs). The purpose of the proof-of-concept project is to develop a computer-based system using this Al approach to assist process plant operators during off-normal plant conditions. The proposed computer-based system will use thermal hydraulic (T-H) signals complemented by other non-T-H signals available in the data stream to provide the process operator with the component which most likely caused the observed process disturbance.To demonstrate the scale-up feasibility of the proposed diagnostic system it is being developed for use with the Chemical Volume Control System (CVCS) of a nuclear power plant. A full-scope operator training simulator representing the Commonwealth Edison Braidwood nuclear power plant is being used both as the source of development data and as the means to evaluate the advantages of the proposed diagnostic system. This is an ongoing multi-year project and this paper presents the results to date of the CRADA phase

  3. Nonbinary tree-based phylogenetic networks

    OpenAIRE

    Jetten, Laura; van Iersel, Leo

    2016-01-01

    Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can for example represent gene transfer events. Such phylogenetic networks are called tree-based. Here, we consider two possible generalizations of this concept to nonbinary networks, which we call tree-based and st...

  4. Directory Enabled Policy Based Networking; TOPICAL

    International Nuclear Information System (INIS)

    KELIIAA, CURTIS M.

    2001-01-01

    This report presents a discussion of directory-enabled policy-based networking with an emphasis on its role as the foundation for securely scalable enterprise networks. A directory service provides the object-oriented logical environment for interactive cyber-policy implementation. Cyber-policy implementation includes security, network management, operational process and quality of service policies. The leading network-technology vendors have invested in these technologies for secure universal connectivity that transverses Internet, extranet and intranet boundaries. Industry standards are established that provide the fundamental guidelines for directory deployment scalable to global networks. The integration of policy-based networking with directory-service technologies provides for intelligent management of the enterprise network environment as an end-to-end system of related clients, services and resources. This architecture allows logical policies to protect data, manage security and provision critical network services permitting a proactive defense-in-depth cyber-security posture. Enterprise networking imposes the consideration of supporting multiple computing platforms, sites and business-operation models. An industry-standards based approach combined with principled systems engineering in the deployment of these technologies allows these issues to be successfully addressed. This discussion is focused on a directory-based policy architecture for the heterogeneous enterprise network-computing environment and does not propose specific vendor solutions. This document is written to present practical design methodology and provide an understanding of the risks, complexities and most important, the benefits of directory-enabled policy-based networking

  5. Agent-based modeling and network dynamics

    CERN Document Server

    Namatame, Akira

    2016-01-01

    The book integrates agent-based modeling and network science. It is divided into three parts, namely, foundations, primary dynamics on and of social networks, and applications. The book begins with the network origin of agent-based models, known as cellular automata, and introduce a number of classic models, such as Schelling’s segregation model and Axelrod’s spatial game. The essence of the foundation part is the network-based agent-based models in which agents follow network-based decision rules. Under the influence of the substantial progress in network science in late 1990s, these models have been extended from using lattices into using small-world networks, scale-free networks, etc. The book also shows that the modern network science mainly driven by game-theorists and sociophysicists has inspired agent-based social scientists to develop alternative formation algorithms, known as agent-based social networks. The book reviews a number of pioneering and representative models in this family. Upon the gi...

  6. Beam diagnostics based on virtual instrument technology for HLS

    International Nuclear Information System (INIS)

    Sun Baogen; Lu Ping; Wang Xiaohui; Wang Baoyun; Wang Junhua; Gu Liming; Fang Jia; Ma Tianji

    2009-01-01

    The paper introduce the beam diagnostics system using virtual instrument technology for Hefei Light Source (HLS), which includes a GPIB bus-based DCCT measurement system to measure the beam DC current and beam life, a VXIbus-based closed orbit measurement system to measure the beam position, a PCIbus-based beam profile measurement system to measure the beam profile and emittance, a GPIB-LAN based bunch length system using photoelectric method, and a Ethernet-based photon beam position measurement system. The software is programmed by LabVIEW, which reduces much developing work. (authors)

  7. Intelligent model-based diagnostics for vehicle health management

    Science.gov (United States)

    Luo, Jianhui; Tu, Fang; Azam, Mohammad S.; Pattipati, Krishna R.; Willett, Peter K.; Qiao, Liu; Kawamoto, Masayuki

    2003-08-01

    The recent advances in sensor technology, remote communication and computational capabilities, and standardized hardware/software interfaces are creating a dramatic shift in the way the health of vehicles is monitored and managed. These advances facilitate remote monitoring, diagnosis and condition-based maintenance of automotive systems. With the increased sophistication of electronic control systems in vehicles, there is a concomitant increased difficulty in the identification of the malfunction phenomena. Consequently, the current rule-based diagnostic systems are difficult to develop, validate and maintain. New intelligent model-based diagnostic methodologies that exploit the advances in sensor, telecommunications, computing and software technologies are needed. In this paper, we will investigate hybrid model-based techniques that seamlessly employ quantitative (analytical) models and graph-based dependency models for intelligent diagnosis. Automotive engineers have found quantitative simulation (e.g. MATLAB/SIMULINK) to be a vital tool in the development of advanced control systems. The hybrid method exploits this capability to improve the diagnostic system's accuracy and consistency, utilizes existing validated knowledge on rule-based methods, enables remote diagnosis, and responds to the challenges of increased system complexity. The solution is generic and has the potential for application in a wide range of systems.

  8. Nuclear reactor pump diagnostics via noise analysis/artificial neural networks

    International Nuclear Information System (INIS)

    Keyvan, S.; Rabelo, L.C.

    1991-01-01

    A feasibility study is performed on the utilization of artificial neural networks as a tool for reactor diagnostics. Reactor pump signals utilized in a wear-out monitoring system developed for early detection of degradation of pump shaft are analyzed as a semi-benchmark test to study the feasibility of neural networks for pattern recognition. The Adaptive Resonance Theory (ART 2) paradigm of artificial neural networks is applied in this study. The signals are collected signals as well as generated signals simulating the wear progress. The wear-out monitoring system applies noise analysis techniques, and is capable of distinguishing between these signals and providing a measure of the progress of the degradation. This paper presents the results of the analysis of these data via the ART 2 paradigm

  9. Design and implementation of a Macintosh-CAMAC based system for neutral beam diagnostics

    International Nuclear Information System (INIS)

    Wight, J.; Hong, R.M.; Phillips, J.C.; Lee, R.L.; Colleraine, A.P.; Kim, J.

    1989-12-01

    An automated personal computer based CAMAC data acquisition system is being implemented on the DIII-D neutral beamlines for certain diagnostics. The waterflow calorimetry (WFC) diagnostic is the first system to be upgraded. It includes data acquisition by a Macintosh II computer containing a National Instruments IEEE-488 card, and running their LabView software. Macintosh to CAMAC communications are carried out through an IEEE-488 crate controller. The Doppler shift spectroscopy, residual gas analysis, and armor tile infrared image diagnostics will be modified in similar ways. To reduce the demand for Macintosh CPU time, the extensive serial high-way data activity is performed by means of a new Kinetic Systems 3982 List sequencing Crate Controller dedicated to these operations. A simple Local Area Network file server is used to store data from all diagnostics together, and in a format readable by a standard commercial database. This reduces the problem of redundant data storage and allows simpler inter-diagnostic analysis. 3 refs., 4 figs

  10. Development of electromagnetic induction diagnostics technology for condition based maintenance

    International Nuclear Information System (INIS)

    Mawatari, Shingo; Oeda, Kaoru; Yatogi, Hideo; Fukuchi, Taira; Ueno, Tadashi

    2008-01-01

    In ROKKASHO Reprocessing Plant (below, called 'RRP'), we have applied Condition Based Maintenance to rotating equipment with vibration diagnostics technology. However, a few rotating equipment are difficult to diagnose definitely, because have structural problems which exercise vibrational noise to peripheral and be impossible to install vibratory sensor. Electromagnetic induction diagnostics technology which measure magnetic fields to eddy current which is induced to rotary through static magnetic field, diagnose deterioration behavior such as abrasion and crack. As a result, it has possibilities to clear above problems. Therefore, we started our basic researches with this technology for Condition Based Maintenance. In this paper, it introduces basic data about 'Non-seal pump' that have installed in RRP. As a result, this technology is a possibility that be able to detect Condition Based Maintenance. (author)

  11. A random network based, node attraction facilitated network evolution method

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2016-03-01

    Full Text Available In present study, I present a method of network evolution that based on random network, and facilitated by node attraction. In this method, I assume that the initial network is a random network, or a given initial network. When a node is ready to connect, it tends to link to the node already owning the most connections, which coincides with the general rule (Barabasi and Albert, 1999 of node connecting. In addition, a node may randomly disconnect a connection i.e., the addition of connections in the network is accompanied by the pruning of some connections. The dynamics of network evolution is determined of the attraction factor Lamda of nodes, the probability of node connection, the probability of node disconnection, and the expected initial connectance. The attraction factor of nodes, the probability of node connection, and the probability of node disconnection are time and node varying. Various dynamics can be achieved by adjusting these parameters. Effects of simplified parameters on network evolution are analyzed. The changes of attraction factor Lamda can reflect various effects of the node degree on connection mechanism. Even the changes of Lamda only will generate various networks from the random to the complex. Therefore, the present algorithm can be treated as a general model for network evolution. Modeling results show that to generate a power-law type of network, the likelihood of a node attracting connections is dependent upon the power function of the node's degree with a higher-order power. Matlab codes for simplified version of the method are provided.

  12. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images. A Japanese multicenter study

    Energy Technology Data Exchange (ETDEWEB)

    Nakajima, Kenichi; Matsuo, Shinro [Kanazawa University Hospital, Kanazawa (Japan); Kudo, Takashi [Nagasaki University Hospital, Nagasaki (Japan); Nakata, Tomoaki [Hakodate Goryoukaku Hospital, Hakodate (Japan); Kiso, Keisuke [National Cerebral and Cardiovascular Center, Suita (Japan); Kasai, Tokuo [Tokyo Medical University Hachioji Medical Center, Hachioji (Japan); Taniguchi, Yasuyo [Hyogo Brain and Heart Center, Himeji (Japan); Momose, Mitsuru [Tokyo Women' s Medical University, Tokyo (Japan); Nakagawa, Masayasu [Akita City Hospital, Akita (Japan); Sarai, Masayoshi [Fujita Health University Hospital, Toyoake (Japan); Hida, Satoshi [Tokyo Medical University Hospital, Tokyo (Japan); Tanaka, Hirokazu [Tokyo Medical University Ibaraki Medical Center, Ibaraki (Japan); Yokoyama, Kunihiko [Public Central Hospital of Matto Ishikawa, Hakusan (Japan); Okuda, Koichi [Kanazawa Medical University, Kahoku (Japan); Edenbrandt, Lars [University of Gothenburg, Gothenburg (Sweden)

    2017-12-15

    Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest {sup 99m}Tc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80. The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease. (orig.)

  13. Diagnostic accuracy of an artificial neural network compared with statistical quantitation of myocardial perfusion images. A Japanese multicenter study

    International Nuclear Information System (INIS)

    Nakajima, Kenichi; Matsuo, Shinro; Kudo, Takashi; Nakata, Tomoaki; Kiso, Keisuke; Kasai, Tokuo; Taniguchi, Yasuyo; Momose, Mitsuru; Nakagawa, Masayasu; Sarai, Masayoshi; Hida, Satoshi; Tanaka, Hirokazu; Yokoyama, Kunihiko; Okuda, Koichi; Edenbrandt, Lars

    2017-01-01

    Artificial neural networks (ANN) might help to diagnose coronary artery disease. This study aimed to determine whether the diagnostic accuracy of an ANN-based diagnostic system and conventional quantitation are comparable. The ANN was trained to classify potentially abnormal areas as true or false based on the nuclear cardiology expert interpretation of 1001 gated stress/rest 99m Tc-MIBI images at 12 hospitals. The diagnostic accuracy of the ANN was compared with 364 expert interpretations that served as the gold standard of abnormality for the validation study. Conventional summed stress/rest/difference scores (SSS/SRS/SDS) were calculated and compared with receiver operating characteristics (ROC) analysis. The ANN generated a better area under the ROC curves (AUC) than SSS (0.92 vs. 0.82, p < 0.0001), indicating better identification of stress defects. The ANN also generated a better AUC than SDS (0.90 vs. 0.75, p < 0.0001) for stress-induced ischemia. The AUC for patients with old myocardial infarction based on rest defects was 0.97 (0.91 for SRS, p = 0.0061), and that for patients with and without a history of revascularization based on stress defects was 0.94 and 0.90 (p = 0.0055 and p < 0.0001 vs. SSS, respectively). The SSS/SRS/SDS steeply increased when ANN values (probability of abnormality) were >0.80. The ANN was diagnostically accurate in various clinical settings, including that of patients with previous myocardial infarction and coronary revascularization. The ANN could help to diagnose coronary artery disease. (orig.)

  14. Intelligent DNA-based molecular diagnostics using linked genetic markers

    Energy Technology Data Exchange (ETDEWEB)

    Pathak, D.K.; Perlin, M.W.; Hoffman, E.P.

    1994-12-31

    This paper describes a knowledge-based system for molecular diagnostics, and its application to fully automated diagnosis of X-linked genetic disorders. Molecular diagnostic information is used in clinical practice for determining genetic risks, such as carrier determination and prenatal diagnosis. Initially, blood samples are obtained from related individuals, and PCR amplification is performed. Linkage-based molecular diagnosis then entails three data analysis steps. First, for every individual, the alleles (i.e., DNA composition) are determined at specified chromosomal locations. Second, the flow of genetic material among the individuals is established. Third, the probability that a given individual is either a carrier of the disease or affected by the disease is determined. The current practice is to perform each of these three steps manually, which is costly, time consuming, labor-intensive, and error-prone. As such, the knowledge-intensive data analysis and interpretation supersede the actual experimentation effort as the major bottleneck in molecular diagnostics. By examining the human problem solving for the task, we have designed and implemented a prototype knowledge-based system capable of fully automating linkage-based molecular diagnostics in X-linked genetic disorders, including Duchenne Muscular Dystrophy (DMD). Our system uses knowledge-based interpretation of gel electrophoresis images to determine individual DNA marker labels, a constraint satisfaction search for consistent genetic flow among individuals, and a blackboard-style problem solver for risk assessment. We describe the system`s successful diagnosis of DMD carrier and affected individuals from raw clinical data.

  15. Bluetooth-based wireless sensor networks

    Science.gov (United States)

    You, Ke; Liu, Rui Qiang

    2007-11-01

    In this work a Bluetooth-based wireless sensor network is proposed. In this bluetooth-based wireless sensor networks, information-driven star topology and energy-saved mode are used, through which a blue master node can control more than seven slave node, the energy of each sensor node is reduced and secure management of each sensor node is improved.

  16. CRISP. Simulation tool for fault detection and diagnostics in high-DG power networks

    International Nuclear Information System (INIS)

    Fontela, M.; Andrieu, C.; Raison, B.

    2004-08-01

    This document gives a description of a tool proposed for fault detection and diagnostics. The main principles of the functions of fault localization are described and detailed for a given MV network that will be used for the ICT experiment in Grenoble (experiment 3B). The aim of the tool is to create a technical, simple and realistic context for testing ICT dedicated to an electrical application. The tool gives the expected inputs and outputs contents of the various distributed ICT components when a fault occurs in a given MV network. So the requirements for the ICT components are given in term of expected data collected, analysed and transmitted. Several examples are given in order to illustrate the inputs/outputs in case of different faults. The tool includes a topology description which is a main aspect to develop in the future for managing the distribution network. Updating topology in real time will become necessary for fault diagnostic and protection, but also necessary for the various possible added applications (local market balance and local electrical power quality for instance). The tool gives a context and a simple view for the ICT components behaviours assuming an ideal response and transmission from them. The real characteristics and possible limitations for the ICT (information latency, congestion, security) will be established during the experiments from the same context described in the HTFD tool

  17. Memristor-based neural networks

    International Nuclear Information System (INIS)

    Thomas, Andy

    2013-01-01

    The synapse is a crucial element in biological neural networks, but a simple electronic equivalent has been absent. This complicates the development of hardware that imitates biological architectures in the nervous system. Now, the recent progress in the experimental realization of memristive devices has renewed interest in artificial neural networks. The resistance of a memristive system depends on its past states and exactly this functionality can be used to mimic the synaptic connections in a (human) brain. After a short introduction to memristors, we present and explain the relevant mechanisms in a biological neural network, such as long-term potentiation and spike time-dependent plasticity, and determine the minimal requirements for an artificial neural network. We review the implementations of these processes using basic electric circuits and more complex mechanisms that either imitate biological systems or could act as a model system for them. (topical review)

  18. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  19. Diagnostic markers of urothelial cancer based on DNA methylation analysis

    International Nuclear Information System (INIS)

    Chihara, Yoshitomo; Hirao, Yoshihiko; Kanai, Yae; Fujimoto, Hiroyuki; Sugano, Kokichi; Kawashima, Kiyotaka; Liang, Gangning; Jones, Peter A; Fujimoto, Kiyohide; Kuniyasu, Hiroki

    2013-01-01

    Early detection and risk assessment are crucial for treating urothelial cancer (UC), which is characterized by a high recurrence rate, and necessitates frequent and invasive monitoring. We aimed to establish diagnostic markers for UC based on DNA methylation. In this multi-center study, three independent sample sets were prepared. First, DNA methylation levels at CpG loci were measured in the training sets (tumor samples from 91 UC patients, corresponding normal-appearing tissue from these patients, and 12 normal tissues from age-matched bladder cancer-free patients) using the Illumina Golden Gate methylation assay to identify differentially methylated loci. Next, these methylated loci were validated by quantitative DNA methylation by pyrosequencing, using another cohort of tissue samples (Tissue validation set). Lastly, methylation of these markers was analyzed in the independent urine samples (Urine validation set). ROC analysis was performed to evaluate the diagnostic accuracy of these 12 selected markers. Of the 1303 CpG sites, 158 were hyper ethylated and 356 were hypo ethylated in tumor tissues compared to normal tissues. In the panel analysis, 12 loci showed remarkable alterations between tumor and normal samples, with 94.3% sensitivity and 97.8% specificity. Similarly, corresponding normal tissue could be distinguished from normal tissues with 76.0% sensitivity and 100% specificity. Furthermore, the diagnostic accuracy for UC of these markers determined in urine samples was high, with 100% sensitivity and 100% specificity. Based on these preliminary findings, diagnostic markers based on differential DNA methylation at specific loci can be useful for non-invasive and reliable detection of UC and epigenetic field defect

  20. Diagnostic of Gravitropism-like Stabilizer of Inspection Drone Using Neural Networks

    Science.gov (United States)

    Kruglova, Tatyana; Sayfeddine, Daher; Bulgakov, Alexey

    2018-03-01

    This paper discusses the enhancement of flight stability of using an inspection drone to scan the condition of buildings on low and high altitude. Due to aerial perturbations and wakes, the drone starts to shake and may be damaged. One of the mechanical optimization methods it so add a built-in stabilizing mechanism. However, the performance of this supporting device becomes critical on certain flying heights, thus to avoid losing the drone. The paper is divided in two parts: the description of the gravitropism-like stabilizer and the diagnostic of its status using wavelet transformation and neural network classification.

  1. Proteinuria: The diagnostic strategy based on urine proteins differentiation

    Directory of Open Access Journals (Sweden)

    Stojimirović Biljana B.

    2004-01-01

    Full Text Available Basal glomerular membrane represents mechanical and electrical barrier for passing of the plasma proteins. Mechanical barrier is composed of cylindrical pores and filtration fissure, and negative layer charge in exterior and interior side of basal glomerular membrane, made of heparan sulphate and sialoglicoproteine, provides certain electrical barrier. Diagnostic strategy based on different serum and urine proteins enables the differentiation of various types of proteinuria. Depending on etiology of proteinuria it can be prerenal, renal and postrenal. By analyzing albumin, armicroglobulin, immunoglobulin G and armacroglobulin, together with total protein in urine, it is possible to detect and differentiate causes of prerenal, renal (glomerular, tubular, glomerulo-tubular and postrenal proteinuria. The adequate and early differentiation of proteinuria type is of an immense diagnostic and therapeutic importance.

  2. Cut Based Method for Comparing Complex Networks.

    Science.gov (United States)

    Liu, Qun; Dong, Zhishan; Wang, En

    2018-03-23

    Revealing the underlying similarity of various complex networks has become both a popular and interdisciplinary topic, with a plethora of relevant application domains. The essence of the similarity here is that network features of the same network type are highly similar, while the features of different kinds of networks present low similarity. In this paper, we introduce and explore a new method for comparing various complex networks based on the cut distance. We show correspondence between the cut distance and the similarity of two networks. This correspondence allows us to consider a broad range of complex networks and explicitly compare various networks with high accuracy. Various machine learning technologies such as genetic algorithms, nearest neighbor classification, and model selection are employed during the comparison process. Our cut method is shown to be suited for comparisons of undirected networks and directed networks, as well as weighted networks. In the model selection process, the results demonstrate that our approach outperforms other state-of-the-art methods with respect to accuracy.

  3. Nontargeted diagnostic ion network analysis (NINA): A software to streamline the analytical workflow for untargeted characterization of natural medicines.

    Science.gov (United States)

    Ye, Hui; Zhu, Lin; Sun, Di; Luo, Xiaozhuo; Lu, Gaoyuan; Wang, Hong; Wang, Jing; Cao, Guoxiu; Xiao, Wei; Wang, Zhenzhong; Wang, Guangji; Hao, Haiping

    2016-11-30

    The characterization of herbal prescriptions serves as a foundation for quality control and regulation of herbal medicines. Previously, the characterization of herbal chemicals from natural medicines often relied on the analysis of signature fragment ions from the acquired tandem mass spectrometry (MS/MS) spectra with prior knowledge of the herbal species present in the herbal prescriptions of interest. Nevertheless, such an approach is often limited to target components, and it risks missing the critical components that we have no prior knowledge of. We previously reported a "diagnostic ion-guided network bridging" strategy. It is a generally applicable and robust approach to analyze unknown substances from complex mixtures in an untargeted manner. In this study, we have developed a standalone software named "Nontargeted Diagnostic Ion Network Analysis (NINA)" with a graphical user interface based on a strategy for post-acquisition data analysis. NINA allows one to rapidly determine the nontargeted diagnostic ions (NIs) by summarizing all of the fragment ions shared by the precursors from the acquired MS/MS spectra. A NI-guided network using bridging components that possess two or more NIs can then be established via NINA. With such a network, we could sequentially identify the structures of all the NIs once a single compound has been identified de novo. The structures of NIs can then be used as "priori" knowledge to narrow the candidates containing the sub-structure of the corresponding NI from the database hits. Subsequently, we applied the NINA software to the characterization of a model herbal prescription, Re-Du-Ning injection, and rapidly identified 56 herbal chemicals from the prescription using an ultra-performance liquid chromatography quadrupole time-of-flight system in the negative mode with no knowledge of the herbal species or herbal chemicals in the mixture. Therefore, we believe the applications of NINA will greatly facilitate the characterization

  4. Network-based Approaches in Pharmacology.

    Science.gov (United States)

    Boezio, Baptiste; Audouze, Karine; Ducrot, Pierre; Taboureau, Olivier

    2017-10-01

    In drug discovery, network-based approaches are expected to spotlight our understanding of drug action across multiple layers of information. On one hand, network pharmacology considers the drug response in the context of a cellular or phenotypic network. On the other hand, a chemical-based network is a promising alternative for characterizing the chemical space. Both can provide complementary support for the development of rational drug design and better knowledge of the mechanisms underlying the multiple actions of drugs. Recent progress in both concepts is discussed here. In addition, a network-based approach using drug-target-therapy data is introduced as an example. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

  5. Cognitive Radio-based Home Area Networks

    NARCIS (Netherlands)

    Sarijari, M.A.B.

    2016-01-01

    A future home area network (HAN) is envisaged to consist of a large number of devices that support various applications such as smart grid, security and safety systems, voice call, and video streaming. Most of these home devices are communicating based on various wireless networking technologies

  6. VLSI Based Multiprocessor Communications Networks.

    Science.gov (United States)

    1982-09-01

    Networks". The contract began on September 1,1980 and was approved on scientific /technical grounds for a duration of three years. Incremental funding was...values for the individual delays will vary from comunicating modules (ij) are shown in Figure 4 module to module due to processing and fabrication

  7. CAD-Based Shielding Analysis for ITER Port Diagnostics

    Directory of Open Access Journals (Sweden)

    Serikov Arkady

    2017-01-01

    Full Text Available Radiation shielding analysis conducted in support of design development of the contemporary diagnostic systems integrated inside the ITER ports is relied on the use of CAD models. This paper presents the CAD-based MCNP Monte Carlo radiation transport and activation analyses for the Diagnostic Upper and Equatorial Port Plugs (UPP #3 and EPP #8, #17. The creation process of the complicated 3D MCNP models of the diagnostics systems was substantially accelerated by application of the CAD-to-MCNP converter programs MCAM and McCad. High performance computing resources of the Helios supercomputer allowed to speed-up the MCNP parallel transport calculations with the MPI/OpenMP interface. The found shielding solutions could be universal, reducing ports R&D costs. The shield block behind the Tritium and Deposit Monitor (TDM optical box was added to study its influence on Shut-Down Dose Rate (SDDR in Port Interspace (PI of EPP#17. Influence of neutron streaming along the Lost Alpha Monitor (LAM on the neutron energy spectra calculated in the Tangential Neutron Spectrometer (TNS of EPP#8. For the UPP#3 with Charge eXchange Recombination Spectroscopy (CXRS-core, an excessive neutron streaming along the CXRS shutter, which should be prevented in further design iteration.

  8. Glycosylation-Based Serum Biomarkers for Cancer Diagnostics and Prognostics.

    Science.gov (United States)

    Kirwan, Alan; Utratna, Marta; O'Dwyer, Michael E; Joshi, Lokesh; Kilcoyne, Michelle

    2015-01-01

    Cancer is the second most common cause of death in developed countries with approximately 14 million newly diagnosed individuals and over 6 million cancer-related deaths in 2012. Many cancers are discovered at a more advanced stage but better survival rates are correlated with earlier detection. Current clinically approved cancer biomarkers are most effective when applied to patients with widespread cancer. Single biomarkers with satisfactory sensitivity and specificity have not been identified for the most common cancers and some biomarkers are ineffective for the detection of early stage cancers. Thus, novel biomarkers with better diagnostic and prognostic performance are required. Aberrant protein glycosylation is well known hallmark of cancer and represents a promising source of potential biomarkers. Glycoproteins enter circulation from tissues or blood cells through active secretion or leakage and patient serum is an attractive option as a source for biomarkers from a clinical and diagnostic perspective. A plethora of technical approaches have been developed to address the challenges of glycosylation structure detection and determination. This review summarises currently utilised glycoprotein biomarkers and novel glycosylation-based biomarkers from the serum glycoproteome under investigation as cancer diagnostics and for monitoring and prognostics and includes details of recent high throughput and other emerging glycoanalytical techniques.

  9. CAD-Based Shielding Analysis for ITER Port Diagnostics

    Science.gov (United States)

    Serikov, Arkady; Fischer, Ulrich; Anthoine, David; Bertalot, Luciano; De Bock, Maartin; O'Connor, Richard; Juarez, Rafael; Krasilnikov, Vitaly

    2017-09-01

    Radiation shielding analysis conducted in support of design development of the contemporary diagnostic systems integrated inside the ITER ports is relied on the use of CAD models. This paper presents the CAD-based MCNP Monte Carlo radiation transport and activation analyses for the Diagnostic Upper and Equatorial Port Plugs (UPP #3 and EPP #8, #17). The creation process of the complicated 3D MCNP models of the diagnostics systems was substantially accelerated by application of the CAD-to-MCNP converter programs MCAM and McCad. High performance computing resources of the Helios supercomputer allowed to speed-up the MCNP parallel transport calculations with the MPI/OpenMP interface. The found shielding solutions could be universal, reducing ports R&D costs. The shield block behind the Tritium and Deposit Monitor (TDM) optical box was added to study its influence on Shut-Down Dose Rate (SDDR) in Port Interspace (PI) of EPP#17. Influence of neutron streaming along the Lost Alpha Monitor (LAM) on the neutron energy spectra calculated in the Tangential Neutron Spectrometer (TNS) of EPP#8. For the UPP#3 with Charge eXchange Recombination Spectroscopy (CXRS-core), an excessive neutron streaming along the CXRS shutter, which should be prevented in further design iteration.

  10. Network-based identification of biomarkers coexpressed with multiple pathways.

    Science.gov (United States)

    Guo, Nancy Lan; Wan, Ying-Wooi

    2014-01-01

    Unraveling complex molecular interactions and networks and incorporating clinical information in modeling will present a paradigm shift in molecular medicine. Embedding biological relevance via modeling molecular networks and pathways has become increasingly important for biomarker identification in cancer susceptibility and metastasis studies. Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility, disease progression, and prognostication. Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic and prognostic biomarkers. The results show that implication networks, implemented in Genet package, identified sets of biomarkers that generated an accurate prediction of lung cancer risk and metastases; meanwhile, implication networks revealed more biologically relevant molecular interactions than Boolean networks, Bayesian networks, and Pearson's correlation networks when evaluated with MSigDB database.

  11. Distribution network topology identification based on synchrophasor

    Directory of Open Access Journals (Sweden)

    Stefania Conti

    2018-03-01

    Full Text Available A distribution system upgrade moving towards Smart Grid implementation is necessary to face the proliferation of distributed generators and electric vehicles, in order to satisfy the increasing demand for high quality, efficient, secure, reliable energy supply. This perspective requires taking into account system vulnerability to cyber attacks. An effective attack could destroy stored information about network structure, historical data and so on. Countermeasures and network applications could be made impracticable since most of them are based on the knowledge of network topology. Usually, the location of each link between nodes in a network is known. Therefore, the methods used for topology identification determine if a link is open or closed. When no information on the location of the network links is available, these methods become totally unfeasible. This paper presents a method to identify the network topology using only nodal measures obtained by means of phasor measurement units.

  12. The application of expert systems and neural networks to gas turbine prognostics and diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    DePold, H.R.; Gass, F.D.

    1999-10-01

    Condition monitoring of engine gas generators plays an essential role in airline fleet management. Adaptive diagnostic systems are becoming available that interpret measured data, furnish diagnosis of problems, provide a prognosis of engine health for planning purposes, and rank engines for scheduled maintenance. More than four hundred operations worldwide currently use versions of the first or second generation diagnostic tools. Development of a third generation system is underway which will provide additional system enhancements and combine the functions of the existing tools. Proposed enhancements include the use of artificial intelligence to automate, improve the quality of the analysis, provide timely alerts, and the use of an Internet link for collaboration. One objective of these enhancements is to have the intelligent system do more of the analysis and decision making, while continuing to support the depth of analysis currently available at experienced operations. This paper presents recent developments in technology and strategies in engine condition monitoring including: (1) application of statistical analysis and artificial neural network filters to improve data quality, (2) neural networks for trend change detection, and classification to diagnose performance change, and (3) expert systems to diagnose, provide alerts and to rank maintenance action recommendations.

  13. Modeling the interdependent network based on two-mode networks

    Science.gov (United States)

    An, Feng; Gao, Xiangyun; Guan, Jianhe; Huang, Shupei; Liu, Qian

    2017-10-01

    Among heterogeneous networks, there exist obviously and closely interdependent linkages. Unlike existing research primarily focus on the theoretical research of physical interdependent network model. We propose a two-layer interdependent network model based on two-mode networks to explore the interdependent features in the reality. Specifically, we construct a two-layer interdependent loan network and develop several dependent features indices. The model is verified to enable us to capture the loan dependent features of listed companies based on loan behaviors and shared shareholders. Taking Chinese debit and credit market as case study, the main conclusions are: (1) only few listed companies shoulder the main capital transmission (20% listed companies occupy almost 70% dependent degree). (2) The control of these key listed companies will be more effective of avoiding the spreading of financial risks. (3) Identifying the companies with high betweenness centrality and controlling them could be helpful to monitor the financial risk spreading. (4) The capital transmission channel among Chinese financial listed companies and Chinese non-financial listed companies are relatively strong. However, under greater pressure of demand of capital transmission (70% edges failed), the transmission channel, which constructed by debit and credit behavior, will eventually collapse.

  14. Turbofan engine diagnostics neuron network size optimization method which takes into account overlaerning effect

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2010-01-01

    Full Text Available  The article is devoted to the problem of gas turbine engine (GTE technical state class automatic recognition with operation parameters by neuron networks. The one of main problems for creation the neuron networks is determination of their optimal structures size (amount of layers in network and count of neurons in each layer.The method of neuron network size optimization intended for classification of GTE technical state is considered in the article. Optimization is cared out with taking into account of overlearning effect possibility when a learning network loses property of generalization and begins strictly describing educational data set. To determinate a moment when overlearning effect is appeared in learning neuron network the method  of three data sets is used. The method is based on the comparison of recognition quality parameters changes which were calculated during recognition of educational and control data sets. As the moment when network overlearning effect is appeared the moment when control data set recognition quality begins deteriorating but educational data set recognition quality continues still improving is used. To determinate this moment learning process periodically is terminated and simulation of network with education and control data sets is fulfilled. The optimization of two-, three- and four-layer networks is conducted and some results of optimization are shown. Also the extended educational set is created and shown. The set describes 16 GTE technical state classes and each class is represented with 200 points (200 possible technical state class realizations instead of 20 points using in the former articles. It was done to increase representativeness of data set.In the article the algorithm of optimization is considered and some results which were obtained with it are shown. The results of experiments were analyzed to determinate most optimal neuron network structure. This structure provides most high-quality GTE

  15. Autonomous power networks based power system

    International Nuclear Information System (INIS)

    Jokic, A.; Van den Bosch, P.P.J.

    2006-01-01

    This paper presented the concept of autonomous networks to cope with this increased complexity in power systems while enhancing market-based operation. The operation of future power systems will be more challenging and demanding than present systems because of increased uncertainties, less inertia in the system, replacement of centralized coordinating activities by decentralized parties and the reliance on dynamic markets for both power balancing and system reliability. An autonomous network includes the aggregation of networked producers and consumers in a relatively small area with respect to the overall system. The operation of an autonomous network is coordinated and controlled with one central unit acting as an interface between internal producers/consumers and the rest of the power system. In this study, the power balance problem and system reliability through provision of ancillary services was formulated as an optimization problem for the overall autonomous networks based power system. This paper described the simulation of an optimal autonomous network dispatching in day ahead markets, based on predicted spot prices for real power, and two ancillary services. It was concluded that large changes occur in a power systems structure and operation, most of them adding to the uncertainty and complexity of the system. The introduced concept of an autonomous power network-based power system was shown to be a realistic and consistent approach to formulate and operate a market-based dispatch of both power and ancillary services. 9 refs., 4 figs

  16. Dynamics-based centrality for directed networks.

    Science.gov (United States)

    Masuda, Naoki; Kori, Hiroshi

    2010-11-01

    Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.

  17. Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses.

    Science.gov (United States)

    Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik; Park, Hyun

    2016-01-01

    Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination.

  18. Smartphone-Based Fluorescent Diagnostic System for Highly Pathogenic H5N1 Viruses

    Science.gov (United States)

    Yeo, Seon-Ju; Choi, Kyunghan; Cuc, Bui Thi; Hong, Nguyen Ngoc; Bao, Duong Tuan; Ngoc, Nguyen Minh; Le, Mai Quynh; Hang, Nguyen Le Khanh; Thach, Nguyen Co; Mallik, Shyam Kumar; Kim, Hak Sung; Chong, Chom-Kyu; Choi, Hak Soo; Sung, Haan Woo; Yu, Kyoungsik; Park, Hyun

    2016-01-01

    Field diagnostic tools for avian influenza (AI) are indispensable for the prevention and controlled management of highly pathogenic AI-related diseases. More accurate, faster and networked on-site monitoring is demanded to detect such AI viruses with high sensitivity as well as to maintain up-to-date information about their geographical transmission. In this work, we assessed the clinical and field-level performance of a smartphone-based fluorescent diagnostic device with an efficient reflective light collection module using a coumarin-derived dendrimer-based fluorescent lateral flow immunoassay. By application of an optimized bioconjugate, a smartphone-based diagnostic device had a two-fold higher detectability as compared to that of the table-top fluorescence strip reader for three different AI subtypes (H5N3, H7N1, and H9N2). Additionally, in a clinical study of H5N1-confirmed patients, the smartphone-based diagnostic device showed a sensitivity of 96.55% (28/29) [95% confidence interval (CI): 82.24 to 99.91] and a specificity of 98.55% (68/69) (95% CI: 92.19 to 99.96). The measurement results from the distributed individual smartphones were wirelessly transmitted via short messaging service and collected by a centralized database system for further information processing and data mining. Smartphone-based diagnosis provided highly sensitive measurement results for H5N1 detection within 15 minutes. Because of its high sensitivity, portability and automatic reporting feature, the proposed device will enable agile identification of patients and efficient control of AI dissemination. PMID:26877781

  19. New advanced netted ground based and topside radio diagnostics for Space Weather Program

    Science.gov (United States)

    Rothkaehl, Hanna; Krankowski, Andrzej; Morawski, Marek; Atamaniuk, Barbara; Zakharenkova, Irina; Cherniak, Iurii

    2014-05-01

    To give a more detailed and complete understanding of physical plasma processes that govern the solar-terrestrial space, and to develop qualitative and quantitative models of the magnetosphere-ionosphere-thermosphere coupling, it is necessary to design and build the next generation of instruments for space diagnostics and monitoring. Novel ground- based wide-area sensor networks, such as the LOFAR (Low Frequency Array) radar facility, comprising wide band, and vector-sensing radio receivers and multi-spacecraft plasma diagnostics should help solve outstanding problems of space physics and describe long-term environmental changes. The LOw Frequency ARray - LOFAR - is a new fully digital radio telescope designed for frequencies between 30 MHz and 240 MHz located in Europe. The three new LOFAR stations will be installed until summer 2015 in Poland. The LOFAR facilities in Poland will be distributed among three sites: Lazy (East of Krakow), Borowiec near Poznan and Baldy near Olsztyn. All they will be connected via PIONIER dedicated links to Poznan. Each site will host one LOFAR station (96 high-band+96 low-band antennas). They will most time work as a part of European network, however, when less charged, they can operate as a national network The new digital radio frequency analyzer (RFA) on board the low-orbiting RELEC satellite was designed to monitor and investigate the ionospheric plasma properties. This two-point ground-based and topside ionosphere-located space plasma diagnostic can be a useful new tool for monitoring and diagnosing turbulent plasma properties. The RFA on board the RELEC satellite is the first in a series of experiments which is planned to be launched into the near-Earth environment. In order to improve and validate the large scales and small scales ionospheric structures we will used the GPS observations collected at IGS/EPN network employed to reconstruct diurnal variations of TEC using all satellite passes over individual GPS stations and the

  20. Cloud-based Networked Visual Servo Control

    OpenAIRE

    Wu, Haiyan; Lu, Lei; Chen, Chih-Chung; Hirche, Sandra; Kühnlenz, Kolja

    2013-01-01

    The performance of vision-based control systems, in particular of highly dynamic vision-based motion control systems, is often limited by the low sampling rate of the visual feedback caused by the long image processing time. In order to overcome this problem, the networked visual servo control, which integrates networked computational resources for cloud image processing, is considered in this article. The main contributions of this article are i) a real-time transport protocol for transmitti...

  1. A Ribeiroia spp. (Class: Trematoda) - Specific PCR-based diagnostic

    Science.gov (United States)

    Reinitz, David M.; Yoshino, T.P.; Cole, Rebecca A.

    2007-01-01

    Increased reporting of amphibian malformations in North America has been noted with concern in light of reports that amphibian numbers and species are declining worldwide. Ribeiroia ondatrae has been shown to cause a variety of types of malformations in amphibians. However, little is known about the prevalence of R. ondatrae in North America. To aid in conducting field studies of Ribeiroia spp., we have developed a polymerase chain reaction (PCR)-based diagnostic. Herein, we describe the development of an accurate, rapid, simple, and cost-effective diagnostic for detection of Ribeiroia spp. infection in snails (Planorbella trivolvis). Candidate oligonucleotide primers for PCR were designed via DNA sequence analyses of multiple ribosomal internal transcribed spacer-2 regions from Ribeiroia spp. and Echinostoma spp. Comparison of consensus sequences determined from both genera identified areas of sequence potentially unique to Ribeiroia spp. The PCR reliably produced a diagnostic 290-base pair (bp) product in the presence of a wide concentration range of snail or frog DNA. Sensitivity was examined with DNA extracted from single R. ondatrae cercaria. The single-tube PCR could routinely detect less than 1 cercariae equivalent, because DNA isolated from a single cercaria could be diluted at least 1:50 and still yield a positive result via gel electrophoresis. An even more sensitive nested PCR also was developed that routinely detected 100 fg of the 290-bp fragment. The assay did not detect furcocercous cercariae of certain Schistosomatidae, Echinostoma sp., or Sphaeridiotrema globulus nor adults of Clinostomum sp. or Cyathocotyle bushiensis. Field testing of 137 P. trivolvis identified 3 positives with no overt environmental cross-reactivity, and results concurred with microscopic examinations in all cases. ?? American Society of Parasitologists 2007.

  2. Electronics and Algorithms for HOM Based Beam Diagnostics

    Science.gov (United States)

    Frisch, Josef; Baboi, Nicoleta; Eddy, Nathan; Nagaitsev, Sergei; Hensler, Olaf; McCormick, Douglas; May, Justin; Molloy, Stephen; Napoly, Olivier; Paparella, Rita; Petrosyan, Lyudvig; Ross, Marc; Simon, Claire; Smith, Tonee

    2006-11-01

    The signals from the Higher Order Mode (HOM) ports on superconducting cavities can be used as beam position monitors and to do survey structure alignment. A HOM-based diagnostic system has been installed to instrument both couplers on each of the 40 cryogenic accelerating structures in the DESY TTF2 Linac. The electronics uses a single stage down conversion from the 1.7 GHz HOM spectral line to a 20MHz IF which has been digitized. The electronics is based on low cost surface mount components suitable for large scale production. The analysis of the HOM data is based on Singular Value Decomposition. The response of the OM modes is calibrated using conventional BPMs.

