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.

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

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.

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...

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

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

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)

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

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.

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.

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.

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.

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.

Advance in study of intelligent diagnostic method for nuclear power plant

International Nuclear Information System (INIS)

Zhou Gang; Yang Li

2008-01-01

The advance of research on the application of three types of intelligent diagnostic approach based on neural network (ANN), fuzzy logic and expert system to the operation status monitoring and fault diagnosis of nuclear power plant (NPP) was reviewed. The research status and characters on status monitoring and fault diagnosis approaches based on neural network, fuzzy logic and expert system for nuclear power plant were analyzed. The development trend of applied research on intelligent diagnostic approaches for nuclear power plant was explored. The analysis results show that the research achievements on intelligent diagnostic approaches based on fuzzy logic and expert system for nuclear power plant are not much relatively. The research of intelligent diagnostic approaches for nuclear power plant concentrate on the aspect of operation status monitoring and fault diagnosis based on neural networks for nuclear power plant. The advancing tendency of intelligent diagnostic approaches for nuclear power plant is the combination of various intelligent diagnostic approaches, the combination of neural network diagnostic approaches and other diagnostic approaches as well as multiple neural network diagnostic approaches. (authors)

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.

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

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

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...

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

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

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.

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

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.

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

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.

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 ...

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.

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

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

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

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

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

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

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…

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

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.

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

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)

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)

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)

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

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.

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

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.

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.

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.

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)

Efficient Probabilistic Diagnostics for Electrical Power Systems

Science.gov (United States)

Mengshoel, Ole J.; Chavira, Mark; Cascio, Keith; Poll, Scott; Darwiche, Adnan; Uckun, Serdar

2008-01-01

We consider in this work the probabilistic approach to model-based diagnosis when applied to electrical power systems (EPSs). Our probabilistic approach is formally well-founded, as it based on Bayesian networks and arithmetic circuits. We investigate the diagnostic task known as fault isolation, and pay special attention to meeting two of the main challenges . model development and real-time reasoning . often associated with real-world application of model-based diagnosis technologies. To address the challenge of model development, we develop a systematic approach to representing electrical power systems as Bayesian networks, supported by an easy-to-use speci.cation language. To address the real-time reasoning challenge, we compile Bayesian networks into arithmetic circuits. Arithmetic circuit evaluation supports real-time diagnosis by being predictable and fast. In essence, we introduce a high-level EPS speci.cation language from which Bayesian networks that can diagnose multiple simultaneous failures are auto-generated, and we illustrate the feasibility of using arithmetic circuits, compiled from Bayesian networks, for real-time diagnosis on real-world EPSs of interest to NASA. The experimental system is a real-world EPS, namely the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. In experiments with the ADAPT Bayesian network, which currently contains 503 discrete nodes and 579 edges, we .nd high diagnostic accuracy in scenarios where one to three faults, both in components and sensors, were inserted. The time taken to compute the most probable explanation using arithmetic circuits has a small mean of 0.2625 milliseconds and standard deviation of 0.2028 milliseconds. In experiments with data from ADAPT we also show that arithmetic circuit evaluation substantially outperforms joint tree propagation and variable elimination, two alternative algorithms for diagnosis using Bayesian network inference.

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)

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)

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

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

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.)

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.)

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

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

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.

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.

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

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.

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

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.

[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.

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...

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

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.

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.

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.

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.

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.

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

Overview of data acquisition system for SST-1 diagnostics

International Nuclear Information System (INIS)

Sharma, Manika; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-01-01

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

Overview of data acquisition system for SST-1 diagnostics

Energy Technology Data Exchange (ETDEWEB)

Sharma, Manika, E-mail: bithi@ipr.res.in; Mansuri, Imran; Raval, Tushar; Sharma, A.L; Pradhan, S.

2016-11-15

Highlights: • An account of architecture and data acquisition activities of SST-1 data acquisition system (DAS) for SST-1 diagnostics and subsystems. • PXI based Data acquisition system and CAMAC based Data acquisition system for slow and fast plasma diagnostics. • SST-1 DAS interface and its communication with SST-1 central control system. Integration of SST-1 DAS with timing system. • SST-1 DAS data archival and data analysis. - Abstract: The recent first phase operations of SST-1 in short pulse mode have provided an excellent opportunity for the essential initial tests and benchmark of the SST-1 Data Acquisition System. This paper describes the SST-1 Data Acquisition systems (DAS), which with its heterogeneous composition and distributed architecture, aims to cover a wide range of slow to fast channels interfaced with a large set of diagnostics. The DAS also provides the essential user interface for data acquisition to cater both on and off-line data usage. The central archiving and retrieval service is based on a dual step architecture involving a combination of Network Attached Server (NAS) and a Storage Area Network (SAN). SST-1 Data Acquisition Systems have been reliably operated in the SST-1 experimental campaigns. At present different distributed DAS caters the need of around 130 channels from different SST-1 diagnostics and its subsystems. PXI based DAS and CAMAC based DAS have been chosen to cater the need, with sampling rates varying from 10Ksamples/sec to 1Msamples/sec. For these large sets of channels acquiring from individual diagnostics and subsystems has been a combined setup, subjected to a gradual phase of optimization and tests resulting into a series of improvisations over the recent operations. In order to facilitate a reliable data acquisition, the model further integrates the objects of the systems with the Central Control System of SST-1 using the TCP/IP communication. The associated DAS software essentially addresses the

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

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....

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.

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).

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.

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

Science.gov (United States)

Satoh, Hitoshi; Niki, Noboru; Eguchi, Kenji; Ohmatsu, Hironobu; Kakinuma, Ryutaru; Moriyama, Noriyuki

2009-02-01

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. Moreover, the doctor who diagnoses a medical image is insufficient in Japan. To overcome these problems, 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 functions 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. We can share the screen of Web medical image conference system from two or more web conference terminals at the same time. An opinion can be exchanged mutually by using a camera and a microphone that are connected with workstation. 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

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.

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.

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...

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.

SNS Diagnostics Timing Integration

CERN Document Server

Long, Cary D; Murphy, Darryl J; Pogge, James; Purcell, John D; Sundaram, Madhan

2005-01-01

The Spallation Neutron Source (SNS) accelerator systems will deliver a 1.0 GeV, 1.4 MW proton beam to a liquid mercury target for neutron scattering research. The accelerator complex consists of a 1 GeV linear accelerator, an accumulator ring and associated transport lines. The SNS diagnostics platform is PC-based running Windows XP Embedded for its OS and LabVIEW as its programming language. Coordinating timing among the various diagnostics instruments with the generation of the beam pulse is a challenging task that we have chosen to divide into three phases. First, timing was derived from VME based systems. In the second phase, described in this paper, timing pulses are generated by an in house designed PCI timing card installed in ten diagnostics PCs. Using fan-out modules, enough triggers were generated for all instruments. This paper describes how the Timing NAD (Network Attached Device) was rapidly developed using our NAD template, LabVIEW's PCI driver wizard, and LabVIEW Channel Access library. The NAD...

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

Microsphere-based immunoassay integrated with a microfluidic network to perform logic operations

International Nuclear Information System (INIS)

Sabhachandani, Pooja; Cohen, Noa; Sarkar, Saheli; Konry, Tania

2015-01-01

Lab on a chip (LOC) intelligent diagnostics can be described by molecular logic-based circuits. We report on the development of an LOC approach with logic capability for screening combinations of antigen and antibody in the same sample. A microsphere-based immunoassay was integrated with a microfluidic network device to perform the logic operations AND and INHIBIT. Using the clinically relevant biomarkers TNF-α cytokine and anti-TNF-α antibody, we obtained a fluorescent output in the presence of both inputs. This results in an AND operation, while the presence of only one specific input results in a different fluorescent signal, thereby indicating the INHIBIT operation. This approach demonstrates the effective use of molecular logic computation for developing portable, point-of-care technologies for diagnostic purposes due to fast detection times, minimal reagent consumption and low costs. This model system may be further expanded to screening of multiple disease markers, combinatorial logic applications, and developing “smart” sensors and therapeutic technologies. (author)

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.

Study on Practical Application of Turboprop Engine Condition Monitoring and Fault Diagnostic System Using Fuzzy-Neuro Algorithms

Science.gov (United States)

Kong, Changduk; Lim, Semyeong; Kim, Keunwoo

2013-03-01

The Neural Networks is mostly used to engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measuring performance data, and proposes a fault diagnostic system using the base performance model and artificial intelligent methods such as Fuzzy and Neural Networks. Each real engine performance model, which is named as the base performance model that can simulate a new engine performance, is inversely made using its performance test data. Therefore the condition monitoring of each engine can be more precisely carried out through comparison with measuring performance data. The proposed diagnostic system identifies firstly the faulted components using Fuzzy Logic, and then quantifies faults of the identified components using Neural Networks leaned by fault learning data base obtained from the developed base performance model. In leaning the measuring performance data of the faulted components, the FFBP (Feed Forward Back Propagation) is used. In order to user's friendly purpose, the proposed diagnostic program is coded by the GUI type using MATLAB.

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.

A new diagnostic concept for the primary circuit of WWER 1000 reactors

International Nuclear Information System (INIS)

Streicher, V.; Liska, J.

1993-01-01

The new concept developed by the Skoda and Siemens companies is based on their own experience, the requirements of the Czech Power Board/NPP Temelin Diagnostic Department, and on the research work of various institutes in Czechoslovakia. The development of a complex diagnostic concept is an iterative process and includes parts with different stages of experience and different goals. They can be divided into five groups: PC-based diagnostic systems with continuous or periodic functions, equipment for condition monitoring, PC-based data collectors, studies, analyses and measures which are mandatory for the correct performance and interpretation of the diagnostic systems, and the integration of all subsystems and data acquisition/evaluation units into a Local Area Network with a graphic workstation. (Z.S.) 2 figs., 4 refs

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

Networked Attached Devices at SNS

CERN Document Server

Blokland, W

2003-01-01

The Spallation Neutron Source (SNS) diagnostic instruments at Oak Ridge National Laboratory are based on the Network Attached Device (NAD) concept. Each pickup or sensor has its own resources such as timing, data acquisition and processing. NADs are individually connected to the network, thus reducing the brittleness inherent in tightly coupled systems. This architecture allows an individual device to fail or to be serviced or removed without disrupting other devices. This paper describes our implementation of the nearly 400 NADs to be deployed. The hardware consists of rack-mounted PCs with standard motherboards and PCI data-acquisition boards. The software environment is based on LabVIEW and EPICS. LabVIEW supports the agile development demanded by modern diagnostic systems. EPICS is the control system standard for the entire SNS facility. To achieve high performance, LabVIEW and EPICS communicate through shared memory. SNS diagnostics are developed by a multi-laboratory partnership including ORNL, BNL, LAN...

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.

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

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

Exploring the underlying structure of mental disorders: cross-diagnostic differences and similarities from a network perspective using both a top-down and a bottom-up approach.

Science.gov (United States)

Wigman, J T W; van Os, J; Borsboom, D; Wardenaar, K J; Epskamp, S; Klippel, A; Viechtbauer, W; Myin-Germeys, I; Wichers, M

2015-08-01

It has been suggested that the structure of psychopathology is best described as a complex network of components that interact in dynamic ways. The goal of the present paper was to examine the concept of psychopathology from a network perspective, combining complementary top-down and bottom-up approaches using momentary assessment techniques. A pooled Experience Sampling Method (ESM) dataset of three groups (individuals with a diagnosis of depression, psychotic disorder or no diagnosis) was used (pooled N = 599). The top-down approach explored the network structure of mental states across different diagnostic categories. For this purpose, networks of five momentary mental states ('cheerful', 'content', 'down', 'insecure' and 'suspicious') were compared between the three groups. The complementary bottom-up approach used principal component analysis to explore whether empirically derived network structures yield meaningful higher order clusters. Individuals with a clinical diagnosis had more strongly connected moment-to-moment network structures, especially the depressed group. This group also showed more interconnections specifically between positive and negative mental states than the psychotic group. In the bottom-up approach, all possible connections between mental states were clustered into seven main components that together captured the main characteristics of the network dynamics. Our combination of (i) comparing network structure of mental states across three diagnostically different groups and (ii) searching for trans-diagnostic network components across all pooled individuals showed that these two approaches yield different, complementary perspectives in the field of psychopathology. The network paradigm therefore may be useful to map transdiagnostic processes.

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.

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...

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.

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.

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.

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)

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)

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....

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

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...

Representation of neutron noise data using neural networks

International Nuclear Information System (INIS)

Korsah, K.; Damiano, B.; Wood, R.T.

1992-01-01

This paper describes a neural network-based method of representing neutron noise spectra using a model developed at the Oak Ridge National Laboratory (ORNL). The backpropagation neural network learned to represent neutron noise data in terms of four descriptors, and the network response matched calculated values to within 3.5 percent. These preliminary results are encouraging, and further research is directed towards the application of neural networks in a diagnostics system for the identification of the causes of changes in structural spectral resonances. This work is part of our current investigation of advanced technologies such as expert systems and neural networks for neutron noise data reduction, analysis, and interpretation. The objective is to improve the state-of-the-art of noise analysis as a diagnostic tool for nuclear power plants and other mechanical systems

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...

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.

Privacy-Preserving Self-Helped Medical Diagnosis Scheme Based on Secure Two-Party Computation in Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Yi Sun

2014-01-01

Full Text Available With the continuing growth of wireless sensor networks in pervasive medical care, people pay more and more attention to privacy in medical monitoring, diagnosis, treatment, and patient care. On one hand, we expect the public health institutions to provide us with better service. On the other hand, we would not like to leak our personal health information to them. In order to balance this contradiction, in this paper we design a privacy-preserving self-helped medical diagnosis scheme based on secure two-party computation in wireless sensor networks so that patients can privately diagnose themselves by inputting a health card into a self-helped medical diagnosis ATM to obtain a diagnostic report just like drawing money from a bank ATM without revealing patients’ health information and doctors’ diagnostic skill. It makes secure self-helped disease diagnosis feasible and greatly benefits patients as well as relieving the heavy pressure of public health institutions.

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

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.

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)

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.

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....

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.

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.

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 ...

Motor Fault Diagnosis Based on Short-time Fourier Transform and Convolutional Neural Network

Science.gov (United States)

Wang, Li-Hua; Zhao, Xiao-Ping; Wu, Jia-Xin; Xie, Yang-Yang; Zhang, Yong-Hong

2017-11-01

With the rapid development of mechanical equipment, the mechanical health monitoring field has entered the era of big data. However, the method of manual feature extraction has the disadvantages of low efficiency and poor accuracy, when handling big data. In this study, the research object was the asynchronous motor in the drivetrain diagnostics simulator system. The vibration signals of different fault motors were collected. The raw signal was pretreated using short time Fourier transform (STFT) to obtain the corresponding time-frequency map. Then, the feature of the time-frequency map was adaptively extracted by using a convolutional neural network (CNN). The effects of the pretreatment method, and the hyper parameters of network diagnostic accuracy, were investigated experimentally. The experimental results showed that the influence of the preprocessing method is small, and that the batch-size is the main factor affecting accuracy and training efficiency. By investigating feature visualization, it was shown that, in the case of big data, the extracted CNN features can represent complex mapping relationships between signal and health status, and can also overcome the prior knowledge and engineering experience requirement for feature extraction, which is used by traditional diagnosis methods. This paper proposes a new method, based on STFT and CNN, which can complete motor fault diagnosis tasks more intelligently and accurately.

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.

Strengthening the diagnostic capacity to detect Bio Safety Level 3 organisms in unusual respiratory viral outbreaks.

Science.gov (United States)

van Asten, Liselotte; van der Lubben, Mariken; van den Wijngaard, Cees; van Pelt, Wilfrid; Verheij, Robert; Jacobi, Andre; Overduin, Pieter; Meijer, Adam; Luijt, Dirk; Claas, Eric; Hermans, Mirjam; Melchers, Willem; Rossen, John; Schuurman, Rob; Wolffs, Petra; Boucher, Charles; Bouchier, Charles; Schirm, Jurjen; Kroes, Louis; Leenders, Sander; Galama, Joep; Peeters, Marcel; van Loon, Anton; Stobberingh, Ellen; Schutten, Martin; Koopmans, Marion

2009-07-01

Experience with a highly pathogenic avian influenza outbreak in the Netherlands (2003) illustrated that the diagnostic demand for respiratory viruses at different biosafety levels (including BSL3), can increase unexpectedly and dramatically. We describe the measures taken since, aimed at strengthening national laboratory surge capacity and improving preparedness for dealing with diagnostic demand during outbreaks of (emerging) respiratory virus infections, including pandemic influenza virus. Academic and peripheral medical-microbiological laboratories collaborated to determine minimal laboratory requirements for the identification of viruses in the early stages of a pandemic or a large outbreak of avian influenza virus. Next, an enhanced collaborative national network of outbreak assistance laboratories (OAL) was set up. An inventory was made of the maximum diagnostic throughput that this network can deliver in a period of intensified demand. For an estimate of the potential magnitude of this surge demand, historical counts were calculated from hospital- and physician-based registries of patients presenting with respiratory symptoms. Number of respiratory physician-visits ranged from 140,000 to 615,000 per month and hospitalizations ranged from 3000 to 11,500 per month. The established OAL-network provides rapid diagnostic response with agreed quality requirements and a maximum throughput capacity of 1275 samples/day (38,000 per month), assuming other routine diagnostic work needs to be maintained. Thus surge demand for diagnostics for hospitalized cases (if not distinguishable from other respiratory illness) could be handled by the OAL network. Assessing etiology of community acquired acute respiratory infection however, may rapidly exceed the capacity of the network. Therefore algorithms are needed for triaging for laboratory diagnostics; currently this is not addressed in pandemic preparedness plans.

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.

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

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.

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....

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....

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.

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...

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...

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.

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.

Diagnostics of nitrogen deficiency in mini-cucumber plant by near ...

African Journals Online (AJOL)

K-nearest neighbors (KNN) and artificial neural network (ANN) were applied to build diagnostics models, respectively. Some parameters of the model were optimized by cross-validation. The performance of the KNN model and the ANN model based on NIRS data was compared. Experiment results showed that the ANN ...

Classification of data patterns using an autoassociative neural network topology

Science.gov (United States)

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

1989-01-01

A diagnostic expert system based on neural networks is developed and applied to the real-time diagnosis of jet and rocket engines. The expert system methodologies are based on the analysis of patterns of behavior of physical mechanisms. In this approach, fault diagnosis is conceptualized as the mapping or association of patterns of sensor data to patterns representing fault conditions. The approach addresses deficiencies inherent in many feedforward neural network models and greatly reduces the number of networks necessary to identify the existence of a fault condition and estimate the duration and severity of the identified fault. The network topology used in the present implementation of the diagnostic system is described, as well as the training regimen used and the response of the system to inputs representing both previously observed and unknown fault scenarios. Noise effects on the integrity of the diagnosis are also evaluated.

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.

Structure Crack Identification Based on Surface-mounted Active Sensor Network with Time-Domain Feature Extraction and Neural Network

Directory of Open Access Journals (Sweden)

Chunling DU

2012-03-01

Full Text Available In this work the condition of metallic structures are classified based on the acquired sensor data from a surface-mounted piezoelectric sensor/actuator network. The structures are aluminum plates with riveted holes and possible crack damage at these holes. A 400 kHz sine wave burst is used as diagnostic signals. The combination of time-domain S0 waves from received sensor signals is directly used as features and preprocessing is not needed for the dam age detection. Since the time sequence of the extracted S0 has a high dimension, principal component estimation is applied to reduce its dimension before entering NN (neural network training for classification. An LVQ (learning vector quantization NN is used to classify the conditions as healthy or damaged. A number of FEM (finite element modeling results are taken as inputs to the NN for training, since the simulated S0 waves agree well with the experimental results on real plates. The performance of the classification is then validated by using these testing results.

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.

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

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...

Backpropagation architecture optimization and an application in nuclear power plant diagnostics

International Nuclear Information System (INIS)

Basu, A.; Bartlett, E.B.

1993-01-01

This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a root network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, another classifier network is trained to recognize the particular transient taking place. These networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at, the Iowa Electric Light and Power Company's Duane Arnold Energy Center (DAEC) operator training simulator

Backpropagation architecture optimization and an application in nuclear power plant diagnostics

International Nuclear Information System (INIS)

Basu, A.; Bartlett, E.B.

1993-01-01

This paper presents a Dynamic Node Architecture (DNA) scheme to optimize the architecture of backpropagation Artificial Neural Networks (ANNs). This network scheme is used to develop an ANN based diagnostic adviser capable of identifying the operating status of a nuclear power plant. Specifically, a ''root'' network is trained to diagnose if the plant is in a normal operating condition or not. In the event of an abnormal condition, and other ''classifier'' network is trained to recognize the particular transient taking place. these networks are trained using plant instrumentation data gathered during simulations of the various transients and normal operating conditions at the Iowa Electric Light and Power Company's Duane Arnold Energy Center (DAEC) operator training simulator

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.

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.

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....

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.

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...

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.

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)

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.

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.

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)

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

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.

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.

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

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.

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.

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 ...

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.

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.

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

Directory of Open Access Journals (Sweden)

Hamidreza Mousavi

2017-01-01

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

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.

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.

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...

Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: A systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database

International Nuclear Information System (INIS)

Dietzel, Matthias; Baltzer, Pascal A.T.; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P.; Bogdan, Martin; Kaiser, Werner A.

2012-01-01

Rationale and objectives: Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. Materials and methods: For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of “Training Epochs” (TE), “Hidden Layers” (HL), “Learning Rate” (LR) and “Neurons” (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Results: Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885–0.892; range: 0.880–0.898). Conclusion: The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data.

Artificial Neural Networks for differential diagnosis of breast lesions in MR-Mammography: a systematic approach addressing the influence of network architecture on diagnostic performance using a large clinical database.

Science.gov (United States)

Dietzel, Matthias; Baltzer, Pascal A T; Dietzel, Andreas; Zoubi, Ramy; Gröschel, Tobias; Burmeister, Hartmut P; Bogdan, Martin; Kaiser, Werner A

2012-07-01

Differential diagnosis of lesions in MR-Mammography (MRM) remains a complex task. The aim of this MRM study was to design and to test robustness of Artificial Neural Network architectures to predict malignancy using a large clinical database. For this IRB-approved investigation standardized protocols and study design were applied (T1w-FLASH; 0.1 mmol/kgBW Gd-DTPA; T2w-TSE; histological verification after MRM). All lesions were evaluated by two experienced (>500 MRM) radiologists in consensus. In every lesion, 18 previously published descriptors were assessed and documented in the database. An Artificial Neural Network (ANN) was developed to process this database (The-MathWorks/Inc., feed-forward-architecture/resilient back-propagation-algorithm). All 18 descriptors were set as input variables, whereas histological results (malignant vs. benign) was defined as classification variable. Initially, the ANN was optimized in terms of "Training Epochs" (TE), "Hidden Layers" (HL), "Learning Rate" (LR) and "Neurons" (N). Robustness of the ANN was addressed by repeated evaluation cycles (n: 9) with receiver operating characteristics (ROC) analysis of the results applying 4-fold Cross Validation. The best network architecture was identified comparing the corresponding Area under the ROC curve (AUC). Histopathology revealed 436 benign and 648 malignant lesions. Enhancing the level of complexity could not increase diagnostic accuracy of the network (P: n.s.). The optimized ANN architecture (TE: 20, HL: 1, N: 5, LR: 1.2) was accurate (mean-AUC 0.888; P: <0.001) and robust (CI: 0.885-0.892; range: 0.880-0.898). The optimized neural network showed robust performance and high diagnostic accuracy for prediction of malignancy on unknown data. Copyright © 2011 Elsevier Ireland Ltd. All rights reserved.

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...

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.

Diagnostic Analyzer for Gearboxes (DAG): User's Guide. Version 3.1 for Microsoft Windows 3.1

Science.gov (United States)

Jammu, Vinay B.; Kourosh, Danai

1997-01-01

This documentation describes the Diagnostic Analyzer for Gearboxes (DAG) software for performing fault diagnosis of gearboxes. First, the user would construct a graphical representation of the gearbox using the gear, bearing, shaft, and sensor tools contained in the DAG software. Next, a set of vibration features obtained by processing the vibration signals recorded from the gearbox using a signal analyzer is required. Given this information, the DAG software uses an unsupervised neural network referred to as the Fault Detection Network (FDN) to identify the occurrence of faults, and a pattern classifier called Single Category-Based Classifier (SCBC) for abnormality scaling of individual vibration features. The abnormality-scaled vibration features are then used as inputs to a Structure-Based Connectionist Network (SBCN) for identifying faults in gearbox subsystems and components. The weights of the SBCN represent its diagnostic knowledge and are derived from the structure of the gearbox graphically presented in DAG. The outputs of SBCN are fault possibility values between 0 and 1 for individual subsystems and components in the gearbox with a 1 representing a definite fault and a 0 representing normality. This manual describes the steps involved in creating the diagnostic gearbox model, along with the options and analysis tools of the DAG software.

Evaluating airline energy efficiency: An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure

International Nuclear Information System (INIS)

Xu, Xin; Cui, Qiang

2017-01-01

This paper focuses on evaluating airline energy efficiency, which is firstly divided into four stages: Operations Stage, Fleet Maintenance Stage, Services Stage and Sales Stage. The new four-stage network structure of airline energy efficiency is a modification of existing models. A new approach, integrated with Network Epsilon-based Measure and Network Slacks-based Measure, is applied to assess the overall energy efficiency and divisional efficiency of 19 international airlines from 2008 to 2014. The influencing factors of airline energy efficiency are analyzed through the regression analysis. The results indicate the followings: 1. The integrated model can identify the benchmarking airlines in the overall system and stages. 2. Most airlines' energy efficiencies keep steady during the period, except for some sharply fluctuations. The efficiency decreases mainly centralized in the year 2008–2011, affected by the financial crisis in the USA. 3. The average age of fleet is positively correlated with the overall energy efficiency, and each divisional efficiency has different significant influencing factors. - Highlights: • An integrated approach with Network Epsilon-based Measure and Network Slacks-based Measure is developed. • 19 airlines' energy efficiencies are evaluated. • Garuda Indonesia has the highest overall energy efficiency.

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.

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

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...

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.

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.

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)

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.

A NEURAL NETWORK BASED TRAFFIC-AWARE FORWARDING STRATEGY IN NAMED DATA NETWORKING

Directory of Open Access Journals (Sweden)

Parisa Bazmi

2016-11-01

Full Text Available 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 strategy (NNTF is introduced in order to determine an optimal path for Interest forwarding. NN is embedded in NDN routers to select next hop dynamically based on the path overload probability achieved from the NN. This solution is characterized by load balancing and QoS-awareness via monitoring the available path and forwarding data on the traffic-aware shortest path. The performance of NNTF is evaluated using ndnSIM which shows the efficiency of this scheme in terms of network QoS improvementof17.5% and 72% reduction in network delay and packet drop respectively.

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.

Diagnostic reasoning and underlying knowledge of students with preclinical patient contacts in PBL.