  3. Network-based Database Course

    DEFF Research Database (Denmark)

    Nielsen, J.N.; Knudsen, Morten; Nielsen, Jens Frederik Dalsgaard

    A course in database design and implementation has been de- signed, utilizing existing network facilities. The course is an elementary course for students of computer engineering. Its purpose is to give the students a theoretical database knowledge as well as practical experience with design...... and implementation. A tutorial relational database and the students self-designed databases are implemented on the UNIX system of Aalborg University, thus giving the teacher the possibility of live demonstrations in the lecture room, and the students the possibility of interactive learning in their working rooms...

  4. Parameter diagnostics of phases and phase transition learning by neural networks

    Science.gov (United States)

    Suchsland, Philippe; Wessel, Stefan

    2018-05-01

    We present an analysis of neural network-based machine learning schemes for phases and phase transitions in theoretical condensed matter research, focusing on neural networks with a single hidden layer. Such shallow neural networks were previously found to be efficient in classifying phases and locating phase transitions of various basic model systems. In order to rationalize the emergence of the classification process and for identifying any underlying physical quantities, it is feasible to examine the weight matrices and the convolutional filter kernels that result from the learning process of such shallow networks. Furthermore, we demonstrate how the learning-by-confusing scheme can be used, in combination with a simple threshold-value classification method, to diagnose the learning parameters of neural networks. In particular, we study the classification process of both fully-connected and convolutional neural networks for the two-dimensional Ising model with extended domain wall configurations included in the low-temperature regime. Moreover, we consider the two-dimensional XY model and contrast the performance of the learning-by-confusing scheme and convolutional neural networks trained on bare spin configurations to the case of preprocessed samples with respect to vortex configurations. We discuss these findings in relation to similar recent investigations and possible further applications.

  5. Plasmon-Based Colorimetric Nanosensors for Ultrasensitive Molecular Diagnostics.

    Science.gov (United States)

    Tang, Longhua; Li, Jinghong

    2017-07-28

    Colorimetric detection of target analytes with high specificity and sensitivity is of fundamental importance to clinical and personalized point-of-care diagnostics. Because of their extraordinary optical properties, plasmonic nanomaterials have been introduced into colorimetric sensing systems, which provide significantly improved sensitivity in various biosensing applications. Here we review the recent progress on these plasmonic nanoparticles-based colorimetric nanosensors for ultrasensitive molecular diagnostics. According to their different colorimetric signal generation mechanisms, these plasmonic nanosensors are classified into two categories: (1) interparticle distance-dependent colorimetric assay based on target-induced forming cross-linking assembly/aggregate of plasmonic nanoparticles; and (2) size/morphology-dependent colorimetric assay by target-controlled growth/etching of the plasmonic nanoparticles. The sensing fundamentals and cutting-edge applications will be provided for each of them, particularly focusing on signal generation and/or amplification mechanisms that realize ultrasensitive molecular detection. Finally, we also discuss the challenge and give our future perspective in this emerging field.

  6. Nuclear based diagnostics in high-power laser applications

    Energy Technology Data Exchange (ETDEWEB)

    Guenther, Marc; Sonnabend, Kerstin; Harres, Knut; Otten, Anke; Roth, Markus [TU Darmstadt, Institut fuer Kernphysik, Darmstadt (Germany); Vogt, Karsten; Bagnoud, Vincent [GSI Helmholtzzentrum fuer Schwerionenforschung, Darmstadt (Germany)

    2010-07-01

    High-power lasers allow focused intensities of >10{sup 18} W/cm{sup 2}. During the laser-solid interaction, an intense relativistic electron current is injected from the plasma into the target. One challenge is to characterize the electron dynamic close to the interaction region. Moreover, next generation high-power laser proton acceleration leads to high proton fluxes, which require novel, nuclear diagnostic techniques. We present an activation-based nuclear pyrometry for the investigation of electrons generated in relativistic laser-solid interactions. We use novel activation targets consisting of several isotopes with different photo-neutron disintegration thresholds. The electrons are decelerated inside the target via bremsstrahlung processes. The high-energy bremsstrahlung induces photo-nuclear reactions. In this energy range no disturbing low energy effects are important. Via the pyrometry the Reconstruction of the absolute yield, spectral and spatial distribution of the electrons is possible. For the characterization of proton beams we present a nuclear activation imaging spectroscopy (NAIS). The diagnostic is based on proton-neutron disintegration reactions of copper stacked in consecutive layers. An autoradiography of copper layers leads to spectrally and spatially reconstruction of the beam profile.

  7. Power System Transient Diagnostics Based on Novel Traveling Wave Detection

    Science.gov (United States)

    Hamidi, Reza Jalilzadeh

    Modern electrical power systems demand novel diagnostic approaches to enhancing the system resiliency by improving the state-of-the-art algorithms. The proliferation of high-voltage optical transducers and high time-resolution measurements provide opportunities to develop novel diagnostic methods of very fast transients in power systems. At the same time, emerging complex configuration, such as multi-terminal hybrid transmission systems, limits the applications of the traditional diagnostic methods, especially in fault location and health monitoring. The impedance-based fault-location methods are inefficient for cross-bounded cables, which are widely used for connection of offshore wind farms to the main grid. Thus, this dissertation first presents a novel traveling wave-based fault-location method for hybrid multi-terminal transmission systems. The proposed method utilizes time-synchronized high-sampling voltage measurements. The traveling wave arrival times (ATs) are detected by observation of the squares of wavelet transformation coefficients. Using the ATs, an over-determined set of linear equations are developed for noise reduction, and consequently, the faulty segment is determined based on the characteristics of the provided equation set. Then, the fault location is estimated. The accuracy and capabilities of the proposed fault location method are evaluated and also compared to the existing traveling-wave-based method for a wide range of fault parameters. In order to improve power systems stability, auto-reclosing (AR), single-phase auto-reclosing (SPAR), and adaptive single-phase auto-reclosing (ASPAR) methods have been developed with the final objectives of distinguishing between the transient and permanent faults to clear the transient faults without de-energization of the solid phases. However, the features of the electrical arcs (transient faults) are severely influenced by a number of random parameters, including the convection of the air and plasma

  8. NASDA knowledge-based network planning system

    Science.gov (United States)

    Yamaya, K.; Fujiwara, M.; Kosugi, S.; Yambe, M.; Ohmori, M.

    1993-01-01

    One of the SODS (space operation and data system) sub-systems, NP (network planning) was the first expert system used by NASDA (national space development agency of Japan) for tracking and control of satellite. The major responsibilities of the NP system are: first, the allocation of network and satellite control resources and, second, the generation of the network operation plan data (NOP) used in automated control of the stations and control center facilities. Up to now, the first task of network resource scheduling was done by network operators. NP system automatically generates schedules using its knowledge base, which contains information on satellite orbits, station availability, which computer is dedicated to which satellite, and how many stations must be available for a particular satellite pass or a certain time period. The NP system is introduced.

  9. Distributed computing methodology for training neural networks in an image-guided diagnostic application.

    Science.gov (United States)

    Plagianakos, V P; Magoulas, G D; Vrahatis, M N

    2006-03-01

    Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

  10. Entropy-Based Clutter Rejection for Intrawall Diagnostics

    Directory of Open Access Journals (Sweden)

    Raffaele Solimene

    2012-01-01

    Full Text Available The intrawall diagnostic problem of detecting localized inhomogeneities possibly present within the wall is addressed. As well known, clutter arising from masonry structure can impair detection of embedded scatterers due to high amplitude reflections that wall front face introduces. Moreover, internal multiple reflections also can make it difficult ground penetrating radar images (radargramms interpretation. To counteract these drawbacks, a clutter rejection method, properly tailored on the wall features, is mandatory. To this end, here we employ a windowing strategy based on entropy measures of temporal traces “similarity.” Accordingly, instants of time for which radargramms exhibit entropy values greater than a prescribed threshold are “silenced.” Numerical results are presented in order to show the effectiveness of the entropy-based clutter rejection algorithm. Moreover, a comparison with the standard average trace subtraction is also included.

  11. A knowledge based on-line diagnostic system for the fast breeder reactor KNKII

    International Nuclear Information System (INIS)

    Eggert, H.; Scherer, K.P.; Stiller, P.

    1989-01-01

    In the nuclear research center at Karlsruhe, a diagnostic expert system is developed to supervise a fast breeder process (KNKII). The problem is to detect critical phases in the beginning state before fault propagation. The expert system itself is integrated in a computer network (realized by a local area network), where different computers are involved as special detection systems (for example acoustic noise, temperature noise, covergas monitoring and so on), which produce partial diagnoses, based on intelligent signal processing techniques like pattern recognition. Additional to the detection systems a process computer is integrated as well as a test computer, which simulates hypothetical and real fault data. On the logical top level the expert system manages the partial diagnoses of the detection systems with the operating data of the process computer and to produce a final diagnosis including the explanation part for operator support. The knowledge base is developed by typical Artificial Intelligence tools. Both fact based and rule based knowledge representations are stored in form of flavors and predications. The inference engine operates on a rule based approach. Specific detail knowledge, based on experience about any years, is available to influence the decision process by increasing or decreasing of the generated hypotheses. In a meta knowledge base, a rule master triggers the special domain experts and contributes the tasks to the specific rule complexes. Such a system management guarantees a problem solving strategy, which operates event triggered and situation specific in a local inference domain. (author). 3 refs, 6 figs, 2 tabs

  12. Nuclear power plant status diagnostics using a neural network with dynamic node architecture

    International Nuclear Information System (INIS)

    Basu, A.

    1992-01-01

    This thesis is part of an ongoing project at Iowa State University to develop ANN based fault diagnostic systems to detect and classify operational transients at nuclear power plants. The project envisages the deployment of such an advisor at Iowa Electric Light and Power Company's Duane Arnold Energy Center nuclear power plant located at Palo, IA. This advisor is expected to make status diagnosis in real time, thus providing the operators with more time for corrective measures

  13. Can and should value-based pricing be applied to molecular diagnostics?

    Science.gov (United States)

    Garau, Martina; Towse, Adrian; Garrison, Louis; Housman, Laura; Ossa, Diego

    2013-01-01

    Current pricing and reimbursement systems for diagnostics are not efficient. Prices for diagnostics are often driven by administrative practices and expected production cost. The purpose of the paper is to discuss how a value-based pricing framework being used to ensure efficient use and price of medicines could also be applied to diagnostics. Diagnostics not only facilitates health gain and cost savings, but also information to guide patients' decisions on interventions and their future 'behaviors'. For value assessment processes we recommend a two-part approach. Companion diagnostics introduced at the launch of the drug should be assessed through new drug assessment processes considering a broad range of value elements and a balanced analysis of diagnostic impacts. A separate diagnostic-dedicated committee using value-based pricing principles should review other diagnostics lying outside the companion diagnostics-and-drug 'at-launch' situation.

  14. Toward Measuring Network Aesthetics Based on Symmetry

    Directory of Open Access Journals (Sweden)

    Zengqiang Chen

    2017-05-01

    Full Text Available In this exploratory paper, we discuss quantitative graph-theoretical measures of network aesthetics. Related work in this area has typically focused on geometrical features (e.g., line crossings or edge bendiness of drawings or visual representations of graphs which purportedly affect an observer’s perception. Here we take a very different approach, abandoning reliance on geometrical properties, and apply information-theoretic measures to abstract graphs and networks directly (rather than to their visual representaions as a means of capturing classical appreciation of structural symmetry. Examples are used solely to motivate the approach to measurement, and to elucidate our symmetry-based mathematical theory of network aesthetics.

  15. Cryptography based on neural networks - analytical results

    International Nuclear Information System (INIS)

    Rosen-Zvi, Michal; Kanter, Ido; Kinzel, Wolfgang

    2002-01-01

    The mutual learning process between two parity feed-forward networks with discrete and continuous weights is studied analytically, and we find that the number of steps required to achieve full synchronization between the two networks in the case of discrete weights is finite. The synchronization process is shown to be non-self-averaging and the analytical solution is based on random auxiliary variables. The learning time of an attacker that is trying to imitate one of the networks is examined analytically and is found to be much longer than the synchronization time. Analytical results are found to be in agreement with simulations. (letter to the editor)

  16. Apriori-based network intrusion detection system

    International Nuclear Information System (INIS)

    Wang Wenjin; Liu Junrong; Liu Baoxu

    2012-01-01

    With the development of network communication technology, more and more social activities run by Internet. In the meantime, the network information security is getting increasingly serious. Intrusion Detection System (IDS) has greatly improved the general security level of whole network. But there are still many problem exists in current IDS, e.g. high leak rate detection/false alarm rates and feature library need frequently upgrade. This paper presents an association-rule based IDS. This system can detect unknown attack by generate rules from training data. Experiment in last chapter proved the system has great accuracy on unknown attack detection. (authors)

  17. Screen printed paper-based diagnostic devices with polymeric inks.

    Science.gov (United States)

    Sun, Ju-Yen; Cheng, Chao-Min; Liao, Ying-Chih

    2015-01-01

    A simple and low-cost fabrication method for paper-based diagnostic devices (PBDDs) is described in this study. Street-available polymer solutions were screen printed onto filter papers to create hydrophobic patterns for fluidic channels. In order to obtain fully functional hydrophobic patterns for fluids, the original polymer solutions were diluted with butyl acetate to yield a suitable viscosity range between 30-200 cP for complete patterning on paper. Typical pH and glucose tests with color indicators were performed on the screen printed PBDDs. Images of the PBDDs were analyzed by computers to obtain calibration curves for pH between 2 and 12 and glucose concentration ranging from 10-1000 mmol dm(-3). Detection of formaldehyde in acetone was also carried out to show the possibility of using this PBBD for analytical detection with organic solvents. An exemplar PBDD with simultaneous pH and glucose detection was also used to demonstrate the feasibility of applying this technique for realistic diagnostic applications.

  18. Extracellular gadolinium-based contrast media: Differences in diagnostic efficacy

    Energy Technology Data Exchange (ETDEWEB)

    Molen, Aart J. van der [Department of Radiology C-2S, Leiden University Medical Centre, Albinusdreef 2, NL-2333 ZA Leiden (Netherlands)], E-mail: molen@lumc.nl; Bellin, Marie-France [Universite Paris-Sud XI, AP-HP, Service de Radiologie, Hopital Paul Brousse, 12-14 Avenue Paul Vaillant Couturier, F-94804 Villejuif Cedex (France)

    2008-05-15

    Since the introduction of the first gadolinium-based contrast agent (Gd-CA) in 1988 it has become clear that these agents significantly improve the diagnostic efficacy of MRI. Studies on single agents have shown that, in comparison to unenhanced sequences, all agents help to improve the detection and delineation of lesions which can alter diagnosis in up to 40% of patients. Doubling or tripling the standard dose of 0.1 mmol/kg body weight may be beneficial for selected indications (e.g. brain perfusion, equivocal single dose study in MRI for brain metastasis, small vessel MR angiography). A more limited number of studies have compared the various agents. These studies do not show clinically significant differences in diagnostic efficacy between the various extracellular Gd-CA. Agents with higher concentration or protein binding may be relatively better suitable for selected applications (e.g. perfusion MRI). The higher relaxivity agents may be used in somewhat lower doses than the extracellular agents.

  19. Extracellular gadolinium-based contrast media: Differences in diagnostic efficacy

    International Nuclear Information System (INIS)

    Molen, Aart J. van der; Bellin, Marie-France

    2008-01-01

    Since the introduction of the first gadolinium-based contrast agent (Gd-CA) in 1988 it has become clear that these agents significantly improve the diagnostic efficacy of MRI. Studies on single agents have shown that, in comparison to unenhanced sequences, all agents help to improve the detection and delineation of lesions which can alter diagnosis in up to 40% of patients. Doubling or tripling the standard dose of 0.1 mmol/kg body weight may be beneficial for selected indications (e.g. brain perfusion, equivocal single dose study in MRI for brain metastasis, small vessel MR angiography). A more limited number of studies have compared the various agents. These studies do not show clinically significant differences in diagnostic efficacy between the various extracellular Gd-CA. Agents with higher concentration or protein binding may be relatively better suitable for selected applications (e.g. perfusion MRI). The higher relaxivity agents may be used in somewhat lower doses than the extracellular agents

  20. Leo satellite-based telecommunication network concepts

    Science.gov (United States)

    Aiken, John G.; Swan, Peter A.; Leopold, Ray J.

    1991-01-01

    Design considerations are discussed for Low Earth Orbit (LEO) satellite based telecommunications networks. The satellites are assumed to be connected to each other via intersatellite links. They are connected to the end user either directly or through gateways to other networks. Frequency reuse, circuit switching, packet switching, call handoff, and routing for these systems are discussed by analogy with terrestrial cellular (mobile radio) telecommunication systems.

  1. Image standards in Tissue-Based Diagnosis (Diagnostic Surgical Pathology

    Directory of Open Access Journals (Sweden)

    Vollmer Ekkehard

    2008-04-01

    Full Text Available Abstract Background Progress in automated image analysis, virtual microscopy, hospital information systems, and interdisciplinary data exchange require image standards to be applied in tissue-based diagnosis. Aims To describe the theoretical background, practical experiences and comparable solutions in other medical fields to promote image standards applicable for diagnostic pathology. Theory and experiences Images used in tissue-based diagnosis present with pathology – specific characteristics. It seems appropriate to discuss their characteristics and potential standardization in relation to the levels of hierarchy in which they appear. All levels can be divided into legal, medical, and technological properties. Standards applied to the first level include regulations or aims to be fulfilled. In legal properties, they have to regulate features of privacy, image documentation, transmission, and presentation; in medical properties, features of disease – image combination, human – diagnostics, automated information extraction, archive retrieval and access; and in technological properties features of image acquisition, display, formats, transfer speed, safety, and system dynamics. The next lower second level has to implement the prescriptions of the upper one, i.e. describe how they are implemented. Legal aspects should demand secure encryption for privacy of all patient related data, image archives that include all images used for diagnostics for a period of 10 years at minimum, accurate annotations of dates and viewing, and precise hardware and software information. Medical aspects should demand standardized patients' files such as DICOM 3 or HL 7 including history and previous examinations, information of image display hardware and software, of image resolution and fields of view, of relation between sizes of biological objects and image sizes, and of access to archives and retrieval. Technological aspects should deal with image

  2. The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders.

    Science.gov (United States)

    Boschloo, Lynn; van Borkulo, Claudia D; Rhemtulla, Mijke; Keyes, Katherine M; Borsboom, Denny; Schoevers, Robert A

    2015-01-01

    Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from the causal interplay between psychiatric symptoms and focuses specifically on these symptoms and their complex associations. By using a sophisticated network analysis technique, this study constructed an empirically based network structure of 120 psychiatric symptoms of twelve major DSM-IV diagnoses using cross-sectional data of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC, second wave; N = 34,653). The resulting network demonstrated that symptoms within the same diagnosis showed differential associations and indicated that the strategy of summing symptoms, as in current classification systems, leads to loss of information. In addition, some symptoms showed strong connections with symptoms of other diagnoses, and these specific symptom pairs, which both concerned overlapping and non-overlapping symptoms, may help to explain the comorbidity across diagnoses. Taken together, our findings indicated that psychopathology is very complex and can be more adequately captured by sophisticated network models than current classification systems. The network approach is, therefore, promising in improving our understanding of psychopathology and moving our field forward.

  3. Elements of Network-Based Assessment

    Science.gov (United States)

    Gibson, David

    2007-01-01

    Elements of network-based assessment systems are envisioned based on recent advances in knowledge and practice in learning theory, assessment design and delivery, and semantic web interoperability. The architecture takes advantage of the meditating role of technology as well as recent models of assessment systems. This overview of the elements…

  4. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

    Hashemi Nekoo, Ali Reza; Ashourizadeh, Shayegheh; Zarei, Behrouz

    2015-01-01

    Survival on dynamic environment is not achieved without a map. Scanning and monitoring of the market show business models as a fruitful tool. But scholars believe that old-fashioned business models are dead; as they are not included the effect of internet and network in themselves. This paper...... is going to propose e-business model ontology from the network point of view and its application in real world. The suggested ontology for network-based businesses is composed of individuals` characteristics and what kind of resources they own. also, their connections and pre-conceptions of connections...... such as shared-mental model and trust. However, it mostly covers previous business model elements. To confirm the applicability of this ontology, it has been implemented in business angel network and showed how it works....

  5. Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

    Science.gov (United States)

    Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

    2013-01-01

    Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

  6. Connectivity diagnostics in the Mediterranean obtained from Lagrangian Flow Networks; global patterns, sensitivity and robustness

    Science.gov (United States)

    Monroy, Pedro; Rossi, Vincent; Ser-Giacomi, Enrico; López, Cristóbal; Hernández-García, Emilio

    2017-04-01

    Lagrangian Flow Network (LFN) is a modeling framework in which geographical sub-areas of the ocean are represented as nodes in a network and are interconnected by links representing the transport of water, substances or propagules (eggs and larvae) by currents. Here we compute for the surface of the whole Mediterranean basin four connectivity metrics derived from LFN that measure retention and exchange processes, thus providing a systematic characterization of propagule dispersal driven by the ocean circulation. Then we assess the sensitivity and robustness of the results with respect to the most relevant parameters: the density of released particles, the node size (spatial-scales of discretization), the Pelagic Larval Duration (PLD) and the modality of spawning. We find a threshold for the number of particles per node that guarantees reliable values for most of the metrics examined, independently of node size. For our setup, this threshold is 100 particles per node. We also find that the size of network nodes has a non-trivial influence on the spatial variability of both exchange and retention metrics. Although the spatio-temporal fluctuations of the circulation affect larval transport in a complex and unpredictable manner, our analyses evidence how specific biological parametrization impact the robustness of connectivity diagnostics. Connectivity estimates for long PLDs are more robust against biological uncertainties (PLD and spawning date) than for short PLDs. Furthermore, our model suggests that for mass-spawners that release propagules over short periods (≃ 2 to 10 days), daily release must be simulated to properly consider connectivity fluctuations. In contrast, average connectivity estimates for species that spawn repeatedly over longer duration (a few weeks to a few months) remain robust even using longer periodicity (5 to 10 days). Our results give a global view of the surface connectivity of the Mediterranean Sea and have implications for the design of

  7. Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

    Energy Technology Data Exchange (ETDEWEB)

    Eswari J, Satya; Chandrakar, Neha [National Institute of Technology Raipur, Raipur (India)

    2016-04-15

    Artificial neural networks (ANNs) can be used to develop a technique to classify lymph node negative breast cancer that is prone to distant metastases based on gene expression signatures. The neural network used is a multilayered feed forward network that employs back propagation algorithm. Once trained with DNA microarraybased gene expression profiles of genes that were predictive of distant metastasis recurrence of lymph node negative breast cancer, the ANNs became capable of correctly classifying all samples and recognizing the genes most appropriate to the classification. To test the ability of the trained ANN models in recognizing lymph node negative breast cancer, we analyzed additional idle samples that were not used beforehand for the training procedure and obtained the correctly classified result in the validation set. For more substantial result, bootstrapping of training and testing dataset was performed as external validation. This study illustrates the potential application of ANN for breast tumor diagnosis and the identification of candidate targets in patients for therapy.

  8. A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

    Science.gov (United States)

    Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

    2015-01-01

    This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

  9. The probability estimate of the defects of the asynchronous motors based on the complex method of diagnostics

    Science.gov (United States)

    Zhukovskiy, Yu L.; Korolev, N. A.; Babanova, I. S.; Boikov, A. V.

    2017-10-01

    This article is devoted to the development of a method for probability estimate of failure of an asynchronous motor as a part of electric drive with a frequency converter. The proposed method is based on a comprehensive method of diagnostics of vibration and electrical characteristics that take into account the quality of the supply network and the operating conditions. The developed diagnostic system allows to increase the accuracy and quality of diagnoses by determining the probability of failure-free operation of the electromechanical equipment, when the parameters deviate from the norm. This system uses an artificial neural networks (ANNs). The results of the system for estimator the technical condition are probability diagrams of the technical state and quantitative evaluation of the defects of the asynchronous motor and its components.

  10. Fuzzy based method for project planning of the infrastructure design for the diagnostic in ITER

    International Nuclear Information System (INIS)

    Piros, Attila; Veres, Gábor

    2013-01-01

    The long-term design projects need special preparation before the start of the execution. This preparation usually includes the drawing of the network diagram for the whole procedure. This diagram includes the time estimation of the individual subtasks and gives us information about the predicted dates of the milestones. The calculated critical path in this network characterizes a specific design project concerning to its duration very well. Several methods are available to support this step of preparation. This paper describes a new method to map the structure of the design process and clarify the milestones and predict the dates of these milestones. The method is based on the PERT (Project Evaluation and Review Technique) network but as a novelty it applies fuzzy logic to find out the concerning times in this graph. With the application of the fuzzy logic the handling of the different kinds of design uncertainties becomes feasible. Many kinds of design uncertainties exist from the possible electric blackout up to the illness of an engineer. In many cases these uncertainties are related with human errors and described with linguistic expressions. The fuzzy logic enables to transform these ambiguous expressions into numeric values for further mathematical evaluation. The method is introduced in the planning of the design project of the infrastructure for the diagnostic systems of ITER. The method not only helps the project in the planning phase, but it will be a powerful tool in mathematical modeling and monitoring of the project execution

  11. Fuzzy based method for project planning of the infrastructure design for the diagnostic in ITER

    Energy Technology Data Exchange (ETDEWEB)

    Piros, Attila, E-mail: attila.piros@gt3.bme.hu [Department of Machine and Product Design, Budapest University of Technology and Economics, Budapest (Hungary); Veres, Gábor [Department of Plasma Physics, Wigner Research Centre for Physics, Hungarian Academy of Sciences, Budapest (Hungary)

    2013-10-15

    The long-term design projects need special preparation before the start of the execution. This preparation usually includes the drawing of the network diagram for the whole procedure. This diagram includes the time estimation of the individual subtasks and gives us information about the predicted dates of the milestones. The calculated critical path in this network characterizes a specific design project concerning to its duration very well. Several methods are available to support this step of preparation. This paper describes a new method to map the structure of the design process and clarify the milestones and predict the dates of these milestones. The method is based on the PERT (Project Evaluation and Review Technique) network but as a novelty it applies fuzzy logic to find out the concerning times in this graph. With the application of the fuzzy logic the handling of the different kinds of design uncertainties becomes feasible. Many kinds of design uncertainties exist from the possible electric blackout up to the illness of an engineer. In many cases these uncertainties are related with human errors and described with linguistic expressions. The fuzzy logic enables to transform these ambiguous expressions into numeric values for further mathematical evaluation. The method is introduced in the planning of the design project of the infrastructure for the diagnostic systems of ITER. The method not only helps the project in the planning phase, but it will be a powerful tool in mathematical modeling and monitoring of the project execution.

  12. Changing Histopathological Diagnostics by Genome-Based Tumor Classification

    Directory of Open Access Journals (Sweden)

    Michael Kloth

    2014-05-01

    Full Text Available Traditionally, tumors are classified by histopathological criteria, i.e., based on their specific morphological appearances. Consequently, current therapeutic decisions in oncology are strongly influenced by histology rather than underlying molecular or genomic aberrations. The increase of information on molecular changes however, enabled by the Human Genome Project and the International Cancer Genome Consortium as well as the manifold advances in molecular biology and high-throughput sequencing techniques, inaugurated the integration of genomic information into disease classification. Furthermore, in some cases it became evident that former classifications needed major revision and adaption. Such adaptations are often required by understanding the pathogenesis of a disease from a specific molecular alteration, using this molecular driver for targeted and highly effective therapies. Altogether, reclassifications should lead to higher information content of the underlying diagnoses, reflecting their molecular pathogenesis and resulting in optimized and individual therapeutic decisions. The objective of this article is to summarize some particularly important examples of genome-based classification approaches and associated therapeutic concepts. In addition to reviewing disease specific markers, we focus on potentially therapeutic or predictive markers and the relevance of molecular diagnostics in disease monitoring.

  13. An indicator cell assay for blood-based diagnostics.

    Directory of Open Access Journals (Sweden)

    Samuel A Danziger

    Full Text Available We have established proof of principle for the Indicator Cell Assay Platform™ (iCAP™, a broadly applicable tool for blood-based diagnostics that uses specifically-selected, standardized cells as biosensors, relying on their innate ability to integrate and respond to diverse signals present in patients' blood. To develop an assay, indicator cells are exposed in vitro to serum from case or control subjects and their global differential response patterns are used to train reliable, disease classifiers based on a small number of features. In a feasibility study, the iCAP detected pre-symptomatic disease in a murine model of amyotrophic lateral sclerosis (ALS with 94% accuracy (p-Value = 3.81E-6 and correctly identified samples from a murine Huntington's disease model as non-carriers of ALS. Beyond the mouse model, in a preliminary human disease study, the iCAP detected early stage Alzheimer's disease with 72% cross-validated accuracy (p-Value = 3.10E-3. For both assays, iCAP features were enriched for disease-related genes, supporting the assay's relevance for disease research.

  14. Nonbinary Tree-Based Phylogenetic Networks

    NARCIS (Netherlands)

    Jetten, L.; van Iersel, L.J.J.

    2018-01-01

    Rooted phylogenetic networks are used to describe evolutionary histories that contain non-treelike evolutionary events such as hybridization and horizontal gene transfer. In some cases, such histories can be described by a phylogenetic base-tree with additional linking arcs, which can for example

  15. Location-based Forwarding in Vehicular Networks

    NARCIS (Netherlands)

    Klein Wolterink, W.

    2013-01-01

    In this thesis we focus on location-based message forwarding in vehicular networks to support intelligent transportation systems (ITSs). ITSs are transport systems that utilise information and communication technologies to increase their level of automation, in this way levering the performance of

  16. An efficient diagnostic technique for distribution systems based on under fault voltages and currents

    Energy Technology Data Exchange (ETDEWEB)

    Campoccia, A.; Di Silvestre, M.L.; Incontrera, I.; Riva Sanseverino, E. [Dipartimento di Ingegneria Elettrica elettronica e delle Telecomunicazioni, Universita degli Studi di Palermo, viale delle Scienze, 90128 Palermo (Italy); Spoto, G. [Centro per la Ricerca Elettronica in Sicilia, Monreale, Via Regione Siciliana 49, 90046 Palermo (Italy)

    2010-10-15

    Service continuity is one of the major aspects in the definition of the quality of the electrical energy, for this reason the research in the field of faults diagnostic for distribution systems is spreading ever more. Moreover the increasing interest around modern distribution systems automation for management purposes gives faults diagnostics more tools to detect outages precisely and in short times. In this paper, the applicability of an efficient fault location and characterization methodology within a centralized monitoring system is discussed. The methodology, appropriate for any kind of fault, is based on the use of the analytical model of the network lines and uses the fundamental components rms values taken from the transient measures of line currents and voltages at the MV/LV substations. The fault location and identification algorithm, proposed by the authors and suitably restated, has been implemented on a microprocessor-based device that can be installed at each MV/LV substation. The speed and precision of the algorithm have been tested against the errors deriving from the fundamental extraction within the prescribed fault clearing times and against the inherent precision of the electronic device used for computation. The tests have been carried out using Matlab Simulink for simulating the faulted system. (author)

  17. Neural Network Based Load Frequency Control for Restructuring ...

    African Journals Online (AJOL)

    Neural Network Based Load Frequency Control for Restructuring Power Industry. ... an artificial neural network (ANN) application of load frequency control (LFC) of a Multi-Area power system by using a neural network controller is presented.

  18. Inferring Trust Relationships in Web-Based Social Networks

    National Research Council Canada - National Science Library

    Golbeck, Jennifer; Hendler, James

    2006-01-01

    The growth of web-based social networking and the properties of those networks have created great potential for producing intelligent software that integrates a user's social network and preferences...

  19. Case-based reasoning diagnostic technique based on multi-attribute similarity

    Energy Technology Data Exchange (ETDEWEB)

    Makoto, Takahashi [Tohoku University, Miyagi (Japan); Akio, Gofuku [Okayama University, Okayamaa (Japan)

    2014-08-15

    Case-based diagnostic technique has been developed based on the multi-attribute similarity. Specific feature of the developed system is to use multiple attributes of process signals for similarity evaluation to retrieve a similar case stored in a case base. The present technique has been applied to the measurement data from Monju with some simulated anomalies. The results of numerical experiments showed that the present technique can be utilizes as one of the methods for a hybrid-type diagnosis system.

  20. Optimising TCP for cloud-based mobile networks

    DEFF Research Database (Denmark)

    Artuso, Matteo; Christiansen, Henrik Lehrmann

    2016-01-01

    Cloud-based mobile networks are foreseen to be a technological enabler for the next generation of mobile networks. Their design requires substantial research as they pose unique challenges, especially from the point of view of additional delays in the fronthaul network. Commonly used network...... implementations of 3 popular operating systems are investigated in our network model. The results on the most influential parameters are used to design an optimized TCP for cloud-based mobile networks....

  1. Network Based High Speed Product Innovation

    DEFF Research Database (Denmark)

    Lindgren, Peter

    In the first decade of the 21st century, New Product Development has undergone major changes in the way NPD is managed and organised. This is due to changes in technology, market demands, and in the competencies of companies. As a result NPD organised in different forms of networks is predicted...... to be of ever-increasing importance to many different kinds of companies. This happens at the same times as the share of new products of total turnover and earnings is increasing at unprecedented speed in many firms and industries. The latter results in the need for very fast innovation and product development...... - a need that can almost only be resolved by organising NPD in some form of network configuration. The work of Peter Lindgren is on several aspects of network based high speed product innovation and contributes to a descriptive understanding of this phenomenon as well as with normative theory on how NPD...

  2. Quantum networks based on cavity QED

    Energy Technology Data Exchange (ETDEWEB)

    Ritter, Stephan; Bochmann, Joerg; Figueroa, Eden; Hahn, Carolin; Kalb, Norbert; Muecke, Martin; Neuzner, Andreas; Noelleke, Christian; Reiserer, Andreas; Uphoff, Manuel; Rempe, Gerhard [Max-Planck-Institut fuer Quantenoptik, Hans-Kopfermann-Strasse 1, 85748 Garching (Germany)

    2014-07-01

    Quantum repeaters require an efficient interface between stationary quantum memories and flying photons. Single atoms in optical cavities are ideally suited as universal quantum network nodes that are capable of sending, storing, retrieving, and even processing quantum information. We demonstrate this by presenting an elementary version of a quantum network based on two identical nodes in remote, independent laboratories. The reversible exchange of quantum information and the creation of remote entanglement are achieved by exchange of a single photon. Quantum teleportation is implemented using a time-resolved photonic Bell-state measurement. Quantum control over all degrees of freedom of the single atom also allows for the nondestructive detection of flying photons and the implementation of a quantum gate between the spin state of the atom and the polarization of a photon upon its reflection from the cavity. Our approach to quantum networking offers a clear perspective for scalability and provides the essential components for the realization of a quantum repeater.

  3. Smartphone-Based Food Diagnostic Technologies: A Review.

    Science.gov (United States)

    Rateni, Giovanni; Dario, Paolo; Cavallo, Filippo

    2017-06-20

    A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.

  4. Smartphone-Based Food Diagnostic Technologies: A Review

    Directory of Open Access Journals (Sweden)

    Giovanni Rateni

    2017-06-01

    Full Text Available A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies.

  5. Beam Diagnostics for Laser Undulator Based on Compton Backward Scattering

    CERN Document Server

    Kuroda, R

    2005-01-01

    A compact soft X-ray source is required in various research fields such as material and biological science. The laser undulator based on Compton backward scattering has been developed as a compact soft X-ray source for the biological observation at Waseda University. It is performed in a water window region (250eV - 500 eV) using the interaction between 1047 nm Nd:YLF laser (10ps FWHM) and about 5 MeV high quality electron beam (10ps FWHM) generated from rf gun system. The range of X-ray energy in the water window region has K-shell absorption edges of Oxygen, Carbon and Nitrogen, which mainly constitute of living body. Since the absorption coefficient of water is much smaller than the protein's coefficient in this range, a dehydration of the specimens is not necessary. To generate the soft X-ray pulse stably, the electron beam diagnostics have been developed such as the emittance measurement using double slit scan technique, the bunch length measurement using two frequency analysis technique. In this confere...

  6. Smartphone-Based Food Diagnostic Technologies: A Review

    Science.gov (United States)

    Rateni, Giovanni; Dario, Paolo; Cavallo, Filippo

    2017-01-01

    A new generation of mobile sensing approaches offers significant advantages over traditional platforms in terms of test speed, control, low cost, ease-of-operation, and data management, and requires minimal equipment and user involvement. The marriage of novel sensing technologies with cellphones enables the development of powerful lab-on-smartphone platforms for many important applications including medical diagnosis, environmental monitoring, and food safety analysis. This paper reviews the recent advancements and developments in the field of smartphone-based food diagnostic technologies, with an emphasis on custom modules to enhance smartphone sensing capabilities. These devices typically comprise multiple components such as detectors, sample processors, disposable chips, batteries and software, which are integrated with a commercial smartphone. One of the most important aspects of developing these systems is the integration of these components onto a compact and lightweight platform that requires minimal power. To date, researchers have demonstrated several promising approaches employing various sensing techniques and device configurations. We aim to provide a systematic classification according to the detection strategy, providing a critical discussion of strengths and weaknesses. We have also extended the analysis to the food scanning devices that are increasingly populating the Internet of Things (IoT) market, demonstrating how this field is indeed promising, as the research outputs are quickly capitalized on new start-up companies. PMID:28632188

  7. Clinical advances of nanocarrier-based cancer therapy and diagnostics.

    Science.gov (United States)

    Luque-Michel, Edurne; Imbuluzqueta, Edurne; Sebastián, Víctor; Blanco-Prieto, María J

    2017-01-01

    Cancer is a leading cause of death worldwide and efficient new strategies are urgently needed to combat its high mortality and morbidity statistics. Fortunately, over the years, nanotechnology has evolved as a frontrunner in the areas of imaging, diagnostics and therapy, giving the possibility of monitoring, evaluating and individualizing cancer treatments in real-time. Areas covered: Polymer-based nanocarriers have been extensively studied to maximize cancer treatment efficacy and minimize the adverse effects of standard therapeutics. Regarding diagnosis, nanomaterials like quantum dots, iron oxide nanoparticles or gold nanoparticles have been developed to provide rapid, sensitive detection of cancer and, therefore, facilitate early treatment and monitoring of the disease. Therefore, multifunctional nanosystems with both imaging and therapy functionalities bring us a step closer to delivering precision/personalized medicine in the cancer setting. Expert opinion: There are multiple barriers for these new nanosystems to enter the clinic, but it is expected that in the near future, nanocarriers, together with new 'targeted drugs', could replace our current treatments and cancer could become a nonfatal disease with good recovery rates. Joint efforts between scientists, clinicians, the pharmaceutical industry and legislative bodies are needed to bring to fruition the application of nanosystems in the clinical management of cancer.

  8. Wind Turbine Bearing Diagnostics Based on Vibration Monitoring

    Science.gov (United States)

    Kadhim, H. T.; Mahmood, F. H.; Resen, A. K.

    2018-05-01

    Reliability maintenance can be considered as an accurate condition monitoring system which increasing beneficial and decreasing the cost production of wind energy. Supporting low friction of wind turbine rotating shaft is the main task of rolling element bearing and it is the main part that suffers from failure. The rolling failures elements have an economic impact and may lead to malfunctions and catastrophic failures. This paper concentrates on the vibration monitoring as a Non-Destructive Technique for assessing and demonstrates the feasibility of vibration monitoring for small wind turbine bearing defects based on LabVIEW software. Many bearings defects were created, such as inner race defect, outer race defect, and ball spin defect. The spectra data were recorded and compared with the theoretical results. The accelerometer with 4331 NI USB DAQ was utilized to acquiring, analyzed, and recorded. The experimental results were showed the vibration technique is suitable for diagnostic the defects that will be occurred in the small wind turbine bearings and developing a fault in the bearing which leads to increasing the vibration amplitude or peaks in the spectrum.

  9. Neural network based multiscale image restoration approach

    Science.gov (United States)

    de Castro, Ana Paula A.; da Silva, José D. S.

    2007-02-01

    This paper describes a neural network based multiscale image restoration approach. Multilayer perceptrons are trained with artificial images of degraded gray level circles, in an attempt to make the neural network learn inherent space relations of the degraded pixels. The present approach simulates the degradation by a low pass Gaussian filter blurring operation and the addition of noise to the pixels at pre-established rates. The training process considers the degraded image as input and the non-degraded image as output for the supervised learning process. The neural network thus performs an inverse operation by recovering a quasi non-degraded image in terms of least squared. The main difference of the approach to existing ones relies on the fact that the space relations are taken from different scales, thus providing relational space data to the neural network. The approach is an attempt to come up with a simple method that leads to an optimum solution to the problem. Considering different window sizes around a pixel simulates the multiscale operation. In the generalization phase the neural network is exposed to indoor, outdoor, and satellite degraded images following the same steps use for the artificial circle image.

  10. Tree-Based Unrooted Phylogenetic Networks.

    Science.gov (United States)

    Francis, A; Huber, K T; Moulton, V

    2018-02-01

    Phylogenetic networks are a generalization of phylogenetic trees that are used to represent non-tree-like evolutionary histories that arise in organisms such as plants and bacteria, or uncertainty in evolutionary histories. An unrooted phylogenetic network on a non-empty, finite set X of taxa, or network, is a connected, simple graph in which every vertex has degree 1 or 3 and whose leaf set is X. It is called a phylogenetic tree if the underlying graph is a tree. In this paper we consider properties of tree-based networks, that is, networks that can be constructed by adding edges into a phylogenetic tree. We show that although they have some properties in common with their rooted analogues which have recently drawn much attention in the literature, they have some striking differences in terms of both their structural and computational properties. We expect that our results could eventually have applications to, for example, detecting horizontal gene transfer or hybridization which are important factors in the evolution of many organisms.

  11. Dynamic social networks based on movement

    Science.gov (United States)

    Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

    2016-01-01

    Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

  12. Experience with model based display for advanced diagnostics and control

    International Nuclear Information System (INIS)

    Staffon, J.D.; Lindsay, R.W.