Science.gov (United States)

Diemers, Agnes D; van de Wiel, Margje W J; Scherpbier, Albert J J A; Baarveld, Frank; Dolmans, Diana H J M

2015-12-01

Medical experts have access to elaborate and integrated knowledge networks consisting of biomedical and clinical knowledge. These coherent knowledge networks enable them to generate more accurate diagnoses in a shorter time. However, students' knowledge networks are less organised and students have difficulties linking theory and practice and transferring acquired knowledge. Therefore we wanted to explore the development and transfer of knowledge of third-year preclinical students on a problem-based learning (PBL) course with real patient contacts. Before and after a 10-week PBL course with real patients, third-year medical students were asked to think out loud while diagnosing four types of paper patient problems (two course cases and two transfer cases), and explain the underlying pathophysiological mechanisms of the patient features. Diagnostic accuracy and time needed to think through the cases were measured. The think-aloud protocols were transcribed verbatim and different types of knowledge were coded and quantitatively analysed. The written pathophysiological explanations were translated into networks of concepts. Both the concepts and the links between concepts in students' networks were compared to model networks. Over the course diagnostic accuracy increased, case-processing time decreased, and students used less biomedical and clinical knowledge during diagnostic reasoning. The quality of the pathophysiological explanations increased: the students used more concepts, especially more model concepts, and they used fewer wrong concepts and links. The findings differed across course and transfer cases. The effects were generally less strong for transfer cases. Students' improved diagnostic accuracy and the improved quality of their knowledge networks suggest that integration of biomedical and clinical knowledge took place during a 10-week course. The differences between course and transfer cases demonstrate that transfer is complex and time-consuming. We

Towards the development of multilevel-multiagent diagnostic aids

International Nuclear Information System (INIS)

Stratton, R.C.; Jarrell, D.B.

1991-10-01

Presented here is our methodology for developing automated aids for diagnosing faults in complex systems. We have designed these aids as multilevel-multiagent diagnostic aids based on principles that should be generally applicable to any complex system. In this methodology, ''multilevel'' refers to information models described at successful levels of abstraction that are tied together in such a way that reasoning is directed to the appropriate level as determined by the problem solving requirements. The concept of ''multiagent'' refers to the method of information processing within the multilevel model network; each model in the network is an independent information processor, i.e., an intelligent agent. 19 refs., 15 figs., 9 tabs

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.

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.

A flexible simulator for training an early fault diagnostic system

International Nuclear Information System (INIS)

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

1997-01-01

An early fault diagnostic system has been developed addressed to timely trouble shooting in process plants during any operational modes. The theory of this diagnostic system is related with the usage of learning methods for automatic generation of knowledge bases. This approach enables the conversion of ''cause→effect'' relations into ''effect→possible-causes'' ones. The diagnostic rules are derived from the operation of a plant simulator according to a specific procedure. Flexibility, accuracy and high speed are the major characteristics of the training simulator, used to generate the diagnostic knowledge base. The simulator structure is very flexible, being based on LEGO code but allowing the use of practically any kind of FORTRAN routines (recently also ACSL macros has been introduced) as plant modules: this permits, when needed, a very accurate description of the malfunctions the diagnostic system should ''known''. The high speed is useful to shorten the ''learning'' phase of the diagnostic system. The feasibility of the overall system has been assessed, using as reference plant the conventional Sampierdarena (Italy) power station, that is a combined cycle plant dedicated to produce both electrical and heat power. The hardware configuration of this prototype system was made up of a network of a Hewlett-Packard workstation and a Digital VAX-Station. The paper illustrates the basic structure of the simulator used for this diagnostic system training purpose, as well as the theoretical background on which the diagnostic system is based. Some evidence of the effectiveness of the concept through the application to Sampierdarena 40 MW cogeneration plant is reported. Finally an outline of an ongoing application to a WWER-1000 plant is given; the operating system is, in this case, UNIX. (author)

Neural networks and their application to nuclear power plant diagnosis

International Nuclear Information System (INIS)

Reifman, J.

1997-01-01

The authors present a survey of artificial neural network-based computer systems that have been proposed over the last decade for the detection and identification of component faults in thermal-hydraulic systems of nuclear power plants. The capabilities and advantages of applying neural networks as decision support systems for nuclear power plant operators and their inherent characteristics are discussed along with their limitations and drawbacks. The types of neural network structures used and their applications are described and the issues of process diagnosis and neural network-based diagnostic systems are identified. A total of thirty-four publications are reviewed

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

Study on Fault Diagnostics of a Turboprop Engine Using Inverse Performance Model and Artificial Intelligent Methods

Science.gov (United States)

Kong, Changduk; Lim, Semyeong

2011-12-01

Recently, the health monitoring system of major gas path components of gas turbine uses mostly the model based method like the Gas Path Analysis (GPA). This method is to find quantity changes of component performance characteristic parameters such as isentropic efficiency and mass flow parameter by comparing between measured engine performance parameters such as temperatures, pressures, rotational speeds, fuel consumption, etc. and clean engine performance parameters without any engine faults which are calculated by the base engine performance model. Currently, the expert engine diagnostic systems using the artificial intelligent methods such as Neural Networks (NNs), Fuzzy Logic and Genetic Algorithms (GAs) have been studied to improve the model based method. Among them the NNs are mostly used to the engine fault diagnostic system due to its good learning performance, but it has a drawback due to low accuracy and long learning time to build learning data base if there are large amount of learning data. In addition, it has a very complex structure for finding effectively single type faults or multiple type faults of gas path components. This work builds inversely a base performance model of a turboprop engine to be used for a high altitude operation UAV using measured performance data, and proposes a fault diagnostic system using the base engine performance model and the artificial intelligent methods such as Fuzzy logic and Neural Network. The proposed diagnostic system isolates firstly the faulted components using Fuzzy Logic, then quantifies faults of the identified components using the NN leaned by fault learning data base, which are obtained from the developed base performance model. In leaning the NN, the Feed Forward Back Propagation (FFBP) method is used. Finally, it is verified through several test examples that the component faults implanted arbitrarily in the engine are well isolated and quantified by the proposed diagnostic system.

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.

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)

Multimodal Hyper-connectivity Networks for MCI Classification.

Science.gov (United States)

Li, Yang; Gao, Xinqiang; Jie, Biao; Yap, Pew-Thian; Kim, Min-Jeong; Wee, Chong-Yaw; Shen, Dinggang

2017-09-01

Hyper-connectivity network is a network where every edge is connected to more than two nodes, and can be naturally denoted using a hyper-graph. Hyper-connectivity brain network, either based on structural or functional interactions among the brain regions, has been used for brain disease diagnosis. However, the conventional hyper-connectivity network is constructed solely based on single modality data, ignoring potential complementary information conveyed by other modalities. The integration of complementary information from multiple modalities has been shown to provide a more comprehensive representation about the brain disruptions. In this paper, a novel multimodal hyper-network modelling method was proposed for improving the diagnostic accuracy of mild cognitive impairment (MCI). Specifically, we first constructed a multimodal hyper-connectivity network by simultaneously considering information from diffusion tensor imaging and resting-state functional magnetic resonance imaging data. We then extracted different types of network features from the hyper-connectivity network, and further exploited a manifold regularized multi-task feature selection method to jointly select the most discriminative features. Our proposed multimodal hyper-connectivity network demonstrated a better MCI classification performance than the conventional single modality based hyper-connectivity networks.

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

Diagnosis and prediction of periodontally compromised teeth using a deep learning-based convolutional neural network algorithm.

Science.gov (United States)

Lee, Jae-Hong; Kim, Do-Hyung; Jeong, Seong-Nyum; Choi, Seong-Ho

2018-04-01

The aim of the current study was to develop a computer-assisted detection system based on a deep convolutional neural network (CNN) algorithm and to evaluate the potential usefulness and accuracy of this system for the diagnosis and prediction of periodontally compromised teeth (PCT). Combining pretrained deep CNN architecture and a self-trained network, periapical radiographic images were used to determine the optimal CNN algorithm and weights. The diagnostic and predictive accuracy, sensitivity, specificity, positive predictive value, negative predictive value, receiver operating characteristic (ROC) curve, area under the ROC curve, confusion matrix, and 95% confidence intervals (CIs) were calculated using our deep CNN algorithm, based on a Keras framework in Python. The periapical radiographic dataset was split into training (n=1,044), validation (n=348), and test (n=348) datasets. With the deep learning algorithm, the diagnostic accuracy for PCT was 81.0% for premolars and 76.7% for molars. Using 64 premolars and 64 molars that were clinically diagnosed as severe PCT, the accuracy of predicting extraction was 82.8% (95% CI, 70.1%-91.2%) for premolars and 73.4% (95% CI, 59.9%-84.0%) for molars. We demonstrated that the deep CNN algorithm was useful for assessing the diagnosis and predictability of PCT. Therefore, with further optimization of the PCT dataset and improvements in the algorithm, a computer-aided detection system can be expected to become an effective and efficient method of diagnosing and predicting PCT.

A unified plant information network

International Nuclear Information System (INIS)

Niederauer, G.F.

1986-01-01

Technology is bringing power plants fully into the age of computerization. Microcomputers, data base managers, networking, and friendly, expert software are principal technology factors. Monitoring will improve, and the number and power of computers is increasing. The huge information flow will cause computers to be integrated into a communication network. The total plant operating triangle includes process, engineering, and management systems. The total network will integrate all of these into a Total Unified Plant Information Network (TUPIN). Software will take the type of information beyond monitored data. Analysis will improve through direct access to logical, physical, and procedural models by end users. Information management will improve through widespread use of hierarchical, relational, and expert data base managers. Expert systems will aid in diagnostics and interpretation. The goal is to automate plant operations to enhance safety and performance and to reduce cost by making both the plants and the personnel more expert

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...

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

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.

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)

Research on method of nuclear power plant operation fault diagnosis based on a combined artificial neural network

International Nuclear Information System (INIS)

Liu Feng; Yu Ren; Li Fengyu; Zhang Meng

2007-01-01

To solve the online real-time diagnosis problem of the nuclear power plant in operating condition, a method based on a combined artificial neural network is put forward in the paper. Its main principle is: using the BP neural network for the fast group diagnosis, and then using the RBF neural network for distinguishing and verifying the diagnostic result. The accuracy of the method is verified using the simulation values of the key parameters in normal status and malfunction status of a nuclear power plant. The results show that the method combining the advantages of the two neural networks can not only diagnose the learned faults in similar power level of the nuclear power plant quickly and accurately, but also can identify the faults in different power status, as well as the unlearned faults. The outputs of the diagnosis system are in form of the reliability of the faults, and are changing with the lasting of the operation time of the plant. This makes the diagnosis results be more acceptable to operators. (authors)

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.

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.

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.

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.

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

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.

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.

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.

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...

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.

Network Approach to Autistic Traits : Group and Subgroup Analyses of ADOS Item Scores

NARCIS (Netherlands)

Anderson, George M.; Montazeri, Farhad; de Bildt, Annelies

2015-01-01

A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of

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.

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

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.

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.

A dynamic Bayesian network based approach to safety decision support in tunnel construction

International Nuclear Information System (INIS)

Wu, Xianguo; Liu, Huitao; Zhang, Limao; Skibniewski, Miroslaw J.; Deng, Qianli; Teng, Jiaying

2015-01-01

This paper presents a systemic decision approach with step-by-step procedures based on dynamic Bayesian network (DBN), aiming to provide guidelines for dynamic safety analysis of the tunnel-induced road surface damage over time. The proposed DBN-based approach can accurately illustrate the dynamic and updated feature of geological, design and mechanical variables as the construction progress evolves, in order to overcome deficiencies of traditional fault analysis methods. Adopting the predictive, sensitivity and diagnostic analysis techniques in the DBN inference, this approach is able to perform feed-forward, concurrent and back-forward control respectively on a quantitative basis, and provide real-time support before and after an accident. A case study in relating to dynamic safety analysis in the construction of Wuhan Yangtze Metro Tunnel in China is used to verify the feasibility of the proposed approach, as well as its application potential. The relationships between the DBN-based and BN-based approaches are further discussed according to analysis results. The proposed approach can be used as a decision tool to provide support for safety analysis in tunnel construction, and thus increase the likelihood of a successful project in a dynamic project environment. - Highlights: • A dynamic Bayesian network (DBN) based approach for safety decision support is developed. • This approach is able to perform feed-forward, concurrent and back-forward analysis and control. • A case concerning dynamic safety analysis in Wuhan Yangtze Metro Tunnel in China is presented. • DBN-based approach can perform a higher accuracy than traditional static BN-based approach

Real-Time Remote Diagnostic Monitoring Test-bed in JET

Energy Technology Data Exchange (ETDEWEB)

Castro, R. [Asociation Euratom/CIEMAT para Fusion, Madrid (Spain); Kneupner, K.; Purahoo, K. [EURATOM/UKAEA Fusion Association, Abingdon (United Kingdom); Vega, J.; Pereira, A.; Portas, A. [Association EuratomCIEMAT para Fusion, Madrid (Spain); De Arcas, G.; Lopez, J.M. [Universidad Politecnica de Madrid (Spain); Murari, A. [Consorzio RFX, Padova (Italy); Fonseca, A. [Associacao URATOM/IST, Lisboa (Portugal); Contributors, J.E. [JET-EFDA, Abingdon (United Kingdom)

2009-07-01

Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. It integrates 2 functionalities. The first one is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The second one is the integration of dotJET (Diagnostic Overview Tool for JET), which internally provides at JET an overview about the current diagnostic systems state, in order to monitor, on remote, JET diagnostics status. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there are two data generators: the acquisition equipment associated with the reflectometer diagnostic that generates data and status information, and dotJET server that centralize the access to the status information of JET diagnostics. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on Java Web Start technology, and a dotJET client application have been used. There are 3 interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of a flexible enough architecture, to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements; and the third one is to have achieved a secure system, taking into account internal networks and firewalls aspects in JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to

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.

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)

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.

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…

Study of Aided Diagnosis of Hepatic Carcinoma Based on Artificial Neural Network Combined with Tumor Marker Group

Science.gov (United States)

Tan, Shanjuan; Feng, Feifei; Wu, Yongjun; Wu, Yiming

To develop a computer-aided diagnostic scheme by using an artificial neural network (ANN) combined with tumor markers for diagnosis of hepatic carcinoma (HCC) as a clinical assistant method. 140 serum samples (50 malignant, 40 benign and 50 normal) were analyzed for α-fetoprotein (AFP), carbohydrate antigen 125 (CA125), carcinoembryonic antigen (CEA), sialic acid (SA) and calcium (Ca). The five tumor marker values were then used as ANN inputs data. The result of ANN was compared with that of discriminant analysis by receiver operating characteristic (ROC) curve (AUC) analysis. The diagnostic accuracy of ANN and discriminant analysis among all samples of the test group was 95.5% and 79.3%, respectively. Analysis of multiple tumor markers based on ANN may be a better choice than the traditional statistical methods for differentiating HCC from benign or normal.

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 ...

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

Science.gov (United States)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Park, June; Jhon, Young Min; Seong, Maeng-Je; Hong, Seunghun

2010-02-01

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ~1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

Energy Technology Data Exchange (ETDEWEB)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun [Department of Physics and Astronomy, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742 (Korea, Republic of); Park, June; Seong, Maeng-Je [Department of Physics, Chung-Ang University, Heukseok-Dong, Dongjak-Gu, Seoul 156-756 (Korea, Republic of); Jhon, Young Min, E-mail: mseong@cau.ac.kr, E-mail: shong@phya.snu.ac.kr [Korea Institute of Science and Technology, Hawolgok-Dong, Seongbuk-Gu, Seoul 136-791 (Korea, Republic of)

2010-02-05

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of {approx}1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

100 nm scale low-noise sensors based on aligned carbon nanotube networks: overcoming the fundamental limitation of network-based sensors

International Nuclear Information System (INIS)

Lee, Minbaek; Lee, Joohyung; Kim, Tae Hyun; Lee, Hyungwoo; Lee, Byung Yang; Hong, Seunghun; Park, June; Seong, Maeng-Je; Jhon, Young Min

2010-01-01

Nanoscale sensors based on single-walled carbon nanotube (SWNT) networks have been considered impractical due to several fundamental limitations such as a poor sensitivity and small signal-to-noise ratio. Herein, we present a strategy to overcome these fundamental problems and build highly-sensitive low-noise nanoscale sensors simply by controlling the structure of the SWNT networks. In this strategy, we prepared nanoscale width channels based on aligned SWNT networks using a directed assembly strategy. Significantly, the aligned network-based sensors with narrower channels exhibited even better signal-to-noise ratio than those with wider channels, which is opposite to conventional random network-based sensors. As a proof of concept, we demonstrated 100 nm scale low-noise sensors to detect mercury ions with the detection limit of ∼1 pM, which is superior to any state-of-the-art portable detection system and is below the allowable limit of mercury ions in drinking water set by most government environmental protection agencies. This is the first demonstration of 100 nm scale low-noise sensors based on SWNT networks. Considering the increased interests in high-density sensor arrays for healthcare and environmental protection, our strategy should have a significant impact on various industrial applications.

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.

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.

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.

Application of Neural Networks for classification of Patau, Edwards, Down, Turner and Klinefelter Syndrome based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics.

Science.gov (United States)

Catic, Aida; Gurbeta, Lejla; Kurtovic-Kozaric, Amina; Mehmedbasic, Senad; Badnjevic, Almir

2018-02-13

feedback was 98.8%. The results presented in this paper prove that an expert diagnostic system based on neural networks can be efficiently used for classification of five aneuploidy syndromes, covered with this study, based on first trimester maternal serum screening data, ultrasonographic findings and patient demographics. Developed Expert System proved to be simple, robust, and powerful in properly classifying prenatal aneuploidy syndromes.

The prediction of the residual life of electromechanical equipment based on the artificial neural network

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 prediction of the residual life based on an estimate of the technical state of the induction motor. The proposed system allows to increase the accuracy and completeness of diagnostics by using an artificial neural network (ANN), and also identify and predict faulty states of an electrical equipment in dynamics. The results of the proposed system for estimation the technical condition are probability technical state diagrams and a quantitative evaluation of the residual life, taking into account electrical, vibrational, indirect parameters and detected defects. Based on the evaluation of the technical condition and the prediction of the residual life, a decision is made to change the control of the operating and maintenance modes of the electric motors.

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

Reliability and diagnostic of modular systems

Directory of Open Access Journals (Sweden)

J. Kohlas

2014-01-01

Full Text Available Reliability and diagnostic are in general two problems discussed separately. Yet the two problems are in fact closely related to each other. Here, this relation is considered in the simple case of modular systems. We show, how the computation of reliability and diagnostic can efficiently be done within the same Bayesian network induced by the modularity of the structure function of the system.

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.

Improved diagnostic accuracy of Alzheimer's disease by combining regional cortical thickness and default mode network functional connectivity: Validated in the Alzheimer's disease neuroimaging initiative set

International Nuclear Information System (INIS)

Park, Ji Eun; Park, Bum Woo; Kim, Sang Joon; Kim, Ho Sung; Choi, Choong Gon; Jung, Seung Jung; Oh, Joo Young; Shim, Woo Hyun; Lee, Jae Hong; Roh, Jee Hoon

2017-01-01

To identify potential imaging biomarkers of Alzheimer's disease by combining brain cortical thickness (CThk) and functional connectivity and to validate this model's diagnostic accuracy in a validation set. Data from 98 subjects was retrospectively reviewed, including a study set (n = 63) and a validation set from the Alzheimer's Disease Neuroimaging Initiative (n = 35). From each subject, data for CThk and functional connectivity of the default mode network was extracted from structural T1-weighted and resting-state functional magnetic resonance imaging. Cortical regions with significant differences between patients and healthy controls in the correlation of CThk and functional connectivity were identified in the study set. The diagnostic accuracy of functional connectivity measures combined with CThk in the identified regions was evaluated against that in the medial temporal lobes using the validation set and application of a support vector machine. Group-wise differences in the correlation of CThk and default mode network functional connectivity were identified in the superior temporal (p < 0.001) and supramarginal gyrus (p = 0.007) of the left cerebral hemisphere. Default mode network functional connectivity combined with the CThk of those two regions were more accurate than that combined with the CThk of both medial temporal lobes (91.7% vs. 75%). Combining functional information with CThk of the superior temporal and supramarginal gyri in the left cerebral hemisphere improves diagnostic accuracy, making it a potential imaging biomarker for Alzheimer's disease

The application of DECnet TTT technique in beam diagnostical system of SSC central region

International Nuclear Information System (INIS)

Zhang Guixu; Huang Tuanhua

1997-01-01

The DECnet TTT technique and the real time network communication based on DECnet TTT are presented. Information can be transferred between console and diagnostical system via DECnet communication, so all kinds of operation can be done on console. The working computer of HIRFL console is VAX8350

A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks

Directory of Open Access Journals (Sweden)

Shibo Luo

2015-12-01

Full Text Available Software-Defined Networking-based Mobile Networks (SDN-MNs are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

A Security Assessment Mechanism for Software-Defined Networking-Based Mobile Networks.

Science.gov (United States)

Luo, Shibo; Dong, Mianxiong; Ota, Kaoru; Wu, Jun; Li, Jianhua

2015-12-17

Software-Defined Networking-based Mobile Networks (SDN-MNs) are considered the future of 5G mobile network architecture. With the evolving cyber-attack threat, security assessments need to be performed in the network management. Due to the distinctive features of SDN-MNs, such as their dynamic nature and complexity, traditional network security assessment methodologies cannot be applied directly to SDN-MNs, and a novel security assessment methodology is needed. In this paper, an effective security assessment mechanism based on attack graphs and an Analytic Hierarchy Process (AHP) is proposed for SDN-MNs. Firstly, this paper discusses the security assessment problem of SDN-MNs and proposes a methodology using attack graphs and AHP. Secondly, to address the diversity and complexity of SDN-MNs, a novel attack graph definition and attack graph generation algorithm are proposed. In order to quantify security levels, the Node Minimal Effort (NME) is defined to quantify attack cost and derive system security levels based on NME. Thirdly, to calculate the NME of an attack graph that takes the dynamic factors of SDN-MN into consideration, we use AHP integrated with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) as the methodology. Finally, we offer a case study to validate the proposed methodology. The case study and evaluation show the advantages of the proposed security assessment mechanism.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch.

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-19

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Directory of Open Access Journals (Sweden)

Tao Huang

2016-01-01

Full Text Available Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture.

Building SDN-Based Agricultural Vehicular Sensor Networks Based on Extended Open vSwitch

Science.gov (United States)

Huang, Tao; Yan, Siyu; Yang, Fan; Pan, Tian; Liu, Jiang

2016-01-01

Software-defined vehicular sensor networks in agriculture, such as autonomous vehicle navigation based on wireless multi-sensor networks, can lead to more efficient precision agriculture. In SDN-based vehicle sensor networks, the data plane is simplified and becomes more efficient by introducing a centralized controller. However, in a wireless environment, the main controller node may leave the sensor network due to the dynamic topology change or the unstable wireless signal, leaving the rest of network devices without control, e.g., a sensor node as a switch may forward packets according to stale rules until the controller updates the flow table entries. To solve this problem, this paper proposes a novel SDN-based vehicular sensor networks architecture which can minimize the performance penalty of controller connection loss. We achieve this by designing a connection state detection and self-learning mechanism. We build prototypes based on extended Open vSwitch and Ryu. The experimental results show that the recovery time from controller connection loss is under 100 ms and it keeps rule updating in real time with a stable throughput. This architecture enhances the survivability and stability of SDN-based vehicular sensor networks in precision agriculture. PMID:26797616

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)

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).

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.

The graphics-based human interface to the DISYS diagnostic/control guidance system at EBR-2

International Nuclear Information System (INIS)

Edwards, R.M.; Chavez, C.; Kamarthi, S.; Dharap, S.; Lindsay, R.W.; Staffon, J.

1990-01-01

An initial graphics based interface to the real-time DISYS diagnostic system has been developed using the multi-tasking capabilities of the UNIX operating system and X-Windows 11 Xlib graphics library. This system is interfaced to live plant data at the Experimental Breeder Reactor (EBR-2) for the Argon Cooling System of fuel handling operations and the steam plant. The interface includes an intelligent process schematic which highlights problematic components and sensors based on the results of the diagnostic computations. If further explanation of a faulted component is required, the user can call up a display of the diagnostic computations presented in a tree-like diagram. Numerical data on the process schematic and optional diagnostic tree are updated as new real-time data becomes available. The initial X-Windows 11 based interface will be further enhanced using VI Corporation DATAVIEWS graphical data base software. 5 refs., 6 figs

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...

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.

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.

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

Approaches to Demonstrating the Reliability and Validity of Core Diagnostic Criteria for Chronic Pain.

Science.gov (United States)

Bruehl, Stephen; Ohrbach, Richard; Sharma, Sonia; Widerstrom-Noga, Eva; Dworkin, Robert H; Fillingim, Roger B; Turk, Dennis C

2016-09-01

The Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks-American Pain Society Pain Taxonomy (AAPT) is designed to be an evidence-based multidimensional chronic pain classification system that will facilitate more comprehensive and consistent chronic pain diagnoses, and thereby enhance research, clinical communication, and ultimately patient care. Core diagnostic criteria (dimension 1) for individual chronic pain conditions included in the initial version of AAPT will be the focus of subsequent empirical research to evaluate and provide evidence for their reliability and validity. Challenges to validating diagnostic criteria in the absence of clear and identifiable pathophysiological mechanisms are described. Based in part on previous experience regarding the development of evidence-based diagnostic criteria for psychiatric disorders, headache, and specific chronic pain conditions (fibromyalgia, complex regional pain syndrome, temporomandibular disorders, pain associated with spinal cord injuries), several potential approaches for documentation of the reliability and validity of the AAPT diagnostic criteria are summarized. The AAPT is designed to be an evidence-based multidimensional chronic pain classification system. Conceptual and methodological issues related to demonstrating the reliability and validity of the proposed AAPT chronic pain diagnostic criteria are discussed. Copyright © 2016 American Pain Society. Published by Elsevier Inc. All rights reserved.

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

Work-based social networks and health status among Japanese employees.

Science.gov (United States)

Suzuki, E; Takao, S; Subramanian, S V; Doi, H; Kawachi, I

2009-09-01

Despite the worldwide trend towards more time being spent at work by employed people, few studies have examined the independent influences of work-based versus home-based social networks on employees' health. We examined the association between work-based social networks and health status by controlling for home-based social networks in a cross-sectional study. By employing a two-stage stratified random sampling procedure, 1105 employees were identified from 46 companies in Okayama, Japan, in 2007. Work-based social networks were assessed by asking the number of co-workers whom they consult with ease on personal issues. The outcome was self-rated health; the adjusted OR for poor health compared employees with no network with those who have larger networks. Although a clear (and inverse) dose-response relationship was found between the size of work-based social networks and poor health (OR 1.53, 95% CI 1.03 to 2.27, comparing those with the lowest versus highest level of social network), the association was attenuated to statistical non-significance after we controlled for the size of home-based social networks. In further analyses stratified on age groups, in older workers (> or =50 years) work-based social networks were apparently associated with better health status, whereas home-based networks were not. The reverse was true among middle-aged workers (30-49 years). No associations were found among younger workers (social support on health according to age groups. We hypothesise that these patterns reflect generational differences in workers' commitment to their workplace.