    1989-01-01

    A full color, model based display system based on the Rankine thermodynamic cycle has been developed for use at the Experimental Breeder Reactor II by plant operators, engineers, and experimenters. The displays generate a real time thermodynamic model of the plant processes on computer screens to provide a direct indication of the plant performance. Operators and others who view the displays are no longer required to mentally ''construct'' a model of the process before acting. The model based display accurately depicts the plant states. It appears to effectively reduce the gulf of evaluation, which should result in a significant reduction in human operator errors if this plant display approach is adopted by the nuclear industry. Preliminary comments from users, including operators, indicate an overwhelming acceptance of the display approach. The displays incorporate alarm functions as well as levels of detail ''paging'' capability. The system is developed on a computer network which allows the easy addition of displays as well as extra computers. Constructing a complete console can be rapid and inexpensive. 1 ref., 2 figs

  13. Untangling the complexity of blood coagulation network: use of computational modelling in pharmacology and diagnostics.

    Science.gov (United States)

    Shibeko, Alexey M; Panteleev, Mikhail A

    2016-05-01

    Blood coagulation is a complex biochemical network that plays critical roles in haemostasis (a physiological process that stops bleeding on injury) and thrombosis (pathological vessel occlusion). Both up- and down-regulation of coagulation remain a major challenge for modern medicine, with the ultimate goal to correct haemostasis without causing thrombosis and vice versa. Mathematical/computational modelling is potentially an important tool for understanding blood coagulation disorders and their treatment. It can save a huge amount of time and resources, and provide a valuable alternative or supplement when clinical studies are limited, or not ethical, or technically impossible. This article reviews contemporary state of the art in the modelling of blood coagulation for practical purposes: to reveal the molecular basis of a disease, to understand mechanisms of drug action, to predict pharmacodynamics and drug-drug interactions, to suggest potential drug targets or to improve quality of diagnostics. Different model types and designs used for this are discussed. Functional mechanisms of procoagulant bypassing agents and investigations of coagulation inhibitors were the two particularly popular applications of computational modelling that gave non-trivial results. Yet, like any other tool, modelling has its limitations, mainly determined by insufficient knowledge of the system, uncertainty and unreliability of complex models. We show how to some extent this can be overcome and discuss what can be expected from the mathematical modelling of coagulation in not-so-far future. © The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

  14. Towards a blood-based diagnostic panel for bipolar disorder

    NARCIS (Netherlands)

    F. Haenisch (Frieder); J.D. Cooper (Jason); A. Reif (Andreas); S. Kittel-Schneider (Sarah); J. Steiner (Johann); F.M. Leweke (Marcus); M. Rothermundt (Matthias); N.J.M. van Beveren (Nico); B. Crespo-Facorro (Benedicto); D. Niebuhr (David); D. Cowan (David); N. Weber (Natalya); R.H. Yolken (Robert); B.W.J.H. Penninx (Brenda W.J.H.); S. Bahn (Sabine)

    2015-01-01

    markdownabstract_Background:_ Bipolar disorder (BD) is a costly, devastating and life shortening mental disorder that is often misdiagnosed, especially on initial presentation. Misdiagnosis frequently results in ineffective treatment. We investigated the utility of a biomarker panel as a diagnostic

  15. Model-based Diagnostics for Propellant Loading Systems

    Data.gov (United States)

    National Aeronautics and Space Administration — The loading of spacecraft propellants is a complex, risky operation. Therefore, diagnostic solutions are neces- sary to quickly identify when a fault occurs, so that...

  16. Development of genomic based diagnostics in various application domains

    DEFF Research Database (Denmark)

    Szallasi, Zoltan Imre

    2017-01-01

    We will review the revolution brought about by low cost next generation sequencing in a wide array of diagnostic and industrial applications with a special emphasis on computational requirements and big data challenges.......We will review the revolution brought about by low cost next generation sequencing in a wide array of diagnostic and industrial applications with a special emphasis on computational requirements and big data challenges....

  17. Maximization Network Throughput Based on Improved Genetic Algorithm and Network Coding for Optical Multicast Networks

    Science.gov (United States)

    Wei, Chengying; Xiong, Cuilian; Liu, Huanlin

    2017-12-01

    Maximal multicast stream algorithm based on network coding (NC) can improve the network's throughput for wavelength-division multiplexing (WDM) networks, which however is far less than the network's maximal throughput in terms of theory. And the existing multicast stream algorithms do not give the information distribution pattern and routing in the meantime. In the paper, an improved genetic algorithm is brought forward to maximize the optical multicast throughput by NC and to determine the multicast stream distribution by hybrid chromosomes construction for multicast with single source and multiple destinations. The proposed hybrid chromosomes are constructed by the binary chromosomes and integer chromosomes, while the binary chromosomes represent optical multicast routing and the integer chromosomes indicate the multicast stream distribution. A fitness function is designed to guarantee that each destination can receive the maximum number of decoding multicast streams. The simulation results showed that the proposed method is far superior over the typical maximal multicast stream algorithms based on NC in terms of network throughput in WDM networks.

  18. Nuclear power plant transient diagnostics using artificial neural networks that allow ''don't-know'' classifications

    International Nuclear Information System (INIS)

    Bartal, Y.; Lin, J.; Uhrig, R.E.

    1995-01-01

    A nuclear power plant's (NPP's) status is usually monitored by a human operator. Any classifier system used to enhance the operator's capability to diagnose a safety-critical system like an NPP should classify a novel transient as ''don't-know'' if it is not contained within its accumulated knowledge base. In particular, the classifier needs some kind of proximity measure between the new data and its training set. Artificial neural networks have been proposed as NPP classifiers, the most popular ones being the multilayered perceptron (MLP) type. However, MLPs do not have a proximity measure, while learning vector quantization, probabilistic neural networks (PNNs), and some others do. This proximity measure may also serve as an explanation to the classifier's decision in the way that case-based-reasoning expert systems do. The capability of a PNN network as a classifier is demonstrated using simulator data for the three-loop 436-MW(electric) Westinghouse San Onofre unit 1 pressurized water reactor. A transient's classification history is used in an ''evidence accumulation'' technique to enhance a classifier's accuracy as well as its consistency

  19. Fast infectious diseases diagnostics based on microfluidic biochip system

    Directory of Open Access Journals (Sweden)

    Qin Huang

    2017-03-01

    Full Text Available Molecular diagnostics is one of the most important tools currently in use for clinical pathogen detection due to its high sensitivity, specificity, and low consume of sample and reagent is keyword to low cost molecular diagnostics. In this paper, a sensitive DNA isothermal amplification method for fast clinical infectious diseases diagnostics at aM concentrations of DNA was developed using a polycarbonate (PC microfluidic chip. A portable confocal optical fluorescence detector was specifically developed for the microfluidic chip that was capable of highly sensitive real-time detection of amplified products for sequence-specific molecular identification near the optical diffraction limit with low background. The molecular diagnostics of Listeria monocytogenes with nucleic acid extracted from stool samples was performed at a minimum DNA template concentration of 3.65aM, and a detection limit of less than five copies of genomic DNA. Contrast to the general polymerase chain reaction (PCR at eppendorf (EP tube, the detection time in our developed method was reduced from 1.5h to 45min for multi-target parallel detection, the consume of sample and reagent was dropped from 25μL to 1.45μL. This novel microfluidic chip system and method can be used to develop a micro total analysis system as a clinically relevant pathogen molecular diagnostics method via the amplification of targets, with potential applications in biotechnology, medicine, and clinical molecular diagnostics.

  20. Autocorrel I: A Neural Network Based Network Event Correlation Approach

    National Research Council Canada - National Science Library

    Japkowicz, Nathalie; Smith, Reuben

    2005-01-01

    .... We use the autoassociator to build prototype software to cluster network alerts generated by a Snort intrusion detection system, and discuss how the results are significant, and how they can be applied to other types of network events.

  1. Quantitative learning strategies based on word networks

    Science.gov (United States)

    Zhao, Yue-Tian-Yi; Jia, Zi-Yang; Tang, Yong; Xiong, Jason Jie; Zhang, Yi-Cheng

    2018-02-01

    Learning English requires a considerable effort, but the way that vocabulary is introduced in textbooks is not optimized for learning efficiency. With the increasing population of English learners, learning process optimization will have significant impact and improvement towards English learning and teaching. The recent developments of big data analysis and complex network science provide additional opportunities to design and further investigate the strategies in English learning. In this paper, quantitative English learning strategies based on word network and word usage information are proposed. The strategies integrate the words frequency with topological structural information. By analyzing the influence of connected learned words, the learning weights for the unlearned words and dynamically updating of the network are studied and analyzed. The results suggest that quantitative strategies significantly improve learning efficiency while maintaining effectiveness. Especially, the optimized-weight-first strategy and segmented strategies outperform other strategies. The results provide opportunities for researchers and practitioners to reconsider the way of English teaching and designing vocabularies quantitatively by balancing the efficiency and learning costs based on the word network.

  2. Comparison analysis on vulnerability of metro networks based on complex network

    Science.gov (United States)

    Zhang, Jianhua; Wang, Shuliang; Wang, Xiaoyuan

    2018-04-01

    This paper analyzes the networked characteristics of three metro networks, and two malicious attacks are employed to investigate the vulnerability of metro networks based on connectivity vulnerability and functionality vulnerability. Meanwhile, the networked characteristics and vulnerability of three metro networks are compared with each other. The results show that Shanghai metro network has the largest transport capacity, Beijing metro network has the best local connectivity and Guangzhou metro network has the best global connectivity, moreover Beijing metro network has the best homogeneous degree distribution. Furthermore, we find that metro networks are very vulnerable subjected to malicious attacks, and Guangzhou metro network has the best topological structure and reliability among three metro networks. The results indicate that the proposed methodology is feasible and effective to investigate the vulnerability and to explore better topological structure of metro networks.

  3. Studies on neutron noise diagnostics of control rod vibrations by neural networks; Obtencion de U{sub 3}O{sub 8} y UO{sub 2} a partir de ADU (diuranato amonico) precipitado con aplicacion de ultrasonido

    Energy Technology Data Exchange (ETDEWEB)

    Roston, G; Kozma, R; Kitamura, M [Tohoku Univ., Sendai (Japan); Garis, N S; Pazsit, I [Chalmers Univ. of Technology, Goeteborg (Sweden). Dept. of Reactor Physics

    1997-12-31

    This work is focussed on the study of a neutron noise based technique for the diagnostics of reactor core internal, in particular, excessively vibrating control rods. The use of a combination of physical models and neural networks offers an alternative way of performing the inversion procedure. The application of a neural network technique to determine the rod position from the detector spectra is much faster, more effective and simpler to use than the conventional method. (author). 5 refs., 1 fig., 1 tab.

  4. Investigation of the network delay on Profibus-DP based network

    OpenAIRE

    Yılmaz, C.; Gürdal, O.; Sayan, H.H.

    2008-01-01

    The mathematical model of the network-induced delay control systems (NDCS) is given. Also the role of the NDCS’s components such as controller, sensor and network environment on the network-induced delay are included in the mathematical model of the system. The network delay is investigated on Profibus-DP based network application and experimental results obtained are presented graphically. The experimental results obtained show that the network induced delay is randomly changed according to ...

  5. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

    Full Text Available An acquaintance network is a social structure made up of a set of actors and the ties between them. These ties change dynamically as a consequence of incessant interactions between the actors. In this paper we introduce a social network model called the Interaction-Based (IB model that involves well-known sociological principles. The connections between the actors and the strength of the connections are influenced by the continuous positive and negative interactions between the actors and, vice versa, the future interactions are more likely to happen between the actors that are connected with stronger ties. The model is also inspired by the social behavior of animal species, particularly that of ants in their colony. A model evaluation showed that the IB model turned out to be sparse. The model has a small diameter and an average path length that grows in proportion to the logarithm of the number of vertices. The clustering coefficient is relatively high, and its value stabilizes in larger networks. The degree distributions are slightly right-skewed. In the mature phase of the IB model, i.e., when the number of edges does not change significantly, most of the network properties do not change significantly either. The IB model was found to be the best of all the compared models in simulating the e-mail URV (University Rovira i Virgili of Tarragona network because the properties of the IB model more closely matched those of the e-mail URV network than the other models

  6. Network-based recommendation algorithms: A review

    Science.gov (United States)

    Yu, Fei; Zeng, An; Gillard, Sébastien; Medo, Matúš

    2016-06-01

    Recommender systems are a vital tool that helps us to overcome the information overload problem. They are being used by most e-commerce web sites and attract the interest of a broad scientific community. A recommender system uses data on users' past preferences to choose new items that might be appreciated by a given individual user. While many approaches to recommendation exist, the approach based on a network representation of the input data has gained considerable attention in the past. We review here a broad range of network-based recommendation algorithms and for the first time compare their performance on three distinct real datasets. We present recommendation topics that go beyond the mere question of which algorithm to use-such as the possible influence of recommendation on the evolution of systems that use it-and finally discuss open research directions and challenges.

  7. Wireless Sensor Network Based Smart Parking System

    Directory of Open Access Journals (Sweden)

    Jeffrey JOSEPH

    2014-01-01

    Full Text Available Ambient Intelligence is a vision in which various devices come together and process information from multiple sources in order to exert control on the physical environment. In addition to computation and control, communication plays a crucial role in the overall functionality of such a system. Wireless Sensor Networks are one such class of networks, which meet these criteria. These networks consist of spatially distributed sensor motes which work in a co-operative manner to sense and control the environment. In this work, an implementation of an energy-efficient and cost-effective, wireless sensor networks based vehicle parking system for a multi-floor indoor parking facility has been introduced. The system monitors the availability of free parking slots and guides the vehicle to the nearest free slot. The amount of time the vehicle has been parked is monitored for billing purposes. The status of the motes (dead/alive is also recorded. Information like slot allocated, directions to the slot and billing data is sent as a message to customer’s mobile phones. This paper extends our previous work 1 with the development of a low cost sensor mote, about one tenth the cost of a commercially available mote, keeping in mind the price sensitive markets of the developing countries.

  8. Optimal Combinations of Diagnostic Tests Based on AUC.

    Science.gov (United States)

    Huang, Xin; Qin, Gengsheng; Fang, Yixin

    2011-06-01

    When several diagnostic tests are available, one can combine them to achieve better diagnostic accuracy. This article considers the optimal linear combination that maximizes the area under the receiver operating characteristic curve (AUC); the estimates of the combination's coefficients can be obtained via a nonparametric procedure. However, for estimating the AUC associated with the estimated coefficients, the apparent estimation by re-substitution is too optimistic. To adjust for the upward bias, several methods are proposed. Among them the cross-validation approach is especially advocated, and an approximated cross-validation is developed to reduce the computational cost. Furthermore, these proposed methods can be applied for variable selection to select important diagnostic tests. The proposed methods are examined through simulation studies and applications to three real examples. © 2010, The International Biometric Society.

  9. Bayesian based Diagnostic Model for Condition based Maintenance of Offshore Wind Farms

    DEFF Research Database (Denmark)

    Asgarpour, Masoud; Sørensen, John Dalsgaard

    2018-01-01

    Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing...... sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using...... for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions....

  10. Application of Bayesian Networks to Diagnostics of Hot Dip Galvanized Coasts

    Directory of Open Access Journals (Sweden)

    A. Adrian

    2007-07-01

    Full Text Available This study presents an output of the application of a probabilistic method of inference based on Bayes' rule in the diagnosis of defects formed during hot-dip galvanising process of casting products. Bayesian cause-effect network for given group of surface defects and its causes was build. Many factors causing defects was taken into consideration like: technological parameters, technological nodes and character of cause. The advantages and drawbacks of a probabilistic method of representation of the incomplete and uncertain empirical knowledge were highlighted.

  11. Intraoral fiber optic-based diagnostic for periodontal disease

    Energy Technology Data Exchange (ETDEWEB)

    Johnson, P W; Gutierrez, D M; Everett, M J; Brown, S B; Langry, K C; Colston, B W; Roe, J N

    2000-01-21

    The purpose of this initial study was to begin development of a new, objective diagnostic instrument that will allow simultaneous quantitation of multiple proteases within a single periodontal pocket using a chemical fiber optic sensor. This approach could potentially be adapted to use specific antibodies and chemiluminescence to detect and quantitate virtually any compound and compare concentrations of different compounds within the same periodontal pocket. The device could also be used to assay secretions in salivary ducts or from a variety of wounds. The applicability is, therefore, not solely limited to dentistry and the device would be important both for clinical diagnostics and as a research tool.

  12. Intraoral fiber-optic-based diagnostic for periodontal disease

    Science.gov (United States)

    Colston, Bill W., Jr.; Gutierrez, Dora M.; Everett, Matthew J.; Brown, Steve B.; Langry, Kevin C.; Cox, Weldon R.; Johnson, Paul W.; Roe, Jeffrey N.

    2000-05-01

    The purpose of this initial study was to begin development of a new, objective diagnostic instrument that will allow simultaneous quantitation of multiple proteases within a single periodontal pocket using a chemical fiber optic senor. This approach could potentially be adapted to use specific antibodies and chemiluminescence to detect and quantitate virtually any compound and compare concentrations of different compounds within the same periodontal pocket. The device could also be used to assay secretions in salivary ducts or from a variety of wounds. The applicability is, therefore, not solely limited to dentistry and the device would be important both for clinical diagnostics and as a research too.

  13. Agent based modeling of energy networks

    International Nuclear Information System (INIS)

    Gonzalez de Durana, José María; Barambones, Oscar; Kremers, Enrique; Varga, Liz

    2014-01-01

    Highlights: • A new approach for energy network modeling is designed and tested. • The agent-based approach is general and no technology dependent. • The models can be easily extended. • The range of applications encompasses from small to large energy infrastructures. - Abstract: Attempts to model any present or future power grid face a huge challenge because a power grid is a complex system, with feedback and multi-agent behaviors, integrated by generation, distribution, storage and consumption systems, using various control and automation computing systems to manage electricity flows. Our approach to modeling is to build upon an established model of the low voltage electricity network which is tested and proven, by extending it to a generalized energy model. But, in order to address the crucial issues of energy efficiency, additional processes like energy conversion and storage, and further energy carriers, such as gas, heat, etc., besides the traditional electrical one, must be considered. Therefore a more powerful model, provided with enhanced nodes or conversion points, able to deal with multidimensional flows, is being required. This article addresses the issue of modeling a local multi-carrier energy network. This problem can be considered as an extension of modeling a low voltage distribution network located at some urban or rural geographic area. But instead of using an external power flow analysis package to do the power flow calculations, as used in electric networks, in this work we integrate a multiagent algorithm to perform the task, in a concurrent way to the other simulation tasks, and not only for the electric fluid but also for a number of additional energy carriers. As the model is mainly focused in system operation, generation and load models are not developed

  14. Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials.

    Science.gov (United States)

    Siontis, George Cm; Mavridis, Dimitris; Greenwood, John P; Coles, Bernadette; Nikolakopoulou, Adriani; Jüni, Peter; Salanti, Georgia; Windecker, Stephan

    2018-02-21

    To evaluate differences in downstream testing, coronary revascularisation, and clinical outcomes following non-invasive diagnostic modalities used to detect coronary artery disease. Systematic review and network meta-analysis. Medline, Medline in process, Embase, Cochrane Library for clinical trials, PubMed, Web of Science, SCOPUS, WHO International Clinical Trials Registry Platform, and Clinicaltrials.gov. Diagnostic randomised controlled trials comparing non-invasive diagnostic modalities in patients presenting with symptoms suggestive of low risk acute coronary syndrome or stable coronary artery disease. A random effects network meta-analysis synthesised available evidence from trials evaluating the effect of non-invasive diagnostic modalities on downstream testing and patient oriented outcomes in patients with suspected coronary artery disease. Modalities included exercise electrocardiograms, stress echocardiography, single photon emission computed tomography-myocardial perfusion imaging, real time myocardial contrast echocardiography, coronary computed tomographic angiography, and cardiovascular magnetic resonance. Unpublished outcome data were obtained from 11 trials. 18 trials of patients with low risk acute coronary syndrome (n=11 329) and 12 trials of those with suspected stable coronary artery disease (n=22 062) were included. Among patients with low risk acute coronary syndrome, stress echocardiography, cardiovascular magnetic resonance, and exercise electrocardiograms resulted in fewer invasive referrals for coronary angiography than coronary computed tomographic angiography (odds ratio 0.28 (95% confidence interval 0.14 to 0.57), 0.32 (0.15 to 0.71), and 0.53 (0.28 to 1.00), respectively). There was no effect on the subsequent risk of myocardial infarction, but estimates were imprecise. Heterogeneity and inconsistency were low. In patients with suspected stable coronary artery disease, an initial diagnostic strategy of stress echocardiography or

  15. Outcomes of non-invasive diagnostic modalities for the detection of coronary artery disease: network meta-analysis of diagnostic randomised controlled trials

    Science.gov (United States)

    Siontis, George CM; Mavridis, Dimitris; Greenwood, John P; Coles, Bernadette; Nikolakopoulou, Adriani; Jüni, Peter; Salanti, Georgia

    2018-01-01

    Abstract Objective To evaluate differences in downstream testing, coronary revascularisation, and clinical outcomes following non-invasive diagnostic modalities used to detect coronary artery disease. Design Systematic review and network meta-analysis. Data sources Medline, Medline in process, Embase, Cochrane Library for clinical trials, PubMed, Web of Science, SCOPUS, WHO International Clinical Trials Registry Platform, and Clinicaltrials.gov. Eligibility criteria for selecting studies Diagnostic randomised controlled trials comparing non-invasive diagnostic modalities in patients presenting with symptoms suggestive of low risk acute coronary syndrome or stable coronary artery disease. Data synthesis A random effects network meta-analysis synthesised available evidence from trials evaluating the effect of non-invasive diagnostic modalities on downstream testing and patient oriented outcomes in patients with suspected coronary artery disease. Modalities included exercise electrocardiograms, stress echocardiography, single photon emission computed tomography-myocardial perfusion imaging, real time myocardial contrast echocardiography, coronary computed tomographic angiography, and cardiovascular magnetic resonance. Unpublished outcome data were obtained from 11 trials. Results 18 trials of patients with low risk acute coronary syndrome (n=11 329) and 12 trials of those with suspected stable coronary artery disease (n=22 062) were included. Among patients with low risk acute coronary syndrome, stress echocardiography, cardiovascular magnetic resonance, and exercise electrocardiograms resulted in fewer invasive referrals for coronary angiography than coronary computed tomographic angiography (odds ratio 0.28 (95% confidence interval 0.14 to 0.57), 0.32 (0.15 to 0.71), and 0.53 (0.28 to 1.00), respectively). There was no effect on the subsequent risk of myocardial infarction, but estimates were imprecise. Heterogeneity and inconsistency were low. In patients with

  16. Molecular diagnostics based on clustering dynamics of magnetic nanobeads

    DEFF Research Database (Denmark)

    Donolato, Marco; Bejhed, Rebecca S.; de la Torre, Teresa Zardán Gómez

    2014-01-01

    transmission modulation caused by the AC magnetic field-stimulated reversible formation and disruption of elongated MNB supra-structures during a cycle of the uniaxial applied magnetic field. As a specific clinically relevant diagnostic case, we detect DNA coils formed via padlock probe recognition...

  17. Social Network Analysis as an Organizational Diagnostic Tool: The Case of Small Business in Russia

    OpenAIRE

    Rasskazov, Sergey; Rubtcova, Mariia; Derugin, Pavel; Pruel, Nikolay; Malychev, Valeriy

    2016-01-01

    The science of social networks is at the intersection of computer science, communication studies, mathematics and sociology. The first area is already ‘invested’ by communications networks, hardware and software, and has attracted many users. Communication science studies the network not only as a computer network, but also as a network of discourse. Contributions of mathematics; the Theory of Graphs and various related calculations. ‘Weak’ in this triad is a sociological interpretation of qu...

  18. Bayesian Based Diagnostic Model for Condition Based Maintenance of Offshore Wind Farms

    Directory of Open Access Journals (Sweden)

    Masoud Asgarpour

    2018-01-01

    Full Text Available Operation and maintenance costs are a major contributor to the Levelized Cost of Energy for electricity produced by offshore wind and can be significantly reduced if existing corrective actions are performed as efficiently as possible and if future corrective actions are avoided by performing sufficient preventive actions. This paper presents an applied and generic diagnostic model for fault detection and condition based maintenance of offshore wind components. The diagnostic model is based on two probabilistic matrices; first, a confidence matrix, representing the probability of detection using each fault detection method, and second, a diagnosis matrix, representing the individual outcome of each fault detection method. Once the confidence and diagnosis matrices of a component are defined, the individual diagnoses of each fault detection method are combined into a final verdict on the fault state of that component. Furthermore, this paper introduces a Bayesian updating model based on observations collected by inspections to decrease the uncertainty of initial confidence matrix. The framework and implementation of the presented diagnostic model are further explained within a case study for a wind turbine component based on vibration, temperature, and oil particle fault detection methods. The last part of the paper will have a discussion of the case study results and present conclusions.

  19. 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.

  20. A Cluster- Based Secure Active Network Environment

    Institute of Scientific and Technical Information of China (English)

    CHEN Xiao-lin; ZHOU Jing-yang; DAI Han; LU Sang-lu; CHEN Gui-hai

    2005-01-01

    We introduce a cluster-based secure active network environment (CSANE) which separates the processing of IP packets from that of active packets in active routers. In this environment, the active code authorized or trusted by privileged users is executed in the secure execution environment (EE) of the active router, while others are executed in the secure EE of the nodes in the distributed shared memory (DSM) cluster. With the supports of a multi-process Java virtual machine and KeyNote, untrusted active packets are controlled to securely consume resource. The DSM consistency management makes that active packets can be parallelly processed in the DSM cluster as if they were processed one by one in ANTS (Active Network Transport System). We demonstrate that CSANE has good security and scalability, but imposing little changes on traditional routers.

  1. Convolution neural-network-based detection of lung structures

    Science.gov (United States)

    Hasegawa, Akira; Lo, Shih-Chung B.; Freedman, Matthew T.; Mun, Seong K.

    1994-05-01

    Chest radiography is one of the most primary and widely used techniques in diagnostic imaging. Nowadays with the advent of digital radiology, the digital medical image processing techniques for digital chest radiographs have attracted considerable attention, and several studies on the computer-aided diagnosis (CADx) as well as on the conventional image processing techniques for chest radiographs have been reported. In the automatic diagnostic process for chest radiographs, it is important to outline the areas of the lungs, the heart, and the diaphragm. This is because the original chest radiograph is composed of important anatomic structures and, without knowing exact positions of the organs, the automatic diagnosis may result in unexpected detections. The automatic extraction of an anatomical structure from digital chest radiographs can be a useful tool for (1) the evaluation of heart size, (2) automatic detection of interstitial lung diseases, (3) automatic detection of lung nodules, and (4) data compression, etc. Based on the clearly defined boundaries of heart area, rib spaces, rib positions, and rib cage extracted, one should be able to use this information to facilitate the tasks of the CADx on chest radiographs. In this paper, we present an automatic scheme for the detection of lung field from chest radiographs by using a shift-invariant convolution neural network. A novel algorithm for smoothing boundaries of lungs is also presented.

  2. Virtualized Network Function Orchestration System and Experimental Network Based QR Recognition for a 5G Mobile Access Network

    Directory of Open Access Journals (Sweden)

    Misun Ahn

    2017-12-01

    Full Text Available This paper proposes a virtualized network function orchestration system based on Network Function Virtualization (NFV, one of the main technologies in 5G mobile networks. This system should provide connectivity between network devices and be able to create flexible network function and distribution. This system focuses more on access networks. By experimenting with various scenarios of user service established and activated in a network, we examine whether rapid adoption of new service is possible and whether network resources can be managed efficiently. The proposed method is based on Bluetooth transfer technology and mesh networking to provide automatic connections between network machines and on a Docker flat form, which is a container virtualization technology for setting and managing key functions. Additionally, the system includes a clustering and recovery measure regarding network function based on the Docker platform. We will briefly introduce the QR code perceived service as a user service to examine the proposal and based on this given service, we evaluate the function of the proposal and present analysis. Through the proposed approach, container relocation has been implemented according to a network device’s CPU usage and we confirm successful service through function evaluation on a real test bed. We estimate QR code recognition speed as the amount of network equipment is gradually increased, improving user service and confirm that the speed of recognition is increased as the assigned number of network devices is increased by the user service.

  3. Network-based analysis of proteomic profiles

    KAUST Repository

    Wong, Limsoon

    2016-01-26

    Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.

  4. Compact Interconnection Networks Based on Quantum Dots

    Science.gov (United States)

    Fijany, Amir; Toomarian, Nikzad; Modarress, Katayoon; Spotnitz, Matthew

    2003-01-01

    Architectures that would exploit the distinct characteristics of quantum-dot cellular automata (QCA) have been proposed for digital communication networks that connect advanced digital computing circuits. In comparison with networks of wires in conventional very-large-scale integrated (VLSI) circuitry, the networks according to the proposed architectures would be more compact. The proposed architectures would make it possible to implement complex interconnection schemes that are required for some advanced parallel-computing algorithms and that are difficult (and in many cases impractical) to implement in VLSI circuitry. The difficulty of implementation in VLSI and the major potential advantage afforded by QCA were described previously in Implementing Permutation Matrices by Use of Quantum Dots (NPO-20801), NASA Tech Briefs, Vol. 25, No. 10 (October 2001), page 42. To recapitulate: Wherever two wires in a conventional VLSI circuit cross each other and are required not to be in electrical contact with each other, there must be a layer of electrical insulation between them. This, in turn, makes it necessary to resort to a noncoplanar and possibly a multilayer design, which can be complex, expensive, and even impractical. As a result, much of the cost of designing VLSI circuits is associated with minimization of data routing and assignment of layers to minimize crossing of wires. Heretofore, these considerations have impeded the development of VLSI circuitry to implement complex, advanced interconnection schemes. On the other hand, with suitable design and under suitable operating conditions, QCA-based signal paths can be allowed to cross each other in the same plane without adverse effect. In principle, this characteristic could be exploited to design compact, coplanar, simple (relative to VLSI) QCA-based networks to implement complex, advanced interconnection schemes. The proposed architectures require two advances in QCA-based circuitry beyond basic QCA-based binary

  5. Computer-aided diagnosis workstation and network system for chest diagnosis based on multislice CT images

    Science.gov (United States)

    Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Moriyama, Noriyuki; Ohmatsu, Hironobu; Masuda, Hideo; Machida, Suguru

    2008-03-01

    Mass screening based on multi-helical CT images requires a considerable number of images to be read. It is this time-consuming step that makes the use of helical CT for mass screening impractical at present. To overcome this problem, we have provided diagnostic assistance methods to medical screening specialists by developing a lung cancer screening algorithm that automatically detects suspected lung cancers in helical CT images, a coronary artery calcification screening algorithm that automatically detects suspected coronary artery calcification and a vertebra body analysis algorithm for quantitative evaluation of osteoporosis likelihood by using helical CT scanner for the lung cancer mass screening. The function to observe suspicious shadow in detail are provided in computer-aided diagnosis workstation with these screening algorithms. We also have developed the telemedicine network by using Web medical image conference system with the security improvement of images transmission, Biometric fingerprint authentication system and Biometric face authentication system. Biometric face authentication used on site of telemedicine makes "Encryption of file" and Success in login" effective. As a result, patients' private information is protected. Based on these diagnostic assistance methods, we have developed a new computer-aided workstation and a new telemedicine network that can display suspected lesions three-dimensionally in a short time. The results of this study indicate that our radiological information system without film by using computer-aided diagnosis workstation and our telemedicine network system can increase diagnostic speed, diagnostic accuracy and security improvement of medical information.

  6. Diagnosing Diagnostic Models: From Von Neumann's Elephant to Model Equivalencies and Network Psychometrics

    Science.gov (United States)

    von Davier, Matthias

    2018-01-01

    This article critically reviews how diagnostic models have been conceptualized and how they compare to other approaches used in educational measurement. In particular, certain assumptions that have been taken for granted and used as defining characteristics of diagnostic models are reviewed and it is questioned whether these assumptions are the…

  7. A Scalable Policy and SNMP Based Network Management Framework

    Institute of Scientific and Technical Information of China (English)

    LIU Su-ping; DING Yong-sheng

    2009-01-01

    Traditional SNMP-based network management can not deal with the task of managing large-scaled distributed network,while policy-based management is one of the effective solutions in network and distributed systems management. However,cross-vendor hardware compatibility is one of the limitations in policy-based management. Devices existing in current network mostly support SNMP rather than Common Open Policy Service (COPS) protocol. By analyzing traditional network management and policy-based network management, a scalable network management framework is proposed. It is combined with Internet Engineering Task Force (IETF) framework for policybased management and SNMP-based network management. By interpreting and translating policy decision to SNMP message,policy can be executed in traditional SNMP-based device.

  8. Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

    OpenAIRE

    Srinivas Kanakala; Venugopal Reddy Ananthula; Prashanthi Vempaty

    2014-01-01

    In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing proto...

  9. Improvement of malaria diagnostic system based on acridine orange staining.

    Science.gov (United States)

    Kimura, Masatsugu; Teramoto, Isao; Chan, Chim W; Idris, Zulkarnain Md; Kongere, James; Kagaya, Wataru; Kawamoto, Fumihiko; Asada, Ryoko; Isozumi, Rie; Kaneko, Akira

    2018-02-07

    Rapid diagnosis of malaria using acridine orange (AO) staining and a light microscope with a halogen lamp and interference filter was deployed in some malaria-endemic countries. However, it has not been widely adopted because: (1) the lamp was weak as an excitation light and the set-up did not work well under unstable power supply; and, (2) the staining of samples was frequently inconsistent. The halogen lamp was replaced by a low-cost, blue light-emitting diode (LED) lamp. Using a reformulated AO solution, the staining protocol was revised to make use of a concentration gradient instead of uniform staining. To evaluate this new AO diagnostic system, a pilot field study was conducted in the Lake Victoria basin in Kenya. Without staining failure, malaria infection status of about 100 samples was determined on-site per one microscopist per day, using the improved AO diagnostic system. The improved AO diagnosis had both higher overall sensitivity (46.1 vs 38.9%: p = 0.08) and specificity (99.0 vs 96.3%) than the Giemsa method (N = 1018), using PCR diagnosis as the standard. Consistent AO staining of thin blood films and rapid evaluation of malaria parasitaemia with the revised protocol produced superior results relative to the Giemsa method. This AO diagnostic system can be set up easily at low cost using an ordinary light microscope. It may supplement rapid diagnostic tests currently used in clinical settings in malaria-endemic countries, and may be considered as an inexpensive tool for case surveillance in malaria-eliminating countries.

  10. An intelligent diagnostic aid (ida) based upon the simulated and operational experience

    International Nuclear Information System (INIS)

    Zuenkov, M.; Poletykin, A.; Marsiletti, M.

    1999-01-01

    types of failures can be detected: leakages, cut off or deterioration of the characteristics of flow/pressure sources, pipeline fluid conductance disturbances, excessive passage of the medium through pipelines, defects of control systems of auxiliary mechanisms, sensor defects, damage of equipment elements. The identification of malfunctions is performed through the use of the fuzzy set logic algorithms. The hardware configuration of the prototype system is made up of a network of a Hewlett-Packard workstation and a Digital VAX-Station. The diagnosable failure were 41 items of the following types: partial reduction of pipes hydraulic resistance, increased pipes hydraulic resistance, 'on-off' behaviour of flowrate variables, pump switch off, tank leaks. The paper reports the theoretical background on which MA is based, as well as a rationale of such approach for diagnostic and some evidence of its effectiveness through an application to Sampierdarena 40 MW cogeneration plant. Finally an outline of an ongoing application to a VVER-1000 plant simulator will be given. (author)

  11. A WAO - ARIA - GA2LEN consensus document on molecular-based allergy diagnostics

    DEFF Research Database (Denmark)

    Canonica, Giorgio Walter; Ansotegui, Ignacio J; Pawankar, Ruby

    2013-01-01

    Molecular-based allergy (MA) diagnostics is an approach used to map the allergen sensitization of a patient at a molecular level, using purified natural or recombinant allergenic molecules (allergen components) instead of allergen extracts. Since its introduction, MA diagnostics has increasingly ...

  12. Fetal Implications of Diagnostic Radiation Exposure During Pregnancy: Evidence-based Recommendations.

    Science.gov (United States)

    Rimawi, Bassam H; Green, Victoria; Lindsay, Michael

    2016-06-01

    The purpose of this article is to review the fetal and long-term implications of diagnostic radiation exposure during pregnancy. Evidence-based recommendations for radiologic imaging modalities utilizing exposure of diagnostic radiation during pregnancy, including conventional screen-film mammography, digital mammography, tomosynthesis, and contrast-enhanced mammography are described.

  13. Classification of CT brain images based on deep learning networks.

    Science.gov (United States)

    Gao, Xiaohong W; Hui, Rui; Tian, Zengmin

    2017-01-01

    While computerised tomography (CT) may have been the first imaging tool to study human brain, it has not yet been implemented into clinical decision making process for diagnosis of Alzheimer's disease (AD). On the other hand, with the nature of being prevalent, inexpensive and non-invasive, CT does present diagnostic features of AD to a great extent. This study explores the significance and impact on the application of the burgeoning deep learning techniques to the task of classification of CT brain images, in particular utilising convolutional neural network (CNN), aiming at providing supplementary information for the early diagnosis of Alzheimer's disease. Towards this end, three categories of CT images (N = 285) are clustered into three groups, which are AD, lesion (e.g. tumour) and normal ageing. In addition, considering the characteristics of this collection with larger thickness along the direction of depth (z) (~3-5 mm), an advanced CNN architecture is established integrating both 2D and 3D CNN networks. The fusion of the two CNN networks is subsequently coordinated based on the average of Softmax scores obtained from both networks consolidating 2D images along spatial axial directions and 3D segmented blocks respectively. As a result, the classification accuracy rates rendered by this elaborated CNN architecture are 85.2%, 80% and 95.3% for classes of AD, lesion and normal respectively with an average of 87.6%. Additionally, this improved CNN network appears to outperform the others when in comparison with 2D version only of CNN network as well as a number of state of the art hand-crafted approaches. As a result, these approaches deliver accuracy rates in percentage of 86.3, 85.6 ± 1.10, 86.3 ± 1.04, 85.2 ± 1.60, 83.1 ± 0.35 for 2D CNN, 2D SIFT, 2D KAZE, 3D SIFT and 3D KAZE respectively. The two major contributions of the paper constitute a new 3-D approach while applying deep learning technique to extract signature information

  14. Curation-Based Network Marketing: Strategies for Network Growth and Electronic Word-of-Mouth Diffusion

    Science.gov (United States)

    Church, Earnie Mitchell, Jr.

    2013-01-01

    In the last couple of years, a new aspect of online social networking has emerged, in which the strength of social network connections is based not on social ties but mutually shared interests. This dissertation studies these "curation-based" online social networks (CBN) and their suitability for the diffusion of electronic word-of-mouth…

  15. CUFID-query: accurate network querying through random walk based network flow estimation.

    Science.gov (United States)

    Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

    2017-12-28

    Functional modules in biological networks consist of numerous biomolecules and their complicated interactions. Recent studies have shown that biomolecules in a functional module tend to have similar interaction patterns and that such modules are often conserved across biological networks of different species. As a result, such conserved functional modules can be identified through comparative analysis of biological networks. In this work, we propose a novel network querying algorithm based on the CUFID (Comparative network analysis Using the steady-state network Flow to IDentify orthologous proteins) framework combined with an efficient seed-and-extension approach. The proposed algorithm, CUFID-query, can accurately detect conserved functional modules as small subnetworks in the target network that are expected to perform similar functions to the given query functional module. The CUFID framework was recently developed for probabilistic pairwise global comparison of biological networks, and it has been applied to pairwise global network alignment, where the framework was shown to yield accurate network alignment results. In the proposed CUFID-query algorithm, we adopt the CUFID framework and extend it for local network alignment, specifically to solve network querying problems. First, in the seed selection phase, the proposed method utilizes the CUFID framework to compare the query and the target networks and to predict the probabilistic node-to-node correspondence between the networks. Next, the algorithm selects and greedily extends the seed in the target network by iteratively adding nodes that have frequent interactions with other nodes in the seed network, in a way that the conductance of the extended network is maximally reduced. Finally, CUFID-query removes irrelevant nodes from the querying results based on the personalized PageRank vector for the induced network that includes the fully extended network and its neighboring nodes. Through extensive

  16. Experimental performance evaluation of software defined networking (SDN) based data communication networks for large scale flexi-grid optical networks.

    Science.gov (United States)

    Zhao, Yongli; He, Ruiying; Chen, Haoran; Zhang, Jie; Ji, Yuefeng; Zheng, Haomian; Lin, Yi; Wang, Xinbo

    2014-04-21

    Software defined networking (SDN) has become the focus in the current information and communication technology area because of its flexibility and programmability. It has been introduced into various network scenarios, such as datacenter networks, carrier networks, and wireless networks. Optical transport network is also regarded as an important application scenario for SDN, which is adopted as the enabling technology of data communication networks (DCN) instead of general multi-protocol label switching (GMPLS). However, the practical performance of SDN based DCN for large scale optical networks, which is very important for the technology selection in the future optical network deployment, has not been evaluated up to now. In this paper we have built a large scale flexi-grid optical network testbed with 1000 virtual optical transport nodes to evaluate the performance of SDN based DCN, including network scalability, DCN bandwidth limitation, and restoration time. A series of network performance parameters including blocking probability, bandwidth utilization, average lightpath provisioning time, and failure restoration time have been demonstrated under various network environments, such as with different traffic loads and different DCN bandwidths. The demonstration in this work can be taken as a proof for the future network deployment.

  17. Modeling online social networks based on preferential linking

    International Nuclear Information System (INIS)

    Hu Hai-Bo; Chen Jun; Guo Jin-Li

    2012-01-01

    We study the phenomena of preferential linking in a large-scale evolving online social network and find that the linear preference holds for preferential creation, preferential acceptance, and preferential attachment. Based on the linear preference, we propose an analyzable model, which illustrates the mechanism of network growth and reproduces the process of network evolution. Our simulations demonstrate that the degree distribution of the network produced by the model is in good agreement with that of the real network. This work provides a possible bridge between the micro-mechanisms of network growth and the macrostructures of online social networks

  18. Networking activities in technology-based entrepreneurial teams

    DEFF Research Database (Denmark)

    Neergaard, Helle

    2005-01-01

    Based on social network theoy, this article investigates the distribution of networking roles and responsibilities in entrepreneurial founding teams. Its focus is on the team as a collection of individuals, thus allowing the research to address differences in networking patterns. It identifies six...... central networking activities and shows that not all founding team members are equally active 'networkers'. The analyses show that team members prioritize different networking activities and that one member in particular has extensive networking activities whereas other memebrs of the team are more...