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.

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...

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.

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.

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

Directory of Open Access Journals (Sweden)

M. Kumar

2016-01-01

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

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...

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.

A Gossip-based Energy Efficient Protocol for Robust In-network Aggregation in Wireless Sensor Networks

Science.gov (United States)

Fauji, Shantanu

We consider the problem of energy efficient and fault tolerant in--network aggregation for wireless sensor networks (WSNs). In-network aggregation is the process of aggregation while collecting data from sensors to the base station. This process should be energy efficient due to the limited energy at the sensors and tolerant to the high failure rates common in sensor networks. Tree based in--network aggregation protocols, although energy efficient, are not robust to network failures. Multipath routing protocols are robust to failures to a certain degree but are not energy efficient due to the overhead in the maintenance of multiple paths. We propose a new protocol for in-network aggregation in WSNs, which is energy efficient, achieves high lifetime, and is robust to the changes in the network topology. Our protocol, gossip--based protocol for in-network aggregation (GPIA) is based on the spreading of information via gossip. GPIA is not only adaptive to failures and changes in the network topology, but is also energy efficient. Energy efficiency of GPIA comes from all the nodes being capable of selective message reception and detecting convergence of the aggregation early. We experimentally show that GPIA provides significant improvement over some other competitors like the Ridesharing, Synopsis Diffusion and the pure version of gossip. GPIA shows ten fold, five fold and two fold improvement over the pure gossip, the synopsis diffusion and Ridesharing protocols in terms of network lifetime, respectively. Further, GPIA retains gossip's robustness to failures and improves upon the accuracy of synopsis diffusion and Ridesharing.

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

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.

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....

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.

A belief network approach for development of a nuclear power plant diagnosis system

Energy Technology Data Exchange (ETDEWEB)

Hwang, I K; Kim, J T; Lee, D Y; Jung, C H; Kim, J Y; Lee, J S; Ham, C S [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1999-12-31

Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

A belief network approach for development of a nuclear power plant diagnosis system

Energy Technology Data Exchange (ETDEWEB)

Hwang, I. K.; Kim, J. T.; Lee, D. Y.; Jung, C. H.; Kim, J. Y.; Lee, J. S.; Ham, C. S. [Korea Atomic Energy Research Institute, Taejon (Korea, Republic of)

1998-12-31

Belief network (or Bayesian network) based on Bayes` rule in probabilistic theory can be applied to the reasoning of diagnostic system. This paper describes the basic theory of concept and feasibility of using the network for diagnosis of nuclear power plants. An example shows that the probabilities of root causes of a failure are calculated from the measured or believed evidences. 6 refs., 3 figs. (Author)

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...

Wavelet Based Diagnosis and Protection of Electric Motors

OpenAIRE

Khan, M. Abdesh Shafiel Kafiey; Rahman, M. Azizur

2010-01-01

In this chapter, a short review of conventional Fourier transforms and new wavelet based faults diagnostic and protection techniques for electric motors is presented. The new hybrid wavelet packet transform (WPT) and neural network (NN) based faults diagnostic algorithm is developed and implemented for electric motors. The proposed WPT and NN

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.

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…

[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.

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.

Genome network medicine: innovation to overcome huge challenges in cancer therapy.

Science.gov (United States)

Roukos, Dimitrios H

2014-01-01

The post-ENCODE era shapes now a new biomedical research direction for understanding transcriptional and signaling networks driving gene expression and core cellular processes such as cell fate, survival, and apoptosis. Over the past half century, the Francis Crick 'central dogma' of single n gene/protein-phenotype (trait/disease) has defined biology, human physiology, disease, diagnostics, and drugs discovery. However, the ENCODE project and several other genomic studies using high-throughput sequencing technologies, computational strategies, and imaging techniques to visualize regulatory networks, provide evidence that transcriptional process and gene expression are regulated by highly complex dynamic molecular and signaling networks. This Focus article describes the linear experimentation-based limitations of diagnostics and therapeutics to cure advanced cancer and the need to move on from reductionist to network-based approaches. With evident a wide genomic heterogeneity, the power and challenges of next-generation sequencing (NGS) technologies to identify a patient's personal mutational landscape for tailoring the best target drugs in the individual patient are discussed. However, the available drugs are not capable of targeting aberrant signaling networks and research on functional transcriptional heterogeneity and functional genome organization is poorly understood. Therefore, the future clinical genome network medicine aiming at overcoming multiple problems in the new fields of regulatory DNA mapping, noncoding RNA, enhancer RNAs, and dynamic complexity of transcriptional circuitry are also discussed expecting in new innovation technology and strong appreciation of clinical data and evidence-based medicine. The problematic and potential solutions in the discovery of next-generation, molecular, and signaling circuitry-based biomarkers and drugs are explored. © 2013 Wiley Periodicals, Inc.

Predicting the Uncertain Future of Aptamer-Based Diagnostics and Therapeutics.

Science.gov (United States)

Bruno, John G

2015-04-16

Despite the great promise of nucleic acid aptamers in the areas of diagnostics and therapeutics for their facile in vitro development, lack of immunogenicity and other desirable properties, few truly successful aptamer-based products exist in the clinical or other markets. Core reasons for these commercial deficiencies probably stem from industrial commitment to antibodies including a huge financial investment in humanized monoclonal antibodies and a general ignorance about aptamers and their performance among the research and development community. Given the early failures of some strong commercial efforts to gain government approval and bring aptamer-based products to market, it may seem that aptamers are doomed to take a backseat to antibodies forever. However, the key advantages of aptamers over antibodies coupled with niche market needs that only aptamers can fill and more recent published data still point to a bright commercial future for aptamers in areas such as infectious disease and cancer diagnostics and therapeutics. As more researchers and entrepreneurs become familiar with aptamers, it seems inevitable that aptamers will at least be considered for expanded roles in diagnostics and therapeutics. This review also examines new aptamer modifications and attempts to predict new aptamer applications that could revolutionize biomedical technology in the future and lead to marketed products.

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.

Implementing an ultrasound-based protocol for diagnosingappendicitis while maintaining diagnostic accuracy

International Nuclear Information System (INIS)

Van Atta, Angela J.; Baskin, Henry J.; Maves, Connie K.; Dansie, David M.; Rollins, Michael D.; Bolte, Robert G.; Mundorff, Michael B.; Andrews, Seth P.

2015-01-01

The use of ultrasound to diagnose appendicitis in children is well-documented but not universally employed outside of pediatric academic centers, especially in the United States. Various obstacles make it difficult for institutions and radiologists to abandon a successful and accurate CT-based imaging protocol in favor of a US-based protocol. To describe how we overcame barriers to implementing a US-based appendicitis protocol among a large group of nonacademic private-practice pediatric radiologists while maintaining diagnostic accuracy and decreasing medical costs. A multidisciplinary team of physicians (pediatric surgery, pediatric emergency medicine and pediatric radiology) approved an imaging protocol using US as the primary modality to evaluate suspected appendicitis with CT for equivocal cases. The protocol addressed potential bias against US and accommodated for institutional limitations of radiologist and sonographer experience and availability. Radiologists coded US reports according to the probability of appendicitis. Radiology reports were compared with clinical outcomes to assess diagnostic accuracy. During the study period, physicians from each group were apprised of the interim US protocol accuracy results. Problematic cases were discussed openly. A total of 512 children were enrolled and underwent US for evaluation of appendicitis over a 30-month period. Diagnostic accuracy was comparable to published results for combined US/CT protocols. Comparing the first 12 months to the last 12 months of the study period, the proportion of children achieving an unequivocal US result increased from 30% (51/169) to 53% (149/282) and the proportion of children undergoing surgery based solely on US findings increased from 55% (23/42) to 84% (92/109). Overall, 63% (325/512) of patients in the protocol did not require a CT. Total patient costs were reduced by $30,182 annually. We overcame several barriers to implementing a US protocol. During the study period our

Implementing an ultrasound-based protocol for diagnosingappendicitis while maintaining diagnostic accuracy

Energy Technology Data Exchange (ETDEWEB)

Van Atta, Angela J. [University of Utah School of Medicine, Salt Lake City, UT (United States); Baskin, Henry J.; Maves, Connie K.; Dansie, David M. [Primary Children' s Hospital, Department of Radiology, Salt Lake City, UT (United States); Rollins, Michael D. [University of Utah School of Medicine, Department of Surgery, Division of Pediatric Surgery, Salt Lake City, UT (United States); Bolte, Robert G. [University of Utah School of Medicine, Department of Pediatrics, Division of Pediatric Emergency Medicine, Salt Lake City, UT (United States); Mundorff, Michael B.; Andrews, Seth P. [Primary Children' s Hospital, Systems Improvement, Salt Lake City, UT (United States)

2015-05-01

The use of ultrasound to diagnose appendicitis in children is well-documented but not universally employed outside of pediatric academic centers, especially in the United States. Various obstacles make it difficult for institutions and radiologists to abandon a successful and accurate CT-based imaging protocol in favor of a US-based protocol. To describe how we overcame barriers to implementing a US-based appendicitis protocol among a large group of nonacademic private-practice pediatric radiologists while maintaining diagnostic accuracy and decreasing medical costs. A multidisciplinary team of physicians (pediatric surgery, pediatric emergency medicine and pediatric radiology) approved an imaging protocol using US as the primary modality to evaluate suspected appendicitis with CT for equivocal cases. The protocol addressed potential bias against US and accommodated for institutional limitations of radiologist and sonographer experience and availability. Radiologists coded US reports according to the probability of appendicitis. Radiology reports were compared with clinical outcomes to assess diagnostic accuracy. During the study period, physicians from each group were apprised of the interim US protocol accuracy results. Problematic cases were discussed openly. A total of 512 children were enrolled and underwent US for evaluation of appendicitis over a 30-month period. Diagnostic accuracy was comparable to published results for combined US/CT protocols. Comparing the first 12 months to the last 12 months of the study period, the proportion of children achieving an unequivocal US result increased from 30% (51/169) to 53% (149/282) and the proportion of children undergoing surgery based solely on US findings increased from 55% (23/42) to 84% (92/109). Overall, 63% (325/512) of patients in the protocol did not require a CT. Total patient costs were reduced by $30,182 annually. We overcame several barriers to implementing a US protocol. During the study period our

Network Approach to Autistic Traits: Group and Subgroup Analyses of ADOS Item Scores

Science.gov (United States)

Anderson, George M.; Montazeri, Farhad; de Bildt, Annelies

2015-01-01

A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of 477 Dutch individuals with developmental disorders.…

Thyroid Disease and Surgery in CHEER: The Nation’s Otolaryngology-Head and Neck Surgery Practice Based Network

Science.gov (United States)

Parham, Kourosh; Chapurin, Nikita; Schulz, Kris; Shin, Jennifer J.; Pynnonen, Melissa A.; Witsell, David L.; Langman, Alan; Nguyen-Huynh, Anh; Ryan, Sheila E.; Vambutas, Andrea; Wolfley, Anne; Roberts, Rhonda; Lee, Walter T.

2017-01-01

Objectives 1) Describe thyroid-related diagnoses and procedures in CHEER across academic and community sites. 2) Compare management of malignant thyroid disease across these sites, and 3) Provide practice based data related to flexible laryngoscopy vocal fold assessment before and after thyroid surgery based on AAO-HNSF Clinical Practice Guidelines. Study Design Review of retrospective data collection (RDC) database of the CHEER network using ICD-9 and CPT codes related to thyroid conditions. Setting Multisite practice based network. Subjects and Methods There were 3,807 thyroid patients (1,392 malignant; 2,415 benign) with 10,160 unique visits identified from 1 year of patient data in the RDC. Analysis was performed for identified cohort of patients using demographics, site characteristics and diagnostic and procedural distribution. Results Mean number of patients with thyroid disease per site was 238 (range 23–715). In community practices, 19% of patients with thyroid disease had cancer versus 45% in the academic setting (pVocal fold function was assessed by flexible laryngoscopy in 34.0% of pre-operative patients and in 3.7% post-operatively. Conclusion This is the first overview of malignant and benign thyroid disease through CHEER. It shows how the RDC can be used alone and with national guidelines to inform of clinical practice patterns in academic and community sites. This demonstrates the potential for future thyroid related studies utilizing the Otolaryngology-H&N Surgery’s practice-based research network. PMID:27371622

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.

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.

Real-time remote diagnostic monitoring test-bed in JET

International Nuclear Information System (INIS)

Castro, R.; Kneupner, K.; Vega, J.; De Arcas, G.; Lopez, J.M.; Purahoo, K.; Murari, A.; Fonseca, A.; Pereira, A.; Portas, A.

2010-01-01

Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. Its main functionality is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there is one data generator, which is the acquisition equipment associated with the reflectometer diagnostic that generates data and status information. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on JAVA Web Start technology has been used. There are three interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of an architecture, flexible enough to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements. Finally, the third result is a secure system, taking into account internal networks and firewalls aspects of JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to monitor diagnostics in real-time, and enabling the integration of this service into the EFDA Federation (Castro et al., 2008 ).

Real-time remote diagnostic monitoring test-bed in JET

Energy Technology Data Exchange (ETDEWEB)

Castro, R., E-mail: rodrigo.castro@ciemat.e [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); Kneupner, K. [EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, OX14 3DB (United Kingdom); Vega, J. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain); De Arcas, G.; Lopez, J.M. [Universidad Politecnica de Madrid, Grupo I2A2, Madrid (Spain); Purahoo, K. [EURATOM/UKAEA Fusion Association, Culham Science Centre, Abingdon, OX14 3DB (United Kingdom); Murari, A. [Associazione EURATOM-ENEA per la Fusione, Consorzio RFX, 4-35127 Padova (Italy); Fonseca, A. [Associacao EURATOM/IST, Lisbon (Portugal); Pereira, A.; Portas, A. [Asociacion EURATOM/CIEMAT para Fusion, Madrid (Spain)

2010-07-15

Based on the remote experimentation concept oriented to long pulse shots, a test-bed system has been implemented in JET. Its main functionality is the real-time monitoring, on remote, of a reflectometer diagnostic, to visualize different data outputs and status information. The architecture of the system is formed by: the data generator components, the data distribution system, an access control service, and the client applications. In the test-bed there is one data generator, which is the acquisition equipment associated with the reflectometer diagnostic that generates data and status information. The data distribution system has been implemented using a publishing-subscribing technology that receives data from data generators and redistributes them to client applications. And finally, for monitoring, a client application based on JAVA Web Start technology has been used. There are three interesting results from this project. The first one is the analysis of different aspects (data formats, data frame rate, data resolution, etc) related with remote real-time diagnostic monitoring oriented to long pulse experiments. The second one is the definition and implementation of an architecture, flexible enough to be applied to different types of data generated from other diagnostics, and that fits with remote access requirements. Finally, the third result is a secure system, taking into account internal networks and firewalls aspects of JET, and securing the access from remote users. For this last issue, PAPI technology has been used, enabling access control based on user attributes, enabling mobile users to monitor diagnostics in real-time, and enabling the integration of this service into the EFDA Federation (Castro et al., 2008 ).

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.

Identification of Gene Biomarkers for Distinguishing Small-Cell Lung Cancer from Non-Small-Cell Lung Cancer Using a Network-Based Approach

Directory of Open Access Journals (Sweden)

Fei Long

2015-01-01

Full Text Available Lung cancer consists of two main subtypes: small-cell lung cancer (SCLC and non-small-cell lung cancer (NSCLC that are classified according to their physiological phenotypes. In this study, we have developed a network-based approach to identify molecular biomarkers that can distinguish SCLC from NSCLC. By identifying positive and negative coexpression gene pairs in normal lung tissues, SCLC, or NSCLC samples and using functional association information from the STRING network, we first construct a lung cancer-specific gene association network. From the network, we obtain gene modules in which genes are highly functionally associated with each other and are either positively or negatively coexpressed in the three conditions. Then, we identify gene modules that not only are differentially expressed between cancer and normal samples, but also show distinctive expression patterns between SCLC and NSCLC. Finally, we select genes inside those modules with discriminating coexpression patterns between the two lung cancer subtypes and predict them as candidate biomarkers that are of diagnostic use.

A novel neural-wavelet approach for process diagnostics and complex system modeling

Science.gov (United States)

Gao, Rong

Neural networks have been effective in several engineering applications because of their learning abilities and robustness. However certain shortcomings, such as slow convergence and local minima, are always associated with neural networks, especially neural networks applied to highly nonlinear and non-stationary problems. These problems can be effectively alleviated by integrating a new powerful tool, wavelets, into conventional neural networks. The multi-resolution analysis and feature localization capabilities of the wavelet transform offer neural networks new possibilities for learning. A neural wavelet network approach developed in this thesis enjoys fast convergence rate with little possibility to be caught at a local minimum. It combines the localization properties of wavelets with the learning abilities of neural networks. Two different testbeds are used for testing the efficiency of the new approach. The first is magnetic flowmeter-based process diagnostics: here we extend previous work, which has demonstrated that wavelet groups contain process information, to more general process diagnostics. A loop at Applied Intelligent Systems Lab (AISL) is used for collecting and analyzing data through the neural-wavelet approach. The research is important for thermal-hydraulic processes in nuclear and other engineering fields. The neural-wavelet approach developed is also tested with data from the electric power grid. More specifically, the neural-wavelet approach is used for performing short-term and mid-term prediction of power load demand. In addition, the feasibility of determining the type of load using the proposed neural wavelet approach is also examined. The notion of cross scale product has been developed as an expedient yet reliable discriminator of loads. Theoretical issues involved in the integration of wavelets and neural networks are discussed and future work outlined.

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.

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.

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

An Artificial Intelligence Approach for Gears Diagnostics in AUVs.

Science.gov (United States)

Marichal, Graciliano Nicolás; Del Castillo, María Lourdes; López, Jesús; Padrón, Isidro; Artés, Mariano

2016-04-12

In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles), where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms) have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

An Artificial Intelligence Approach for Gears Diagnostics in AUVs

Directory of Open Access Journals (Sweden)

Graciliano Nicolás Marichal

2016-04-01

Full Text Available In this paper, an intelligent scheme for detecting incipient defects in spur gears is presented. In fact, the study has been undertaken to determine these defects in a single propeller system of a small-sized unmanned helicopter. It is important to remark that although the study focused on this particular system, the obtained results could be extended to other systems known as AUVs (Autonomous Unmanned Vehicles, where the usage of polymer gears in the vehicle transmission is frequent. Few studies have been carried out on these kinds of gears. In this paper, an experimental platform has been adapted for the study and several samples have been prepared. Moreover, several vibration signals have been measured and their time-frequency characteristics have been taken as inputs to the diagnostic system. In fact, a diagnostic system based on an artificial intelligence strategy has been devised. Furthermore, techniques based on several paradigms of the Artificial Intelligence (Neural Networks, Fuzzy systems and Genetic Algorithms have been applied altogether in order to design an efficient fault diagnostic system. A hybrid Genetic Neuro-Fuzzy system has been developed, where it is possible, at the final stage of the learning process, to express the fault diagnostic system as a set of fuzzy rules. Several trials have been carried out and satisfactory results have been achieved.

Automated mode shape estimation in agent-based wireless sensor networks

Science.gov (United States)

Zimmerman, Andrew T.; Lynch, Jerome P.

2010-04-01

Recent advances in wireless sensing technology have made it possible to deploy dense networks of sensing transducers within large structural systems. Because these networks leverage the embedded computing power and agent-based abilities integral to many wireless sensing devices, it is possible to analyze sensor data autonomously and in-network. In this study, market-based techniques are used to autonomously estimate mode shapes within a network of agent-based wireless sensors. Specifically, recent work in both decentralized Frequency Domain Decomposition and market-based resource allocation is leveraged to create a mode shape estimation algorithm derived from free-market principles. This algorithm allows an agent-based wireless sensor network to autonomously shift emphasis between improving mode shape accuracy and limiting the consumption of certain scarce network resources: processing time, storage capacity, and power consumption. The developed algorithm is validated by successfully estimating mode shapes using a network of wireless sensor prototypes deployed on the mezzanine balcony of Hill Auditorium, located on the University of Michigan campus.

Cooperative research and development for artificial intelligence based reactor diagnostic system

International Nuclear Information System (INIS)

Reifman, J.; Wei, T.Y.C.; Abboud, R.G.; Chasensky, T.M.

1994-01-01

Artificial Intelligence (AI) techniques in the form of knowledge-based Expert Systems (ESs) have been proposed to provide on-line decision-making support for plant operators during both normal and emergency conditions. However, in spite of the great interest in these advanced techniques, their application in the diagnosis of large-scale processes has not yet reached its full potential because of limitations of the knowledge base. These limitations include problems with knowledge acquisition and the use of an event-oriented approach for process diagnosis. To investigate the capabilities of this two-level hierarchical knowledge structure, 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 the proposed diagnostic system. Investigations are being performed in the construction of a physics-based plant level process diagnostic ES and the characterization of component-level fault 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 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. This is an ongoing multi-year project and the remainder of this paper presents a mid-term status report

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.

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...

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.

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.

Vortex network community based reduced-order force model

Science.gov (United States)

Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya; Taira, Kunihiko

2017-11-01

We characterize the vortical wake interactions by utilizing network theory and cluster-based approaches, and develop a data-inspired unsteady force model. In the present work, the vortical interaction network is defined by nodes representing vortical elements and the edges quantified by induced velocity measures amongst the vortices. The full vorticity field is reduced to a finite number of vortical clusters based on network community detection algorithm, which serves as a basis for a skeleton network that captures the essence of the wake dynamics. We use this reduced representation of the wake to develop a data-inspired reduced-order force model that can predict unsteady fluid forces on the body. The overall formulation is demonstrated for laminar flows around canonical bluff body wake and stalled flow over an airfoil. We also show the robustness of the present network-based model against noisy data, which motivates applications towards turbulent flows and experimental measurements. Supported by the National Science Foundation (Grant 1632003).

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

2016-05-01

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 closely reflected these recommendations (increased adherence) and increased waiting times. Retrospective, cross-sectional case note analysis was applied to data from 80 case notes. Adherence ranged from a possible 0 (no adherence) to 19 (full adherence). Overall, 17/22 of the recommendations were adhered to in over 50 of the 80 cases and in 70 or more cases for 11/22 of the recommendations, with a mean adherence score of 16 (standard deviation = 1.9). No significant correlation was found between adherence and total wait time for untransformed (r = 0.15, p = 0.32) or transformed data (r = 0.12, p = 0.20). The results indicated that the assessment and diagnostic practices were consistent with the relevant Scottish Intercollegiate Guidelines Network 98 guideline recommendations. Increased adherence to the 19 included recommendations was not significantly related to increased total waiting times, indicating that the Scottish Intercollegiate Guidelines Network 98 recommendations have generally been integrated into practice, without a resultant increase in patient waits. © The Author(s) 2015.

BWR recirculation pump diagnostic expert system

International Nuclear Information System (INIS)

Chiang, S.C.; Morimoto, C.N.; Torres, M.R.

2004-01-01

At General Electric (GE), an on-line expert system to support maintenance decisions for BWR recirculation pumps for nuclear power plants has been developed. This diagnostic expert system is an interactive on-line system that furnishes diagnostic information concerning BWR recirculation pump operational problems. It effectively provides the recirculation pump diagnostic expertise in the plant control room continuously 24 hours a day. The expert system is interfaced to an on-line monitoring system, which uses existing plant sensors to acquire non-safety related data in real time. The expert system correlates and evaluates process data and vibration data by applying expert rules to determine the condition of a BWR recirculation pump system by applying knowledge based rules. Any diagnosis will be automatically displayed, indicating which pump may have a problem, the category of the problem, and the degree of concern expressed by the validity index and color hierarchy. The rules incorporate the expert knowledge from various technical sources such as plant experience, engineering principles, and published reports. These rules are installed in IF-THEN formats and the resulting truth values are also expressed in fuzzy terms and a certainty factor called a validity index. This GE Recirculation Pump Expert System uses industry-standard software, hardware, and network access to provide flexible interfaces with other possible data acquisition systems. Gensym G2 Real-Time Expert System is used for the expert shell and provides the graphical user interface, knowledge base, and inference engine capabilities. (author)

Neural networks: Application to medical imaging

Science.gov (United States)

Clarke, Laurence P.

1994-01-01

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

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...

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.

Influence of the Atmospheric Model on Hanle Diagnostics

Science.gov (United States)

Ishikawa, Ryohko; Uitenbroek, Han; Goto, Motoshi; Iida, Yusuke; Tsuneta, Saku

2018-05-01

We clarify the uncertainty in the inferred magnetic field vector via the Hanle diagnostics of the hydrogen Lyman-α line when the stratification of the underlying atmosphere is unknown. We calculate the anisotropy of the radiation field with plane-parallel semi-empirical models under the nonlocal thermal equilibrium condition and derive linear polarization signals for all possible parameters of magnetic field vectors based on an analytical solution of the atomic polarization and Hanle effect. We find that the semi-empirical models of the inter-network region (FAL-A) and network region (FAL-F) show similar degrees of anisotropy in the radiation field, and this similarity results in an acceptable inversion error ( e.g., {˜} 40 G instead of 50 G in field strength and {˜} 100° instead of 90° in inclination) when FAL-A and FAL-F are swapped. However, the semi-empirical models of FAL-C (averaged quiet-Sun model including both inter-network and network regions) and FAL-P (plage regions) yield an atomic polarization that deviates from all other models, which makes it difficult to precisely determine the magnetic field vector if the correct atmospheric model is not known ( e.g., the inversion error is much larger than 40% of the field strength; {>} 70 G instead of 50 G). These results clearly demonstrate that the choice of model atmosphere is important for Hanle diagnostics. As is well known, one way to constrain the average atmospheric stratification is to measure the center-to-limb variation of the linear polarization signals. The dependence of the center-to-limb variations on the atmospheric model is also presented in this paper.

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.

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.

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.

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.

On effectiveness of network sensor-based defense framework

Science.gov (United States)

Zhang, Difan; Zhang, Hanlin; Ge, Linqiang; Yu, Wei; Lu, Chao; Chen, Genshe; Pham, Khanh

2012-06-01

Cyber attacks are increasing in frequency, impact, and complexity, which demonstrate extensive network vulnerabilities with the potential for serious damage. Defending against cyber attacks calls for the distributed collaborative monitoring, detection, and mitigation. To this end, we develop a network sensor-based defense framework, with the aim of handling network security awareness, mitigation, and prediction. We implement the prototypical system and show its effectiveness on detecting known attacks, such as port-scanning and distributed denial-of-service (DDoS). Based on this framework, we also implement the statistical-based detection and sequential testing-based detection techniques and compare their respective detection performance. The future implementation of defensive algorithms can be provisioned in our proposed framework for combating cyber attacks.