  19. Shared protection based virtual network mapping in space division multiplexing optical networks

    Science.gov (United States)

    Zhang, Huibin; Wang, Wei; Zhao, Yongli; Zhang, Jie

    2018-05-01

    Space Division Multiplexing (SDM) has been introduced to improve the capacity of optical networks. In SDM optical networks, there are multiple cores/modes in each fiber link, and spectrum resources are multiplexed in both frequency and core/modes dimensions. Enabled by network virtualization technology, one SDM optical network substrate can be shared by several virtual networks operators. Similar with point-to-point connection services, virtual networks (VN) also need certain survivability to guard against network failures. Based on customers' heterogeneous requirements on the survivability of their virtual networks, this paper studies the shared protection based VN mapping problem and proposes a Minimum Free Frequency Slots (MFFS) mapping algorithm to improve spectrum efficiency. Simulation results show that the proposed algorithm can optimize SDM optical networks significantly in terms of blocking probability and spectrum utilization.

  20. Advances in molecular-based diagnostics in meeting crop biosecurity and phytosanitary issues.

    Science.gov (United States)

    Schaad, Norman W; Frederick, Reid D; Shaw, Joe; Schneider, William L; Hickson, Robert; Petrillo, Michael D; Luster, Douglas G

    2003-01-01

    Awareness of crop biosecurity and phytosanitation has been heightened since 9/11 and the unresolved anthrax releases in October 2001. Crops are highly vulnerable to accidental or deliberate introductions of crop pathogens from outside U.S. borders. Strategic thinking about protection against deliberate or accidental release of a plant pathogen is an urgent priority. Rapid detection will be the key to success. This review summarizes recent progress in the development of rapid real-time PCR protocols and evaluates their effectiveness in a proposed nationwide network of diagnostic laboratories that will facilitate rapid diagnostics and improved communication.

  1. Resilient Disaster Network Based on Software Defined Cognitive Wireless Network Technology

    Directory of Open Access Journals (Sweden)

    Goshi Sato

    2015-01-01

    Full Text Available In order to temporally recover the information network infrastructure in disaster areas from the Great East Japan Earthquake in 2011, various wireless network technologies such as satellite IP network, 3G, and Wi-Fi were effectively used. However, since those wireless networks are individually introduced and installed but not totally integrated, some of networks were congested due to the sudden network traffic generation and unbalanced traffic distribution, and eventually the total network could not effectively function. In this paper, we propose a disaster resilient network which integrates various wireless networks into a cognitive wireless network that users can use as an access network to the Internet at the serious disaster occurrence. We designed and developed the disaster resilient network based on software defined network (SDN technology to automatically select the best network link and route among the possible access networks to the Internet by periodically monitoring their network states and evaluate those using extended AHP method. In order to verify the usefulness of our proposed system, a prototype system is constructed and its performance is evaluated.

  2. Internet-Based Mobile Ad Hoc Networking (Preprint)

    National Research Council Canada - National Science Library

    Corson, M. S; Macker, Joseph P; Cirincione, Gregory H

    1999-01-01

    Internet-based Mobile Ad Hoc Networking is an emerging technology that supports self-organizing, mobile networking infrastructures, and is one which appears well-suited for use in future commercial...

  3. 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

  4. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

    This monograph describes the synthesis and use of biologically-inspired artificial hydrocarbon networks (AHNs) for approximation models associated with machine learning and a novel computational algorithm with which to exploit them. The reader is first introduced to various kinds of algorithms designed to deal with approximation problems and then, via some conventional ideas of organic chemistry, to the creation and characterization of artificial organic networks and AHNs in particular. The advantages of using organic networks are discussed with the rules to be followed to adapt the network to its objectives. Graph theory is used as the basis of the necessary formalism. Simulated and experimental examples of the use of fuzzy logic and genetic algorithms with organic neural networks are presented and a number of modeling problems suitable for treatment by AHNs are described: ·        approximation; ·        inference; ·        clustering; ·        control; ·        class...

  5. Electron Beam Diagnostics in Plasmas Based on Electron Beam Ionization

    Science.gov (United States)

    Leonhardt, Darrin; Leal-Quiros, Edbertho; Blackwell, David; Walton, Scott; Murphy, Donald; Fernsler, Richard; Meger, Robert

    2001-10-01

    Over the last few years, electron beam ionization has been shown to be a viable generator of high density plasmas with numerous applications in materials modification. To better understand these plasmas, we have fielded electron beam diagnostics to more clearly understand the propagation of the beam as it travels through the background gas and creates the plasma. These diagnostics vary greatly in sophistication, ranging from differentially pumped systems with energy selective elements to metal 'hockey pucks' covered with thin layers of insulation to electrically isolate the detector from the plasma but pass high energy beam electrons. Most importantly, absolute measurements of spatially resolved beam current densities are measured in a variety of pulsed and continuous beam sources. The energy distribution of the beam current(s) will be further discussed, through experiments incorporating various energy resolving elements such as simple grids and more sophisticated cylindrical lens geometries. The results are compared with other experiments of high energy electron beams through gases and appropriate disparities and caveats will be discussed. Finally, plasma parameters are correlated to the measured beam parameters for a more global picture of electron beam produced plasmas.

  6. EAP-Based Authentication for Ad Hoc Network

    OpenAIRE

    Bhakti, Muhammad Agni Catur; Abdullah, Azween; Jung, Low Tan

    2007-01-01

    Wireless network has been deployed worldwide, but some security issues in wireless network might haveprevented its further acceptance. One of the solutions to overcome the limitation of wireless network security isthe IEEE 802.1X specification, a mechanism for port-based network access control, which is based onExtensible Authentication Protocol (EAP). It is an authentication framework that can support multipleauthentication methods. EAP can run over many types of data-link layer and it is fl...

  7. Systematic classification and identification of noise spectra using perception-based neural networks

    Energy Technology Data Exchange (ETDEWEB)

    Racz, A.; Kiss, S. (KFKI-Atomic Energy Research Inst., Budapest (Hungary). Applied Reactor Physics Lab.)

    1994-01-01

    A general framework for the detection of gradually developing changes in a noise generating system is presented. The procedure is based on a new learning algorithm developed for neural networks with dynamically building architecture. The method has been tested by using almost a thousand noise spectra recorded from different detector types and from different detector positions. This work is part of a larger project, aimed at developing a noise diagnostic expert system. (author).

  8. High-Throughput Array Instrument for DNA-Based Breast Cancer Diagnostics

    National Research Council Canada - National Science Library

    Swerdlow, Harold

    2000-01-01

    ...) for breast-cancer diagnostics. These methods are based upon large numbers of discrete DNA spots placed on glass microscope slides typically, and hybridized to a probe derived from a tIssue or blood sample...

  9. A Laser-Based Diagnostic Suite for Hypersonic Test Facilities, Phase I

    Data.gov (United States)

    National Aeronautics and Space Administration — In this SBIR effort, Los Gatos Research (LGR) proposes to develop a suite of laser-based diagnostics for the study of reactive and non-reactive hypersonic flows....

  10. Application of artificial neural network for medical image recognition and diagnostic decision making

    International Nuclear Information System (INIS)

    Asada, N.; Eiho, S.; Doi, K.; MacMahon, H.; Montner, S.M.; Giger, M.L.

    1989-01-01

    An artificial neural network has been applied for pattern recognition and used as a tool in an expert system. The purpose of this study is to examine the potential usefulness of the neural network approach in medical applications for image recognition and decision making. The authors designed multilayer feedforward neural networks with a back-propagation algorithm for our study. Using first-pass radionuclide ventriculograms, we attempted to identify the right and left ventricles of the heart and the lungs by training the neural network from patterns of time-activity curves. In a preliminary study, the neural network enabled identification of the lungs and heart chambers once the network was trained sufficiently by means of repeated entries of data from the same case

  11. Diagnostic Value of Nineteen Different Imaging Methods for Patients with Breast Cancer: a Network Meta-Analysis

    Directory of Open Access Journals (Sweden)

    Xiao-Hong Zhang

    2018-04-01

    Full Text Available Background/Aims: We performed a network meta-analysis (NMA to investigate and compare the diagnostic value of 19 different imaging methods used for breast cancer (BC. Methods: Cochrane Library, PubMed and EMBASE were searched to collect the relevant literature from the inception of the study until November 2016. A combination of direct and indirect comparisons was performed using an NMA to evaluate the combined odd ratios (OR and draw the surface under the cumulative ranking curves (SUCRA of the diagnostic value of different imaging methods for BC. Results: A total of 39 eligible diagnostic tests regarding 19 imaging methods (mammography [MG], breast-specific gamma imaging [BSGI], color Doppler sonography [CD], contrast-enhanced magnetic resonance imaging [CE-MRI], digital breast tomosynthesis [DBT], fluorodeoxyglucose positron-emission tomography/computed tomography [FDG PET/CT], fluorodeoxyglucose positron-emission tomography [FDG-PET], full field digital mammography [FFDM], handheld breast ultrasound [HHUS], magnetic resonance imaging [MRI], automated breast volume scanner [ABUS], magnetic resonance mammography [MRM], scintimammography [SMM], single photon emission computed tomography scintimammography [SPECT SMM], ultrasound elastography [UE], ultrasonography [US], mammography + ultrasonography [MG + US], mammography + scintimammography [MG + SMM], and ultrasound elastography + ultrasonography [UE + US] were included in the study. According to this network meta-analysis, in comparison to the MG method, the CE-MRI, MRI, MRM, MG + SMM and UE + US methods exhibited relatively higher sensitivity, and the specificity of the FDG PET/CT method was higher, while the BSGI and MRI methods exhibited higher accuracy. Conclusion: The results from this NMA indicate that the diagnostic value of the BSGI, MG + SMM, MRI and CE-MRI methods for BC were relatively higher in terms of sensitivity, specificity and accuracy.

  12. Adaptive Technology Application for Vibration-Based Diagnostics of Roller Bearings on Industrial Plants

    Directory of Open Access Journals (Sweden)

    Mironov Aleksey

    2014-09-01

    Full Text Available Roller bearings are widely used in equipment of different applications; therefore, the issues related to the assessment of bearing technical state and localization of bearing faults are quite important and relevant. The reason is that technical state of a bearing is a critical component, which determines efficiency of a mechanism or equipment. For bearings inspection and diagnostics, various methods of vibration-based diagnostics are used. The adaptive technology for vibration-based diagnostics developed in „D un D centrs” is an effective tool for evaluation of technical state of bearings in operation compared to the existing SKF method.

  13. Laser-based diagnostics on NO in a diesel engine

    International Nuclear Information System (INIS)

    Brugman, T.M.

    1999-01-01

    The non-intrusive two-dimensional detection of nitric oxide (NO) in the cylinder of a diesel engine by means of laser-induced fluorescence (LIF) is the central theme of this thesis. Chapter 1 provides a general introduction including a brief discussion of the underlying environmental considerations as well as an overview of the laser-based imaging diagnostics in i.c. engines as reported in the literature. In the same chapter the LIF spectroscopy of NO is discussed in detail and the dependence of the LIF signal to several parameters is studied on the basis of a two-level rate equation model. This chapter concludes with an overview of the imaging techniques used in the experiments discussed in this thesis. The principal components of the experimental setup are described in great detail in chapter 2. Some of the issues discussed there have turned out to be crucial for the success of the experiments as reported in the subsequent chapters. In chapter 3 the results of the first imaging experiments in the idling engine are reported for two different fuels: n-heptane and standard diesel fuel. Besides the in-cylinder NO fluorescence distributions at various crank angles presented in this chapter, excitation spectra recorded from in-cylinder NO at atmospheric pressure using the respective fuels are also reported. In chapter 4 the detection method is further validated on the basis of a number of experiments in the standard-diesel -fuel -driven engine testing the various underlying assumptions. The sensitivity of the LIF signal to photo-chemically- induced effects possibly arising from the use of a high-power UV excimer laser, is investigated by means of a double-resonance experiment. The dependence of the LIF signal on the actual laser power is experimentally verified as well. The degree as to which saturation might occur at the in-cylinder laser intensities pertinent to this work, is estimated using data and relations found in the literature. Finally, in this chapter image

  14. Strategies to Enhance Online Learning Teams. Team Assessment and Diagnostics Instrument and Agent-based Modeling

    Science.gov (United States)

    2010-08-12

    Strategies to Enhance Online Learning Teams Team Assessment and Diagnostics Instrument and Agent-based Modeling Tristan E. Johnson, Ph.D. Learning ...REPORT DATE AUG 2010 2. REPORT TYPE 3. DATES COVERED 00-00-2010 to 00-00-2010 4. TITLE AND SUBTITLE Strategies to Enhance Online Learning ...TeamsTeam Strategies to Enhance Online Learning Teams: Team Assessment and Diagnostics Instrument and Agent-based Modeling 5a. CONTRACT NUMBER 5b. GRANT

  15. Body-Sensor-Network-Based Spasticity Detection.

    Science.gov (United States)

    Misgeld, Berno J E; Luken, Markus; Heitzmann, Daniel; Wolf, Sebastian I; Leonhardt, Steffen

    2016-05-01

    Spasticity is a common disorder of the skeletal muscle with a high incidence in industrialised countries. A quantitative measure of spasticity using body-worn sensors is important in order to assess rehabilitative motor training and to adjust the rehabilitative therapy accordingly. We present a new approach to spasticity detection using the Integrated Posture and Activity Network by Medit Aachen body sensor network (BSN). For this, a new electromyography (EMG) sensor node was developed and employed in human locomotion. Following an analysis of the clinical gait data of patients with unilateral cerebral palsy, a novel algorithm was developed based on the idea to detect coactivation of antagonistic muscle groups as observed in the exaggerated stretch reflex with associated joint rigidity. The algorithm applies a cross-correlation function to the EMG signals of two antagonistically working muscles and subsequent weighting using a Blackman window. The result is a coactivation index which is also weighted by the signal equivalent energy to exclude positive detection of inactive muscles. Our experimental study indicates good performance in the detection of coactive muscles associated with spasticity from clinical data as well as measurements from a BSN in qualitative comparison with the Modified Ashworth Scale as classified by clinical experts. Possible applications of the new algorithm include (but are not limited to) use in robotic sensorimotor therapy to reduce the effect of spasticity.

  16. CLASSIFICATION OF NEURAL NETWORK FOR TECHNICAL CONDITION OF TURBOFAN ENGINES BASED ON HYBRID ALGORITHM

    Directory of Open Access Journals (Sweden)

    Valentin Potapov

    2016-12-01

    Full Text Available Purpose: This work presents a method of diagnosing the technical condition of turbofan engines using hybrid neural network algorithm based on software developed for the analysis of data obtained in the aircraft life. Methods: allows the engine diagnostics with deep recognition to the structural assembly in the presence of single structural damage components of the engine running and the multifaceted damage. Results: of the optimization of neural network structure to solve the problems of evaluating technical state of the bypass turbofan engine, when used with genetic algorithms.

  17. Communication Network Architectures Based on Ethernet Passive Optical Network for Offshore Wind Power Farms

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2016-03-01

    Full Text Available Nowadays, with large-scale offshore wind power farms (WPFs becoming a reality, more efforts are needed to maintain a reliable communication network for WPF monitoring. Deployment topologies, redundancy, and network availability are the main items to enhance the communication reliability between wind turbines (WTs and control centers. Traditional communication networks for monitoring and control (i.e., supervisory control and data acquisition (SCADA systems using switched gigabit Ethernet will not be sufficient for the huge amount of data passing through the network. In this paper, the optical power budget, optical path loss, reliability, and network cost of the proposed Ethernet Passive Optical Network (EPON-based communication network for small-size offshore WPFs have been evaluated for five different network architectures. The proposed network model consists of an optical network unit device (ONU deployed on the WT side for collecting data from different internal networks. All ONUs from different WTs are connected to a central optical line terminal (OLT, placed in the control center. There are no active electronic elements used between the ONUs and the OLT, which reduces the costs and complexity of maintenance and deployment. As fiber access networks without any protection are characterized by poor reliability, three different protection schemes have been configured, explained, and discussed. Considering the cost of network components, the total implementation expense of different architectures with, or without, protection have been calculated and compared. The proposed network model can significantly contribute to the communication network architecture for next generation WPFs.

  18. Network-Aware DHT-Based P2P Systems

    Science.gov (United States)

    Fayçal, Marguerite; Serhrouchni, Ahmed

    P2P networks lay over existing IP networks and infrastructure. This chapter investigates the relation between both layers, details the motivations for network awareness in P2P systems, and elucidates the requirements P2P systems have to meet for efficient network awareness. Since new P2P systems are mostly based on DHTs, we also present and analyse DHT-based architectures. And after a brief presentation of different existing network-awareness solutions, the chapter goes on effective cooperation between P2P traffic and network providers' business agreements, and introduces emerging DHT-based P2P systems that are network aware through a semantic defined for resource sharing. These new systems ensure also a certain context-awareness. So, they are analyzed and compared before an open end on prospects of network awareness in P2P systems.

  19. Evaluation of PC-based diagnostic radiology workstations

    International Nuclear Information System (INIS)

    Pollack, T.; Brueggenwerth, G.; Kaulfuss, K.; Niederlag, W.

    2000-01-01

    Material and Methods: During February 1999 and September 1999 medical users at the hospital Dresden-Friedrichstadt Germany had tested 7 types of radiology diagnostic workstations. Two types of test methods were used: In test type 1 ergonomic and handling functions were evaluated impartial according to 78 selected user requirements. In test type 2 radiologists and radiographers (3+4) performed 23 work flow steps with a subjectively evaluation. Results: By using a progressive rating no product could fully meet the user requirements. As a result of the summary evaluation for test 1 and test 2 the following compliance rating was calculated for the different products: Rad Works (66%), Magic View (63%), ID-Report (58%), Impax 3000 (53%), Medical Workstation (52%), Pathspeed (46%) and Autorad (39%). (orig.) [de

  20. Introducing the ESAT-6 free IGRA, a companion diagnostic for TB vaccines based on ESAT-6

    DEFF Research Database (Denmark)

    Ruhwald, Morten; de Thurah, Lena; Kuchaka, Davis

    2017-01-01

    tests unspecific after vaccination. This challenge has prompted the development of a companion diagnostic for ESAT-6 based vaccines, an ESAT-6 free IGRA. We screened a panel of seven potential new diagnostic antigens not recognized in BCG vaccinated individuals. Three highly recognized antigens Esp......C, EspF and Rv2348c were identified and combined with CFP10 in an ESAT-6 free antigen cocktail. The cocktail was prepared in a field-friendly format, lyophilized with heparin in ready-to-use vacutainer tubes. The diagnostic performance of the ESAT-6 free IGRA was determined in a cross-validation study....... Compared IGRA, the ESAT-6 free IGRA induced a comparable magnitude of IFN-γ release, and the diagnostic performance was on par with Quantiferon (sensitivity 84% vs 79%; specificity 99% vs 97%). The comparable performance of the ESAT-6 free IGRA to IGRA suggests potential as companion diagnostic for ESAT-6...

  1. Strategies for adding adaptive learning mechanisms to rule-based diagnostic expert systems

    Science.gov (United States)

    Stclair, D. C.; Sabharwal, C. L.; Bond, W. E.; Hacke, Keith

    1988-01-01

    Rule-based diagnostic expert systems can be used to perform many of the diagnostic chores necessary in today's complex space systems. These expert systems typically take a set of symptoms as input and produce diagnostic advice as output. The primary objective of such expert systems is to provide accurate and comprehensive advice which can be used to help return the space system in question to nominal operation. The development and maintenance of diagnostic expert systems is time and labor intensive since the services of both knowledge engineer(s) and domain expert(s) are required. The use of adaptive learning mechanisms to increment evaluate and refine rules promises to reduce both time and labor costs associated with such systems. This paper describes the basic adaptive learning mechanisms of strengthening, weakening, generalization, discrimination, and discovery. Next basic strategies are discussed for adding these learning mechanisms to rule-based diagnostic expert systems. These strategies support the incremental evaluation and refinement of rules in the knowledge base by comparing the set of advice given by the expert system (A) with the correct diagnosis (C). Techniques are described for selecting those rules in the in the knowledge base which should participate in adaptive learning. The strategies presented may be used with a wide variety of learning algorithms. Further, these strategies are applicable to a large number of rule-based diagnostic expert systems. They may be used to provide either immediate or deferred updating of the knowledge base.

  2. Mobile Diagnostics Based on Motion? A Close Look at Motility Patterns in the Schistosome Life Cycle

    Directory of Open Access Journals (Sweden)

    Ewert Linder

    2016-06-01

    Full Text Available Imaging at high resolution and subsequent image analysis with modified mobile phones have the potential to solve problems related to microscopy-based diagnostics of parasitic infections in many endemic regions. Diagnostics using the computing power of “smartphones” is not restricted by limited expertise or limitations set by visual perception of a microscopist. Thus diagnostics currently almost exclusively dependent on recognition of morphological features of pathogenic organisms could be based on additional properties, such as motility characteristics recognizable by computer vision. Of special interest are infectious larval stages and “micro swimmers” of e.g., the schistosome life cycle, which infect the intermediate and definitive hosts, respectively. The ciliated miracidium, emerges from the excreted egg upon its contact with water. This means that for diagnostics, recognition of a swimming miracidium is equivalent to recognition of an egg. The motility pattern of miracidia could be defined by computer vision and used as a diagnostic criterion. To develop motility pattern-based diagnostics of schistosomiasis using simple imaging devices, we analyzed Paramecium as a model for the schistosome miracidium. As a model for invasive nematodes, such as strongyloids and filaria, we examined a different type of motility in the apathogenic nematode Turbatrix, the “vinegar eel.” The results of motion time and frequency analysis suggest that target motility may be expressed as specific spectrograms serving as “diagnostic fingerprints.”

  3. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  4. Computer Networks as a New Data Base.

    Science.gov (United States)

    Beals, Diane E.

    1992-01-01

    Discusses the use of communication on computer networks as a data source for psychological, social, and linguistic research. Differences between computer-mediated communication and face-to-face communication are described, the Beginning Teacher Computer Network is discussed, and examples of network conversations are appended. (28 references) (LRW)

  5. Personalized Network-Based Treatments in Oncology

    DEFF Research Database (Denmark)

    Robin, Xavier; Creixell, Pau; Radetskaya, Oxana

    2013-01-01

    Network medicine aims at unraveling cell signaling networks to propose personalized treatments for patients suffering from complex diseases. In this short review, we show the relevance of network medicine to cancer treatment by outlining the potential convergence points of the most recent technol...

  6. Target Diagnostic Instrument-Based Controls Framework for the National Ignition Facility

    International Nuclear Information System (INIS)

    Shelton, R; O'Brien, D; Nelson, J; Kamperschroer, J

    2007-01-01

    NIF target diagnostics are being developed to observe and measure the extreme physics of targets irradiated by the 192-beam laser. The response time of target materials can be on the order of 100ps--the time it takes light to travel 3 cm--temperatures more than 100 times hotter than the surface of the sun, and pressures that exceed 109 atmospheres. Optical and x-ray diagnostics were developed and fielded to observe and record the results of the first 4-beam experiments at NIF. Hard and soft x-ray spectra were measured, and time-integrated and gated x-ray images of hydrodynamics experiments were recorded. Optical diagnostics recorded backscatter from the target, and VISAR laser velocimetry measurements were taken of laser-shocked target surfaces. Additional diagnostics are being developed and commissioned to observe and diagnose ignition implosions, including various neutron and activation diagnostics. NIF's diagnostics are being developed at LLNL and with collaborators at other sites. To accommodate the growing number of target diagnostics, an Instrument-Based Controls hardware-software framework has been developed to facilitate development and ease integration into the NIF Integrated Computer Control System (ICCS). Individual WindowsXP PC controllers for each digitizer, power supply and camera (i.e., instruments) execute controls software unique to each instrument model. Each hardware-software controller manages a single instrument, in contrast to the complexity of combining all the controls software needed for a diagnostic into a single controller. Because of this simplification, controllers can be more easily tested on the actual hardware, evaluating all normal and off-normal conditions. Each target diagnostic is then supported by a number of instruments, each with its own hardware-software instrument-based controller. Advantages of the instrument-based control architecture and framework include reusability, testability, and improved reliability of the deployed

  7. Target Diagnostic Instrument-Based Controls Framework for the National Ignition Facility

    Energy Technology Data Exchange (ETDEWEB)

    Shelton, R; O' Brien, D; Nelson, J; Kamperschroer, J

    2007-05-07

    NIF target diagnostics are being developed to observe and measure the extreme physics of targets irradiated by the 192-beam laser. The response time of target materials can be on the order of 100ps--the time it takes light to travel 3 cm--temperatures more than 100 times hotter than the surface of the sun, and pressures that exceed 109 atmospheres. Optical and x-ray diagnostics were developed and fielded to observe and record the results of the first 4-beam experiments at NIF. Hard and soft x-ray spectra were measured, and time-integrated and gated x-ray images of hydrodynamics experiments were recorded. Optical diagnostics recorded backscatter from the target, and VISAR laser velocimetry measurements were taken of laser-shocked target surfaces. Additional diagnostics are being developed and commissioned to observe and diagnose ignition implosions, including various neutron and activation diagnostics. NIF's diagnostics are being developed at LLNL and with collaborators at other sites. To accommodate the growing number of target diagnostics, an Instrument-Based Controls hardware-software framework has been developed to facilitate development and ease integration into the NIF Integrated Computer Control System (ICCS). Individual WindowsXP PC controllers for each digitizer, power supply and camera (i.e., instruments) execute controls software unique to each instrument model. Each hardware-software controller manages a single instrument, in contrast to the complexity of combining all the controls software needed for a diagnostic into a single controller. Because of this simplification, controllers can be more easily tested on the actual hardware, evaluating all normal and off-normal conditions. Each target diagnostic is then supported by a number of instruments, each with its own hardware-software instrument-based controller. Advantages of the instrument-based control architecture and framework include reusability, testability, and improved reliability of the

  8. NM-Net Gigabit-based Implementation on Core Network Facilities and Network Design Hierarchy

    International Nuclear Information System (INIS)

    Raja Murzaferi Raja Moktar; Mohd Fauzi Haris; Siti Nurbahyah Hamdan

    2011-01-01

    Nuclear Malaysia computing network or NM the main backbone of internet working on operational staffs. Main network operating center or NOC is situated in Block 15 and linkup via fiber cabling to adjacent main network blocks (18, 29, 11 connections. Pre 2009 infrastructure; together to form the core networking switch. of the core network infrastructure were limited by the up link between core switches that is the Pair (UTP) Category 6 Cable. Furthermore, majority of the networking infrastructure throughout the agency were mainly built with Fast Ethernet Based specifications to date. With current research and operational tasks highly dependent on IT infrastructure that is being enabled through NM-Net, the performance NM-Net implementing gigabit-based networking system achieve optimal performance of internet networking services in the agency thus catalyze initiative. (author)

  9. Network-based automation for SMEs

    DEFF Research Database (Denmark)

    Parizi, Mohammad Shahabeddini; Radziwon, Agnieszka

    2017-01-01

    The implementation of appropriate automation concepts which increase productivity in Small and Medium Sized Enterprises (SMEs) requires a lot of effort, due to their limited resources. Therefore, it is strongly recommended for small firms to open up for the external sources of knowledge, which...... could be obtained through network interaction. Based on two extreme cases of SMEs representing low-tech industry and an in-depth analysis of their manufacturing facilities this paper presents how collaboration between firms embedded in a regional ecosystem could result in implementation of new...... with other members of the same regional ecosystem. The findings highlight two main automation related areas where manufacturing SMEs could leverage on external sources on knowledge – these are assistance in defining automation problem as well as appropriate solution and provider selection. Consequently...

  10. 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 ...

  11. Rapid tomographic reconstruction based on machine learning for time-resolved combustion diagnostics

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

    Optical tomography has attracted surged research efforts recently due to the progress in both the imaging concepts and the sensor and laser technologies. The high spatial and temporal resolutions achievable by these methods provide unprecedented opportunity for diagnosis of complicated turbulent combustion. However, due to the high data throughput and the inefficiency of the prevailing iterative methods, the tomographic reconstructions which are typically conducted off-line are computationally formidable. In this work, we propose an efficient inversion method based on a machine learning algorithm, which can extract useful information from the previous reconstructions and build efficient neural networks to serve as a surrogate model to rapidly predict the reconstructions. Extreme learning machine is cited here as an example for demonstrative purpose simply due to its ease of implementation, fast learning speed, and good generalization performance. Extensive numerical studies were performed, and the results show that the new method can dramatically reduce the computational time compared with the classical iterative methods. This technique is expected to be an alternative to existing methods when sufficient training data are available. Although this work is discussed under the context of tomographic absorption spectroscopy, we expect it to be useful also to other high speed tomographic modalities such as volumetric laser-induced fluorescence and tomographic laser-induced incandescence which have been demonstrated for combustion diagnostics.

  12. Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

    Directory of Open Access Journals (Sweden)

    Laisen Nie

    2018-01-01

    Full Text Available Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods.

  13. Analyzing the factors affecting network lifetime cluster-based wireless sensor network

    International Nuclear Information System (INIS)

    Malik, A.S.; Qureshi, A.

    2010-01-01

    Cluster-based wireless sensor networks enable the efficient utilization of the limited energy resources of the deployed sensor nodes and hence prolong the node as well as network lifetime. Low Energy Adaptive Clustering Hierarchy (Leach) is one of the most promising clustering protocol proposed for wireless sensor networks. This paper provides the energy utilization and lifetime analysis for cluster-based wireless sensor networks based upon LEACH protocol. Simulation results identify some important factors that induce unbalanced energy utilization between the sensor nodes and hence affect the network lifetime in these types of networks. These results highlight the need for a standardized, adaptive and distributed clustering technique that can increase the network lifetime by further balancing the energy utilization among sensor nodes. (author)

  14. Scaling architecture-on-demand based optical networks

    NARCIS (Netherlands)

    Meyer, Hugo; Sancho, Jose Carlos; Mrdakovic, Milica; Peng, Shuping; Simeonidou, Dimitra; Miao, Wang; Calabretta, Nicola

    2016-01-01

    This paper analyzes methodologies that allow scaling properly Architecture-On-Demand (AoD) based optical networks. As Data Centers and HPC systems are growing in size and complexity, optical networks seem to be the way to scale the bandwidth of current network infrastructures. To scale the number of

  15. Prediction based chaos control via a new neural network

    International Nuclear Information System (INIS)

    Shen Liqun; Wang Mao; Liu Wanyu; Sun Guanghui

    2008-01-01

    In this Letter, a new chaos control scheme based on chaos prediction is proposed. To perform chaos prediction, a new neural network architecture for complex nonlinear approximation is proposed. And the difficulty in building and training the neural network is also reduced. Simulation results of Logistic map and Lorenz system show the effectiveness of the proposed chaos control scheme and the proposed neural network

  16. Analysis of Computer Network Information Based on "Big Data"

    Science.gov (United States)

    Li, Tianli

    2017-11-01

    With the development of the current era, computer network and large data gradually become part of the people's life, people use the computer to provide convenience for their own life, but at the same time there are many network information problems has to pay attention. This paper analyzes the information security of computer network based on "big data" analysis, and puts forward some solutions.

  17. On Emulation-Based Network Intrusion Detection Systems

    NARCIS (Netherlands)

    Abbasi, Ali; Wetzel, Jos; Bokslag, Wouter; Zambon, Emmanuele; Etalle, Sandro

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an in- strumented environment and checking the execution traces for signs of shellcode activity.

  18. On emulation-based network intrusion detection systems

    NARCIS (Netherlands)

    Abbasi, A.; Wetzels, J.; Bokslag, W.; Zambon, E.; Etalle, S.; Stavrou, A.; Bos, H.; Portokalidis, G.

    2014-01-01

    Emulation-based network intrusion detection systems have been devised to detect the presence of shellcode in network traffic by trying to execute (portions of) the network packet payloads in an instrumented environment and checking the execution traces for signs of shellcode activity.

  19. Novel Ethernet Based Optical Local Area Networks for Computer Interconnection

    NARCIS (Netherlands)

    Radovanovic, Igor; van Etten, Wim; Taniman, R.O.; Kleinkiskamp, Ronny

    2003-01-01

    In this paper we present new optical local area networks for fiber-to-the-desk application. Presented networks are expected to bring a solution for having optical fibers all the way to computers. To bring the overall implementation costs down we have based our networks on short-wavelength optical

  20. ORGANIZATION OF CLOUD COMPUTING INFRASTRUCTURE BASED ON SDN NETWORK

    Directory of Open Access Journals (Sweden)

    Alexey A. Efimenko

    2013-01-01

    Full Text Available The article presents the main approaches to cloud computing infrastructure based on the SDN network in present data processing centers (DPC. The main indexes of management effectiveness of network infrastructure of DPC are determined. The examples of solutions for the creation of virtual network devices are provided.

  1. Arresting Strategy Based on Dynamic Criminal Networks Changing over Time

    Directory of Open Access Journals (Sweden)

    Junqing Yuan

    2013-01-01

    Full Text Available We investigate a sequence of dynamic criminal networks on a time series based on the dynamic network analysis (DNA. According to the change of networks’ structure, networks’ variation trend is analyzed to forecast its future structure. Finally, an optimal arresting time and priority list are designed based on our analysis. Better results can be expected than that based on social network analysis (SNA.

  2. Next Generation Campus Network Deployment Project Based on Softswitch

    OpenAIRE

    HU Feng; LIU Ziyan

    2011-01-01

    After analyzing the current networks of Guizhou University,we brought forward a scheme of next generation campus networks based on softswitch technology by choosing SoftX3000 switching system of HuaWei and provided the specific solution of accessing campus networks in this paper. It is proved that this scheme is feasible by using OPNET, which not only accomplished the integration of the PSTN and IP networks but also achieved the combining of voice services and data services.

  3. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  4. Towards a Diagnostic Instrument to Identify Improvement Opportunities for Quality Controlled Logistics in Agrifood Supply Chain Networks

    Directory of Open Access Journals (Sweden)

    Jack G.A.J. van der Vorst

    2011-10-01

    Full Text Available  Western-European consumers have become not only more demanding on product availability in retail outlets but also on other food attributes such as quality, integrity, and safety. When (redesigning food supply-chain networks, from a logistics point of view, one has to consider these demands next to traditional efficiency and responsiveness requirements. The concept ‘quality controlled logistics’ (QCL hypothesizes that if product quality in each step of the supply chain can be predicted in advance, goods flows can be controlled in a pro-active manner and better chain designs can be established resulting in higher product availability, constant quality, and less product losses. The paper discusses opportunities of using real-time product quality information for improvement of the design and management of ‘AgriFood Supply Chain Networks’, and presents a preliminary diagnostic instrument for assessment of ‘critical quality’ and ‘logistics control’ points in the supply chain network. Results of a tomato-chain case illustrate the added value of the QCL concept for identifying improvement opportunities in the supply chain as to increase both product availability and quality. Future research aims for the further development of the diagnostic instrument and the quantification of costs and benefits of QCL scenarios.

  5. Community Based Networks and 5G Wi-Fi

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2018-01-01

    This paper argues on why Community Based Networks should be recognized as potential 5G providers using 5G Wi-Fi. The argument is hinged on findings in a research to understand why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out...... inductively using Grounded Theory. Six cases were investigated. Two Community Based Network Mobilization Models were identified. The findings indicate that 5G Wi-Fi deployment by Community Based Networks is possible if policy initiatives and the 5G Wi-Fi standards are developed to facilitate the causal...

  6. A pilot study on diagnostic sensor networks for structure health monitoring.

    Science.gov (United States)

    2013-08-01

    The proposal was submitted in an effort to obtain some preliminary results on using sensor networks for real-time structure health : monitoring. The proposed work has twofold: to develop and validate an elective algorithm for the diagnosis of coupled...

  7. The Network Structure of Symptoms of the Diagnostic and Statistical Manual of Mental Disorders

    NARCIS (Netherlands)

    Boschloo, Lynn; van Borkulo, Claudia D.; Rhemtulla, Mijke; Keyes, Katherine M.; Borsboom, Denny; Schoevers, Robert A.

    2015-01-01

    Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from

  8. The network structure of symptoms of the diagnostic and statistical manual of mental disorders

    NARCIS (Netherlands)

    Boschloo, L.; van Borkulo, C.D.; Rhemtulla, M.; Keyes, K.M.; Borsboom, D.; Schoevers, R.A.

    2015-01-01

    Although current classification systems have greatly contributed to the reliability of psychiatric diagnoses, they ignore the unique role of individual symptoms and, consequently, potentially important information is lost. The network approach, in contrast, assumes that psychopathology results from

  9. An Improved Car-Following Model in Vehicle Networking Based on Network Control

    Directory of Open Access Journals (Sweden)

    D. Y. Kong

    2014-01-01

    Full Text Available Vehicle networking is a system to realize information interoperability between vehicles and people, vehicles and roads, vehicles and vehicles, and cars and transport facilities, through the network information exchange, in order to achieve the effective monitoring of the vehicle and traffic flow. Realizing information interoperability between vehicles and vehicles, which can affect the traffic flow, is an important application of network control system (NCS. In this paper, a car-following model using vehicle networking theory is established, based on network control principle. The car-following model, which is an improvement of the traditional traffic model, describes the traffic in vehicle networking condition. The impact that vehicle networking has on the traffic flow is quantitatively assessed in a particular scene of one-way, no lane changing highway. The examples show that the capacity of the road is effectively enhanced by using vehicle networking.

  10. Virtual network embedding in cross-domain network based on topology and resource attributes

    Science.gov (United States)

    Zhu, Lei; Zhang, Zhizhong; Feng, Linlin; Liu, Lilan

    2018-03-01

    Aiming at the network architecture ossification and the diversity of access technologies issues, this paper researches the cross-domain virtual network embedding algorithm. By analysing the topological attribute from the local and global perspective of nodes in the virtual network and the physical network, combined with the local network resource property, we rank the embedding priority of the nodes with PCA and TOPSIS methods. Besides, the link load distribution is considered. Above all, We proposed an cross-domain virtual network embedding algorithm based on topology and resource attributes. The simulation results depicts that our algorithm increases the acceptance rate of multi-domain virtual network requests, compared with the existing virtual network embedding algorithm.

  11. Cascade-based attacks on complex networks

    Science.gov (United States)

    Motter, Adilson E.; Lai, Ying-Cheng

    2002-12-01

    We live in a modern world supported by large, complex networks. Examples range from financial markets to communication and transportation systems. In many realistic situations the flow of physical quantities in the network, as characterized by the loads on nodes, is important. We show that for such networks where loads can redistribute among the nodes, intentional attacks can lead to a cascade of overload failures, which can in turn cause the entire or a substantial part of the network to collapse. This is relevant for real-world networks that possess a highly heterogeneous distribution of loads, such as the Internet and power grids. We demonstrate that the heterogeneity of these networks makes them particularly vulnerable to attacks in that a large-scale cascade may be triggered by disabling a single key node. This brings obvious concerns on the security of such systems.

  12. Current and future developments in nucleic acid-based diagnostics

    International Nuclear Information System (INIS)

    Viljoen, G.J.; Romito, M.; Kara, P.D.

    2005-01-01

    The detection and characterization of specific nucleic acids of medico-veterinary pathogens have proven invaluable for diagnostic purposes. Apart from hybridization and sequencing techniques, polymerase chain reaction (PCR) and numerous other methods have contributed significantly to this process. The integration of amplification and signal detection systems, including on-line real-time devices, have increased speed and sensitivity and greatly facilitated the quantification of target nucleic acids. They have also allowed for sequence characterization using melting or hybridization curves. Rugged portable real-time instruments for field use and robotic devices for processing samples are already available commercially. Various stem-loop DNA probes have been designed to have greater specificity for target recognition during real-time PCR. Various DNA fingerprinting techniques or post amplification sequencing are used to type pathogenic strains. Characterization according to DNA sequence is becoming more readily available as automated sequencers become more widely used. Reverse hybridization and to a greater degree DNA micro-arrays, are being used for genotyping related organisms and can allow for the detection of a large variety of different pathogens simultaneously. Non-radioactive labelling of DNA, especially using fluorophores and the principles of fluorescence resonance energy transfer, is now widely used, especially in real-time detection devices. Other detection methods include the use of surface plasmon resonance and MALDI-TOF mass spectrometry. In addition to these technological advances, contributions by bioinformatics and the description of unique signatures of DNA sequences from pathogens will contribute to the development of further assays for monitoring presence of pathogens. An important goal will be the development of robust devices capable of sensitively and specifically detecting a broad spectrum of pathogens that will be applicable for point

  13. Interlaboratory diagnostic accuracy of a Salmonella specific PCR-based method

    DEFF Research Database (Denmark)

    Malorny, B.; Hoorfar, Jeffrey; Hugas, M.

    2003-01-01

    A collaborative study involving four European laboratories was conducted to investigate the diagnostic accuracy of a Salmonella specific PCR-based method, which was evaluated within the European FOOD-PCR project (http://www.pcr.dk). Each laboratory analysed by the PCR a set of independent obtained...... presumably naturally contaminated samples and compared the results with the microbiological culture method. The PCR-based method comprised a preenrichment step in buffered peptone water followed by a thermal cell lysis using a closed tube resin-based method. Artificially contaminated minced beef and whole......-based diagnostic methods and is currently proposed as international standard document....

  14. Advanced DNA-Based Point-of-Care Diagnostic Methods for Plant Diseases Detection

    OpenAIRE

    Lau, Han Yih; Botella, Jose R.

    2017-01-01

    Diagnostic technologies for the detection of plant pathogens with point-of-care capability and high multiplexing ability are an essential tool in the fight to reduce the large agricultural production losses caused by plant diseases. The main desirable characteristics for such diagnostic assays are high specificity, sensitivity, reproducibility, quickness, cost efficiency and high-throughput multiplex detection capability. This article describes and discusses various DNA-based point-of care di...

  15. Network-based production quality control

    Science.gov (United States)

    Kwon, Yongjin; Tseng, Bill; Chiou, Richard

    2007-09-01

    This study investigates the feasibility of remote quality control using a host of advanced automation equipment with Internet accessibility. Recent emphasis on product quality and reduction of waste stems from the dynamic, globalized and customer-driven market, which brings opportunities and threats to companies, depending on the response speed and production strategies. The current trends in industry also include a wide spread of distributed manufacturing systems, where design, production, and management facilities are geographically dispersed. This situation mandates not only the accessibility to remotely located production equipment for monitoring and control, but efficient means of responding to changing environment to counter process variations and diverse customer demands. To compete under such an environment, companies are striving to achieve 100%, sensor-based, automated inspection for zero-defect manufacturing. In this study, the Internet-based quality control scheme is referred to as "E-Quality for Manufacturing" or "EQM" for short. By its definition, EQM refers to a holistic approach to design and to embed efficient quality control functions in the context of network integrated manufacturing systems. Such system let designers located far away from the production facility to monitor, control and adjust the quality inspection processes as production design evolves.