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…

Analysis of blocking probability for OFDM-based variable bandwidth optical network

Science.gov (United States)

Gong, Lei; Zhang, Jie; Zhao, Yongli; Lin, Xuefeng; Wu, Yuyao; Gu, Wanyi

2011-12-01

Orthogonal Frequency Division Multiplexing (OFDM) has recently been proposed as a modulation technique. For optical networks, because of its good spectral efficiency, flexibility, and tolerance to impairments, optical OFDM is much more flexible compared to traditional WDM systems, enabling elastic bandwidth transmissions, and optical networking is the future trend of development. In OFDM-based optical network the research of blocking rate has very important significance for network assessment. Current research for WDM network is basically based on a fixed bandwidth, in order to accommodate the future business and the fast-changing development of optical network, our study is based on variable bandwidth OFDM-based optical networks. We apply the mathematical analysis and theoretical derivation, based on the existing theory and algorithms, research blocking probability of the variable bandwidth of optical network, and then we will build a model for blocking probability.

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)

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.

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...

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....

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

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.”

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.

U.S. Department of Defense Multiple-Parameter Biodosimetry Network

International Nuclear Information System (INIS)

Blakely, William F.; Hoefer, Matthew H.; Huff, L. Andrew; Romanyukha, Alexander; Hayes, Selena M.; Williams, Anthony; Sharp, Thad; Reyes, Ricardo A.; Stewart, H. Michael Jr

2016-01-01

The U.S. Department of Defense (US-DOD) service members are at risk of exposure to ionizing radiation due to radiation accidents, terrorist attacks and national defense activities. The use of biodosimetry is a standard of care for the triage and treatment of radiation injuries. Resources and procedures need to be established to implement a multiple-parameter biodosimetry system coupled with expert medial guidance to provide an integrated radiation diagnostic system to meet US-DOD requirements. Current US-DOD biodosimetry capabilities were identified and recommendations to fill the identified gaps are provided. A US-DOD Multi-parametric Biodosimetry Network, based on the expertise that resides at the Armed Forces Radiobiology Research Institute and the Naval Dosimetry Center, was designed. This network based on the use of multiple biodosimetry modalities would provide diagnostic and triage capabilities needed to meet US-DOD requirements. These are not available with sufficient capacity elsewhere but could be needed urgently after a major radiological/nuclear event. (authors)

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

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.

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.

Restoration in multi-domain GMPLS-based networks

DEFF Research Database (Denmark)

Manolova, Anna; Ruepp, Sarah Renée; Dittmann, Lars

2011-01-01

In this paper, we evaluate the efficiency of using restoration mechanisms in a dynamic multi-domain GMPLS network. Major challenges and solutions are introduced and two well-known restoration schemes (End-to-End and Local-to-End) are evaluated. Additionally, new restoration mechanisms are introdu......In this paper, we evaluate the efficiency of using restoration mechanisms in a dynamic multi-domain GMPLS network. Major challenges and solutions are introduced and two well-known restoration schemes (End-to-End and Local-to-End) are evaluated. Additionally, new restoration mechanisms...... are introduced: one based on the position of a failed link, called Location-Based, and another based on minimizing the additional resources consumed during restoration, called Shortest-New. A complete set of simulations in different network scenarios show where each mechanism is more efficient in terms, such as...

Tree-based server-middleman-client architecture: improving scalability and reliability for voting-based network games in ad hoc wireless networks

Science.gov (United States)

Guo, Y.; Fujinoki, H.

2006-10-01

The concept of a new tree-based architecture for networked multi-player games was proposed by Matuszek to improve scalability in network traffic at the same time to improve reliability. The architecture (we refer it as "Tree-Based Server- Middlemen-Client architecture") will solve the two major problems in ad-hoc wireless networks: frequent link failures and significance in battery power consumption at wireless transceivers by using two new techniques, recursive aggregation of client messages and subscription-based propagation of game state. However, the performance of the TBSMC architecture has never been quantitatively studied. In this paper, the TB-SMC architecture is compared with the client-server architecture using simulation experiments. We developed an event driven simulator to evaluate the performance of the TB-SMC architecture. In the network traffic scalability experiments, the TB-SMC architecture resulted in less than 1/14 of the network traffic load for 200 end users. In the reliability experiments, the TB-SMC architecture improved the number of successfully delivered players' votes by 31.6, 19.0, and 12.4% from the clientserver architecture at high (failure probability of 90%), moderate (50%) and low (10%) failure probability.

A method for identifying hierarchical sub-networks / modules and weighting network links based on their similarity in sub-network / module affiliation

Directory of Open Access Journals (Sweden)

WenJun Zhang

2016-06-01

Full Text Available Some networks, including biological networks, consist of hierarchical sub-networks / modules. Based on my previous study, in present study a method for both identifying hierarchical sub-networks / modules and weighting network links is proposed. It is based on the cluster analysis in which between-node similarity in sets of adjacency nodes is used. Two matrices, linkWeightMat and linkClusterIDs, are achieved by using the algorithm. Two links with both the same weight in linkWeightMat and the same cluster ID in linkClusterIDs belong to the same sub-network / module. Two links with the same weight in linkWeightMat but different cluster IDs in linkClusterIDs belong to two sub-networks / modules at the same hirarchical level. However, a link with an unique cluster ID in linkClusterIDs does not belong to any sub-networks / modules. A sub-network / module of the greater weight is the more connected sub-network / modules. Matlab codes of the algorithm are presented.

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

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.

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

Network-level accident-mapping: Distance based pattern matching using artificial neural network.

Science.gov (United States)

Deka, Lipika; Quddus, Mohammed

2014-04-01

The objective of an accident-mapping algorithm is to snap traffic accidents onto the correct road segments. Assigning accidents onto the correct segments facilitate to robustly carry out some key analyses in accident research including the identification of accident hot-spots, network-level risk mapping and segment-level accident risk modelling. Existing risk mapping algorithms have some severe limitations: (i) they are not easily 'transferable' as the algorithms are specific to given accident datasets; (ii) they do not perform well in all road-network environments such as in areas of dense road network; and (iii) the methods used do not perform well in addressing inaccuracies inherent in and type of road environment. The purpose of this paper is to develop a new accident mapping algorithm based on the common variables observed in most accident databases (e.g. road name and type, direction of vehicle movement before the accident and recorded accident location). The challenges here are to: (i) develop a method that takes into account uncertainties inherent to the recorded traffic accident data and the underlying digital road network data, (ii) accurately determine the type and proportion of inaccuracies, and (iii) develop a robust algorithm that can be adapted for any accident set and road network of varying complexity. In order to overcome these challenges, a distance based pattern-matching approach is used to identify the correct road segment. This is based on vectors containing feature values that are common in the accident data and the network data. Since each feature does not contribute equally towards the identification of the correct road segments, an ANN approach using the single-layer perceptron is used to assist in "learning" the relative importance of each feature in the distance calculation and hence the correct link identification. The performance of the developed algorithm was evaluated based on a reference accident dataset from the UK confirming that

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)

Infection with Pathogens Transmitted Commonly Through Food and the Effect of Increasing Use of Culture-Independent Diagnostic Tests on Surveillance--Foodborne Diseases Active Surveillance Network, 10 U.S. Sites, 2012-2015.

Science.gov (United States)

Huang, Jennifer Y; Henao, Olga L; Griffin, Patricia M; Vugia, Duc J; Cronquist, Alicia B; Hurd, Sharon; Tobin-D'Angelo, Melissa; Ryan, Patricia; Smith, Kirk; Lathrop, Sarah; Zansky, Shelley; Cieslak, Paul R; Dunn, John; Holt, Kristin G; Wolpert, Beverly J; Patrick, Mary E

2016-04-15

To evaluate progress toward prevention of enteric and foodborne illnesses in the United States, the Foodborne Diseases Active Surveillance Network (FoodNet) monitors the incidence of laboratory-confirmed infections caused by nine pathogens transmitted commonly through food in 10 U.S. sites. This report summarizes preliminary 2015 data and describes trends since 2012. In 2015, FoodNet reported 20,107 confirmed cases (defined as culture-confirmed bacterial infections and laboratory-confirmed parasitic infections), 4,531 hospitalizations, and 77 deaths. FoodNet also received reports of 3,112 positive culture-independent diagnostic tests (CIDTs) without culture-confirmation, a number that has markedly increased since 2012. Diagnostic testing practices for enteric pathogens are rapidly moving away from culture-based methods. The continued shift from culture-based methods to CIDTs that do not produce the isolates needed to distinguish between strains and subtypes affects the interpretation of public health surveillance data and ability to monitor progress toward prevention efforts. Expanded case definitions and strategies for obtaining bacterial isolates are crucial during this transition period.

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.

Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

Science.gov (United States)

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.

Exposure from diagnostic nuclear medicine procedures

International Nuclear Information System (INIS)

Iacob, O.; Diaconescu, C.; Isac, R.

2002-01-01

According to our last national study on population exposures from natural and artificial sources of ionizing radiation, 16% of overall annual collective effective dose represent the contribution of diagnostic medical exposures. Of this value, 92% is due to diagnostic X-ray examinations and only 8% arise from diagnostic nuclear medicine procedures. This small contribution to collective dose is mainly the result of their lower frequency compared to that of the X-ray examinations, doses delivered to patients being, on average, ten times higher. The purpose of this review was to reassess the population exposure from in vivo diagnostic nuclear medicine procedures and to evaluate the temporal trends of diagnostic usage of radiopharmaceuticals in Romania. The current survey is the third one conducted in the last decade. As in the previous ones (1990 and 1995), the contribution of the Radiation Hygiene Laboratories Network of the Ministry of Health and Family in collecting data from nuclear medicine departments in hospitals was very important

The Diagnostic Challenge Competition: Probabilistic Techniques for Fault Diagnosis in Electrical Power Systems

Science.gov (United States)

Ricks, Brian W.; Mengshoel, Ole J.

2009-01-01

Reliable systems health management is an important research area of NASA. A health management system that can accurately and quickly diagnose faults in various on-board systems of a vehicle will play a key role in the success of current and future NASA missions. We introduce in this paper the ProDiagnose algorithm, a diagnostic algorithm that uses a probabilistic approach, accomplished with Bayesian Network models compiled to Arithmetic Circuits, to diagnose these systems. We describe the ProDiagnose algorithm, how it works, and the probabilistic models involved. We show by experimentation on two Electrical Power Systems based on the ADAPT testbed, used in the Diagnostic Challenge Competition (DX 09), that ProDiagnose can produce results with over 96% accuracy and less than 1 second mean diagnostic time.

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.

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.

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.

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 ...

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

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Han Yih Lau

2017-12-01

Full Text Available 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 diagnostic methods for applications in plant disease detection. Polymerase chain reaction (PCR is the most common DNA amplification technology used for detecting various plant and animal pathogens. However, subsequent to PCR based assays, several types of nucleic acid amplification technologies have been developed to achieve higher sensitivity, rapid detection as well as suitable for field applications such as loop-mediated isothermal amplification, helicase-dependent amplification, rolling circle amplification, recombinase polymerase amplification, and molecular inversion probe. The principle behind these technologies has been thoroughly discussed in several review papers; herein we emphasize the application of these technologies to detect plant pathogens by outlining the advantages and disadvantages of each technology in detail.

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.

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.

[Self-owned versus accredited network: comparative cost analysis in a Brazilian health insurance provider].

Science.gov (United States)

Souza, Marcos Antônio de; Salvalaio, Dalva

2010-10-01

to analyze the cost of a self-owned network maintained by a Brazilian health insurance provider as compared to the price charged by accredited service providers, so as to identify whether or not the self-owned network is economically advantageous. for this exploratory study, the company's management reports were reviewed. The cost associated with the self-owned network was calculated based on medical and dental office visits and diagnostic/laboratory tests performed at one of the company's most representative facilities. The costs associated with third parties were derived from price tables used by the accredited network for the same services analyzed in the self-owned network. The full-cost method was used for cost quantification. Costs are presented as absolute values (in R$) and percent comparisons between self-owned network costs versus accredited network costs. overall, the self-owned network was advantageous for medical and dental consultations as well as diagnostic and laboratory tests. Pediatric and labor medicine consultations and x-rays were less costly in the accredited network. the choice of verticalization has economic advantages for the health care insurance operator in comparison with services provided by third parties.

An Attractor-Based Complexity Measurement for Boolean Recurrent Neural Networks

Science.gov (United States)

Cabessa, Jérémie; Villa, Alessandro E. P.

2014-01-01

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

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.

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....

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.

Recommender systems for location-based social networks

CERN Document Server

Symeonidis, Panagiotis; Manolopoulos, Yannis

2014-01-01

Online social networks collect information from users' social contacts and their daily interactions (co-tagging of photos, co-rating of products etc.) to provide them with recommendations of new products or friends. Lately, technological progressions in mobile devices (i.e. smart phones) enabled the incorporation of geo-location data in the traditional web-based online social networks, bringing the new era of Social and Mobile Web. The goal of this book is to bring together important research in a new family of recommender systems aimed at serving Location-based Social Networks (LBSNs). The chapters introduce a wide variety of recent approaches, from the most basic to the state-of-the-art, for providing recommendations in LBSNs. The book is organized into three parts. Part 1 provides introductory material on recommender systems, online social networks and LBSNs. Part 2 presents a wide variety of recommendation algorithms, ranging from basic to cutting edge, as well as a comparison of the characteristics of t...

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

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

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

Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors

Science.gov (United States)

Li, Huajiao; Fang, Wei; An, Haizhong; Gao, Xiangyun; Yan, Lili

2016-05-01

Economic networks in the real world are not homogeneous; therefore, it is important to study economic networks with heterogeneous nodes and edges to simulate a real network more precisely. In this paper, we present an empirical study of the one-mode derivative holding-based network constructed by the two-mode affiliation network of two sets of actors using the data of worldwide listed energy companies and their shareholders. First, we identify the primitive relationship in the two-mode affiliation network of the two sets of actors. Then, we present the method used to construct the derivative network based on the shareholding relationship between two sets of actors and the affiliation relationship between actors and events. After constructing the derivative network, we analyze different topological features on the node level, edge level and entire network level and explain the meanings of the different values of the topological features combining the empirical data. This study is helpful for expanding the usage of complex networks to heterogeneous economic networks. For empirical research on the worldwide listed energy stock market, this study is useful for discovering the inner relationships between the nations and regions from a new perspective.