  16. Connecting Land-Based Networks to Ships

    Science.gov (United States)

    2013-06-01

    multipoint wireless broadband systems, and WiMAX networks were initially deployed for fixed and nomadic (portable) applications. These standards...CAPABILITIES OF SHIP-TO-SHORE COMMUNICATIONS A. US Navy Automated Digital Network System (ADNS) The U.S. Navy’s Automated Digital Network System (ADNS...submit digitally any necessary documents to the terminal operators, contact their logistics providers, access tidal information and receive

  17. A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

    Science.gov (United States)

    Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

    2018-02-01

    Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

  18. A Secure Network Coding-based Data Gathering Model and Its Protocol in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Qian Xiao

    2012-09-01

    Full Text Available To provide security for data gathering based on network coding in wireless sensor networks (WSNs, a secure network coding-based data gathering model is proposed, and a data-privacy preserving and pollution preventing (DPPaamp;PP protocol using network coding is designed. DPPaamp;PP makes use of a new proposed pollution symbol selection and pollution (PSSP scheme based on a new obfuscation idea to pollute existing symbols. Analyses of DPPaamp;PP show that it not only requires low overhead on computation and communication, but also provides high security on resisting brute-force attacks.

  19. Carrier ethernet network control plane based on the Next Generation Network

    DEFF Research Database (Denmark)

    Fu, Rong; Wang, Yanmeng; Berger, Michael Stubert

    2008-01-01

    This paper contributes on presenting a step towards the realization of Carrier Ethernet control plane based on the next generation network (NGN). Specifically, transport MPLS (T-MPLS) is taken as the transport technology in Carrier Ethernet. It begins with providing an overview of the evolving...... architecture of the next generation network (NGN). As an essential candidate among the NGN transport technologies, the definition of Carrier Ethernet (CE) is also introduced here. The second part of this paper depicts the contribution on the T-MPLS based Carrier Ethernet network with control plane based on NGN...... at illustrating the improvement of the Carrier Ethernet network with the NGN control plane....

  20. Policy-based Network Management in Home Area Networks: Interim Test Results

    OpenAIRE

    Ibrahim Rana, Annie; Ó Foghlú, Mícheál

    2009-01-01

    This paper argues that Home Area Networks (HANs) are a good candidate for advanced network management automation techniques, such as Policy-Based Network Management (PBNM). What is proposed is a simple use of policy based network management to introduce some level of Quality of Service (QoS) and Security management in the HAN, whilst hiding this complexity from the home user. In this paper we have presented the interim test results of our research experiments (based on a scenario) using the H...

  1. Investigation of neural network paradigms for the development of automatic noise diagnostic/reactor surveillance systems

    International Nuclear Information System (INIS)

    Korsah, K.; Uhrig, R.E.

    1991-01-01

    The use of artificial intelligence (AI) techniques as an aid in the maintenance and operation of nuclear power plant systems has been recognized for the past several years, and several applications using expert systems technology currently exist. The authors investigated the backpropagation paradigm for the recognition of neutron noise power spectral density (PSD) signatures as a possible alternative to current methods based on statistical techniques. The goal is to advance the state of the art in the application of noise analysis techniques to monitor nuclear reactor internals. Continuous surveillance of reactor systems for structural degradation can be quite cost-effective because (1) the loss of mechanical integrity of the reactor internal components can be detected at an early stage before severe damage occurs, (2) unnecessary periodic maintenance can be avoided, (3) plant downtime can be reduced to a minimum, (4) a high level of plant safety can be maintained, and (5) it can be used to help justify the extension of a plant's operating license. The initial objectives were to use neutron noise PSD data from a pressurized water reactor, acquired over a period of ∼2 years by the Oak Ridge National Laboratory (ORNL) Power Spectral Density RECognition (PSDREC) system to develop networks that can (1) differentiate between normal neutron spectral data and anomalous spectral data (e.g., malfunctioning instrumentation); and (2) detect significant shifts in the positions of spectral resonances while reducing the effect of small, random shifts (in neutron noise analysis, shifts in the resonance(s) present in a neutron PSD spectrum are the primary means for diagnosing degradation of reactor internals). 11 refs, 8 figs

  2. What's in a Name? Health Care Providers' Perceptions of Pediatric Pain Patients Based on Diagnostic Labels.

    Science.gov (United States)

    Betsch, Taylor A; Gorodzinsky, Ayala Y; Finley, G A; Sangster, Michael; Chorney, Jill

    2017-08-01

    Diagnostic labels can help patients better understand their symptoms and can influence providers' treatment planning and patient interactions. Recurrent pain is common in childhood; however, there are various diagnostic labels used. The objective of this study was to evaluate the influence of diagnostic labels on pediatric health care providers' perceptions of pediatric chronic pain patients. Using an online survey, providers were randomly assigned to 1 of 2 vignette conditions (differing only in diagnostic label provided) and completed questionnaires about their perceptions of the vignette patient. Responses from 58 participants were analyzed. The 2 groups, based on diagnostic conditions used (fibromyalgia and chronic widespread pain) did not differ significantly on general demographics and health care providers' perceptions of the patient. Perceived origin of the pain influenced providers' perceptions; pain of a perceived medical origin was negatively correlated with stigmatization and positively correlated with sympathy. Perceived psychological origin was positively correlated with stigmatization and providers' age. Health care providers' perceptions of children's pain are more likely influenced by the presumed etiology rather than the diagnostic label used. Pain believed to be more medically based was associated with more positive reactions from providers (ie, less stigmatization). Older providers in particular perceived the patient more negatively if they believe the pain to be psychologically based. The findings of this pediatric study replicated findings from adult literature on chronic pain, suggesting that children and adults are subject to negative perceptions from health care providers when the providers believe the pain to be psychological in origin.

  3. Automated extraction of knowledge for model-based diagnostics

    Science.gov (United States)

    Gonzalez, Avelino J.; Myler, Harley R.; Towhidnejad, Massood; Mckenzie, Frederic D.; Kladke, Robin R.

    1990-01-01

    The concept of accessing computer aided design (CAD) design databases and extracting a process model automatically is investigated as a possible source for the generation of knowledge bases for model-based reasoning systems. The resulting system, referred to as automated knowledge generation (AKG), uses an object-oriented programming structure and constraint techniques as well as internal database of component descriptions to generate a frame-based structure that describes the model. The procedure has been designed to be general enough to be easily coupled to CAD systems that feature a database capable of providing label and connectivity data from the drawn system. The AKG system is capable of defining knowledge bases in formats required by various model-based reasoning tools.

  4. Cost Implications of Value-Based Pricing for Companion Diagnostic Tests in Precision Medicine.

    Science.gov (United States)

    Zaric, Gregory S

    2016-07-01

    Many interpretations of personalized medicine, also referred to as precision medicine, include discussions of companion diagnostic tests that allow drugs to be targeted to those individuals who are most likely to benefit or that allow treatment to be designed in a way such that individuals who are unlikely to benefit do not receive treatment. Many authors have commented on the clinical and competitive implications of companion diagnostics, but there has been relatively little formal analysis of the cost implications of companion diagnostics, although cost reduction is often cited as a significant benefit of precision medicine. We investigate the potential impact on costs of precision medicine implemented through the use of companion diagnostics. We develop a framework in which the costs of companion diagnostic tests are determined by considerations of profit maximization and cost effectiveness. We analyze four scenarios that are defined by the incremental cost-effectiveness ratio of the new drug in the absence of a companion diagnostic test. We find that, in most scenarios, precision medicine strategies based on companion diagnostics should be expected to lead to increases in costs in the short term and that costs would fall only in a limited number of situations.

  5. Complex networks-based energy-efficient evolution model for wireless sensor networks

    Energy Technology Data Exchange (ETDEWEB)

    Zhu Hailin [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China)], E-mail: zhuhailin19@gmail.com; Luo Hong [Beijing Key Laboratory of Intelligent Telecommunications Software and Multimedia, Beijing University of Posts and Telecommunications, P.O. Box 106, Beijing 100876 (China); Peng Haipeng; Li Lixiang; Luo Qun [Information Secure Center, State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, P.O. Box 145, Beijing 100876 (China)

    2009-08-30

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  6. Complex networks-based energy-efficient evolution model for wireless sensor networks

    International Nuclear Information System (INIS)

    Zhu Hailin; Luo Hong; Peng Haipeng; Li Lixiang; Luo Qun

    2009-01-01

    Based on complex networks theory, we present two self-organized energy-efficient models for wireless sensor networks in this paper. The first model constructs the wireless sensor networks according to the connectivity and remaining energy of each sensor node, thus it can produce scale-free networks which have a performance of random error tolerance. In the second model, we not only consider the remaining energy, but also introduce the constraint of links to each node. This model can make the energy consumption of the whole network more balanced. Finally, we present the numerical experiments of the two models.

  7. Design and development of AXUV-based soft X-ray diagnostic camera for Aditya Tokamak

    International Nuclear Information System (INIS)

    Raval, Jayesh V.; Purohit, Shishir; Joisa, Y. Shankara

    2015-01-01

    The hot tokamak plasma emits Soft X-rays (SXR) in accordance with the temperature and density which are important to be studied. A silicon photo diode array (AXUV16ELG, Opto-diode, USA) based prototype SXR diagnostics is designed and developed for ADITYA tokamak for the study of SXR radial intensity profile, internal disruption (Saw-tooth crash), MHD instabilities. The diagnostic is having an array of 16 detector of millimeter dimension in a linear configuration. Absolute Extreme Ultra Violate (AXUV) detector offers compact size, improved time response with considerably good quantum efficiency in the soft X-ray range (200 eV to 10 keV). The diagnostic is designed in competence with the ADITYA tokamak protocol. The diagnostic design geometry allows detector view the plasma through a slot hole (0.5 cm X 0.05 cm), 10 μm Beryllium foil filter window, cutting off energies below 750 eV. The diagnostic was installed on Aditya vacuum vessel at radial port no 7 enabling the diagnostics to view the core plasma. The spatial resolution designed for diagnostic configuration is 1.3 cm at plasma centre. The signal generated from SXR detector is acquired with a dedicated single board computer based data acquisition system at 50 kHz. The diagnostic took observation for the ohmically heated plasma. The data was then processed to construct spatial and temporal profile of SXR intensity for Aditya plasma. This information was complimentary to the Silicon surface barrier detector (SBD) based array for the same plasma discharge. The cross calibration between the two was considerably satisfactory under the assumptions considered. (author)

  8. The guitar chord-generating algorithm based on complex network

    Science.gov (United States)

    Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

    2016-02-01

    This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

  9. Dynamics of subway networks based on vehicles operation timetable

    Science.gov (United States)

    Xiao, Xue-mei; Jia, Li-min; Wang, Yan-hui

    2017-05-01

    In this paper, a subway network is represented as a dynamic, directed and weighted graph, in which vertices represent subway stations and weights of edges represent the number of vehicles passing through the edges by considering vehicles operation timetable. Meanwhile the definitions of static and dynamic metrics which can represent vertices' and edges' local and global attributes are proposed. Based on the model and metrics, standard deviation is further introduced to study the dynamic properties (heterogeneity and vulnerability) of subway networks. Through a detailed analysis of the Beijing subway network, we conclude that with the existing network structure, the heterogeneity and vulnerability of the Beijing subway network varies over time when the vehicle operation timetable is taken into consideration, and the distribution of edge weights affects the performance of the network. In other words, although the vehicles operation timetable is restrained by the physical structure of the network, it determines the performances and properties of the Beijing subway network.

  10. Network-Based Community Brings forth Sustainable Society

    Science.gov (United States)

    Kikuchi, Toshiko

    It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

  11. Diagnostic system based on condition turbogenerator Petri nets

    International Nuclear Information System (INIS)

    Kachur, S.A.; Shakhova, N.V.

    2016-01-01

    A stochastic model of the automated monitoring systems and process control turbine generator based on Petri nets, allowing to detect local changes in the state of the stator windings of turbogenerator, is presented in the paper [ru

  12. Understanding Event-based Business Networks

    OpenAIRE

    2008-01-01

    Abstract This article deals with the temporality in business networks. Marketing as networks approach stresses interaction processes and interdependence among actors noting that business markets are mainly socially constructed. The approach has increased our understanding of business marketing but further attention for theory development and empirical validation is needed. Theoretical foundations of the approach are conceptually analysed here, taking time and timing into particular...

  13. Quantum networks based on spins in diamond

    International Nuclear Information System (INIS)

    Ronald Hanson

    2014-01-01

    Entanglement of spatially separated objects is one of the most intriguing phenomena that can occur in physics. Besides being of fundamental interest, entanglement is also a valuable resource in quantum information technology enabling secure quantum communication networks and distributed quantum computing. Here we present our most recent results towards the realization of scalable quantum networks with solid-state qubits. (author)

  14. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

    Roč. 10, č. 3 (2000), s. 417-428 ISSN 1210-0552. [Neural Network World 2000. Prague, 09.07.2000-12.07.2000] Grant - others:MŠMT ČR(CZ) VS96047; MPO(CZ) RP-4210 Institutional research plan: AV0Z1030915 Keywords : neural network * classifier * hyperspheres * big -dimensional data Subject RIV: BA - General Mathematics

  15. Building Trust-Based Sustainable Networks

    Science.gov (United States)

    2013-06-05

    entities to build sustainable networks with limited resources or misbehaving entities by learning from the lessons in the social sciences. We discuss...their individuality); and ■ Misbehaving nodes in terms of environmental, economic, and social perspectives. The sustainable network concerns...equitable access to particular services which are otherwise abused by misbehaving or malicious users. Such approaches provide a fair and

  16. Electrodiffusion Diagnostics of the Flow and Mass Transfer inside a Network of Crossing Minichannels.

    Czech Academy of Sciences Publication Activity Database

    Huchet, F.; Comiti, J.; Tihon, Jaroslav; Montillet, A.; Legentilhomme, P.

    2007-01-01

    Roč. 37, 1 (2007) , s. 49-55 ISSN 0021-891X R&D Projects: GA ČR(CZ) GA101/04/0745 Grant - others:HPMT(XE) CT/2000/00074 Institutional research plan: CEZ:AV0Z40720504 Keywords : flow diagnostics * crossing minichannels * electrodiffusion method Subject RIV: CI - Industrial Chemistry, Chemical Engineering Impact factor: 1.417, year: 2007

  17. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  18. Network Anomaly Detection Based on Wavelet Analysis

    Science.gov (United States)

    Lu, Wei; Ghorbani, Ali A.

    2008-12-01

    Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  19. Optimization-based topology identification of complex networks

    International Nuclear Information System (INIS)

    Tang Sheng-Xue; Chen Li; He Yi-Gang

    2011-01-01

    In many cases, the topological structures of a complex network are unknown or uncertain, and it is of significance to identify the exact topological structure. An optimization-based method of identifying the topological structure of a complex network is proposed in this paper. Identification of the exact network topological structure is converted into a minimal optimization problem by using the estimated network. Then, an improved quantum-behaved particle swarm optimization algorithm is used to solve the optimization problem. Compared with the previous adaptive synchronization-based method, the proposed method is simple and effective and is particularly valid to identify the topological structure of synchronization complex networks. In some cases where the states of a complex network are only partially observable, the exact topological structure of a network can also be identified by using the proposed method. Finally, numerical simulations are provided to show the effectiveness of the proposed method. (general)

  20. Evidence That Calls-Based and Mobility Networks Are Isomorphic.

    Directory of Open Access Journals (Sweden)

    Michele Coscia

    Full Text Available Social relations involve both face-to-face interaction as well as telecommunications. We can observe the geography of phone calls and of the mobility of cell phones in space. These two phenomena can be described as networks of connections between different points in space. We use a dataset that includes billions of phone calls made in Colombia during a six-month period. We draw the two networks and find that the call-based network resembles a higher order aggregation of the mobility network and that both are isomorphic except for a higher spatial decay coefficient of the mobility network relative to the call-based network: when we discount distance effects on the call connections with the same decay observed for mobility connections, the two networks are virtually indistinguishable.

  1. On Determining if Tree-based Networks Contain Fixed Trees.

    Science.gov (United States)

    Anaya, Maria; Anipchenko-Ulaj, Olga; Ashfaq, Aisha; Chiu, Joyce; Kaiser, Mahedi; Ohsawa, Max Shoji; Owen, Megan; Pavlechko, Ella; St John, Katherine; Suleria, Shivam; Thompson, Keith; Yap, Corrine

    2016-05-01

    We address an open question of Francis and Steel about phylogenetic networks and trees. They give a polynomial time algorithm to decide if a phylogenetic network, N, is tree-based and pose the problem: given a fixed tree T and network N, is N based on T? We show that it is [Formula: see text]-hard to decide, by reduction from 3-Dimensional Matching (3DM) and further that the problem is fixed-parameter tractable.

  2. Product qualification: a barrier to point-of-care microfluidic-based diagnostics?

    Science.gov (United States)

    Tantra, Ratna; van Heeren, Henne

    2013-06-21

    One of the most exciting applications of microfluidics-based diagnostics is its potential use in next generation point-of-care (POC) devices. Many prototypes are already in existence, but, as of yet, few have achieved commercialisation. In this article, we consider the issue surrounding product qualification as a potential barrier to market success. The study discusses, in the context of POC microfluidics-based diagnostics, what the generic issues are and potential solutions. Our findings underline the need for a community-based effort that is necessary to speed up the product qualification process.

  3. DIVA and DIAPO: two diagnostic knowledge based systems used for French nuclear power plants

    International Nuclear Information System (INIS)

    Porcheron, M.; Ricard, B.; Joussellin, A.

    1997-01-01

    In order to improve monitoring and diagnosis capabilities in nuclear power plants, Electricite de France (EDF) has designed an integrated monitoring and diagnosis assistance system: PSAD-Poste de Surveillance et d'Aide au Diagnostic. The development of such a sophisticated monitoring and data processing systems has emphasized the need for the addition of analysis and diagnosis assistance capabilities. Therefore, diagnostic knowledge based systems have been added to the functions monitored in PSAD: DIVA for turbine generators, and DIAPO for reactor coolant pumps. These systems were designed from a representation of the diagnostic reasoning process of experts and of the supporting knowledge. Diagnosis in both systems relies on an abduction reasoning process applied to component fault models and observations derived from their actual behavior, as provided by the monitoring functions. The basic theoretical elements of this diagnostic model are summarized in a first part. In a second part, DIVA and DIAPO specific elements are described

  4. Smart Home System Based on GSM Network

    Directory of Open Access Journals (Sweden)

    Bakhtiar Ali Karim

    2018-04-01

    Full Text Available Due to increasing robbery and intrusion, establishing home-security system has become a correlated part of the modern houses, buildings, and offices. As the family members are not at home all the time, the traditional home security system, which makes alarm sound only, may not be efficient enough. Alternatively, Global System for Mobile communications (GSM based security system can provide higher level of security and convenience compared to the traditionally used systems. The main objective of the current paper is to design and implement cost-efficient and reliable security, safety and home automation system for protection and occupants’ convenience. If any undesired events, such as intrusion, gas leakage and fire occurs in the house, our system warns the homeowner in real-time using Short Message Service (SMS. With the proposed system home appliances can also be controlled in three ways, namely sending SMS from the authorized numbers to the system through GSM network, smartphone app using Bluetooth module and infrared (IR control using IR module

  5. An Embedded Rule-Based Diagnostic Expert System in Ada

    Science.gov (United States)

    Jones, Robert E.; Liberman, Eugene M.

    1992-01-01

    Ada is becoming an increasingly popular programming language for large Government-funded software projects. Ada with it portability, transportability, and maintainability lends itself well to today's complex programming environment. In addition, expert systems have also assumed a growing role in providing human-like reasoning capability expertise for computer systems. The integration is discussed of expert system technology with Ada programming language, especially a rule-based expert system using an ART-Ada (Automated Reasoning Tool for Ada) system shell. NASA Lewis was chosen as a beta test site for ART-Ada. The test was conducted by implementing the existing Autonomous Power EXpert System (APEX), a Lisp-based power expert system, in ART-Ada. Three components, the rule-based expert systems, a graphics user interface, and communications software make up SMART-Ada (Systems fault Management with ART-Ada). The rules were written in the ART-Ada development environment and converted to Ada source code. The graphics interface was developed with the Transportable Application Environment (TAE) Plus, which generates Ada source code to control graphics images. SMART-Ada communicates with a remote host to obtain either simulated or real data. The Ada source code generated with ART-Ada, TAE Plus, and communications code was incorporated into an Ada expert system that reads the data from a power distribution test bed, applies the rule to determine a fault, if one exists, and graphically displays it on the screen. The main objective, to conduct a beta test on the ART-Ada rule-based expert system shell, was achieved. The system is operational. New Ada tools will assist in future successful projects. ART-Ada is one such tool and is a viable alternative to the straight Ada code when an application requires a rule-based or knowledge-based approach.

  6. Development of a Real-Time Thermal Performance Diagnostic Monitoring system Using Self-Organizing Neural Network for Kori-2 Nuclear Power Unit

    International Nuclear Information System (INIS)

    Kang, Hyun Gook; Seong, Poong Hyun

    1996-01-01

    In this work, a PC-based thermal performance monitoring system is developed for the nuclear power plants. the system performs real-time thermal performance monitoring and diagnosis during plant operation. Specifically, a prototype for the Kori-2 nuclear power unit is developed and examined is very difficult because the system structure is highly complex and the components are very much inter-related. In this study, some major diagnostic performance parameters are selected in order to represent the thermal cycle effectively and to reduce the computing time. The Fuzzy ARTMAP, a self-organizing neural network, is used to recognize the characteristic pattern change of the performance parameters in abnormal situation. By examination, the algorithm is shown to be ale to detect abnormality and to identify the fault component or the change of system operation condition successfully. For the convenience of operators, a graphical user interface is also constructed in this work. 5 figs., 3 tabs., 11 refs. (Author)

  7. Stabilization of model-based networked control systems

    Energy Technology Data Exchange (ETDEWEB)

    Miranda, Francisco [CIDMA, Universidade de Aveiro, Aveiro (Portugal); Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); Abreu, Carlos [Instituto Politécnico de Viana do Castelo, Viana do Castelo (Portugal); CMEMS-UMINHO, Universidade do Minho, Braga (Portugal); Mendes, Paulo M. [CMEMS-UMINHO, Universidade do Minho, Braga (Portugal)

    2016-06-08

    A class of networked control systems called Model-Based Networked Control Systems (MB-NCSs) is considered. Stabilization of MB-NCSs is studied using feedback controls and simulation of stabilization for different feedbacks is made with the purpose to reduce the network trafic. The feedback control input is applied in a compensated model of the plant that approximates the plant dynamics and stabilizes the plant even under slow network conditions. Conditions for global exponential stabilizability and for the choosing of a feedback control input for a given constant time between the information moments of the network are derived. An optimal control problem to obtain an optimal feedback control is also presented.

  8. A Spectrum Handoff Scheme for Optimal Network Selection in NEMO Based Cognitive Radio Vehicular Networks

    Directory of Open Access Journals (Sweden)

    Krishan Kumar

    2017-01-01

    Full Text Available When a mobile network changes its point of attachments in Cognitive Radio (CR vehicular networks, the Mobile Router (MR requires spectrum handoff. Network Mobility (NEMO in CR vehicular networks is concerned with the management of this movement. In future NEMO based CR vehicular networks deployment, multiple radio access networks may coexist in the overlapping areas having different characteristics in terms of multiple attributes. The CR vehicular node may have the capability to make call for two or more types of nonsafety services such as voice, video, and best effort simultaneously. Hence, it becomes difficult for MR to select optimal network for the spectrum handoff. This can be done by performing spectrum handoff using Multiple Attributes Decision Making (MADM methods which is the objective of the paper. The MADM methods such as grey relational analysis and cost based methods are used. The application of MADM methods provides wider and optimum choice among the available networks with quality of service. Numerical results reveal that the proposed scheme is effective for spectrum handoff decision for optimal network selection with reduced complexity in NEMO based CR vehicular networks.

  9. Music-based Autism Diagnostics (MUSAD) - A newly developed diagnostic measure for adults with intellectual developmental disabilities suspected of autism.

    Science.gov (United States)

    Bergmann, Thomas; Sappok, Tanja; Diefenbacher, Albert; Dames, Sibylle; Heinrich, Manuel; Ziegler, Matthias; Dziobek, Isabel

    2015-01-01

    The MUSAD was developed as a diagnostic observational instrument in an interactional music framework. It is based on the ICD-10/DSM-5 criteria for autism spectrum disorder (ASD) and was designed to assess adults on a lower level of functioning, including individuals with severe language impairments. This study aimed to evaluate the psychometric properties of the newly developed instrument. Calculations were based on a consecutive clinical sample of N=76 adults with intellectual and developmental disabilities (IDD) suspected of ASD. Objectivity, test-retest reliability, and construct validity were calculated and a confirmatory factor analysis was applied to verify a reduced and optimized test version. The structural model showed a good fit, while internal consistency of the subscales was excellent (ω>.92). Item difficulties ranged between .04≤pi≤.82 and item-total correlation from .21 to .85. Objectivity was assessed by comparing the scorings of two external raters based on a subsample of n=12; interrater agreement was .71 (ICC 2, 1). Reliability was calculated for four test repetitions: the average ICC (3, 1) was .69. Convergent ASD measures correlated significantly with the MUSAD, while the discriminant Modified Overt Aggression Scale (MOAS) showed no significant overlap. Confirmation of factorial structure and acceptable psychometric properties suggest that the MUSAD is a promising new instrument for diagnosing ASD in adults with IDD. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Named data networking-based smart home

    OpenAIRE

    Syed Hassan Ahmed; Dongkyun Kim

    2016-01-01

    Named data networking (NDN) treats content/data as a “first class citizen” of the network by giving it a “name”. This content “name” is used to retrieve any information, unlike in device-centric networks (i.e., the current Internet), which depend on physical IP addresses. Meanwhile, the smart home concept has been gaining attention in academia and industries; various low-cost embedded devices are considered that can sense, process, store, and communicate data autonomously. In this paper, we s...

  11. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-09-01

    Full Text Available Introduction: Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders. Methods: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is signi.cantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion: The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  12. Сhoosing the best type neural network jet contour diagnostics engines

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2006-01-01

    Full Text Available  In the paper the  choice problems of neurons  type for neural network is considered. The neurons types has to be , optimal from the point of work stability, training speed and quality of gas turbine engine  technical condition class recognition by work process parameters. Results of researches are given.

  13. Correlation of Social Network Attributes with Individuals’ Score on Bipolar Spectrum Diagnostic Scale

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-12-01

    Full Text Available Bipolar Spectrum Disorders include a variety of mood disorders from bipolar II disorder to conditions characterized by hyperthymic mood states. It has been suggested that psychosocial factors also play an important role in bipolar disorders, in this study we have used social network analysis in order to better understand the social positions of those affected by bipolar spectrum disorders.Methods and Materials: In this cross sectional study 90 individuals within a bounded network were included and studied by using a standard questionnaire for bipolar spectrum disorder scale (BSDS and a sociometric questionnaire for analyzing the social network of those individuals.Results: This study showed that BSDS score is significantly correlated with the Bonacich power of the participants (P= 0.009 as well as with their Outdegree Strength (P= 0.013.Discussion:The results of this study show that there is interplay between social attributes and Bipolar Spectrum Disorders. This emphasizes the need for understanding the role of social networks and performing further research into quantifying social aspects of psychiatric disorders.

  14. Challenges to the Use of Artificial Neural Networks for Diagnostic Classifications with Student Test Data

    Science.gov (United States)

    Briggs, Derek C.; Circi, Ruhan

    2017-01-01

    Artificial Neural Networks (ANNs) have been proposed as a promising approach for the classification of students into different levels of a psychological attribute hierarchy. Unfortunately, because such classifications typically rely upon internally produced item response patterns that have not been externally validated, the instability of ANN…

  15. An expert system for vibration based diagnostics of rotating machines

    International Nuclear Information System (INIS)

    Korteniemi, A.

    1990-01-01

    Very often changes in the mechanical condition of the rotating machinery can be observed as changes in its vibration. This paper presents an expert system for vibration-based diagnosis of rotating machines by describing the architecture of the developed prototype system. The importance of modelling the problem solving knowledge as well as the domain knowledge is emphasized by presenting the knowledge in several levels

  16. A tool for model based diagnostics of the AGS Booster

    International Nuclear Information System (INIS)

    Luccio, A.

    1993-01-01

    A model-based algorithmic tool was developed to search for lattice errors by a systematic analysis of orbit data in the AGS Booster synchrotron. The algorithm employs transfer matrices calculated with MAD between points in the ring. Iterative model fitting of the data allows one to find and eventually correct magnet displacements and angles or field errors. The tool, implemented on a HP-Apollo workstation system, has proved very general and of immediate physical interpretation

  17. Laser-Based Diagnostic Measurements of Low Emissions Combustor Concepts

    Science.gov (United States)

    Hicks, Yolanda R.

    2011-01-01

    This presentation provides a summary of primarily laser-based measurement techniques we use at NASA Glenn Research Center to characterize fuel injection, fuel/air mixing, and combustion. The report highlights using Planar Laser-Induced Fluorescence, Particle Image Velocimetry, and Phase Doppler Interferometry to obtain fuel injector patternation, fuel and air velocities, and fuel drop sizes and turbulence intensities during combustion. We also present a brief comparison between combustors burning standard JP-8 Jet fuel and an alternative fuels. For this comparison, we used flame chemiluminescence and high speed imaging.

  18. Distribution Network Design--literature study based

    OpenAIRE

    LI, ANG

    2012-01-01

    The focus of this research is companies' outbound distribution network design in supply chain management. Within the present competitive market, it is a fundamental importance for companies to achieve high level business performance with an effective supply chain. Outbound distribution network design as an important part in supply chain management, to a large extent decides whether companies can fulfill customers' requirement or not. Therefore, such a study is important for manufacturers and ...

  19. ETHERNET BASED EMBEDDED SYSTEM FOR FEL DIAGNOSTICS AND CONTROLS

    International Nuclear Information System (INIS)

    Jianxun Yan; Daniel Sexton; Steven Moore; Albert Grippo; Kevin Jordan

    2006-01-01

    An Ethernet based embedded system has been developed to upgrade the Beam Viewer and Beam Position Monitor (BPM) systems within the free-electron laser (FEL) project at Jefferson Lab. The embedded microcontroller was mounted on the front-end I/O cards with software packages such as Experimental Physics and Industrial Control System (EPICS) and Real Time Executive for Multiprocessor System (RTEMS) running as an Input/Output Controller (IOC). By cross compiling with the EPICS, the RTEMS kernel, IOC device supports, and databases all of these can be downloaded into the microcontroller. The first version of the BPM electronics based on the embedded controller was built and is currently running in our FEL system. The new version of BPM that will use a Single Board IOC (SBIOC), which integrates with an Field Programming Gate Array (FPGA) and a ColdFire embedded microcontroller, is presently under development. The new system has the features of a low cost IOC, an open source real-time operating system, plug and play-like ease of installation and flexibility, and provides a much more localized solution

  20. 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.

  1. Mining human mobility in location-based social networks

    CERN Document Server

    Gao, Huiji

    2015-01-01

    In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook Places, which have attracted an increasing number of users and greatly enriched their urban experience. Typical location-based social networking sites allow a user to ""check in"" at a real-world POI (point of interest, e.g., a hotel, restaurant, theater, etc.), leave tips toward the POI, and share the check-in with their online friends. The check-in action bridges the gap between real world and online social networks, resulting in a new type of social networks, namely l

  2. A link prediction method for heterogeneous networks based on BP neural network

    Science.gov (United States)

    Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

    2018-04-01

    Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

  3. Nucleic acid-based diagnostics for infectious diseases in public health affairs.

    Science.gov (United States)

    Yu, Albert Cheung-Hoi; Vatcher, Greg; Yue, Xin; Dong, Yan; Li, Mao Hua; Tam, Patrick H K; Tsang, Parker Y L; Wong, April K Y; Hui, Michael H K; Yang, Bin; Tang, Hao; Lau, Lok-Ting

    2012-06-01

    Infectious diseases, mostly caused by bacteria and viruses but also a result of fungal and parasitic infection, have been one of the most important public health concerns throughout human history. The first step in combating these pathogens is to get a timely and accurate diagnosis at an affordable cost. Many kinds of diagnostics have been developed, such as pathogen culture, biochemical tests and serological tests, to help detect and fight against the causative agents of diseases. However, these diagnostic tests are generally unsatisfactory because they are not particularly sensitive and specific and are unable to deliver speedy results. Nucleic acid-based diagnostics, detecting pathogens through the identification of their genomic sequences, have shown promise to overcome the above limitations and become more widely adopted in clinical tests. Here we review some of the most popular nucleic acid-based diagnostics and focus on their adaptability and applicability to routine clinical usage. We also compare and contrast the characteristics of different types of nucleic acid-based diagnostics.

  4. Cointegration-based financial networks study in Chinese stock market

    Science.gov (United States)

    Tu, Chengyi

    2014-05-01

    We propose a method based on cointegration instead of correlation to construct financial complex network in Chinese stock market. The network is obtained starting from the matrix of p-value calculated by Engle-Granger cointegration test between all pairs of stocks. Then some tools for filtering information in complex network are implemented to prune the complete graph described by the above matrix, such as setting a level of statistical significance as a threshold and Planar Maximally Filtered Graph. We also calculate Partial Correlation Planar Graph of these stocks to compare the above networks. Last, we analyze these directed, weighted and non-symmetric networks by using standard methods of network analysis, including degree centrality, PageRank, HITS, local clustering coefficient, K-shell and strongly and weakly connected components. The results shed a new light on the underlying mechanisms and driving forces in a financial market and deepen our understanding of financial complex network.

  5. Multiagent Based Information Dissemination in Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    S.S. Manvi

    2009-01-01

    Full Text Available Vehicular Ad hoc Networks (VANETs are a compelling application of ad hoc networks, because of the potential to access specific context information (e.g. traffic conditions, service updates, route planning and deliver multimedia services (Voice over IP, in-car entertainment, instant messaging, etc.. This paper proposes an agent based information dissemination model for VANETs. A two-tier agent architecture is employed comprising of the following: 1 'lightweight', network-facing, mobile agents; 2 'heavyweight', application-facing, norm-aware agents. The limitations of VANETs lead us to consider a hybrid wireless network architecture that includes Wireless LAN/Cellular and ad hoc networking for analyzing the proposed model. The proposed model provides flexibility, adaptability and maintainability for traffic information dissemination in VANETs as well as supports robust and agile network management. The proposed model has been simulated in various network scenarios to evaluate the effectiveness of the approach.

  6. Road Network Vulnerability Analysis Based on Improved Ant Colony Algorithm

    Directory of Open Access Journals (Sweden)

    Yunpeng Wang

    2014-01-01

    Full Text Available We present an improved ant colony algorithm-based approach to assess the vulnerability of a road network and identify the critical infrastructures. This approach improves computational efficiency and allows for its applications in large-scale road networks. This research involves defining the vulnerability conception, modeling the traffic utility index and the vulnerability of the road network, and identifying the critical infrastructures of the road network. We apply the approach to a simple test road network and a real road network to verify the methodology. The results show that vulnerability is directly related to traffic demand and increases significantly when the demand approaches capacity. The proposed approach reduces the computational burden and may be applied in large-scale road network analysis. It can be used as a decision-supporting tool for identifying critical infrastructures in transportation planning and management.

  7. Mass spectrometry based proteomics profiling as diagnostic tool in oncology: current status and future perspective.

    Science.gov (United States)

    Findeisen, Peter; Neumaier, Michael

    2009-01-01

    Proteomics analysis has been heralded as a novel tool for identifying new and specific biomarkers that may improve diagnosis and monitoring of various disease states. Recent years have brought a number of proteomics profiling technologies. Although proteomics profiling has resulted in the detection of disease-associated differences and modification of proteins, current proteomics technologies display certain limitations that are hampering the introduction of these new technologies into clinical laboratory diagnostics and routine applications. In this review, we summarize current advances in mass spectrometry based biomarker discovery. The promises and challenges of this new technology are discussed with particular emphasis on diagnostic perspectives of mass-spectrometry based proteomics profiling for malignant diseases.

  8. Diagnostic performance of a Lattice Boltzmann-based method for CT-based fractional flow reserve.

    Science.gov (United States)

    Giannopoulos, Andreas A; Tang, Anji; Ge, Yin; Cheezum, Michael K; Steigner, Michael L; Fujimoto, Shinichiro; Kumamaru, Kanako K; Chiappino, Dante; Della Latta, Daniele; Berti, Sergio; Chiappino, Sara; Rybicki, Frank J; Melchionna, Simone; Mitsouras, Dimitrios

    2018-02-20

    Fractional flow reserve (FFR) estimated from coronary computed tomography angiography (CT-FFR) offers non-invasive detection of lesion-specific ischaemia. We aimed to develop and validate a fast CT-FFR algorithm utilising the Lattice Boltzmann method for blood flow simulation (LBM CT-FFR). Sixty-four patients with clinically indicated CTA and invasive FFR measurement from three institutions were retrospectively analysed. CT-FFR was performed using an onsite tool interfacing with a commercial Lattice Boltzmann fluid dynamics cloud-based platform. Diagnostic accuracy of LBM CT-FFR ≤0.8 and percent diameter stenosis >50% by CTA to detect invasive FFR ≤0.8 were compared using area under the receiver operating characteristic curve (AUC). Sixty patients successfully underwent LBM CT-FFR analysis; 29 of 73 lesions in 69 vessels had invasive FFR ≤0.8. Total time to perform LBM CT-FFR was 40±10 min. Compared to invasive FFR, LBM CT-FFR had good correlation (r=0.64), small bias (0.009) and good limits of agreement (-0.223 to 0.206). The AUC of LBM CT-FFR (AUC=0.894, 95% confidence interval [CI]: 0.792-0.996) was significantly higher than CTA (AUC=0.685, 95% CI: 0.576-0.794) to detect FFR ≤0.8 (p=0.0021). Per-lesion specificity, sensitivity, and accuracy of LBM CT-FFR were 97.7%, 79.3%, and 90.4%, respectively. LBM CT-FFR has very good diagnostic accuracy to detect lesion-specific ischaemia (FFR ≤0.8) and can be performed in less than one hour.

  9. Plasma Channel Diagnostic Based on Laser Centroid Oscillations

    International Nuclear Information System (INIS)

    Gonsalves, Anthony; Nakamura, Kei; Lin, Chen; Osterhoff, Jens; Shiraishi, Satomi; Schroeder, Carl; Geddes, Cameron; Toth, Csaba; Esarey, Eric; Leemans, Wim

    2010-01-01

    A technique has been developed for measuring the properties of discharge-based plasma channels by monitoring the centroid location of a laser beam exiting the channel as a function of input alignment offset between the laser and the channel. The centroid position of low-intensity ( 14 Wcm -2 ) laser pulses focused at the input of a hydrogen-filled capillary discharge waveguide was scanned and the exit positions recorded to determine the channel shape and depth with an accuracy of a few %. In addition, accurate alignment of the laser beam through the plasma channel can be provided by minimizing laser centroid motion at the channel exit as the channel depth is scanned either by scanning the plasma density or the discharge timing. The improvement in alignment accuracy provided by this technique will be crucial for minimizing electron beam pointing errors in laser plasma accelerators.

  10. Lanthanide-based laser-induced phosphorescence for spray diagnostics

    Energy Technology Data Exchange (ETDEWEB)

    Voort, D. D. van der, E-mail: d.d.v.d.voort@tue.nl; Water, W. van de; Kunnen, R. P. J.; Clercx, H. J. H.; Heijst, G. J. F. van [Applied Physics Department, Eindhoven University of Technology, 5612 AZ Eindhoven (Netherlands); Maes, N. C. J.; Sweep, A. M.; Dam, N. J. [Mechanical Engineering Department, Eindhoven University of Technology, 5612 AZ Eindhoven (Netherlands); Lamberts, T. [Institute of Theoretical Chemistry, University of Stuttgart, D-70569 Stuttgart (Germany)

    2016-03-15

    Laser-induced phosphorescence (LIP) is a relatively recent and versatile development for studying flow dynamics. This work investigates certain lanthanide-based molecular complexes for their use in LIP for high-speed sprays. Lanthanide complexes in solutions have been shown to possess long phosphorescence lifetimes (∼1-2 ms) and to emit light in the visible wavelength range. In particular, europium and terbium complexes are investigated using fluorescence/phosphorescence spectrometry, showing that europium-thenoyltrifluoracetone-trioctylphosphineoxide (Eu-TTA-TOPO) can be easily and efficiently excited using a standard frequency-tripled Nd:YAG laser. The emitted spectrum, with maximum intensity at a wavelength of 614 nm, is shown not to vary strongly with temperature (293-383 K). The decay constant of the phosphorescence, while independent of ambient pressure, decreases by approximately 12 μs/K between 323 and 373 K, with the base level of the decay constant dependent on the used solvent. The complex does not luminesce in the gas or solid state, meaning only the liquid phase is visualized, even in an evaporating spray. By using an internally excited spray containing the phosphorescent complex, the effect of vaporization is shown through the decrease in measured intensity over the length of the spray, together with droplet size measurements using interferometric particle imaging. This study shows that LIP, using the Eu-TTA-TOPO complex, can be used with different solvents, including diesel surrogates. Furthermore, it can be easily handled and used in sprays to investigate spray breakup and evaporation.

  11. Inference of time-delayed gene regulatory networks based on dynamic Bayesian network hybrid learning method.

    Science.gov (United States)

    Yu, Bin; Xu, Jia-Meng; Li, Shan; Chen, Cheng; Chen, Rui-Xin; Wang, Lei; Zhang, Yan; Wang, Ming-Hui

    2017-10-06

    Gene regulatory networks (GRNs) research reveals complex life phenomena from the perspective of gene interaction, which is an important research field in systems biology. Traditional Bayesian networks have a high computational complexity, and the network structure scoring model has a single feature. Information-based approaches cannot identify the direction of regulation. In order to make up for the shortcomings of the above methods, this paper presents a novel hybrid learning method (DBNCS) based on dynamic Bayesian network (DBN) to construct the multiple time-delayed GRNs for the first time, combining the comprehensive score (CS) with the DBN model. DBNCS algorithm first uses CMI2NI (conditional mutual inclusive information-based network inference) algorithm for network structure profiles learning, namely the construction of search space. Then the redundant regulations are removed by using the recursive optimization algorithm (RO), thereby reduce the false positive rate. Secondly, the network structure profiles are decomposed into a set of cliques without loss, which can significantly reduce the computational complexity. Finally, DBN model is used to identify the direction of gene regulation within the cliques and search for the optimal network structure. The performance of DBNCS algorithm is evaluated by the benchmark GRN datasets from DREAM challenge as well as the SOS DNA repair network in Escherichia coli , and compared with other state-of-the-art methods. The experimental results show the rationality of the algorithm design and the outstanding performance of the GRNs.

  12. Research and Development of Hybrid Electric Vehicles CAN-Bus Data Monitor and Diagnostic System through OBD-II and Android-Based Smartphones

    Directory of Open Access Journals (Sweden)

    Yalian Yang

    2013-01-01

    Full Text Available With the rapid development of the smartphone market, future cars seem to have more connections with intelligent cell phone and Internet. Intelligent transportation system (ITS and telematics system have become research focus in recent years. There is an increasing demand for remote monitoring and diagnostic system as the further research of hybrid electric vehicle (HEV goes on. In this paper, a remote controller area network bus (CAN-Bus data monitor and diagnostic system for HEV is presented using on board diagnostic version-II (OBD-II and Android-based smartphone. It is low-cost, convenient, and extensible with smartphone used in the system to realize communication with ELM327 and remote monitoring center wirelessly. The prototype of client and server is developed in Java language, and it is proved by the test that the system works stably and the collected data have practical values.

  13. Development of a rule-based diagnostic platform on an object-oriented expert system shell

    International Nuclear Information System (INIS)

    Wang, Wenlin; Yang, Ming; Seong, Poong Hyun

    2016-01-01

    Highlights: • Multilevel Flow Model represents system knowledge as a domain map in expert system. • Rule-based fault diagnostic expert system can identify root cause via a causal chain. • Rule-based fault diagnostic expert system can be used for fault simulation training. - Abstract: This paper presents the development and implementation of a real-time rule-based diagnostic platform. The knowledge is acquired from domain experts and textbooks and the design of the fault diagnosis expert system was performed in the following ways: (i) establishing of corresponding classes and instances to build the domain map, (ii) creating of generic fault models based on events, and (iii) building of diagnostic reasoning based on rules. Knowledge representation is a complicated issue of expert systems. One highlight of this paper is that the Multilevel Flow Model has been used to represent the knowledge, which composes the domain map within the expert system as well as providing a concise description of the system. The developed platform is illustrated using the pressure safety system of a pressurized water reactor as an example of the simulation test bed; the platform is developed using the commercial and industrially validated software G2. The emulation test was conducted and it has been proven that the fault diagnosis expert system can identify the faults correctly and in a timely way; this system can be used as a simulation-based training tool to assist operators to make better decisions.

  14. Turning the Page: Advancing Paper-Based Microfluidics for Broad Diagnostic Application.

    Science.gov (United States)

    Gong, Max M; Sinton, David

    2017-06-28

    Infectious diseases are a major global health issue. Diagnosis is a critical first step in effectively managing their spread. Paper-based microfluidic diagnostics first emerged in 2007 as a low-cost alternative to conventional laboratory testing, with the goal of improving accessibility to medical diagnostics in developing countries. In this review, we examine the advances in paper-based microfluidic diagnostics for medical diagnosis in the context of global health from 2007 to 2016. The theory of fluid transport in paper is first presented. The next section examines the strategies that have been employed to control fluid and analyte transport in paper-based assays. Tasks such as mixing, timing, and sequential fluid delivery have been achieved in paper and have enabled analytical capabilities comparable to those of conventional laboratory methods. The following section examines paper-based sample processing and analysis. The most impactful advancement here has been the translation of nucleic acid analysis to a paper-based format. Smartphone-based analysis is another exciting development with potential for wide dissemination. The last core section of the review highlights emerging health applications, such as male fertility testing and wearable diagnostics. We conclude the review with the future outlook, remaining challenges, and emerging opportunities.

  15. Acquaintance to Artificial Neural Networks and use of artificial intelligence as a diagnostic tool for tuberculosis: A review.

    Science.gov (United States)

    Dande, Payal; Samant, Purva

    2018-01-01

    Tuberculosis [TB] has afflicted numerous nations in the world. As per a report by the World Health Organization [WHO], an estimated 1.4 million TB deaths in 2015 and an additional 0.4 million deaths resulting from TB disease among people living with HIV, were observed. Most of the TB deaths can be prevented if it is detected at an early stage. The existing processes of diagnosis like blood tests or sputum tests are not only tedious but also take a long time for analysis and cannot differentiate between different drug resistant stages of TB. The need to find newer prompt methods for disease detection has been aided by the latest Artificial Intelligence [AI] tools. Artificial Neural Network [ANN] is one of the important tools that is being used widely in diagnosis and evaluation of medical conditions. This review aims at providing brief introduction to various AI tools that are used in TB detection and gives a detailed description about the utilization of ANN as an efficient diagnostic technique. The paper also provides a critical assessment of ANN and the existing techniques for their diagnosis of TB. Researchers and Practitioners in the field are looking forward to use ANN and other upcoming AI tools such as Fuzzy-logic, genetic algorithms and artificial intelligence simulation as a promising current and future technology tools towards tackling the global menace of Tuberculosis. Latest advancements in the diagnostic field include the combined use of ANN with various other AI tools like the Fuzzy-logic, which has led to an increase in the efficacy and specificity of the diagnostic techniques. Copyright © 2017 Elsevier Ltd. All rights reserved.

  16. Fair and efficient network congestion control based on minority game

    Science.gov (United States)

    Wang, Zuxi; Wang, Wen; Hu, Hanping; Deng, Zhaozhang

    2011-12-01

    Low link utility, RTT unfairness and unfairness of Multi-Bottleneck network are the existing problems in the present network congestion control algorithms at large. Through the analogy of network congestion control with the "El Farol Bar" problem, we establish a congestion control model based on minority game(MG), and then present a novel network congestion control algorithm based on the model. The result of simulations indicates that the proposed algorithm can make the achievements of link utility closing to 100%, zero packet lose rate, and small of queue size. Besides, the RTT unfairness and the unfairness of Multi-Bottleneck network can be solved, to achieve the max-min fairness in Multi-Bottleneck network, while efficiently weaken the "ping-pong" oscillation caused by the overall synchronization.

  17. Reconstruction of biological networks based on life science data integration

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

    Full Text Available For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH - an integration toolkit for building life science data warehouses, CardioVINEdb - a information system for biological data in cardiovascular-disease and VANESA- a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  18. Reconstruction of biological networks based on life science data integration.

    Science.gov (United States)

    Kormeier, Benjamin; Hippe, Klaus; Arrigo, Patrizio; Töpel, Thoralf; Janowski, Sebastian; Hofestädt, Ralf

    2010-10-27

    For the implementation of the virtual cell, the fundamental question is how to model and simulate complex biological networks. Therefore, based on relevant molecular database and information systems, biological data integration is an essential step in constructing biological networks. In this paper, we will motivate the applications BioDWH--an integration toolkit for building life science data warehouses, CardioVINEdb--a information system for biological data in cardiovascular-disease and VANESA--a network editor for modeling and simulation of biological networks. Based on this integration process, the system supports the generation of biological network models. A case study of a cardiovascular-disease related gene-regulated biological network is also presented.

  19. Vibration-based monitoring and diagnostics using compressive sensing

    Science.gov (United States)

    Ganesan, Vaahini; Das, Tuhin; Rahnavard, Nazanin; Kauffman, Jeffrey L.

    2017-04-01

    Vibration data from mechanical systems carry important information that is useful for characterization and diagnosis. Standard approaches rely on continually streaming data at a fixed sampling frequency. For applications involving continuous monitoring, such as Structural Health Monitoring (SHM), such approaches result in high volume data and rely on sensors being powered for prolonged durations. Furthermore, for spatial resolution, structures are instrumented with a large array of sensors. This paper shows that both volume of data and number of sensors can be reduced significantly by applying Compressive Sensing (CS) in vibration monitoring applications. The reduction is achieved by using random sampling and capitalizing on the sparsity of vibration signals in the frequency domain. Preliminary experimental results validating CS-based frequency recovery are also provided. By exploiting the sparsity of mode shapes, CS can also enable efficient spatial reconstruction using fewer spatially distributed sensors. CS can thereby reduce the cost and power requirement of sensing as well as streamline data storage and processing in monitoring applications. In well-instrumented structures, CS can enable continued monitoring in case of sensor or computational failures.

  20. Vibration-based Fault Diagnostic of a Spur Gearbox

    Directory of Open Access Journals (Sweden)

    Hartono Dennis

    2016-01-01

    Full Text Available This paper presents comparative studies of Fast Fourier Transform (FFT, Short Time Fourier Transform (STFT and Continuous Wavelet Transform (CWT as several advanced time-frequency analysis methods for diagnosing an early stage of spur gear tooth failure. An incipient fault of a chipped tooth was investigated in this work using vibration measurements from a spur gearbox test rig. Time Synchronous Averaging was implemented for the analysis to enhance the clarity of fault feature from the gear of interest. Based on the experimental results and analysis, it was shown that FFT method could identify the location of the faulty gear with sufficient accuracy. On the other hand, Short Time Fourier Transform method could not provide the angular location information of the faulty gear. It was found that the Continuous Wavelet Transform method offered the best representation of angle-frequency representation. It was not only able to distinguish the difference between the normal and faulty gearboxes from the joint angle-frequency results but could also provide an accurate angular location of the faulty gear tooth in the gearbox.

  1. Solutions to Improve Road Circulation in the Pitesti City Based on Analysis-Diagnostics of Road Traffic

    Science.gov (United States)

    Vîlcan, A.; Neagu, E.; Badarau Suster, H.; Boroiu, A. A.

    2017-10-01

    Road traffic congestion has become a daily phenomenon in the central area of Pitesti in the peak traffic periods. In order to achieve the mobility plan of Pitesti, an important stage is the diagnostic analysis of the road traffic. For this purpose, the urban road network was formalized through a graph containing the most important 40 intersections and traffic measurements were made at all these intersections and on the main roads connecting the peri-urban area. The data obtained by traffic macrosimulation confirmed the overloading of the street network during peak traffic hours and the analyzes made for various road traffic organization scenarios have shown that there are sustainable solutions for urban mobility only if the road network is fundamentally reconfigured (a belt outside the city and a median ring). Thus, the necessity of realizing the road passage in the Prundu neighbourhood and the finishing of the city belt by realizing the “detour West” of the city is argued. The importance of the work is that it brings scientific arguments for the realization of these road infrastructure projects, integrated in the urban mobility plan, which will base the development strategy of the Pitesti municipality.

  2. Cancer physics: diagnostics based on damped cellular elastoelectrical vibrations in microtubules.

    Science.gov (United States)

    Pokorný, Jiří; Vedruccio, Clarbruno; Cifra, Michal; Kučera, Ondřej

    2011-06-01

    This paper describes a proposed biophysical mechanism of a novel diagnostic method for cancer detection developed recently by Vedruccio. The diagnostic method is based on frequency selective absorption of electromagnetic waves by malignant tumors. Cancer is connected with mitochondrial malfunction (the Warburg effect) suggesting disrupted physical mechanisms. In addition to decreased energy conversion and nonutilized energy efflux, mitochondrial malfunction is accompanied by other negative effects in the cell. Diminished proton space charge layer and the static electric field around the outer membrane result in a lowered ordering level of cellular water and increased damping of microtubule-based cellular elastoelectrical vibration states. These changes manifest themselves in a dip in the amplitude of the signal with the fundamental frequency of the nonlinear microwave oscillator-the core of the diagnostic device-when coupled to the investigated cancerous tissue via the near-field. The dip is not present in the case of healthy tissue.

  3. A random spatial network model based on elementary postulates

    Science.gov (United States)

    Karlinger, Michael R.; Troutman, Brent M.

    1989-01-01

    A model for generating random spatial networks that is based on elementary postulates comparable to those of the random topology model is proposed. In contrast to the random topology model, this model ascribes a unique spatial specification to generated drainage networks, a distinguishing property of some network growth models. The simplicity of the postulates creates an opportunity for potential analytic investigations of the probabilistic structure of the drainage networks, while the spatial specification enables analyses of spatially dependent network properties. In the random topology model all drainage networks, conditioned on magnitude (number of first-order streams), are equally likely, whereas in this model all spanning trees of a grid, conditioned on area and drainage density, are equally likely. As a result, link lengths in the generated networks are not independent, as usually assumed in the random topology model. For a preliminary model evaluation, scale-dependent network characteristics, such as geometric diameter and link length properties, and topologic characteristics, such as bifurcation ratio, are computed for sets of drainage networks generated on square and rectangular grids. Statistics of the bifurcation and length ratios fall within the range of values reported for natural drainage networks, but geometric diameters tend to be relatively longer than those for natural networks.

  4. Protecting infrastructure networks from cost-based attacks

    International Nuclear Information System (INIS)

    Wang Xingang; Guan Shuguang; Lai, Choy Heng

    2009-01-01

    It is well known that heterogeneous networks are vulnerable to the intentional removal of a small fraction of highly connected or loaded nodes, implying that to protect the network effectively, the important nodes should be allocated more defense resource than the others. However, if too much resource is allocated to the few important nodes, the numerous less-important nodes will be less protected, which if attacked together can still lead to devastating damage. A natural question is therefore how to efficiently distribute the limited defense resource among the network nodes such that the network damage is minimized against any attack strategy. In this paper, taking into account the factor of attack cost, the problem of network security is reconsidered in terms of efficient network defense against cost-based attacks. The results show that, for a general complex network, there exists an optimal distribution of the defense resource with which the network is best protected from cost-based attacks. Furthermore, it is found that the configuration of the optimal defense is dependent on the network parameters. Specifically, networks of larger size, sparser connection and more heterogeneous structure will more likely benefit from the defense optimization.

  5. A Neural Network-Based Interval Pattern Matcher

    Directory of Open Access Journals (Sweden)

    Jing Lu

    2015-07-01

    Full Text Available One of the most important roles in the machine learning area is to classify, and neural networks are very important classifiers. However, traditional neural networks cannot identify intervals, let alone classify them. To improve their identification ability, we propose a neural network-based interval matcher in our paper. After summarizing the theoretical construction of the model, we take a simple and a practical weather forecasting experiment, which show that the recognizer accuracy reaches 100% and that is promising.

  6. Interconnection network architectures based on integrated orbital angular momentum emitters

    Science.gov (United States)

    Scaffardi, Mirco; Zhang, Ning; Malik, Muhammad Nouman; Lazzeri, Emma; Klitis, Charalambos; Lavery, Martin; Sorel, Marc; Bogoni, Antonella

    2018-02-01

    Novel architectures for two-layer interconnection networks based on concentric OAM emitters are presented. A scalability analysis is done in terms of devices characteristics, power budget and optical signal to noise ratio by exploiting experimentally measured parameters. The analysis shows that by exploiting optical amplifications, the proposed interconnection networks can support a number of ports higher than 100. The OAM crosstalk induced-penalty, evaluated through an experimental characterization, do not significantly affect the interconnection network performance.

  7. A simple network agreement-based approach for combining evidences in a heterogeneous sensor network

    Directory of Open Access Journals (Sweden)

    Raúl Eusebio-Grande

    2015-12-01

    Full Text Available In this research we investigate how the evidences provided by both static and mobile nodes that are part of a heterogenous sensor network can be combined to have trustworthy results. A solution relying on a network agreement-based approach was implemented and tested.

  8. 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)

  9. Analysis of Chronic Temporomandibular Disorders Based on the Latest Diagnostic Criteria

    Directory of Open Access Journals (Sweden)

    Svechtarov V.

    2015-05-01

    Full Text Available The objective of this study is to analyze the distribution of the most common diagnoses observed in patients with chronic temporomandibular disorders, based on the new diagnostic criteria (DC/TMD adopted in 2014. The previous Research Diagnostic Criteria (RDC/TMD adopted in 1992, consisted of three main groups of eight diagnostic subgroups and is currently transformed into two main groups and twelve subgroups, respectively. All subgroups correspond to the nomenclature of the ICD-10. The new clinical diagnostic indices are also modified. The analysis showed a prevalence of Pain-Related TMD compared with that of intra-articular disorders in ratio 57.89% to 42.10%. In Pain-Related TMD arthralgia was represented in 55% of cases; local myalgia - in 12%, myofascial pain - in 18%, myofascial pain with referral - in 14%, headache attributed to TMD - in 1%. In Intra-articular TMD disc displacement with reduction was found in 23% of the cases, disc displacement with reduction with intermittent locking - in 3%, disc displacement without reduction with limited opening - in 25%, disc displacement without reduction and without limited opening - in 8%. Degenerative diseases were found in 14.28%, and hypermobility and subluxations - in 26.98%. These analyzes differ and can only partly be compared with previous analyzes based on RDC system. The changes in the diagnostic criteria require new clinical studies in order to refine the picture of temporomandibular pathology in accordance with the modern views on the matter.

  10. Pre-examination factors affecting molecular diagnostic test results and interpretation: A case-based approach.

    Science.gov (United States)

    Payne, Deborah A; Baluchova, Katarina; Peoc'h, Katell H; van Schaik, Ron H N; Chan, K C Allen; Maekawa, Masato; Mamotte, Cyril; Russomando, Graciela; Rousseau, François; Ahmad-Nejad, Parviz

    2017-04-01

    Multiple organizations produce guidance documents that provide opportunities to harmonize quality practices for diagnostic testing. The International Organization for Standardization ISO 15189 standard addresses requirements for quality in management and technical aspects of the clinical laboratory. One technical aspect addresses the complexities of the pre-examination phase prior to diagnostic testing. The Committee for Molecular Diagnostics of the International Federation for Clinical Chemistry and Laboratory Medicine (also known as, IFCC C-MD) conducted a survey of international molecular laboratories and determined ISO 15189 to be the most referenced guidance document. In this review, the IFCC C-MD provides case-based examples illustrating the value of select pre-examination processes as these processes relate to molecular diagnostic testing. Case-based examples in infectious disease, oncology, inherited disease and pharmacogenomics address the utility of: 1) providing information to patients and users, 2) designing requisition forms, 3) obtaining informed consent and 4) maintaining sample integrity prior to testing. The pre-examination phase requires extensive and consistent communication between the laboratory, the healthcare provider and the end user. The clinical vignettes presented in this paper illustrate the value of applying select ISO 15189 recommendations for general laboratory to the more specialized area of Molecular Diagnostics. Copyright © 2016 Elsevier B.V. All rights reserved.

  11. Bayesian modeling and inference for diagnostic accuracy and probability of disease based on multiple diagnostic biomarkers with and without a perfect reference standard.

    Science.gov (United States)

    Jafarzadeh, S Reza; Johnson, Wesley O; Gardner, Ian A

    2016-03-15

    The area under the receiver operating characteristic (ROC) curve (AUC) is used as a performance metric for quantitative tests. Although multiple biomarkers may be available for diagnostic or screening purposes, diagnostic accuracy is often assessed individually rather than in combination. In this paper, we consider the interesting problem of combining multiple biomarkers for use in a single diagnostic criterion with the goal of improving the diagnostic accuracy above that of an individual biomarker. The diagnostic criterion created from multiple biomarkers is based on the predictive probability of disease, conditional on given multiple biomarker outcomes. If the computed predictive probability exceeds a specified cutoff, the corresponding subject is allocated as 'diseased'. This defines a standard diagnostic criterion that has its own ROC curve, namely, the combined ROC (cROC). The AUC metric for cROC, namely, the combined AUC (cAUC), is used to compare the predictive criterion based on multiple biomarkers to one based on fewer biomarkers. A multivariate random-effects model is proposed for modeling multiple normally distributed dependent scores. Bayesian methods for estimating ROC curves and corresponding (marginal) AUCs are developed when a perfect reference standard is not available. In addition, cAUCs are computed to compare the accuracy of different combinations of biomarkers for diagnosis. The methods are evaluated using simulations and are applied to data for Johne's disease (paratuberculosis) in cattle. Copyright © 2015 John Wiley & Sons, Ltd.

  12. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

    Full Text Available The article deals with detection of network anomalies. Network anomalies include everything that is quite different from the normal operation. For detection of anomalies were used machine learning systems. Machine learning can be considered as a support or a limited type of artificial intelligence. A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyse, and test the knowledge acquired. There are several machine learning techniques available. We tested Decision tree learning and Bayesian networks. The open source data-mining framework WEKA was the tool we used for testing the classify, cluster, association algorithms and for visualization of our results. The WEKA is a collection of machine learning algorithms for data mining tasks.

  13. Named data networking-based smart home

    Directory of Open Access Journals (Sweden)

    Syed Hassan Ahmed

    2016-09-01

    Full Text Available Named data networking (NDN treats content/data as a “first class citizen” of the network by giving it a “name”. This content “name” is used to retrieve any information, unlike in device-centric networks (i.e., the current Internet, which depend on physical IP addresses. Meanwhile, the smart home concept has been gaining attention in academia and industries; various low-cost embedded devices are considered that can sense, process, store, and communicate data autonomously. In this paper, we study NDN in the context of smart-home communications, discuss the preliminary evaluations, and describe the future challenges of applying NDN in smart-home applications.

  14. Computational Approach for Securing Radiology-Diagnostic Data in Connected Health Network using High-Performance GPU-Accelerated AES.

    Science.gov (United States)

    Adeshina, A M; Hashim, R

    2017-03-01

    Diagnostic radiology is a core and integral part of modern medicine, paving ways for the primary care physicians in the disease diagnoses, treatments and therapy managements. Obviously, all recent standard healthcare procedures have immensely benefitted from the contemporary information technology revolutions, apparently revolutionizing those approaches to acquiring, storing and sharing of diagnostic data for efficient and timely diagnosis of diseases. Connected health network was introduced as an alternative to the ageing traditional concept in healthcare system, improving hospital-physician connectivity and clinical collaborations. Undoubtedly, the modern medicinal approach has drastically improved healthcare but at the expense of high computational cost and possible breach of diagnosis privacy. Consequently, a number of cryptographical techniques are recently being applied to clinical applications, but the challenges of not being able to successfully encrypt both the image and the textual data persist. Furthermore, processing time of encryption-decryption of medical datasets, within a considerable lower computational cost without jeopardizing the required security strength of the encryption algorithm, still remains as an outstanding issue. This study proposes a secured radiology-diagnostic data framework for connected health network using high-performance GPU-accelerated Advanced Encryption Standard. The study was evaluated with radiology image datasets consisting of brain MR and CT datasets obtained from the department of Surgery, University of North Carolina, USA, and the Swedish National Infrastructure for Computing. Sample patients' notes from the University of North Carolina, School of medicine at Chapel Hill were also used to evaluate the framework for its strength in encrypting-decrypting textual data in the form of medical report. Significantly, the framework is not only able to accurately encrypt and decrypt medical image datasets, but it also

  15. Target recognition based on convolutional neural network

    Science.gov (United States)

    Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

    2017-11-01

    One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

  16. Transmission network expansion planning based on hybridization model of neural networks and harmony search algorithm

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ameli

    2012-01-01

    Full Text Available Transmission Network Expansion Planning (TNEP is a basic part of power network planning that determines where, when and how many new transmission lines should be added to the network. So, the TNEP is an optimization problem in which the expansion purposes are optimized. Artificial Intelligence (AI tools such as Genetic Algorithm (GA, Simulated Annealing (SA, Tabu Search (TS and Artificial Neural Networks (ANNs are methods used for solving the TNEP problem. Today, by using the hybridization models of AI tools, we can solve the TNEP problem for large-scale systems, which shows the effectiveness of utilizing such models. In this paper, a new approach to the hybridization model of Probabilistic Neural Networks (PNNs and Harmony Search Algorithm (HSA was used to solve the TNEP problem. Finally, by considering the uncertain role of the load based on a scenario technique, this proposed model was tested on the Garver’s 6-bus network.

  17. SNMS: an intelligent transportation system network architecture based on WSN and P2P network

    Institute of Scientific and Technical Information of China (English)

    LI Li; LIU Yuan-an; TANG Bi-hua

    2007-01-01

    With the development of city road networks, the question of how to obtain information about the roads is becoming more and more important. In this article, sensor network with mobile station (SNMS), a novel two-tiered intelligent transportation system (ITS) network architecture based on wireless sensor network (WSN) and peer-to-peer (P2P) network, is proposed to provide significant traffic information about the road and thereby, assist travelers to take optimum decisions when they are driving. A detailed explanation with regard to the strategy of each level as well as the design of two main components in the network, sensor unit (SU) and mobile station (MS), is presented. Finally, a representative scenario is described to display the operation of the system.

  18. Behavior of gadolinium-based diagnostics in water treatment

    Energy Technology Data Exchange (ETDEWEB)

    Cyris, Maike

    2013-04-25

    determined, however, it is strongly assumed that the anthropogenic gadolinium fraction is present as chelate. Adsorption characteristics were evaluated by bottle point isotherm experiments on different activated carbon types and activated polymer based sorbents. The Freundlich coefficients vary between 0.013 and 2.83 (μmol kg{sup -1})(L μmol{sup -1}){sup 1/n} for Gd-BT-DO3A, on Chemviron RD 90 {sup registered} and on the best synthetic adsorbent, respectively. Lab scale experiments with small adsorber columns in a drinking water matrix gave insight in the behavior during fixed-bed adsorption processes. The breakthrough was described successfully by the Linear Driving Force model. Modeling has shown that a description of experimental results is only possible by including dissolved organic carbon isotherm results from drinking water in the model, to describe an additional competitive adsorption effect within the fixed-bed adsorber, different from direct competition. First investigations in a wastewater treatment plant proved a poor adsorption of gadolinium similar to iodinated X-ray contrast media such as iopamidole. Therefore, gadolinium will hardly be removed from wastewater by implementation of a further adsorptive treatment step. However, gadolinium may be utilized as indicator substance for breakthrough. Rate constants of the chelates with ozone and hydroxyl radicals have been determined under pseudo-first-order conditions. Rate constants for the ozone reaction were determined to be < 50 M{sup -1}s{sup -1} for all tested chelates. Hence, the chelates may be considered ozone refractory. For determination of hydroxyl radical rate constants different methods were applied. Radicals were generated either by pulse radiolysis, in this case rate constant were determined directly and by competition with thiocyanate, or by the peroxone process, where only competition kinetics were applied (para-chlorobenzoic acid and tert-butanol as competitors). From pulse radiolysis

  19. Behavior of gadolinium-based diagnostics in water treatment

    International Nuclear Information System (INIS)

    Cyris, Maike

    2013-01-01

    , however, it is strongly assumed that the anthropogenic gadolinium fraction is present as chelate. Adsorption characteristics were evaluated by bottle point isotherm experiments on different activated carbon types and activated polymer based sorbents. The Freundlich coefficients vary between 0.013 and 2.83 (μmol kg -1 )(L μmol -1 ) 1/n for Gd-BT-DO3A, on Chemviron RD 90 registered and on the best synthetic adsorbent, respectively. Lab scale experiments with small adsorber columns in a drinking water matrix gave insight in the behavior during fixed-bed adsorption processes. The breakthrough was described successfully by the Linear Driving Force model. Modeling has shown that a description of experimental results is only possible by including dissolved organic carbon isotherm results from drinking water in the model, to describe an additional competitive adsorption effect within the fixed-bed adsorber, different from direct competition. First investigations in a wastewater treatment plant proved a poor adsorption of gadolinium similar to iodinated X-ray contrast media such as iopamidole. Therefore, gadolinium will hardly be removed from wastewater by implementation of a further adsorptive treatment step. However, gadolinium may be utilized as indicator substance for breakthrough. Rate constants of the chelates with ozone and hydroxyl radicals have been determined under pseudo-first-order conditions. Rate constants for the ozone reaction were determined to be < 50 M -1 s -1 for all tested chelates. Hence, the chelates may be considered ozone refractory. For determination of hydroxyl radical rate constants different methods were applied. Radicals were generated either by pulse radiolysis, in this case rate constant were determined directly and by competition with thiocyanate, or by the peroxone process, where only competition kinetics were applied (para-chlorobenzoic acid and tert-butanol as competitors). From pulse radiolysis determinations (rate constants > 10 9 M -1 s -1

  20. Self-organized topology of recurrence-based complex networks

    International Nuclear Information System (INIS)

    Yang, Hui; Liu, Gang

    2013-01-01

    With the rapid technological advancement, network is almost everywhere in our daily life. Network theory leads to a new way to investigate the dynamics of complex systems. As a result, many methods are proposed to construct a network from nonlinear time series, including the partition of state space, visibility graph, nearest neighbors, and recurrence approaches. However, most previous works focus on deriving the adjacency matrix to represent the complex network and extract new network-theoretic measures. Although the adjacency matrix provides connectivity information of nodes and edges, the network geometry can take variable forms. The research objective of this article is to develop a self-organizing approach to derive the steady geometric structure of a network from the adjacency matrix. We simulate the recurrence network as a physical system by treating the edges as springs and the nodes as electrically charged particles. Then, force-directed algorithms are developed to automatically organize the network geometry by minimizing the system energy. Further, a set of experiments were designed to investigate important factors (i.e., dynamical systems, network construction methods, force-model parameter, nonhomogeneous distribution) affecting this self-organizing process. Interestingly, experimental results show that the self-organized geometry recovers the attractor of a dynamical system that produced the adjacency matrix. This research addresses a question, i.e., “what is the self-organizing geometry of a recurrence network?” and provides a new way to reproduce the attractor or time series from the recurrence plot. As a result, novel network-theoretic measures (e.g., average path length and proximity ratio) can be achieved based on actual node-to-node distances in the self-organized network topology. The paper brings the physical models into the recurrence analysis and discloses the spatial geometry of recurrence networks

  1. A construct-network approach to bridging diagnostic and physiological domains: application to assessment of externalizing psychopathology.

    Science.gov (United States)

    Patrick, Christopher J; Venables, Noah C; Yancey, James R; Hicks, Brian M; Nelson, Lindsay D; Kramer, Mark D

    2013-08-01

    A crucial challenge in efforts to link psychological disorders to neural systems, with the aim of developing biologically informed conceptions of such disorders, is the problem of method variance (Campbell & Fiske, 1959). Since even measures of the same construct in differing domains correlate only moderately, it is unsurprising that large sample studies of diagnostic biomarkers yield only modest associations. To address this challenge, a construct-network approach is proposed in which psychometric operationalizations of key neurobehavioral constructs serve as anchors for identifying neural indicators of psychopathology-relevant dispositions, and as vehicles for bridging between domains of clinical problems and neurophysiology. An empirical illustration is provided for the construct of inhibition-disinhibition, which is of central relevance to problems entailing deficient impulse control. Findings demonstrate that: (1) a well-designed psychometric index of trait disinhibition effectively predicts externalizing problems of multiple types, (2) this psychometric measure of disinhibition shows reliable brain response correlates, and (3) psychometric and brain-response indicators can be combined to form a joint psychoneurometric factor that predicts effectively across clinical and physiological domains. As a methodology for bridging between clinical problems and neural systems, the construct-network approach provides a concrete means by which existing conceptions of psychological disorders can accommodate and be reshaped by neurobiological insights. PsycINFO Database Record (c) 2013 APA, all rights reserved.

  2. Friend suggestion in social network based on user log

    Science.gov (United States)

    Kaviya, R.; Vanitha, M.; Sumaiya Thaseen, I.; Mangaiyarkarasi, R.

    2017-11-01

    Simple friend recommendation algorithms such as similarity, popularity and social aspects is the basic requirement to be explored to methodically form high-performance social friend recommendation. Suggestion of friends is followed. No tags of character were followed. In the proposed system, we use an algorithm for network correlation-based social friend recommendation (NC-based SFR).It includes user activities like where one lives and works. A new friend recommendation method, based on network correlation, by considering the effect of different social roles. To model the correlation between different networks, we develop a method that aligns these networks through important feature selection. We consider by preserving the network structure for a more better recommendations so that it significantly improves the accuracy for better friend-recommendation.

  3. A WAO - ARIA - GA²LEN consensus document on molecular-based allergy diagnostics.

    Science.gov (United States)

    Canonica, Giorgio Walter; Ansotegui, Ignacio J; Pawankar, Ruby; Schmid-Grendelmeier, Peter; van Hage, Marianne; Baena-Cagnani, Carlos E; Melioli, Giovanni; Nunes, Carlos; Passalacqua, Giovanni; Rosenwasser, Lanny; Sampson, Hugh; Sastre, Joaquin; Bousquet, Jean; Zuberbier, Torsten

    2013-10-03

    Molecular-based allergy (MA) diagnostics is an approach used to map the allergen sensitization of a patient at a molecular level, using purified natural or recombinant allergenic molecules (allergen components) instead of allergen extracts. Since its introduction, MA diagnostics has increasingly entered routine care, with currently more than 130 allergenic molecules commercially available for in vitro specific IgE (sIgE) testing.MA diagnostics allows for an increased accuracy in allergy diagnosis and prognosis and plays an important role in three key aspects of allergy diagnosis: (1) resolving genuine versus cross-reactive sensitization in poly-sensitized patients, thereby improving the understanding of triggering allergens; (2) assessing, in selected cases, the risk of severe, systemic versus mild, local reactions in food allergy, thereby reducing unnecessary anxiety for the patient and the need for food challenge testing; and (3) identifying patients and triggering allergens for specific immunotherapy (SIT).Singleplex and multiplex measurement platforms are available for MA diagnostics. The Immuno-Solid phase Allergen Chip (ISAC) is the most comprehensive platform currently available, which involves a biochip technology to measure sIgE antibodies against more than one hundred allergenic molecules in a single assay. As the field of MA diagnostics advances, future work needs to focus on large-scale, population-based studies involving practical applications, elucidation and expansion of additional allergenic molecules, and support for appropriate test interpretation. With the rapidly expanding evidence-base for MA diagnosis, there is a need for allergists to keep abreast of the latest information. The aim of this consensus document is to provide a practical guide for the indications, determination, and interpretation of MA diagnostics for clinicians trained in allergology.

  4. A web-based test of residents' skills in diagnostic radiology

    International Nuclear Information System (INIS)

    Finlay, K.; Norman, G.R.; Keane, D.R.; Stolberg, H.

    2006-01-01

    To develop an objective, Web-based tool for evaluating residents' knowledge of diagnostic radiology. We developed and tested a Web-based evaluation tool (the Diagnostic Radiology Skills Test) that consists of 3 tests, one in each of 3 domains of diagnostic radiology: chest, gastrointestinal, and musculoskeletal imaging. Each test comprises 30 cases representing a range of difficulty in the domain, including normal states, normal variants, typical cases of common diagnoses, and cases with more subtle findings. Cases are presented with a long menu of domain-specific possible diagnoses (response options), each coded for diagnostic appropriateness. Our subjects were 21 residents in postgraduate year (PGY) 2 to 5 and 11 experts in diagnostic radiology. Subjects accessed the tool via a Web site on our Web server. Residents test results were compared for reliability and validity across domain, case, and training level. In addition, results were correlated with commonly used established and objective evaluation tools. The tool demonstrated consistent monotonic improvement in performance with training level. It showed acceptable reliability in discriminating between residents at different performance levels, both within and across training levels (r = 0.53 within level and 0.69 across levels). Test results also had concurrent validity against the American College of Radiology In-Training Examination, a widely accepted objective assessment tool (r = 0.65, P < 0.01), and 2 Objective Structured Clinical Examinations (OSCEs) focusing on diagnostic skills (r = 0.78 and r 0.69, P < 0.01, respectively). Our study demonstrates the feasibility of a Web-based, standardized, objective assessment method for evaluating residents' performance. (author)

  5. Gene expression-based molecular diagnostic system for malignant gliomas is superior to histological diagnosis.

    Science.gov (United States)

    Shirahata, Mitsuaki; Iwao-Koizumi, Kyoko; Saito, Sakae; Ueno, Noriko; Oda, Masashi; Hashimoto, Nobuo; Takahashi, Jun A; Kato, Kikuya

    2007-12-15

    Current morphology-based glioma classification methods do not adequately reflect the complex biology of gliomas, thus limiting their prognostic ability. In this study, we focused on anaplastic oligodendroglioma and glioblastoma, which typically follow distinct clinical courses. Our goal was to construct a clinically useful molecular diagnostic system based on gene expression profiling. The expression of 3,456 genes in 32 patients, 12 and 20 of whom had prognostically distinct anaplastic oligodendroglioma and glioblastoma, respectively, was measured by PCR array. Next to unsupervised methods, we did supervised analysis using a weighted voting algorithm to construct a diagnostic system discriminating anaplastic oligodendroglioma from glioblastoma. The diagnostic accuracy of this system was evaluated by leave-one-out cross-validation. The clinical utility was tested on a microarray-based data set of 50 malignant gliomas from a previous study. Unsupervised analysis showed divergent global gene expression patterns between the two tumor classes. A supervised binary classification model showed 100% (95% confidence interval, 89.4-100%) diagnostic accuracy by leave-one-out cross-validation using 168 diagnostic genes. Applied to a gene expression data set from a previous study, our model correlated better with outcome than histologic diagnosis, and also displayed 96.6% (28 of 29) consistency with the molecular classification scheme used for these histologically controversial gliomas in the original article. Furthermore, we observed that histologically diagnosed glioblastoma samples that shared anaplastic oligodendroglioma molecular characteristics tended to be associated with longer survival. Our molecular diagnostic system showed reproducible clinical utility and prognostic ability superior to traditional histopathologic diagnosis for malignant glioma.

  6. Smartphone-Based Mobile Detection Platform for Molecular Diagnostics and Spatiotemporal Disease Mapping.

    Science.gov (United States)

    Song, Jinzhao; Pandian, Vikram; Mauk, Michael G; Bau, Haim H; Cherry, Sara; Tisi, Laurence C; Liu, Changchun

    2018-04-03

    Rapid and quantitative molecular diagnostics in the field, at home, and at remote clinics is essential for evidence-based disease management, control, and prevention. Conventional molecular diagnostics requires extensive sample preparation, relatively sophisticated instruments, and trained personnel, restricting its use to centralized laboratories. To overcome these limitations, we designed a simple, inexpensive, hand-held, smartphone-based mobile detection platform, dubbed "smart-connected cup" (SCC), for rapid, connected, and quantitative molecular diagnostics. Our platform combines bioluminescent assay in real-time and loop-mediated isothermal amplification (BART-LAMP) technology with smartphone-based detection, eliminating the need for an excitation source and optical filters that are essential in fluorescent-based detection. The incubation heating for the isothermal amplification is provided, electricity-free, with an exothermic chemical reaction, and incubation temperature is regulated with a phase change material. A custom Android App was developed for bioluminescent signal monitoring and analysis, target quantification, data sharing, and spatiotemporal mapping of disease. SCC's utility is demonstrated by quantitative detection of Zika virus (ZIKV) in urine and saliva and HIV in blood within 45 min. We demonstrate SCC's connectivity for disease spatiotemporal mapping with a custom-designed website. Such a smart- and connected-diagnostic system does not require any lab facilities and is suitable for use at home, in the field, in the clinic, and particularly in resource-limited settings in the context of Internet of Medical Things (IoMT).

  7. Power supply system on HT-7 tokamak for diagnostic neutral beam based on PLC

    International Nuclear Information System (INIS)

    Zhang Jian; Liu Baohua; Ding Tonghai; Du Shaowu

    2006-01-01

    A power supply system for diagnostic neutral beam on the HT-7 Tokamak was developed. Its logic control system based on S7-300 PLC was described. The experimental results show that the system is easy to operate and its performance is reliable. (authors)

  8. Virtual standards of vibration-based defects diagnostics in railway industry

    Directory of Open Access Journals (Sweden)

    Vladimir TETTER

    2009-01-01

    Full Text Available The issues related to testing the functionality stated by producers of vibration-based diagnostic equipment have been considered. The introduction of virtual standards of defects found in bearing and geared assemblies of rolling stock is offered. The variants of virtual standards realization have been considered.

  9. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    Science.gov (United States)

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  10. Wireless Sensor Network Based Subsurface Contaminant Plume Monitoring

    Science.gov (United States)

    2012-04-16

    Sensor Network (WSN) to monitor contaminant plume movement in naturally heterogeneous subsurface formations to advance the sensor networking based...time to assess the source and predict future plume behavior. This proof-of-concept research aimed at demonstrating the use of an intelligent Wireless

  11. Identifying key nodes in multilayer networks based on tensor decomposition.

    Science.gov (United States)

    Wang, Dingjie; Wang, Haitao; Zou, Xiufen

    2017-06-01

    The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

  12. Effectiveness of firefly algorithm based neural network in time series ...

    African Journals Online (AJOL)

    Effectiveness of firefly algorithm based neural network in time series forecasting. ... In the experiments, three well known time series were used to evaluate the performance. Results obtained were compared with ... Keywords: Time series, Artificial Neural Network, Firefly Algorithm, Particle Swarm Optimization, Overfitting ...

  13. Distributed network generation based on preferential attachment in ABS

    NARCIS (Netherlands)

    K. Azadbakht (Keyvan); N. Bezirgiannis (Nikolaos); F.S. de Boer (Frank)

    2017-01-01

    textabstractGeneration of social networks using Preferential Attachment (PA) mechanism is proposed in the Barabasi-Albert model. In this mechanism, new nodes are introduced to the network sequentially and they attach to the existing nodes preferentially where the preference can be based on the

  14. Optimization-based Method for Automated Road Network Extraction

    International Nuclear Information System (INIS)

    Xiong, D

    2001-01-01

    Automated road information extraction has significant applicability in transportation. It provides a means for creating, maintaining, and updating transportation network databases that are needed for purposes ranging from traffic management to automated vehicle navigation and guidance. This paper is to review literature on the subject of road extraction and to describe a study of an optimization-based method for automated road network extraction

  15. Energy-Efficient Cluster Based Routing Protocol in Mobile Ad Hoc Networks Using Network Coding

    Directory of Open Access Journals (Sweden)

    Srinivas Kanakala

    2014-01-01

    Full Text Available In mobile ad hoc networks, all nodes are energy constrained. In such situations, it is important to reduce energy consumption. In this paper, we consider the issues of energy efficient communication in MANETs using network coding. Network coding is an effective method to improve the performance of wireless networks. COPE protocol implements network coding concept to reduce number of transmissions by mixing the packets at intermediate nodes. We incorporate COPE into cluster based routing protocol to further reduce the energy consumption. The proposed energy-efficient coding-aware cluster based routing protocol (ECCRP scheme applies network coding at cluster heads to reduce number of transmissions. We also modify the queue management procedure of COPE protocol to further improve coding opportunities. We also use an energy efficient scheme while selecting the cluster head. It helps to increase the life time of the network. We evaluate the performance of proposed energy efficient cluster based protocol using simulation. Simulation results show that the proposed ECCRP algorithm reduces energy consumption and increases life time of the network.