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

Directory of Open Access Journals (Sweden)

Zhisheng Zhang

2016-01-01

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

Opto-electronic DNA chip-based integrated card for clinical diagnostics.

Science.gov (United States)

Marchand, Gilles; Broyer, Patrick; Lanet, Véronique; Delattre, Cyril; Foucault, Frédéric; Menou, Lionel; Calvas, Bernard; Roller, Denis; Ginot, Frédéric; Campagnolo, Raymond; Mallard, Frédéric

2008-02-01

Clinical diagnostics is one of the most promising applications for microfluidic lab-on-a-chip or lab-on-card systems. DNA chips, which provide multiparametric data, are privileged tools for genomic analysis. However, automation of molecular biology protocol and use of these DNA chips in fully integrated systems remains a great challenge. Simplicity of chip and/or card/instrument interfaces is amongst the most critical issues to be addressed. Indeed, current detection systems for DNA chip reading are often complex, expensive, bulky and even limited in terms of sensitivity or accuracy. Furthermore, for liquid handling in the lab-on-cards, many devices use complex and bulky systems, either to directly manipulate fluids, or to ensure pneumatic or mechanical control of integrated valves. All these drawbacks prevent or limit the use of DNA-chip-based integrated systems, for point-of-care testing or as a routine diagnostics tool. We present here a DNA-chip-based protocol integration on a plastic card for clinical diagnostics applications including: (1) an opto-electronic DNA-chip, (2) fluid handling using electrically activated embedded pyrotechnic microvalves with closing/opening functions. We demonstrate both fluidic and electric packaging of the optoelectronic DNA chip without major alteration of its electronical and biological functionalities, and fluid control using novel electrically activable pyrotechnic microvalves. Finally, we suggest a complete design of a card dedicated to automation of a complex biological protocol with a fully electrical fluid handling and DNA chip reading.

A super base station based centralized network architecture for 5G mobile communication systems

Directory of Open Access Journals (Sweden)

Manli Qian

2015-04-01

Full Text Available To meet the ever increasing mobile data traffic demand, the mobile operators are deploying a heterogeneous network with multiple access technologies and more and more base stations to increase the network coverage and capacity. However, the base stations are isolated from each other, so different types of radio resources and hardware resources cannot be shared and allocated within the overall network in a cooperative way. The mobile operators are thus facing increasing network operational expenses and a high system power consumption. In this paper, a centralized radio access network architecture, referred to as the super base station (super BS, is proposed, as a possible solution for an energy-efficient fifth-generation (5G mobile system. The super base station decouples the logical functions and physical entities of traditional base stations, so different types of system resources can be horizontally shared and statistically multiplexed among all the virtual base stations throughout the entire system. The system framework and main functionalities of the super BS are described. Some key technologies for system implementation, i.e., the resource pooling, real-time virtualization, adaptive hardware resource allocation are also highlighted.

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.

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.

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)

A range-based predictive localization algorithm for WSID networks

Science.gov (United States)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

The Evolution of Reputation-Based Cooperation in Regular Networks

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Tatsuya Sasaki

2017-01-01

Full Text Available Despite recent advances in reputation technologies, it is not clear how reputation systems can affect human cooperation in social networks. Although it is known that two of the major mechanisms in the evolution of cooperation are spatial selection and reputation-based reciprocity, theoretical study of the interplay between both mechanisms remains almost uncharted. Here, we present a new individual-based model for the evolution of reciprocal cooperation between reputation and networks. We comparatively analyze four of the leading moral assessment rules—shunning, image scoring, stern judging, and simple standing—and base the model on the giving game in regular networks for Cooperators, Defectors, and Discriminators. Discriminators rely on a proper moral assessment rule. By using individual-based models, we show that the four assessment rules are differently characterized in terms of how cooperation evolves, depending on the benefit-to-cost ratio, the network-node degree, and the observation and error conditions. Our findings show that the most tolerant rule—simple standing—is the most robust among the four assessment rules in promoting cooperation in regular networks.

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

Arresting Strategy Based on Dynamic Criminal Networks Changing over Time

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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.

L-GRAAL: Lagrangian graphlet-based network aligner.

Science.gov (United States)

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at

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.

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

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.

AAPT Diagnostic Criteria for Chronic Cancer Pain Conditions.

Science.gov (United States)

Paice, Judith A; Mulvey, Matt; Bennett, Michael; Dougherty, Patrick M; Farrar, John T; Mantyh, Patrick W; Miaskowski, Christine; Schmidt, Brian; Smith, Thomas J

2017-03-01

Chronic cancer pain is a serious complication of malignancy or its treatment. Currently, no comprehensive, universally accepted cancer pain classification system exists. Clarity in classification of common cancer pain syndromes would improve clinical assessment and management. Moreover, an evidence-based taxonomy would enhance cancer pain research efforts by providing consistent diagnostic criteria, ensuring comparability across clinical trials. As part of a collaborative effort between the Analgesic, Anesthetic, and Addiction Clinical Trial Translations, Innovations, Opportunities, and Networks (ACTTION) and the American Pain Society (APS), the ACTTION-APS Pain Taxonomy initiative worked to develop the characteristics of an optimal diagnostic system. After the establishment of these characteristics, a working group consisting of clinicians and clinical and basic scientists with expertise in cancer and cancer-related pain was convened to generate core diagnostic criteria for an illustrative sample of 3 chronic pain syndromes associated with cancer (ie, bone pain and pancreatic cancer pain as models of pain related to a tumor) or its treatment (ie, chemotherapy-induced peripheral neuropathy). A systematic review and synthesis was conducted to provide evidence for the dimensions that comprise this cancer pain taxonomy. Future efforts will subject these diagnostic categories and criteria to systematic empirical evaluation of their feasibility, reliability, and validity and extension to other cancer-related pain syndromes. The ACTTION-APS chronic cancer pain taxonomy provides an evidence-based classification for 3 prevalent syndromes, namely malignant bone pain, pancreatic cancer pain, and chemotherapy-induced peripheral neuropathy. This taxonomy provides consistent diagnostic criteria, common features, comorbidities, consequences, and putative mechanisms for these potentially serious cancer pain conditions that can be extended and applied with other cancer

Relationship between Entropy and Dimension of Financial Correlation-Based Network

Directory of Open Access Journals (Sweden)

Chun-xiao Nie

2018-03-01

Full Text Available We analyze the dimension of a financial correlation-based network and apply our analysis to characterize the complexity of the network. First, we generalize the volume-based dimension and find that it is well defined by the correlation-based network. Second, we establish the relationship between the Rényi index and the volume-based dimension. Third, we analyze the meaning of the dimensions sequence, which characterizes the level of departure from the comparison benchmark based on the randomized time series. Finally, we use real stock market data from three countries for empirical analysis. In some cases, our proposed analysis method can more accurately capture the structural differences of networks than the power law index commonly used in previous studies.

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

An Entropy-Based Network Anomaly Detection Method

Directory of Open Access Journals (Sweden)

Przemysław Bereziński

2015-04-01

Full Text Available Data mining is an interdisciplinary subfield of computer science involving methods at the intersection of artificial intelligence, machine learning and statistics. One of the data mining tasks is anomaly detection which is the analysis of large quantities of data to identify items, events or observations which do not conform to an expected pattern. Anomaly detection is applicable in a variety of domains, e.g., fraud detection, fault detection, system health monitoring but this article focuses on application of anomaly detection in the field of network intrusion detection.The main goal of the article is to prove that an entropy-based approach is suitable to detect modern botnet-like malware based on anomalous patterns in network. This aim is achieved by realization of the following points: (i preparation of a concept of original entropy-based network anomaly detection method, (ii implementation of the method, (iii preparation of original dataset, (iv evaluation of the method.

Instantiating a Global Network Measurement Framework

Energy Technology Data Exchange (ETDEWEB)

Tierney, Brian L.; Boote, Jeff; Boyd, Eric; Brown, Aaron; Grigoriev, Maxim; Metzger, Joe; Swany, Martin; Zekauskas, Matt; Zurawski, Jason

2008-12-15

perfSONAR is a web services-based infrastructure for collecting and publishing network performance monitoring. A primary goal of perfSONAR is making it easier to solve end-to-end performance problems on paths crossing several networks. It contains a set of services delivering performance measurements in a federated environment. These services act as an intermediate layer, between the performance measurement tools and the diagnostic or visualization applications. This layer is aimed at making and exchanging performance measurements across multiple networks and multiple user communities, using well-defined protocols. This paper summarizes the key perfSONAR components, and describes how they are deployed by the US-LHC community to monitor the networks distributing LHC data from CERN. All monitoring data described herein is publicly available, and we hope the availability of this data via a standard schema will inspire others to contribute to the effort by building network data analysis applications that use perfSONAR.

Intercluster Connection in Cognitive Wireless Mesh Networks Based on Intelligent Network Coding

Science.gov (United States)

Chen, Xianfu; Zhao, Zhifeng; Jiang, Tao; Grace, David; Zhang, Honggang

2009-12-01

Cognitive wireless mesh networks have great flexibility to improve spectrum resource utilization, within which secondary users (SUs) can opportunistically access the authorized frequency bands while being complying with the interference constraint as well as the QoS (Quality-of-Service) requirement of primary users (PUs). In this paper, we consider intercluster connection between the neighboring clusters under the framework of cognitive wireless mesh networks. Corresponding to the collocated clusters, data flow which includes the exchanging of control channel messages usually needs four time slots in traditional relaying schemes since all involved nodes operate in half-duplex mode, resulting in significant bandwidth efficiency loss. The situation is even worse at the gateway node connecting the two colocated clusters. A novel scheme based on network coding is proposed in this paper, which needs only two time slots to exchange the same amount of information mentioned above. Our simulation shows that the network coding-based intercluster connection has the advantage of higher bandwidth efficiency compared with the traditional strategy. Furthermore, how to choose an optimal relaying transmission power level at the gateway node in an environment of coexisting primary and secondary users is discussed. We present intelligent approaches based on reinforcement learning to solve the problem. Theoretical analysis and simulation results both show that the intelligent approaches can achieve optimal throughput for the intercluster relaying in the long run.

REDISTRIBUTION OF BASE STATIONS LOAD IN MOBILE COMMUNICATION NETWORKS

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Igor Ruban

2017-09-01

Full Text Available The subject matter of the article is the processes of load distribution in mobile communication networks. The object of research is the handover. The goal is to develop a method for redistributing the load between neighboring areas for mobile nodes. The considered base stations are supposed to have the signal-to-noise ratios that are equal or close. The methods that are used: methods of system analysis, methods of digital signal processing. The following results are obtained. The method that allows mobile nodes, whose signal-to-noise ratios are equal or close, to switch to a less loaded base station. This method allows the base station to launch the handover process enabling more even distribution of the load from mobile nodes among neighboring base stations in wireless and mobile networks. In the suggested modification of the method, the function assessing the bandwidth of the uplink channel is added to the base stations, as well a threshold value for using its bandwidth. Thus, when the current value of bandwidth reaches the threshold, the base station starts sending out a message to all mobile nodes and verifies free neighboring areas for switching over mobile nodes. If there are adjacent areas with a lower load, the base station notifies all potential candidates about the necessity of their switching over. The handover process is launched when the available bandwidth of the base station decreases below a certain threshold. Therefore, it is possible to optimize the operation of the WiMAX network with respect to the criterion of the total bandwidth capacity of the base stations. Besides, the results of the comparative analysis of the handover process in networks based on the WiMAX technology that are obtained using the OpNet simulation environment are presented. Conclusions.The suggested approach can be used to improve the basic software of mobile communication networks. When moving a node from one area to another one in access servers, the

Systematic review of proposed definitions of nocturnal polyuria and population-based evidence of their diagnostic accuracy.

Science.gov (United States)

Olesen, Tine Kold; Denys, Marie-Astrid; Vande Walle, Johan; Everaert, Karel

2018-02-06

Background Evidence of diagnostic accuracy for proposed definitions of nocturnal polyuria is currently unclear. Purpose Systematic review to determine population-based evidence of the diagnostic accuracy of proposed definitions of nocturnal polyuria based on data from frequency-volume charts. Methods Seventeen pre-specified search terms identified 351 unique investigations published from 1990 to 2016 in BIOSIS, Embase, Embase Alerts, International Pharmaceutical Abstract, Medline, and Cochrane. Thirteen original communications were included in this review based on pre-specified exclusion criteria. Data were extracted from each paper regarding subject age, sex, ethnicity, health status, sample size, data collection methods, and diagnostic discrimination of proposed definitions including sensitivity, specificity, positive and negative predictive value. Results The sample size of study cohorts, participant age, sex, ethnicity, and health status varied considerably in 13 studies reporting on the diagnostic performance of seven different definitions of nocturnal polyuria using frequency-volume chart data from 4968 participants. Most study cohorts were small, mono-ethnic, including only Caucasian males aged 50 or higher with primary or secondary polyuria that were compared to a control group of healthy men without nocturia in prospective or retrospective settings. Proposed definitions had poor discriminatory accuracy in evaluations based on data from subjects independent from the original study cohorts with findings being similar regarding the most widely evaluated definition endorsed by ICS. Conclusions Diagnostic performance characteristics for proposed definitions of nocturnal polyuria show poor to modest discrimination and are not based on sufficient level of evidence from representative, multi-ethnic population-based data from both females and males of all adult ages.

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.

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