  16. Social networking for web-based communities

    NARCIS (Netherlands)

    Issa, T.; Kommers, Petrus A.M.

    2013-01-01

    In the 21st century, a new technology was introduced to facilitate communication, collaboration, and interaction between individuals and businesses. This technology is called social networking; this technology is now part of Internet commodities like email, browsing and blogging. From the 20th

  17. Cloud-based Networked Visual Servo Control

    DEFF Research Database (Denmark)

    Wu, Haiyan; Lu, Lei; Chen, Chih-Chung

    2013-01-01

    , which integrates networked computational resources for cloud image processing, is considered in this article. The main contributions of this article are i) a real-time transport protocol for transmitting large volume image data on a cloud computing platform, which enables high sampling rate visual...

  18. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  19. Ontology-Based Peer Exchange Network (OPEN)

    Science.gov (United States)

    Dong, Hui

    2010-01-01

    In current Peer-to-Peer networks, distributed and semantic free indexing is widely used by systems adopting "Distributed Hash Table" ("DHT") mechanisms. Although such systems typically solve a. user query rather fast in a deterministic way, they only support a very narrow search scheme, namely the exact hash key match. Furthermore, DHT systems put…

  20. Evidence-based medical research on diagnostic criteria and screening technique of vascular mild cognitive impairment

    Directory of Open Access Journals (Sweden)

    Xia-wei LIU

    2015-07-01

    Full Text Available Background Vascular mild cognitive impairment (VaMCI is the prodromal syndrome of vascular dementia (VaD and key target for drug treatment. There is controversy over the diagnostic criteria and screening tools of VaMCI, which affects its clinical diagnosis. This paper aims to explore the clinical features, diagnostic criteria and screening technique of VaMCI.  Methods Taking "vascular mild cognitive impairment OR vascular cognitive impairment no dementia" as retrieval terms, search in PubMed database from January 1997 to March 2015 and screen relevant literatures concerning VaMCI. According to Guidance for the Preparation of Neurological Management Guidelines revised by European Federation of Neurological Societies (EFNS in 2004, evidence grading was performed on literatures. Results A total of 32 literatures in English were selected according to inclusion and exclusion criteria, including 3 guidelines and consensus and 29 clinical studies. Seven literatures (2 on Level Ⅰ, 5 on Level Ⅱ studied on neuropsychological features in VaMCI patients and found reduced processing speed and executive function impairment were main features. Two literatures reported the diagnostic criteria of VaMCI, including VaMCI criteria published by American Heart Association (AHA/American Stroke Association (ASA in 2011 and "Diagnostic Criteria for Vascular Cognitive Disorders" published by International Society for Vascular Behavioral and Cognitive Disorders (VASCOG in 2014. Fifteen literatures (4 on LevelⅠ, 11 on Level Ⅱ described the diagnostic criteria of VaMCI used in clinical research, from which 6 operational diagnostic items were extracted. Fourteen literatures (4 on Level Ⅰ, 10 on Level Ⅱ described neuropsychological assessment tools for VaMCI screening, and found the 5-minute protocol recommended by National Institute of Neurological Disorders and Stroke-Canadian Stroke Network (NINDS-CSN was being good consistency with other neuropsychological

  1. Two port network analysis for three impedance based oscillators

    KAUST Repository

    Said, Lobna A.

    2011-12-01

    Two-port network representations are applied to analyze complex networks which can be dissolved into sub-networks connected in series, parallel or cascade. In this paper, the concept of two-port network has been studied for oscillators. Three impedance oscillator based on two port concept has been analyzed using different impedance structures. The effect of each structure on the oscillation condition and the frequency of oscillation have been introduced. Two different implementations using MOS and BJT have been introduced. © 2011 IEEE.

  2. Optical-Correlator Neural Network Based On Neocognitron

    Science.gov (United States)

    Chao, Tien-Hsin; Stoner, William W.

    1994-01-01

    Multichannel optical correlator implements shift-invariant, high-discrimination pattern-recognizing neural network based on paradigm of neocognitron. Selected as basic building block of this neural network because invariance under shifts is inherent advantage of Fourier optics included in optical correlators in general. Neocognitron is conceptual electronic neural-network model for recognition of visual patterns. Multilayer processing achieved by iteratively feeding back output of feature correlator to input spatial light modulator and updating Fourier filters. Neural network trained by use of characteristic features extracted from target images. Multichannel implementation enables parallel processing of large number of selected features.

  3. Development and Testing of Atomic Beam-Based Plasma Edge Diagnostics in the CIEMAT Fusion Devices

    International Nuclear Information System (INIS)

    Tafalla, D.; Tabares, F.L.; Ortiz, P.; Herrero, V.J.; Tanarro, I.

    1998-01-01

    In this report the development of plasma edge diagnostic based on atomic beam techniques fir their application in the CIEMAT fusion devices is described. The characterisation of the beams in laboratory experiments at the CSIC, together with first results in the Torsatron TJ-II are reported. Two types of beam diagnostics have been developed: a thermal (effusive) Li and a supersonic, pulsed He beams. This work has been carried out in collaboration between the institutions mentioned above under partial financial support by EURATOM. (Author) 17 refs

  4. Diagnostic method for photovoltaic systems based on light I-V measurements

    DEFF Research Database (Denmark)

    Spataru, Sergiu; Sera, Dezso; Kerekes, Tamas

    2015-01-01

    , be it external, such as shading or soiling, or degradation or failure of the PV modules and balance-of-system components. This allows for performing preventive and/or reparative maintenance, thus minimizing further losses and costs. This article proposes a complete diagnostic method for detecting shading...... and analysis of the diagnostic parameters and logic was performed based on module level tests on standard crystalline silicon PV modules, and were optimized to detect even small partial shading and increase series-resistance losses. To demonstrate the practical application and operation of this method...

  5. Hybrid network defense model based on fuzzy evaluation.

    Science.gov (United States)

    Cho, Ying-Chiang; Pan, Jen-Yi

    2014-01-01

    With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  6. Face recognition based on improved BP neural network

    Directory of Open Access Journals (Sweden)

    Yue Gaili

    2017-01-01

    Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

  7. Web-based networking within the framework of ANENT

    International Nuclear Information System (INIS)

    Han, K.W.; Lee, E.J.; Kim, Y.T.; Nam, Y.M.; Kim, H.K.

    2004-01-01

    The Korea Atomic Energy Research Institute (KAERI) is actively participating in the Asian Network for Education in Nuclear Technology (ANENT), which is an IAEA activity to promote nuclear knowledge management. This has led KAERI to conduct a web-based networking for nuclear education and training in Asia. The networking encompasses the establishment of a relevant website and a system for a sustainable operation of the website. The established ANENT website features function as a database providing collected information, a link facilitating a systematic worldwide access to relevant websites, and an activity implementation for supporting the individual tasks of ANENT. The required information is being collected and loaded onto the database, and the website will be improved step by step. Consequently, networking is expected to play an important role, through cooperating with other networks, and thus contributing to a future global network for a sustainable development of nuclear technology. (author)

  8. Physical parameters collection based on wireless senor network

    Science.gov (United States)

    Chen, Xin; Wu, Hong; Ji, Lei

    2013-12-01

    With the development of sensor technology, wireless senor network has been applied in the medical, military, entertainment field and our daily life. But the existing available wireless senor networks applied in human monitoring system still have some problems, such as big power consumption, low security and so on. To improve senor network applied in health monitoring system, the paper introduces a star wireless senor networks based on msp430 and DSP. We design a low-cost heart-rate monitor senor node. The communication between senor node and sink node is realized according to the newest protocol proposed by the IEEE 802.15.6 Task Group. This wireless senor network will be more energy-efficient and faster compared to traditional senor networks.

  9. Rumor Diffusion in an Interests-Based Dynamic Social Network

    Directory of Open Access Journals (Sweden)

    Mingsheng Tang

    2013-01-01

    Full Text Available To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1 positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2 with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3 a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4 a network with a smaller clustering coefficient has a larger efficiency.

  10. The Relationship between Waiting Times and "Adherence" to the Scottish Intercollegiate Guidelines Network 98 Guideline in Autism Spectrum Disorder Diagnostic Services in Scotland

    Science.gov (United States)

    McKenzie, Karen; Forsyth, Kirsty; O'Hare, Anne; McClure, Iain; Rutherford, Marion; Murray, Aja; Irvine, Linda

    2016-01-01

    The aim of this study was to explore the extent to which the Scottish Intercollegiate Guidelines Network 98 guidelines on the assessment and diagnosis of autism spectrum disorder were adhered to in child autism spectrum disorder diagnostic services in Scotland and whether there was a significant relationship between routine practice which more…

  11. Evaluating conducting network based transparent electrodes from geometrical considerations

    Energy Technology Data Exchange (ETDEWEB)

    Kumar, Ankush [Chemistry and Physics of Materials Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, 560064 Bangalore (India); Kulkarni, G. U., E-mail: guk@cens.res.in [Centre for Nano and Soft Matter Sciences, 560013 Bangalore (India)

    2016-01-07

    Conducting nanowire networks have been developed as viable alternative to existing indium tin oxide based transparent electrode (TE). The nature of electrical conduction and process optimization for electrodes have gained much from the theoretical models based on percolation transport using Monte Carlo approach and applying Kirchhoff's law on individual junctions and loops. While most of the literature work pertaining to theoretical analysis is focussed on networks obtained from conducting rods (mostly considering only junction resistance), hardly any attention has been paid to those made using template based methods, wherein the structure of network is neither similar to network obtained from conducting rods nor similar to well periodic geometry. Here, we have attempted an analytical treatment based on geometrical arguments and applied image analysis on practical networks to gain deeper insight into conducting networked structure particularly in relation to sheet resistance and transmittance. Many literature examples reporting networks with straight or curvilinear wires with distributions in wire width and length have been analysed by treating the networks as two dimensional graphs and evaluating the sheet resistance based on wire density and wire width. The sheet resistance values from our analysis compare well with the experimental values. Our analysis on various examples has revealed that low sheet resistance is achieved with high wire density and compactness with straight rather than curvilinear wires and with narrower wire width distribution. Similarly, higher transmittance for given sheet resistance is possible with narrower wire width but of higher thickness, minimal curvilinearity, and maximum connectivity. For the purpose of evaluating active fraction of the network, the algorithm was made to distinguish and quantify current carrying backbone regions as against regions containing only dangling or isolated wires. The treatment can be helpful in

  12. Evaluating conducting network based transparent electrodes from geometrical considerations

    International Nuclear Information System (INIS)

    Kumar, Ankush; Kulkarni, G. U.

    2016-01-01

    Conducting nanowire networks have been developed as viable alternative to existing indium tin oxide based transparent electrode (TE). The nature of electrical conduction and process optimization for electrodes have gained much from the theoretical models based on percolation transport using Monte Carlo approach and applying Kirchhoff's law on individual junctions and loops. While most of the literature work pertaining to theoretical analysis is focussed on networks obtained from conducting rods (mostly considering only junction resistance), hardly any attention has been paid to those made using template based methods, wherein the structure of network is neither similar to network obtained from conducting rods nor similar to well periodic geometry. Here, we have attempted an analytical treatment based on geometrical arguments and applied image analysis on practical networks to gain deeper insight into conducting networked structure particularly in relation to sheet resistance and transmittance. Many literature examples reporting networks with straight or curvilinear wires with distributions in wire width and length have been analysed by treating the networks as two dimensional graphs and evaluating the sheet resistance based on wire density and wire width. The sheet resistance values from our analysis compare well with the experimental values. Our analysis on various examples has revealed that low sheet resistance is achieved with high wire density and compactness with straight rather than curvilinear wires and with narrower wire width distribution. Similarly, higher transmittance for given sheet resistance is possible with narrower wire width but of higher thickness, minimal curvilinearity, and maximum connectivity. For the purpose of evaluating active fraction of the network, the algorithm was made to distinguish and quantify current carrying backbone regions as against regions containing only dangling or isolated wires. The treatment can be helpful in

  13. Neural Networks. Diagnostic and inferential measurements; Reti neurali. Diagnostica e misure inferenziali

    Energy Technology Data Exchange (ETDEWEB)

    Bonavita, N. [Apc Group Leader, Abb Industria, Genua (Italy); Parisini, T. [Milan Politecnico, Milan (Italy). Dipt. di Elettronica e Informazione

    2000-09-01

    In this work, the use of neural approximating networks is described in the context of fault diagnosis of industrial plants with a particular emphasis to the technique of inferential measurements. The proposed methodology is related to the current literature emphasizing advantages and disadvantages of the analytical redundancy concept. The use of neural approximators for the generation of inferential measurement is described in the context of industrial distributed control systems. [Italian] In questo articolo viene descritta l'utilizzazione degli approssimatori neurali in problemi di diagnostica d'impianto con particolare riferimento alla tecnica delle misure inferenziali. Viene fornito un inquadramento della metodologia rispetto alla letteratura attuale mettendo in risalto vantaggi e svantaggi del concetto di ridondanza analitica. L'uso degli approssimatori neurali per la generazione di misure inferenziali e' illustrato in un contesto di sistemi di controllo distribuito di tipo industriale.

  14. Advanced Laser-Based Techniques for Gas-Phase Diagnostics in Combustion and Aerospace Engineering.

    Science.gov (United States)

    Ehn, Andreas; Zhu, Jiajian; Li, Xuesong; Kiefer, Johannes

    2017-03-01

    Gaining information of species, temperature, and velocity distributions in turbulent combustion and high-speed reactive flows is challenging, particularly for conducting measurements without influencing the experimental object itself. The use of optical and spectroscopic techniques, and in particular laser-based diagnostics, has shown outstanding abilities for performing non-intrusive in situ diagnostics. The development of instrumentation, such as robust lasers with high pulse energy, ultra-short pulse duration, and high repetition rate along with digitized cameras exhibiting high sensitivity, large dynamic range, and frame rates on the order of MHz, has opened up for temporally and spatially resolved volumetric measurements of extreme dynamics and complexities. The aim of this article is to present selected important laser-based techniques for gas-phase diagnostics focusing on their applications in combustion and aerospace engineering. Applicable laser-based techniques for investigations of turbulent flows and combustion such as planar laser-induced fluorescence, Raman and Rayleigh scattering, coherent anti-Stokes Raman scattering, laser-induced grating scattering, particle image velocimetry, laser Doppler anemometry, and tomographic imaging are reviewed and described with some background physics. In addition, demands on instrumentation are further discussed to give insight in the possibilities that are offered by laser flow diagnostics.

  15. Case-based reasoning combined with statistics for diagnostics and prognosis

    International Nuclear Information System (INIS)

    Olsson, T; Funk, P

    2012-01-01

    Many approaches used for diagnostics today are based on a precise model. This excludes diagnostics of many complex types of machinery that cannot be modelled and simulated easily or without great effort. Our aim is to show that by including human experience it is possible to diagnose complex machinery when there is no or limited models or simulations available. This also enables diagnostics in a dynamic application where conditions change and new cases are often added. In fact every new solved case increases the diagnostic power of the system. We present a number of successful projects where we have used feature extraction together with case-based reasoning to diagnose faults in industrial robots, welding, cutting machinery and we also present our latest project for diagnosing transmissions by combining Case-Based Reasoning (CBR) with statistics. We view the fault diagnosis process as three consecutive steps. In the first step, sensor fault signals from machines and/or input from human operators are collected. Then, the second step consists of extracting relevant fault features. In the final diagnosis/prognosis step, status and faults are identified and classified. We view prognosis as a special case of diagnosis where the prognosis module predicts a stream of future features.

  16. Hemispheric asymmetry of electroencephalography-based functional brain networks.

    Science.gov (United States)

    Jalili, Mahdi

    2014-11-12

    Electroencephalography (EEG)-based functional brain networks have been investigated frequently in health and disease. It has been shown that a number of graph theory metrics are disrupted in brain disorders. EEG-based brain networks are often studied in the whole-brain framework, where all the nodes are grouped into a single network. In this study, we studied the brain networks in two hemispheres and assessed whether there are any hemispheric-specific patterns in the properties of the networks. To this end, resting state closed-eyes EEGs from 44 healthy individuals were processed and the network structures were extracted separately for each hemisphere. We examined neurophysiologically meaningful graph theory metrics: global and local efficiency measures. The global efficiency did not show any hemispheric asymmetry, whereas the local connectivity showed rightward asymmetry for a range of intermediate density values for the constructed networks. Furthermore, the age of the participants showed significant direct correlations with the global efficiency of the left hemisphere, but only in the right hemisphere, with local connectivity. These results suggest that only local connectivity of EEG-based functional networks is associated with brain hemispheres.

  17. Array-based sensing using nanoparticles: an alternative approach for cancer diagnostics.

    Science.gov (United States)

    Le, Ngoc D B; Yazdani, Mahdieh; Rotello, Vincent M

    2014-07-01

    Array-based sensing using nanoparticles (NPs) provides an attractive alternative to specific biomarker-focused strategies for cancer diagnosis. The physical and chemical properties of NPs provide both the recognition and transduction capabilities required for biosensing. Array-based sensors utilize a combined response from the interactions between sensors and analytes to generate a distinct pattern (fingerprint) for each analyte. These interactions can be the result of either the combination of multiple specific biomarker recognition (specific binding) or multiple selective binding responses, known as chemical nose sensing. The versatility of the latter array-based sensing using NPs can facilitate the development of new personalized diagnostic methodologies in cancer diagnostics, a necessary evolution in the current healthcare system to better provide personalized treatments. This review will describe the basic principle of array-based sensors, along with providing examples of both invasive and noninvasive samples used in cancer diagnosis.

  18. A network-based dynamical ranking system for competitive sports

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

    From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

  19. An FPGA-based torus communication network

    Energy Technology Data Exchange (ETDEWEB)

    Pivanti, Marcello; Schifano, Sebastiano Fabio [INFN, Ferrara (Italy); Ferrara Univ. (Italy); Simma, Hubert [DESY, Zeuthen (Germany). John von Neumann-Institut fuer Computing NIC

    2011-02-15

    We describe the design and FPGA implementation of a 3D torus network (TNW) to provide nearest-neighbor communications between commodity multi-core processors. The aim of this project is to build up tightly interconnected and scalable parallel systems for scientific computing. The design includes the VHDL code to implement on latest FPGA devices a network processor, which can be accessed by the CPU through a PCIe interface and which controls the external PHYs of the physical links. Moreover, a Linux driver and a library implementing custom communication APIs are provided. The TNW has been successfully integrated in two recent parallel machine projects, QPACE and AuroraScience. We describe some details of the porting of the TNW for the AuroraScience system and report performance results. (orig.)

  20. An FPGA-based torus communication network

    International Nuclear Information System (INIS)

    Pivanti, Marcello; Schifano, Sebastiano Fabio; Simma, Hubert

    2011-02-01

    We describe the design and FPGA implementation of a 3D torus network (TNW) to provide nearest-neighbor communications between commodity multi-core processors. The aim of this project is to build up tightly interconnected and scalable parallel systems for scientific computing. The design includes the VHDL code to implement on latest FPGA devices a network processor, which can be accessed by the CPU through a PCIe interface and which controls the external PHYs of the physical links. Moreover, a Linux driver and a library implementing custom communication APIs are provided. The TNW has been successfully integrated in two recent parallel machine projects, QPACE and AuroraScience. We describe some details of the porting of the TNW for the AuroraScience system and report performance results. (orig.)

  1. Ionic liquid based multifunctional double network gel

    Science.gov (United States)

    Ahmed, Kumkum; Higashihara, Tomoya; Arafune, Hiroyuki; Kamijo, Toshio; Morinaga, Takashi; Sato, Takaya; Furukawa, Hidemitsu

    2015-04-01

    Gels are a promising class of soft and wet materials with diverse application in tissue engineering and bio-medical purpose. In order to accelerate the development of gels, it is required to synthesize multi-functional gels of high mechanical strength, ultra low surface friction and suitable elastic modulus with a variety of methods and new materials. Among many types of gel ionic gel made from ionic liquids (ILs) could be used for diverse applications in electrochemical devices and in the field of tribology. IL, a promising materials for lubrication, is a salt with a melting point lower than 100 °C. As a lubricant, ILs are characterized by an extremely low vapor pressure, high thermal stability and high ion conductivity. In this work a novel approach of making double network DN ionic gel using IL has been made utilizing photo polymerization process. A hydrophobic monomer Methyl methacrylate (MMA) has been used as a first network and a hydrophobic IL monomer, N,N-diethyl-N-(2-mthacryloylethyl)-N-methylammonium bistrifluoromethylsulfonyl)imide (DEMM-TFSI) has been used as a second network using photo initiator benzophenon and crosslinker triethylene glycol dimethacrylate (TEGDMA). The resulting DN ionic gel shows transparency, flexibility, high thermal stability, good mechanical toughness and low friction coefficient value which can be a potential candidate as a gel slider in different mechanical devices and can open a new area in the field of gel tribology.

  2. Flexible optical network components based on densely integrated microring resonators

    NARCIS (Netherlands)

    Geuzebroek, D.H.

    2005-01-01

    This thesis addresses the design, realization and characterization of reconfigurable optical network components based on multiple microring resonators. Since thermally tunable microring resonators can be used as wavelength selective space switches, very compact devices with high complexity and

  3. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  4. Building a satellite climate diagnostics data base for real-time climate monitoring

    International Nuclear Information System (INIS)

    Ropelewski, C.F.

    1991-01-01

    The paper discusses the development of a data base, the Satellite Climate Diagnostic Data Base (SCDDB), for real time operational climate monitoring utilizing current satellite data. Special attention is given to the satellite-derived quantities useful for monitoring global climate changes, the requirements of SCDDB, and the use of conventional meteorological data and model assimilated data in developing the SCDDB. Examples of prototype SCDDB products are presented. 10 refs

  5. Network meta-analysis of diagnostic test accuracy studies identifies and ranks the optimal diagnostic tests and thresholds for health care policy and decision-making.

    Science.gov (United States)

    Owen, Rhiannon K; Cooper, Nicola J; Quinn, Terence J; Lees, Rosalind; Sutton, Alex J

    2018-07-01

    Network meta-analyses (NMA) have extensively been used to compare the effectiveness of multiple interventions for health care policy and decision-making. However, methods for evaluating the performance of multiple diagnostic tests are less established. In a decision-making context, we are often interested in comparing and ranking the performance of multiple diagnostic tests, at varying levels of test thresholds, in one simultaneous analysis. Motivated by an example of cognitive impairment diagnosis following stroke, we synthesized data from 13 studies assessing the efficiency of two diagnostic tests: Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA), at two test thresholds: MMSE accounting for the correlations between multiple test accuracy measures from the same study. We developed and successfully fitted a model comparing multiple tests/threshold combinations while imposing threshold constraints. Using this model, we found that MoCA at threshold decision making. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

  6. [Overcoming the limitations of the descriptive and categorical approaches in psychiatric diagnosis: a proposal based on Bayesian networks].

    Science.gov (United States)

    Sorias, Soli

    2015-01-01

    Efforts to overcome the problems of descriptive and categorical approaches have not yielded results. In the present article, psychiatric diagnosis using Bayesian networks is proposed. Instead of a yes/no decision, Bayesian networks give the probability of diagnostic category inclusion, thereby yielding both a graded, i.e., dimensional diagnosis, and a value of the certainty of the diagnosis. With the use of Bayesian networks in the diagnosis of mental disorders, information about etiology, associated features, treatment outcome, and laboratory results may be used in addition to clinical signs and symptoms, with each of these factors contributing proportionally to their own specificity and sensitivity. Furthermore, a diagnosis (albeit one with a lower probability) can be made even with incomplete, uncertain, or partially erroneous information, and patients whose symptoms are below the diagnostic threshold can be evaluated. Lastly, there is no need of NOS or "unspecified" categories, and comorbid disorders become different dimensions of the diagnostic evaluation. Bayesian diagnoses allow the preservation of current categories and assessment methods, and may be used concurrently with criteria-based diagnoses. Users need not put in extra effort except to collect more comprehensive information. Unlike the Research Domain Criteria (RDoC) project, the Bayesian approach neither increases the diagnostic validity of existing categories nor explains the pathophysiological mechanisms of mental disorders. It, however, can be readily integrated to present classification systems. Therefore, the Bayesian approach may be an intermediate phase between criteria-based diagnosis and the RDoC ideal.

  7. Trojan detection model based on network behavior analysis

    International Nuclear Information System (INIS)

    Liu Junrong; Liu Baoxu; Wang Wenjin

    2012-01-01

    Based on the analysis of existing Trojan detection technology, this paper presents a Trojan detection model based on network behavior analysis. First of all, we abstract description of the Trojan network behavior, then according to certain rules to establish the characteristic behavior library, and then use the support vector machine algorithm to determine whether a Trojan invasion. Finally, through the intrusion detection experiments, shows that this model can effectively detect Trojans. (authors)

  8. Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network

    Directory of Open Access Journals (Sweden)

    Kai Lin

    2016-07-01

    Full Text Available With the development of wireless technology, the widespread use of 5G is already an irreversible trend, and millimeter-wave sensor networks are becoming more and more common. However, due to the high degree of complexity and bandwidth bottlenecks, the millimeter-wave sensor network still faces numerous problems. In this paper, we propose a novel content-based multi-channel network coding algorithm, which uses the functions of data fusion, multi-channel and network coding to improve the data transmission; the algorithm is referred to as content-based multi-channel network coding (CMNC. The CMNC algorithm provides a fusion-driven model based on the Dempster-Shafer (D-S evidence theory to classify the sensor nodes into different classes according to the data content. By using the result of the classification, the CMNC algorithm also provides the channel assignment strategy and uses network coding to further improve the quality of data transmission in the millimeter-wave sensor network. Extensive simulations are carried out and compared to other methods. Our simulation results show that the proposed CMNC algorithm can effectively improve the quality of data transmission and has better performance than the compared methods.

  9. Theory of fractional order elements based impedance matching networks

    KAUST Repository

    Radwan, Ahmed G.

    2011-03-01

    Fractional order circuit elements (inductors and capacitors) based impedance matching networks are introduced for the first time. In comparison to the conventional integer based L-type matching networks, fractional matching networks are much simpler and versatile. Any complex load can be matched utilizing a single series fractional element, which generally requires two elements for matching in the conventional approach. It is shown that all the Smith chart circles (resistance and reactance) are actually pairs of completely identical circles. They appear to be single for the conventional integer order case, where the identical circles completely overlap each other. The concept is supported by design equations and impedance matching examples. © 2010 IEEE.

  10. Soft silicone based interpenetrating networks as materials for actuators

    DEFF Research Database (Denmark)

    Yu, Liyun; Gonzalez, Lidia; Hvilsted, Søren

    2014-01-01

    A new approach based on silicone interpenetrating networks with orthogonal chemistries has been investigated with focus on developing soft and flexible elastomers with high energy densities and small viscous losses. The interpenetrating networks are made as simple two pot mixtures...... as for the commercial available silylation based elastomers such as Elastosil RT625. The resulting interpenetrating networks are formulated to be softer than RT625 to increase the actuation caused when applying a voltage due to their softness combined with the significantly higher permittivity than the pure silicone...

  11. Supervised Learning Based on Temporal Coding in Spiking Neural Networks.

    Science.gov (United States)

    Mostafa, Hesham

    2017-08-01

    Gradient descent training techniques are remarkably successful in training analog-valued artificial neural networks (ANNs). Such training techniques, however, do not transfer easily to spiking networks due to the spike generation hard nonlinearity and the discrete nature of spike communication. We show that in a feedforward spiking network that uses a temporal coding scheme where information is encoded in spike times instead of spike rates, the network input-output relation is differentiable almost everywhere. Moreover, this relation is piecewise linear after a transformation of variables. Methods for training ANNs thus carry directly to the training of such spiking networks as we show when training on the permutation invariant MNIST task. In contrast to rate-based spiking networks that are often used to approximate the behavior of ANNs, the networks we present spike much more sparsely and their behavior cannot be directly approximated by conventional ANNs. Our results highlight a new approach for controlling the behavior of spiking networks with realistic temporal dynamics, opening up the potential for using these networks to process spike patterns with complex temporal information.

  12. American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: Evidence-Based Report on Diagnostic Guidelines

    Science.gov (United States)

    Conwell, Darwin L.; Lee, Linda S.; Yadav, Dhiraj; Longnecker, Daniel S.; Miller, Frank H.; Mortele, Koenraad J.; Levy, Michael J.; Kwon, Richard; Lieb, John G.; Stevens, Tyler; Toskes, Philip P.; Gardner, Timothy B.; Gelrud, Andres; Wu, Bechien U.; Forsmark, Christopher E.; Vege, Santhi S.

    2016-01-01

    The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed and evidence based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable or insufficient evidence. A diagnostic (STEP-wise; S-survey, T-tomography, E-endoscopy and P-pancreas function testing) algorithm is proposed that proceeds from a non-invasive to a more invasive approach. This algorithm maximizes specificity (low false positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Futhermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (T-toxic, I-idiopathic, G-genetic, A- autoimmune, R-recurrent and O-obstructive) etiology, gland morphology (Cambridge criteria) and physiologic state (exocrine, endocrine function) for uniformity across future multi-center research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves. PMID:25333398

  13. American Pancreatic Association Practice Guidelines in Chronic Pancreatitis: evidence-based report on diagnostic guidelines.

    Science.gov (United States)

    Conwell, Darwin L; Lee, Linda S; Yadav, Dhiraj; Longnecker, Daniel S; Miller, Frank H; Mortele, Koenraad J; Levy, Michael J; Kwon, Richard; Lieb, John G; Stevens, Tyler; Toskes, Phillip P; Gardner, Timothy B; Gelrud, Andres; Wu, Bechien U; Forsmark, Christopher E; Vege, Santhi S

    2014-11-01

    The diagnosis of chronic pancreatitis remains challenging in early stages of the disease. This report defines the diagnostic criteria useful in the assessment of patients with suspected and established chronic pancreatitis. All current diagnostic procedures are reviewed, and evidence-based statements are provided about their utility and limitations. Diagnostic criteria for chronic pancreatitis are classified as definitive, probable, or insufficient evidence. A diagnostic (STEP-wise; survey, tomography, endoscopy, and pancreas function testing) algorithm is proposed that proceeds from a noninvasive to a more invasive approach. This algorithm maximizes specificity (low false-positive rate) in subjects with chronic abdominal pain and equivocal imaging changes. Furthermore, a nomenclature is suggested to further characterize patients with established chronic pancreatitis based on TIGAR-O (toxic, idiopathic, genetic, autoimmune, recurrent, and obstructive) etiology, gland morphology (Cambridge criteria), and physiologic state (exocrine, endocrine function) for uniformity across future multicenter research collaborations. This guideline will serve as a baseline manuscript that will be modified as new evidence becomes available and our knowledge of chronic pancreatitis improves.

  14. Reliability analysis of cluster-based ad-hoc networks

    International Nuclear Information System (INIS)

    Cook, Jason L.; Ramirez-Marquez, Jose Emmanuel

    2008-01-01

    The mobile ad-hoc wireless network (MAWN) is a new and emerging network scheme that is being employed in a variety of applications. The MAWN varies from traditional networks because it is a self-forming and dynamic network. The MAWN is free of infrastructure and, as such, only the mobile nodes comprise the network. Pairs of nodes communicate either directly or through other nodes. To do so, each node acts, in turn, as a source, destination, and relay of messages. The virtue of a MAWN is the flexibility this provides; however, the challenge for reliability analyses is also brought about by this unique feature. The variability and volatility of the MAWN configuration makes typical reliability methods (e.g. reliability block diagram) inappropriate because no single structure or configuration represents all manifestations of a MAWN. For this reason, new methods are being developed to analyze the reliability of this new networking technology. New published methods adapt to this feature by treating the configuration probabilistically or by inclusion of embedded mobility models. This paper joins both methods together and expands upon these works by modifying the problem formulation to address the reliability analysis of a cluster-based MAWN. The cluster-based MAWN is deployed in applications with constraints on networking resources such as bandwidth and energy. This paper presents the problem's formulation, a discussion of applicable reliability metrics for the MAWN, and illustration of a Monte Carlo simulation method through the analysis of several example networks

  15. Mutual information-based LPI optimisation for radar network

    Science.gov (United States)

    Shi, Chenguang; Zhou, Jianjiang; Wang, Fei; Chen, Jun

    2015-07-01

    Radar network can offer significant performance improvement for target detection and information extraction employing spatial diversity. For a fixed number of radars, the achievable mutual information (MI) for estimating the target parameters may extend beyond a predefined threshold with full power transmission. In this paper, an effective low probability of intercept (LPI) optimisation algorithm is presented to improve LPI performance for radar network. Based on radar network system model, we first provide Schleher intercept factor for radar network as an optimisation metric for LPI performance. Then, a novel LPI optimisation algorithm is presented, where for a predefined MI threshold, Schleher intercept factor for radar network is minimised by optimising the transmission power allocation among radars in the network such that the enhanced LPI performance for radar network can be achieved. The genetic algorithm based on nonlinear programming (GA-NP) is employed to solve the resulting nonconvex and nonlinear optimisation problem. Some simulations demonstrate that the proposed algorithm is valuable and effective to improve the LPI performance for radar network.

  16. Topological Embedding Feature Based Resource Allocation in Network Virtualization

    Directory of Open Access Journals (Sweden)

    Hongyan Cui

    2014-01-01

    Full Text Available Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

  17. Get the Diagnosis: an evidence-based medicine collaborative Wiki for diagnostic test accuracy.

    Science.gov (United States)

    Hammer, Mark M; Kohlberg, Gavriel D

    2017-04-01

    Despite widespread calls for its use, there are challenges to the implementation of evidence-based medicine (EBM) in clinical practice. In response to the challenges of finding timely, pertinent information on diagnostic test accuracy, we developed an online, crowd-sourced Wiki on diagnostic test accuracy called Get the Diagnosis (GTD, http://www.getthediagnosis.org). Since its launch in November 2008 till October 2015, GTD has accumulated information on 300 diagnoses, with 1617 total diagnostic entries. There are a total of 1097 unique diagnostic tests with a mean of 5.4 tests (range 0-38) per diagnosis. 73% of entries (1182 of 1617) have an associated sensitivity and specificity and 89% of entries (1432 of 1617) have associated peer-reviewed literature citations. Altogether, GTD contains 474 unique literature citations. For a sample of three diagnoses, the search precision (percentage of relevant results in the first 30 entries) in GTD was 100% as compared with a range of 13.3%-63.3% for PubMed and between 6.7% and 76.7% for Google Scholar. GTD offers a fast, precise and efficient way to look up diagnostic test accuracy. On three selected examples, GTD had a greater precision rate compared with PubMed and Google Scholar in identifying diagnostic test information. GTD is a free resource that complements other currently available resources. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  18. Experimental and numerical studies on liquid wicking into filter papers for paper-based diagnostics

    International Nuclear Information System (INIS)

    Liu, Zhi; Hu, Jie; Zhao, Yimeng; Qu, Zhiguo; Xu, Feng

    2015-01-01

    Paper-based diagnostics have shown promising potential applications in human disease surveillance and food safety analysis at the point-of-care (POC). The liquid wicking behavior in diagnostic fibrous paper plays an important role in development of paper-based diagnostics. In the current study, we performed experimental and numerical research on the liquid wicking height and mass with three width strips into filter paper. The effective porosity could be conveniently measured in the light of the linear correlation between wicking height and mass by the experimental system. A modified model with considering evaporation effect was proposed to predict wicking height and mass. The predicted wicking height and mass using the evaporation model was much closer to the experimental data compared with the model without evaporation. The wicking speed initially decreased significantly and then maintained at a constant value at lower level. The evaporation effect tends to reduce the wicking flow speed. More wicking mass could be obtained at larger strip width but the corresponding reagent loss became significant. The proposed model with evaporation paved a way to understanding the fundamental of fluid flow in diagnostic paper and was essential to provide meaningful and useful reference for the research and development of paper-based diagnostics devices. - Highlights: • A model with considering evaporation was proposed to predict wicking height and mass. • Flow characteristics of filter paper were experimentally and theoretically studied. • Effective porosity could be conveniently measured by the experimental platform. • The evaporation effect tended to reduce the wicking flow speed

  19. Qualitative Event-Based Diagnosis: Case Study on the Second International Diagnostic Competition

    Science.gov (United States)

    Daigle, Matthew; Roychoudhury, Indranil

    2010-01-01

    We describe a diagnosis algorithm entered into the Second International Diagnostic Competition. We focus on the first diagnostic problem of the industrial track of the competition in which a diagnosis algorithm must detect, isolate, and identify faults in an electrical power distribution testbed and provide corresponding recovery recommendations. The diagnosis algorithm embodies a model-based approach, centered around qualitative event-based fault isolation. Faults produce deviations in measured values from model-predicted values. The sequence of these deviations is matched to those predicted by the model in order to isolate faults. We augment this approach with model-based fault identification, which determines fault parameters and helps to further isolate faults. We describe the diagnosis approach, provide diagnosis results from running the algorithm on provided example scenarios, and discuss the issues faced, and lessons learned, from implementing the approach

  20. Combining Host-based and network-based intrusion detection system

    African Journals Online (AJOL)

    These attacks were simulated using hping. The proposed system is implemented in Java. The results show that the proposed system is able to detect attacks both from within (host-based) and outside sources (network-based). Key Words: Intrusion Detection System (IDS), Host-based, Network-based, Signature, Security log.

  1. IGENPRO knowledge-based digital system for process transient diagnostics and management

    International Nuclear Information System (INIS)

    Morman, J.A.; Reifman, J.; Vitela, J.E.; Wei, T.Y.C.; Applequist, C.A.; Hippely, P.; Kuk, W.; Tsoukalas, L.H.

    1998-01-01

    Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R and D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R and D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management. (author)

  2. IGENPRO knowledge-based digital system for process transient diagnostics and management

    International Nuclear Information System (INIS)

    Morman, J.A.; Reifman, J.; Wei, T.Y.C.

    1997-01-01

    Verification and validation issues have been perceived as important factors in the large scale deployment of knowledge-based digital systems for plant transient diagnostics and management. Research and development (R ampersand D) is being performed on the IGENPRO package to resolve knowledge base issues. The IGENPRO approach is to structure the knowledge bases on generic thermal-hydraulic (T-H) first principles and not use the conventional event-basis structure. This allows for generic comprehensive knowledge, relatively small knowledge bases and above all the possibility of T-H system/plant independence. To demonstrate concept feasibility the knowledge structure has been implemented in the diagnostic module PRODIAG. Promising laboratory testing results have been obtained using data from the full scope Braidwood PWR operator training simulator. This knowledge structure is now being implemented in the transient management module PROMANA to treat unanticipated events and the PROTREN module is being developed to process actual plant data. Achievement of the IGENPRO R ampersand D goals should contribute to the acceptance of knowledge-based digital systems for transient diagnostics and management

  3. Single Frequency Network Based Distributed Passive Radar Technology

    Directory of Open Access Journals (Sweden)

    Wan Xian-rong

    2015-01-01

    Full Text Available The research and application of passive radar are heading from single transmitter-receiver pair to multiple transmitter-receiver pairs. As an important class of the illuminators of opportunity, most of modern digital broadcasting and television systems work on Single Frequency Network (SFN, which intrinsically determines that the passive radar based on such illuminators must be distributed and networked. In consideration of the remarkable working and processing mode of passive radar under SFN configuration, this paper proposes the concept of SFN-based Distributed Passive Radar (SDPR. The main characteristics and key problems of SDPR are first described. Then several potential solutions are discussed for part of the key technologies. The feasibility of SDPR is demonstrated by preliminary experimental results. Finally, the concept of four network convergence that includes the broadcast based passive radar network is conceived, and its application prospects are discussed.

  4. Passivity-based control and estimation in networked robotics

    CERN Document Server

    Hatanaka, Takeshi; Fujita, Masayuki; Spong, Mark W

    2015-01-01

    Highlighting the control of networked robotic systems, this book synthesizes a unified passivity-based approach to an emerging cross-disciplinary subject. Thanks to this unified approach, readers can access various state-of-the-art research fields by studying only the background foundations associated with passivity. In addition to the theoretical results and techniques,  the authors provide experimental case studies on testbeds of robotic systems  including networked haptic devices, visual robotic systems,  robotic network systems and visual sensor network systems. The text begins with an introduction to passivity and passivity-based control together with the other foundations needed in this book. The main body of the book consists of three parts. The first examines how passivity can be utilized for bilateral teleoperation and demonstrates the inherent robustness of the passivity-based controller against communication delays. The second part emphasizes passivity’s usefulness for visual feedback control ...

  5. Cooperative and Adaptive Network Coding for Gradient Based Routing in Wireless Sensor Networks with Multiple Sinks

    Directory of Open Access Journals (Sweden)

    M. E. Migabo

    2017-01-01

    Full Text Available Despite its low computational cost, the Gradient Based Routing (GBR broadcast of interest messages in Wireless Sensor Networks (WSNs causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size GF(2S at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC techniques.

  6. Caries treatment in a dental practice-based research network

    DEFF Research Database (Denmark)

    Gilbert, Gregg H; Gordan, Valeria V; Funkhouser, Ellen M

    2012-01-01

    OBJECTIVES: Practice-based research networks (PBRNs) provide a venue to foster evidence-based care. We tested the hypothesis that a higher level of participation in a dental PBRN is associated with greater stated change toward evidence-based practice. METHODS: A total of 565 dental PBRN practitio......OBJECTIVES: Practice-based research networks (PBRNs) provide a venue to foster evidence-based care. We tested the hypothesis that a higher level of participation in a dental PBRN is associated with greater stated change toward evidence-based practice. METHODS: A total of 565 dental PBRN......) of 36.0 (3.8) months later. A total of 224 were 'full participants' (enrolled in clinical studies and attended at least one network meeting); 181 were 'partial participants' (did not meet 'full' criteria). RESULTS: From 10% to 62% of practitioners were 'surgically invasive' at baseline, depending...

  7. Detection rates in pediatric diagnostic imaging: a picture archive and communication system compared with a web-based imaging system

    International Nuclear Information System (INIS)

    McDonald, L.; Cramer, B.; Barrett, B.

    2006-01-01

    This prospective study assesses whether there are differences in accuracy of interpretation of diagnostic images among users of a picture archive and communication system (PACS) diagnostic workstation, compared with a less costly Web-based imaging system on a personal computer (PC) with a high resolution monitor. One hundred consecutive pediatric chest or abdomen and skeletal X-rays were selected from hospital inpatient and outpatient studies over a 5-month interval. They were classified as normal (n = 32), obviously abnormal (n = 33), or having subtle abnormal findings (n = 35) by 2 senior radiologists who reached a consensus for each individual case. Subsequently, 5 raters with varying degrees of experience independently viewed and interpreted the cases as normal or abnormal. Raters viewed each image 1 month apart on a PACS and on the Web-based PC imaging system. There was no relation between accuracy of detection and the system used to evaluate X-ray images (P = 0.92). The total percentage of incorrect interpretations on the Web-based PC imaging system was 23.2%, compared with 23.6% on the PACS (P = 0.92). For all raters combined, the overall difference in proportion assessed incorrectly on the PACS, compared with the PC system, was not significant at 0.4% (95%CI, -3.5% to 4.3%). The high-resolution Web-based imaging system via PC is an adequate alternative to a PACS clinical workstation. Accordingly, the provision of a more extensive network of workstations throughout the hospital setting could have potentially significant cost savings. (author)

  8. Resource Discovery in Activity-Based Sensor Networks

    DEFF Research Database (Denmark)

    Bucur, Doina; Bardram, Jakob

    This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networ....... ABSN enhances the generic Extended Zone Routing Protocol with logical sensor grouping and greatly lowers network overhead during the process of discovery, while keeping discovery latency close to optimal.......This paper proposes a service discovery protocol for sensor networks that is specifically tailored for use in humancentered pervasive environments. It uses the high-level concept of computational activities (as logical bundles of data and resources) to give sensors in Activity-Based Sensor Networks...... (ABSNs) knowledge about their usage even at the network layer. ABSN redesigns classical network-level service discovery protocols to include and use this logical structuring of the network for a more practically applicable service discovery scheme. Noting that in practical settings activity-based sensor...

  9. Efficient Vector-Based Forwarding for Underwater Sensor Networks

    Directory of Open Access Journals (Sweden)

    Peng Xie

    2010-01-01

    Full Text Available Underwater Sensor Networks (UWSNs are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, node mobility, high error probability, and 3-dimensional space. These new features bring many challenges to the network protocol design of UWSNs. In this paper, we tackle one fundamental problem in UWSNs: robust, scalable, and energy efficient routing. We propose vector-based forwarding (VBF, a geographic routing protocol. In VBF, the forwarding path is guided by a vector from the source to the target, no state information is required on the sensor nodes, and only a small fraction of the nodes is involved in routing. To improve the robustness, packets are forwarded in redundant and interleaved paths. Further, a localized and distributed self-adaptation algorithm allows the nodes to reduce energy consumption by discarding redundant packets. VBF performs well in dense networks. For sparse networks, we propose a hop-by-hop vector-based forwarding (HH-VBF protocol, which adapts the vector-based approach at every hop. We evaluate the performance of VBF and HH-VBF through extensive simulations. The simulation results show that VBF achieves high packet delivery ratio and energy efficiency in dense networks and HH-VBF has high packet delivery ratio even in sparse networks.

  10. FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Zhan Wei Siew

    2012-12-01

    Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.

  11. On the neutron noise diagnostics of pressurized water reactor control rod vibrations. 4: Application of neural networks

    International Nuclear Information System (INIS)

    Pazsit, I.; Garis, N.S.

    1996-01-01

    A neutron noise-based technique for the localization of excessively vibrating control rods is elaborated upon in the previous three papers of this series. The method is based on the inversion of a formula that expresses the auto- and cross spectra of three neutron detector signals through the parameters of the vibrating rod, i.e., equilibrium position and displacement components. Successful tests of the algorithm with both simulated and real data were reported in the previous papers. The algorithm had nevertheless certain drawbacks, namely, that its use requires expert knowledge, the redundancy of extra detectors cannot be utilized, and with realistic transfer functions the calculations are rather lengthy. The use of neural networks offers an alternative way of performing the inversion procedure. This possibility was investigated by constructing a network that was trained to determine the rod position from the detector spectra. It was found that all shortcomings of the traditional localization method can be eliminated. The neural network-based identification was also tested with success

  12. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  13. Incentive-Based Voltage Regulation in Distribution Networks

    Energy Technology Data Exchange (ETDEWEB)

    Dall-Anese, Emiliano [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Baker, Kyri A [National Renewable Energy Laboratory (NREL), Golden, CO (United States); Zhou, Xinyang [University of Colorado; Chen, Lijun [University of Colorado

    2017-07-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  14. A complex network-based importance measure for mechatronics systems

    Science.gov (United States)

    Wang, Yanhui; Bi, Lifeng; Lin, Shuai; Li, Man; Shi, Hao

    2017-01-01

    In view of the negative impact of functional dependency, this paper attempts to provide an alternative importance measure called Improved-PageRank (IPR) for measuring the importance of components in mechatronics systems. IPR is a meaningful extension of the centrality measures in complex network, which considers usage reliability of components and functional dependency between components to increase importance measures usefulness. Our work makes two important contributions. First, this paper integrates the literature of mechatronic architecture and complex networks theory to define component network. Second, based on the notion of component network, a meaningful IPR is brought into the identifying of important components. In addition, the IPR component importance measures, and an algorithm to perform stochastic ordering of components due to the time-varying nature of usage reliability of components and functional dependency between components, are illustrated with a component network of bogie system that consists of 27 components.

  15. Dynamic Evolution Model Based on Social Network Services

    Science.gov (United States)

    Xiong, Xi; Gou, Zhi-Jian; Zhang, Shi-Bin; Zhao, Wen

    2013-11-01

    Based on the analysis of evolutionary characteristics of public opinion in social networking services (SNS), in the paper we propose a dynamic evolution model, in which opinions are coupled with topology. This model shows the clustering phenomenon of opinions in dynamic network evolution. The simulation results show that the model can fit the data from a social network site. The dynamic evolution of networks accelerates the opinion, separation and aggregation. The scale and the number of clusters are influenced by confidence limit and rewiring probability. Dynamic changes of the topology reduce the number of isolated nodes, while the increased confidence limit allows nodes to communicate more sufficiently. The two effects make the distribution of opinion more neutral. The dynamic evolution of networks generates central clusters with high connectivity and high betweenness, which make it difficult to control public opinions in SNS.

  16. Incentive-Based Voltage Regulation in Distribution Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Xinyang; Chen, Lijun; Dall' Anese, Emiliano; Baker, Kyri

    2017-03-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  17. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  18. Newborn Congenital Cytomegalovirus Screening Based on Clinical Manifestations and Evaluation of DNA-based Assays for In Vitro Diagnostics.

    Science.gov (United States)

    Fujii, Tomoyuki; Oka, Akira; Morioka, Ichiro; Moriuchi, Hiroyuki; Koyano, Shin; Yamada, Hideto; Saito, Shigeru; Sameshima, Hiroshi; Nagamatsu, Takeshi; Tsuchida, Shinya; Inoue, Naoki

    2017-10-01

    To establish a strategy for congenital cytomegalovirus (cCMV) screening and to establish confirmatory assays approved as in vitro diagnostics by the regulatory authorities, we evaluated the clinical risks and performance of diagnostic assays developed by commercial companies, since cCMV infection has significant clinical consequences. Newborns with clinical manifestations considered to be consequences of cCMV infection (n = 575) were screened for the presence of cytomegalovirus (CMV) DNA in urine specimens collected onto filter paper placed in their diapers using the polymerase chain reaction-based assay reported previously. Liquid urine specimens were obtained from all of 20 CMV-positive newborns and 107 of the CMV-negative newborns identified in the screening. We used these 127 specimens, as well as 12 from cCMV cases identified in a previous study and 41 from healthy newborns, to compare the performance of 2 commercial assays and 1 in-house assay. The risk-based screening allowed the identification of cCMV cases at least 10-fold more efficiently than our previous universal screening, although there appears to be a limit to the identification of asymptomatically infected newborns. Although CMV-specific IgM during pregnancy was found frequently in mothers of cCMV newborns, CMV-IgM alone is not an effective diagnostic marker. The urine-filter-based assay and the 3 diagnostic assays yielded identical results. Although risk-based and universal newborn screening strategies for cCMV infection each have their respective advantages and disadvantages, urine-filter-based assay followed by confirmatory in vitro diagnostics assays is able to identify cCMV cases efficiently.

  19. Diagnosing Appendicitis: Evidence-Based Review of the Diagnostic Approach in 2014

    Science.gov (United States)

    Shogilev, Daniel J.; Duus, Nicolaj; Odom, Stephen R.; Shapiro, Nathan I.

    2014-01-01

    Introduction Acute appendicitis is the most common abdominal emergency requiring emergency surgery. However, the diagnosis is often challenging and the decision to operate, observe or further work-up a patient is often unclear. The utility of clinical scoring systems (namely the Alvarado score), laboratory markers, and the development of novel markers in the diagnosis of appendicitis remains controversial. This article presents an update on the diagnostic approach to appendicitis through an evidence-based review. Methods We performed a broad Medline search of radiological imaging, the Alvarado score, common laboratory markers, and novel markers in patients with suspected appendicitis. Results Computed tomography (CT) is the most accurate mode of imaging for suspected cases of appendicitis, but the associated increase in radiation exposure is problematic. The Alvarado score is a clinical scoring system that is used to predict the likelihood of appendicitis based on signs, symptoms and laboratory data. It can help risk stratify patients with suspected appendicitis and potentially decrease the use of CT imaging in patients with certain Alvarado scores. White blood cell (WBC), C-reactive protein (CRP), granulocyte count and proportion of polymorphonuclear (PMN) cells are frequently elevated in patients with appendicitis, but are insufficient on their own as a diagnostic modality. When multiple markers are used in combination their diagnostic utility is greatly increased. Several novel markers have been proposed to aid in the diagnosis of appendicitis; however, while promising, most are only in the preliminary stages of being studied. Conclusion While CT is the most accurate mode of imaging in suspected appendicitis, the accompanying radiation is a concern. Ultrasound may help in the diagnosis while decreasing the need for CT in certain circumstances. The Alvarado Score has good diagnostic utility at specific cutoff points. Laboratory markers have very limited

  20. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  1. Constructing financial network based on PMFG and threshold method

    Science.gov (United States)

    Nie, Chun-Xiao; Song, Fu-Tie

    2018-04-01

    Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

  2. Non-invasive diagnostics of the maxillary and frontal sinuses based on diode laser gas spectroscopy.

    Science.gov (United States)

    Lewander, Märta; Lindberg, Sven; Svensson, Tomas; Siemund, Roger; Svanberg, Katarina; Svanberg, Sune

    2012-03-01

    Suspected, but objectively absent, rhinosinusitis constitutes a major cause of visits to the doctor, high health care costs, and the over-prescription of antibiotics, contributing to the serious problem of resistant bacteria. This situation is largely due to a lack of reliable and widely applicable diagnostic methods. A novel method for the diagnosis of rhinosinusitis based on non-intrusive diode laser gas spectroscopy is presented. The technique is based on light absorption by free gas (oxygen and water vapour) inside the sinuses, and has the potential to be a complementary diagnostic tool in primary health care. The method was evaluated on 40 patients with suspected sinus problems, referred to the diagnostic radiology clinic for low-dose computed tomography (CT), which was used as the reference technique. The data obtained with the new laser-based method correlated well with the grading of opacification and ventilation using CT. The sensitivity and specificity were estimated to be 93% and 61%, respectively, for the maxillary sinuses, and 94% and 86%, respectively, for the frontal sinuses. Good reproducibility was shown. The laser-based technique presents real-time clinical data that correlate well to CT findings, while being non-intrusive and avoiding the use of ionizing radiation.

  3. Appraising and applying evidence about a diagnostic test during a performance-based assessment

    Directory of Open Access Journals (Sweden)

    Franklin Ellen

    2004-10-01

    Full Text Available Abstract Background The practice of Evidence-based Medicine requires that clinicians assess the validity of published research and then apply the results to patient care. We wanted to assess whether our soon-to-graduate medical students could appraise and apply research about a diagnostic test within a clinical context and to compare our students with peers trained at other institutions. Methods 4th year medical students who previously had demonstrated competency at probability revision and just starting first-year Internal Medicine residents were used for this research. Following an encounter with a simulated patient, subjects critically appraised a paper about an applicable diagnostic test and revised the patient's pretest probability given the test result. Results The medical students and residents demonstrated similar skills at critical appraisal, correctly answering 4.7 and 4.9, respectively, of 6 questions (p = 0.67. Only one out of 28 (3% medical students and none of the 15 residents were able to correctly complete the probability revision task (p = 1.00. Conclusions This study found that most students completing medical school are able to appraise an article about a diagnostic test but few are able to apply the information from the article to a patient. These findings raise questions about the clinical usefulness of the EBM skills possessed by graduating medical students within the area of diagnostic testing.

  4. Feedback Gating Control for Network Based on Macroscopic Fundamental Diagram

    Directory of Open Access Journals (Sweden)

    YangBeibei Ji

    2016-01-01

    Full Text Available Empirical data from Yokohama, Japan, showed that a macroscopic fundamental diagram (MFD of urban traffic provides for different network regions a unimodal low-scatter relationship between network vehicle density and network space-mean flow. This provides new tools for network congestion control. Based on MFD, this paper proposed a feedback gating control policy which can be used to mitigate network congestion by adjusting signal timings of gating intersections. The objective of the feedback gating control model is to maximize the outflow and distribute the allowed inflows properly according to external demand and capacity of each gating intersection. An example network is used to test the performance of proposed feedback gating control model. Two types of background signalization types for the intersections within the test network, fixed-time and actuated control, are considered. The results of extensive simulation validate that the proposed feedback gating control model can get a Pareto improvement since the performance of both gating intersections and the whole network can be improved significantly especially under heavy demand situations. The inflows and outflows can be improved to a higher level, and the delay and queue length at all gating intersections are decreased dramatically.

  5. Software defined network architecture based research on load balancing strategy

    Science.gov (United States)

    You, Xiaoqian; Wu, Yang

    2018-05-01

    As a new type network architecture, software defined network has the key idea of separating the control place of the network from the transmission plane, to manage and control the network in a concentrated way; in addition, the network interface is opened on the control layer and the data layer, so as to achieve programmable control of the network. Considering that only the single shortest route is taken into the calculation of traditional network data flow transmission, and congestion and resource consumption caused by excessive load of link circuits are ignored, a link circuit load based flow media business QoS gurantee system is proposed in this article to divide the flow in the network into ordinary data flow and QoS flow. In this way, it supervises the link circuit load with the controller so as to calculate reasonable route rapidly and issue the flow table to the exchanger, to finish rapid data transmission. In addition, it establishes a simulation platform to acquire optimized result through simulation experiment.

  6. A Concept of Location-Based Social Network Marketing

    DEFF Research Database (Denmark)

    Tussyadiah, Iis

    2012-01-01

    A stimulus-response model of location-based social network marketing is conceptualized based on an exploratory investigation. Location-based social network applications are capable of generating marketing stimuli from merchant, competition-based, and connection-based rewards resulted from relevance...... and connectivity. Depending on consumption situations, consumer characteristics, and social network structure, these rewards lead to actual behavior that manifests in variety behavior (i.e., patronage to new places) and loyalty behavior (i.e., increased frequency of patronage to familiar places). This behavior...... implies changes in patterns of mobility, making this marketing approach particularly relevant for tourism and hospitality businesses. Managerial implications and recommendations for further studies are provided....

  7. Neural network-based sensor signal accelerator.

    Energy Technology Data Exchange (ETDEWEB)

    Vogt, M. C.

    2000-10-16

    A strategy has been developed to computationally accelerate the response time of a generic electronic sensor. The strategy can be deployed as an algorithm in a control system or as a physical interface (on an embedded microcontroller) between a slower responding external sensor and a higher-speed control system. Optional code implementations are available to adjust algorithm performance when computational capability is limited. In one option, the actual sensor signal can be sampled at the slower rate with adaptive linear neural networks predicting the sensor's future output and interpolating intermediate synthetic output values. In another option, a synchronized collection of predictors sequentially controls the corresponding synthetic output voltage. Error is adaptively corrected in both options. The core strategy has been demonstrated with automotive oxygen sensor data. A prototype interface device is under construction. The response speed increase afforded by this strategy could greatly offset the cost of developing a replacement sensor with a faster physical response time.

  8. EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

    International Nuclear Information System (INIS)

    Ha, Jun Su; Seong, Poong Hyun

    2005-01-01

    In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4

  9. USXR Based MHD, Transport, Equilibria and Current Profile Diagnostics for NSTX. Final Report

    International Nuclear Information System (INIS)

    Finkenthal, Michael

    2009-01-01

    The present report resumes the research activities of the Plasma Spectroscopy/Diagnostics Group at Johns Hopkins University performed on the NSTX tokamak at PPPL during the period 1999-2009. During this period we have designed and implemented XUV based diagnostics for a large number of tasks: study of impurity content and particle transport, MHD activity, time-resolved electron temperature measeurements, ELM research, etc. Both line emission and continuum were used in the XUV range. New technics and novel methods have been devised within the framework of the present research. Graduate and post-graduate students have been involved at all times in addition to the senior research personnel. Several tens of papers have been published and lectures have been given based on the obtained results at conferences and various research institutions (lists of these activities were attached both in each proposal and in the annual reports submitted to our supervisors at OFES)

  10. Innovation in diagnostic imaging services: assessing the potential for value-based reimbursement.

    Science.gov (United States)

    Garrison, Louis P; Bresnahan, Brian W; Higashi, Mitchell K; Hollingworth, William; Jarvik, Jeffrey G

    2011-09-01

    Innovation in the field of diagnostic imaging is based primarily on the availability of new and improved equipment that opens the door for new clinical applications. Payments for these imaging procedures are subject to complex Medicare price control schemes, affecting incentives for appropriate use and innovation. Achieving a "dynamically efficient" health care system-one that elicits a socially optimal amount of innovation-requires that innovators be rewarded in relation to the value they add and can demonstrate with evidence. The authors examine how and whether value-based reimbursement for diagnostic imaging services might better reward innovation explicitly for expected improvements in health and economic outcomes. Copyright © 2011 AUR. Published by Elsevier Inc. All rights reserved.

  11. EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

    Energy Technology Data Exchange (ETDEWEB)

    Ha, Jun Su; Seong, Poong Hyun [Korea Advanced Institute of Science and Technology, Taejon (Korea, Republic of)

    2005-07-01

    In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4.

  12. ICF implosion hotspot ion temperature diagnostic techniques based on neutron time-of-flight method

    International Nuclear Information System (INIS)

    Tang Qi; Song Zifeng; Chen Jiabin; Zhan Xiayu

    2013-01-01

    Ion temperature of implosion hotspot is a very important parameter for inertial confinement fusion. It reflects the energy level of the hotspot, and it is very sensitive to implosion symmetry and implosion speed. ICF implosion hotspot ion temperature diagnostic techniques based on neutron time-of-flight method were described. A neutron TOF spectrometer was developed using a ultrafast plastic scintillator as the neutron detector. Time response of the spectrometer has 1.1 ns FWHM and 0.5 ns rising time. TOF spectrum resolving method based on deconvolution and low pass filter was illuminated. Implosion hotspot ion temperature in low neutron yield and low ion temperature condition at Shenguang-Ⅲ facility was acquired using the diagnostic techniques. (authors)

  13. Diagnostic of the temperature and differential emission measure (DEM based on Hinode/XRT data

    Directory of Open Access Journals (Sweden)

    P. Rudawy

    2008-10-01

    Full Text Available We discuss here various methodologies and an optimal strategy of the temperature and emission measure diagnostics based on Hinode X-Ray Telescope data. As an example of our results we present the determination of the temperature distribution of the X-rays emitting plasma using a filters ratio method and three various methods of the calculation of the differential emission measure (DEM. We have found that all these methods give results similar to the two filters ratio method. Additionally, all methods of the DEM calculation gave similar solutions. We can state that the majority of the pairs of the Hinode filters allows one to derive the temperature and emission measure in the isothermal plasma approximation using standard diagnostics based on the two filters ratio method. In cases of strong flares one can also expect good conformity of the results obtained using a Withbroe – Sylwester, genetic algorithm and least-squares methods of the DEM evaluation.

  14. Error tolerance analysis of wave diagnostic based on coherent modulation imaging in high power laser system

    Science.gov (United States)

    Pan, Xingchen; Liu, Cheng; Zhu, Jianqiang

    2018-02-01

    Coherent modulation imaging providing fast convergence speed and high resolution with single diffraction pattern is a promising technique to satisfy the urgent demands for on-line multiple parameter diagnostics with single setup in high power laser facilities (HPLF). However, the influence of noise on the final calculated parameters concerned has not been investigated yet. According to a series of simulations with twenty different sampling beams generated based on the practical parameters and performance of HPLF, the quantitative analysis based on statistical results was first investigated after considering five different error sources. We found the background noise of detector and high quantization error will seriously affect the final accuracy and different parameters have different sensitivity to different noise sources. The simulation results and the corresponding analysis provide the potential directions to further improve the final accuracy of parameter diagnostics which is critically important to its formal applications in the daily routines of HPLF.

  15. Electrical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers

    Directory of Open Access Journals (Sweden)

    Issouf Fofana

    2016-08-01

    Full Text Available The condition of the internal cellulosic paper and oil insulation are of concern for the performance of power transformers. Over the years, a number of methods have been developed to diagnose and monitor the degradation/aging of the transformer internal insulation system. Some of this degradation/aging can be assessed from electrical responses. Currently there are a variety of electrical-based diagnostic techniques available for insulation condition monitoring of power transformers. In most cases, the electrical signals being monitored are due to mechanical or electric changes caused by physical changes in resistivity, inductance or capacitance, moisture, contamination or aging by-products in the insulation. This paper presents a description of commonly used and modern electrical-based diagnostic techniques along with their interpretation schemes.

  16. Activity and longevity of antibody in paper-based blood typing diagnostics

    Science.gov (United States)

    Henderson, Clare A.; McLiesh, Heather; Then, Whui L.; Garnier, Gil

    2018-05-01

    Paper-based diagnostics provide a low-cost, reliable and easy to use mode of blood typing. The shelf-life of such products, however, can be limited due to the reduced activity of reagent antibodies sorbed on the paper cellulose fibres. This study explores the effects of ageing on antibody activity for periods up to twelve months on paper and in solution under different ageing and drying conditions - air-dried, lyophilised and kept as a liquid. Paper kept wet with undiluted antibody is shown to have the longest shelf-life and the clearest negatives. Antibody diluted with bovine serum albumin (BSA) protects against the lyophilisation process, however, beyond nine months ageing, false positives are seen. Paper with air-dried antibodies is not suitable for use after one month ageing. These results inform preparation and storage conditions for the development of long shelf-life blood grouping paper-based diagnostics.

  17. Activity and Longevity of Antibody in Paper-Based Blood Typing Diagnostics

    Directory of Open Access Journals (Sweden)

    Clare A. Henderson

    2018-05-01

    Full Text Available Paper-based diagnostics provide a low-cost, reliable and easy to use mode of blood typing. The shelf-life of such products, however, can be limited due to the reduced activity of reagent antibodies sorbed on the paper cellulose fibers. This study explores the effects of aging on antibody activity for periods up to 12 months on paper and in solution under different aging and drying conditions—air-dried, lyophilized, and kept as a liquid. Paper kept wet with undiluted antibody is shown to have the longest shelf-life and the clearest negatives. Antibody diluted with bovine serum albumin (BSA protects against the lyophilization process, however, beyond 9 months aging, false positives are seen. Paper with air-dried antibodies is not suitable for use after 1 month aging. These results inform preparation and storage conditions for the development of long shelf-life blood grouping paper-based diagnostics.

  18. SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cancer.

    Science.gov (United States)

    Petricoin, Emanuel F; Liotta, Lance A

    2004-02-01

    Proteomics is more than just generating lists of proteins that increase or decrease in expression as a cause or consequence of pathology. The goal should be to characterize the information flow through the intercellular protein circuitry that communicates with the extracellular microenvironment and then ultimately to the serum/plasma macroenvironment. The nature of this information can be a cause, or a consequence, of disease and toxicity-based processes. Serum proteomic pattern diagnostics is a new type of proteomic platform in which patterns of proteomic signatures from high dimensional mass spectrometry data are used as a diagnostic classifier. This approach has recently shown tremendous promise in the detection of early-stage cancers. The biomarkers found by SELDI-TOF-based pattern recognition analysis are mostly low molecular weight fragments produced at the specific tumor microenvironment.

  19. Systematic construction of qualitative physics-based rules for process diagnostics

    International Nuclear Information System (INIS)

    Reifman, J.; Wei, T.Y.C.

    1995-01-01

    A novel first-principles-based expert system is proposed for on-line detection and identification of faulty component candidates during incipient off-normal process operations. The system performs function-oriented diagnostics and can be reused for diagnosing single-component failures in different processes and different plants through the provision of the appropriate process schematics information. The function-oriented and process-independent diagnostic features of the proposed expert system are achieved by constructing a knowledge base containing three distinct types of information, qualitative balance equation rules, functional classification of process components, and the process piping and instrumentation diagram. The various types of qualitative balance equation rules for processes utilizing single-phase liquids are derived and their usage is illustrated through simulation results of a realistic process in a nuclear power plant

  20. Neural network-based model reference adaptive control system.

    Science.gov (United States)

    Patino, H D; Liu, D

    2000-01-01

    In this paper, an approach to model reference adaptive control based on neural networks is proposed and analyzed for a class of first-order continuous-time nonlinear dynamical systems. The controller structure can employ either a radial basis function network or a feedforward neural network to compensate adaptively the nonlinearities in the plant. A stable controller-parameter adjustment mechanism, which is determined using the Lyapunov theory, is constructed using a sigma-modification-type updating law. The evaluation of control error in terms of the neural network learning error is performed. That is, the control error converges asymptotically to a neighborhood of zero, whose size is evaluated and depends on the approximation error of the neural network. In the design and analysis of neural network-based control systems, it is important to take into account the neural network learning error and its influence on the control error of the plant. Simulation results showing the feasibility and performance of the proposed approach are given.

  1. An image fiber based fluorescent probe with associated signal processing scheme for biomedical diagnostics

    International Nuclear Information System (INIS)

    Vaishakh, M; Murukeshan, V M; Seah, L K

    2008-01-01

    A dual-modality image fiber based fluorescent probe that can be used for depth sensitive imaging and suppression of fluorescent emissions with nanosecond lifetime difference is proposed and illustrated in this paper. The system can give high optical sectioning and employs an algorithm for obtaining phase sensitive images. The system can find main application in in vivo biomedical diagnostics for detecting biochemical changes for distinguishing malignant tissue from healthy tissue

  2. Exposure criteria for medical diagnostic ultrasound: 1, Criteria based on thermal mechanisms

    International Nuclear Information System (INIS)

    1992-01-01

    A previous report (NCRP, 1983) contains a comprehensive review of biological effects and mechanisms of action of ultrasound and an analysis of their implications for medical ultrasound. This Report presents background material for a scientifically-based approach to safety assessment of ultrasound. It is intended to help the medical community take advantage of new developments, while maintaining the excellent safety record which now exists for diagnostic ultrasound

  3. Sequencing-based breast cancer diagnostics as an alternative to routine biomarkers.

    Science.gov (United States)

    Rantalainen, Mattias; Klevebring, Daniel; Lindberg, Johan; Ivansson, Emma; Rosin, Gustaf; Kis, Lorand; Celebioglu, Fuat; Fredriksson, Irma; Czene, Kamila; Frisell, Jan; Hartman, Johan; Bergh, Jonas; Grönberg, Henrik

    2016-11-30

    Sequencing-based breast cancer diagnostics have the potential to replace routine biomarkers and provide molecular characterization that enable personalized precision medicine. Here we investigate the concordance between sequencing-based and routine diagnostic biomarkers and to what extent tumor sequencing contributes clinically actionable information. We applied DNA- and RNA-sequencing to characterize tumors from 307 breast cancer patients with replication in up to 739 patients. We developed models to predict status of routine biomarkers (ER, HER2,Ki-67, histological grade) from sequencing data. Non-routine biomarkers, including mutations in BRCA1, BRCA2 and ERBB2(HER2), and additional clinically actionable somatic alterations were also investigated. Concordance with routine diagnostic biomarkers was high for ER status (AUC = 0.95;AUC(replication) = 0.97) and HER2 status (AUC = 0.97;AUC(replication) = 0.92). The transcriptomic grade model enabled classification of histological grade 1 and histological grade 3 tumors with high accuracy (AUC = 0.98;AUC(replication) = 0.94). Clinically actionable mutations in BRCA1, BRCA2 and ERBB2(HER2) were detected in 5.5% of patients, while 53% had genomic alterations matching ongoing or concluded breast cancer studies. Sequencing-based molecular profiling can be applied as an alternative to histopathology to determine ER and HER2 status, in addition to providing improved tumor grading and clinically actionable mutations and molecular subtypes. Our results suggest that sequencing-based breast cancer diagnostics in a near future can replace routine biomarkers.

  4. Greening radio access networks using distributed base station architectures

    DEFF Research Database (Denmark)

    Kardaras, Georgios; Soler, José; Dittmann, Lars

    2010-01-01

    Several actions for developing environmentally friendly technologies have been taken in most industrial fields. Significant resources have also been devoted in mobile communications industry. Moving towards eco-friendly alternatives is primarily a social responsibility for network operators....... However besides this, increasing energy efficiency represents a key factor for reducing operating expenses and deploying cost effective mobile networks. This paper presents how distributed base station architectures can contribute in greening radio access networks. More specifically, the advantages...... energy saving. Different subsystems have to be coordinated real-time and intelligent network nodes supporting complicated functionalities are necessary. Distributed base station architectures are ideal for this purpose mainly because of their high degree of configurability and self...

  5. A family of quantization based piecewise linear filter networks

    DEFF Research Database (Denmark)

    Sørensen, John Aasted

    1992-01-01

    A family of quantization-based piecewise linear filter networks is proposed. For stationary signals, a filter network from this family is a generalization of the classical Wiener filter with an input signal and a desired response. The construction of the filter network is based on quantization...... of the input signal x(n) into quantization classes. With each quantization class is associated a linear filter. The filtering at time n is carried out by the filter belonging to the actual quantization class of x(n ) and the filters belonging to the neighbor quantization classes of x(n) (regularization......). This construction leads to a three-layer filter network. The first layer consists of the quantization class filters for the input signal. The second layer carries out the regularization between neighbor quantization classes, and the third layer constitutes a decision of quantization class from where the resulting...

  6. Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Ruiyun Yu

    2014-01-01

    Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.

  7. Agent Based Modeling on Organizational Dynamics of Terrorist Network

    Directory of Open Access Journals (Sweden)

    Bo Li

    2015-01-01

    Full Text Available Modeling organizational dynamics of terrorist network is a critical issue in computational analysis of terrorism research. The first step for effective counterterrorism and strategic intervention is to investigate how the terrorists operate with the relational network and what affects the performance. In this paper, we investigate the organizational dynamics by employing a computational experimentation methodology. The hierarchical cellular network model and the organizational dynamics model are developed for modeling the hybrid relational structure and complex operational processes, respectively. To intuitively elucidate this method, the agent based modeling is used to simulate the terrorist network and test the performance in diverse scenarios. Based on the experimental results, we show how the changes of operational environments affect the development of terrorist organization in terms of its recovery and capacity to perform future tasks. The potential strategies are also discussed, which can be used to restrain the activities of terrorists.

  8. Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xu Luo

    2017-01-01

    Full Text Available Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

  9. Morphometric relations of fractal-skeletal based channel network model

    Directory of Open Access Journals (Sweden)

    B. S. Daya Sagar

    1998-01-01

    Full Text Available A fractal-skeletal based channel network (F-SCN model is proposed. Four regular sided initiator-basins are transformed as second order fractal basins by following a specific generating mechanism with non-random rule. The morphological skeletons, hereafter referred to as channel networks, are extracted from these fractal basins. The morphometric and fractal relationships of these F-SCNs are shown. The fractal dimensions of these fractal basins, channel networks, and main channel lengths (computed through box counting method are compared with those of estimated length–area measures. Certain morphometric order ratios to show fractal relations are also highlighted.

  10. Bulk Restoration for SDN-Based Transport Network

    Directory of Open Access Journals (Sweden)

    Yang Zhao

    2016-01-01

    Full Text Available We propose a bulk restoration scheme for software defined networking- (SDN- based transport network. To enhance the network survivability and improve the throughput, we allow disrupted flows to be recovered synchronously in dynamic order. In addition backup paths are scheduled globally by applying the principles of load balance. We model the bulk restoration problem using a mixed integer linear programming (MILP formulation. Then, a heuristic algorithm is devised. The proposed algorithm is verified by simulation and the results are analyzed comparing with sequential restoration schemes.

  11. Facial expression recognition based on improved deep belief networks

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

  12. Advanced diagnostic system for piston slap faults in IC engines, based on the non-stationary characteristics of the vibration signals

    Science.gov (United States)

    Chen, Jian; Randall, Robert Bond; Peeters, Bart

    2016-06-01

    Artificial Neural Networks (ANNs) have the potential to solve the problem of automated diagnostics of piston slap faults, but the critical issue for the successful application of ANN is the training of the network by a large amount of data in various engine conditions (different speed/load conditions in normal condition, and with different locations/levels of faults). On the other hand, the latest simulation technology provides a useful alternative in that the effect of clearance changes may readily be explored without recourse to cutting metal, in order to create enough training data for the ANNs. In this paper, based on some existing simplified models of piston slap, an advanced multi-body dynamic simulation software was used to simulate piston slap faults with different speeds/loads and clearance conditions. Meanwhile, the simulation models were validated and updated by a series of experiments. Three-stage network systems are proposed to diagnose piston faults: fault detection, fault localisation and fault severity identification. Multi Layer Perceptron (MLP) networks were used in the detection stage and severity/prognosis stage and a Probabilistic Neural Network (PNN) was used to identify which cylinder has faults. Finally, it was demonstrated that the networks trained purely on simulated data can efficiently detect piston slap faults in real tests and identify the location and severity of the faults as well.

  13. 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...

  14. Research on Fault Diagnosis Method Based on Rule Base Neural Network

    Directory of Open Access Journals (Sweden)

    Zheng Ni

    2017-01-01

    Full Text Available The relationship between fault phenomenon and fault cause is always nonlinear, which influences the accuracy of fault location. And neural network is effective in dealing with nonlinear problem. In order to improve the efficiency of uncertain fault diagnosis based on neural network, a neural network fault diagnosis method based on rule base is put forward. At first, the structure of BP neural network is built and the learning rule is given. Then, the rule base is built by fuzzy theory. An improved fuzzy neural construction model is designed, in which the calculated methods of node function and membership function are also given. Simulation results confirm the effectiveness of this method.

  15. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data.

    Science.gov (United States)

    Kang, Tianyu; Ding, Wei; Zhang, Luoyan; Ziemek, Daniel; Zarringhalam, Kourosh

    2017-12-19

    Stratification of patient subpopulations that respond favorably to treatment or experience and adverse reaction is an essential step toward development of new personalized therapies and diagnostics. It is currently feasible to generate omic-scale biological measurements for all patients in a study, providing an opportunity for machine learning models to identify molecular markers for disease diagnosis and progression. However, the high variability of genetic background in human populations hampers the reproducibility of omic-scale markers. In this paper, we develop a biological network-based regularized artificial neural network model for prediction of phenotype from transcriptomic measurements in clinical trials. To improve model sparsity and the overall reproducibility of the model, we incorporate regularization for simultaneous shrinkage of gene sets based on active upstream regulatory mechanisms into the model. We benchmark our method against various regression, support vector machines and artificial neural network models and demonstrate the ability of our method in predicting the clinical outcomes using clinical trial data on acute rejection in kidney transplantation and response to Infliximab in ulcerative colitis. We show that integration of prior biological knowledge into the classification as developed in this paper, significantly improves the robustness and generalizability of predictions to independent datasets. We provide a Java code of our algorithm along with a parsed version of the STRING DB database. In summary, we present a method for prediction of clinical phenotypes using baseline genome-wide expression data that makes use of prior biological knowledge on gene-regulatory interactions in order to increase robustness and reproducibility of omic-scale markers. The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets.

  16. Severe childhood asthma and allergy to furry animals: refined assessment using molecular-based allergy diagnostics.

    Science.gov (United States)

    Konradsen, Jon R; Nordlund, Björn; Onell, Annica; Borres, Magnus P; Grönlund, Hans; Hedlin, Gunilla

    2014-03-01

    Allergy to cats and dogs and polysensitization towards these animals are associated with severe childhood asthma. Molecular-based allergy diagnostics offers new opportunities for improved characterization and has been suggested to be particularly useful in patients with polysensitization and/or severe asthma. The aim was to use extract- and molecular-based allergy diagnostics to compare patterns of IgE sensitization towards aeroallergens in children with problematic severe and controlled asthma. Children with a positive ImmunoCAP towards any furry animal (cat, dog or horse) were recruited from a Nationwide Swedish study on severe childhood asthma. Severe (n = 37, age 13 years) and controlled (n = 28, age 14 years) asthmatics underwent assessment of allergic sensitization by ImmunoCap (kUA /l) and immunosolid-phase allergen chip (ISAC). In addition, Asthma Control Test, spirometry and a methacholine challenge were performed. Children with severe asthma had lower asthma control (p Molecular-based allergy diagnostics revealed a more complex molecular spreading of allergen components in children with the most severe disease. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  17. OpenID connect as a security service in Cloud-based diagnostic imaging systems

    Science.gov (United States)

    Ma, Weina; Sartipi, Kamran; Sharghi, Hassan; Koff, David; Bak, Peter

    2015-03-01

    The evolution of cloud computing is driving the next generation of diagnostic imaging (DI) systems. Cloud-based DI systems are able to deliver better services to patients without constraining to their own physical facilities. However, privacy and security concerns have been consistently regarded as the major obstacle for adoption of cloud computing by healthcare domains. Furthermore, traditional computing models and interfaces employed by DI systems are not ready for accessing diagnostic images through mobile devices. RESTful is an ideal technology for provisioning both mobile services and cloud computing. OpenID Connect, combining OpenID and OAuth together, is an emerging REST-based federated identity solution. It is one of the most perspective open standards to potentially become the de-facto standard for securing cloud computing and mobile applications, which has ever been regarded as "Kerberos of Cloud". We introduce OpenID Connect as an identity and authentication service in cloud-based DI systems and propose enhancements that allow for incorporating this technology within distributed enterprise environment. The objective of this study is to offer solutions for secure radiology image sharing among DI-r (Diagnostic Imaging Repository) and heterogeneous PACS (Picture Archiving and Communication Systems) as well as mobile clients in the cloud ecosystem. Through using OpenID Connect as an open-source identity and authentication service, deploying DI-r and PACS to private or community clouds should obtain equivalent security level to traditional computing model.

  18. Prosthetic joint infection development of an evidence-based diagnostic algorithm.

    Science.gov (United States)

    Mühlhofer, Heinrich M L; Pohlig, Florian; Kanz, Karl-Georg; Lenze, Ulrich; Lenze, Florian; Toepfer, Andreas; Kelch, Sarah; Harrasser, Norbert; von Eisenhart-Rothe, Rüdiger; Schauwecker, Johannes

    2017-03-09

    Increasing rates of prosthetic joint infection (PJI) have presented challenges for general practitioners, orthopedic surgeons and the health care system in the recent years. The diagnosis of PJI is complex; multiple diagnostic tools are used in the attempt to correctly diagnose PJI. Evidence-based algorithms can help to identify PJI using standardized diagnostic steps. We reviewed relevant publications between 1990 and 2015 using a systematic literature search in MEDLINE and PUBMED. The selected search results were then classified into levels of evidence. The keywords were prosthetic joint infection, biofilm, diagnosis, sonication, antibiotic treatment, implant-associated infection, Staph. aureus, rifampicin, implant retention, pcr, maldi-tof, serology, synovial fluid, c-reactive protein level, total hip arthroplasty (THA), total knee arthroplasty (TKA) and combinations of these terms. From an initial 768 publications, 156 publications were stringently reviewed. Publications with class I-III recommendations (EAST) were considered. We developed an algorithm for the diagnostic approach to display the complex diagnosis of PJI in a clear and logically structured process according to ISO 5807. The evidence-based standardized algorithm combines modern clinical requirements and evidence-based treatment principles. The algorithm provides a detailed transparent standard operating procedure (SOP) for diagnosing PJI. Thus, consistently high, examiner-independent process quality is assured to meet the demands of modern quality management in PJI diagnosis.

  19. Novel gas sensors based on carbon nanotube networks

    International Nuclear Information System (INIS)

    Sayago, I; Aleixandre, M; Horrillo, M C; Fernandez, M J; Gutierrez, J; Terrado, E; Lafuente, E; Maser, W K; Benito, A M; Martinez, M T; Munoz, E; Urriolabeitia, E P; Navarro, R

    2008-01-01

    Novel resistive gas sensors based on single-walled carbon nanotube (SWNT) networks as the active sensing element nave been investigated for gas detection. SWNTs networks were fabricated by airbrushing on alumina substrates. As-produced- and Pd-decorated SWNT materials were used as sensitive layers for the detection of NO 2 and H 2 , respectively. The studied sensors provided good response to NO 2 and H 2 as well as excellent selectivities to interfering gases.

  20. Attention-based Memory Selection Recurrent Network for Language Modeling

    OpenAIRE

    Liu, Da-Rong; Chuang, Shun-Po; Lee, Hung-yi

    2016-01-01

    Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information about the words it have seen before in the sentence, and thus the useful long-term information may be ignored when predicting the next words. In this paper, we propose Attention-based Memory Selection Recurrent Network (AMSRN), in which the model can review the information stored in the memory at each previous time ...