WorldWideScience

Sample records for network based diagnostic

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-12-16

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

  2. Modeling Diagnostic Assessments with Bayesian Networks

    Science.gov (United States)

    Almond, Russell G.; DiBello, Louis V.; Moulder, Brad; Zapata-Rivera, Juan-Diego

    2007-01-01

    This paper defines Bayesian network models and examines their applications to IRT-based cognitive diagnostic modeling. These models are especially suited to building inference engines designed to be synchronous with the finer grained student models that arise in skills diagnostic assessment. Aspects of the theory and use of Bayesian network models…

  3. [Study on diagnostic methods of breathing disorders based on fuzzy logic inference and the neural network].

    Science.gov (United States)

    Chen, Min; Yin, Xuezhi

    2011-07-01

    This paper descries a new non-invasive method for diagnosis of breathing disorders based on adaptive-network-based fuzzy inference system (ANFIS). In this method, PetCO2, SpO2 and HR are chosen as inputs, and the breathing condition is selected as output ofANFIS. The inputs and output are then classified into fuzzy subsets by experts' knowledge. After, the fuzzy IF-THEN rules are built up according to the corresponding membership functions by set up of fuzzy subsets. The neural network was finally established and the membership functions and fuzzy rules were optimized by training. The results of experiment shows that ANFIS is more effective than BP Network regarding the diagnosis of breathing disorders.

  4. Specialist integrated haematological malignancy diagnostic services: an Activity Based Cost (ABC) analysis of a networked laboratory service model.

    Science.gov (United States)

    Dalley, C; Basarir, H; Wright, J G; Fernando, M; Pearson, D; Ward, S E; Thokula, P; Krishnankutty, A; Wilson, G; Dalton, A; Talley, P; Barnett, D; Hughes, D; Porter, N R; Reilly, J T; Snowden, J A

    2015-04-01

    Specialist Integrated Haematological Malignancy Diagnostic Services (SIHMDS) were introduced as a standard of care within the UK National Health Service to reduce diagnostic error and improve clinical outcomes. Two broad models of service delivery have become established: 'co-located' services operating from a single-site and 'networked' services, with geographically separated laboratories linked by common management and information systems. Detailed systematic cost analysis has never been published on any established SIHMDS model. We used Activity Based Costing (ABC) to construct a cost model for our regional 'networked' SIHMDS covering a two-million population based on activity in 2011. Overall estimated annual running costs were £1 056 260 per annum (£733 400 excluding consultant costs), with individual running costs for diagnosis, staging, disease monitoring and end of treatment assessment components of £723 138, £55 302, £184 152 and £94 134 per annum, respectively. The cost distribution by department was 28.5% for haematology, 29.5% for histopathology and 42% for genetics laboratories. Costs of the diagnostic pathways varied considerably; pathways for myelodysplastic syndromes and lymphoma were the most expensive and the pathways for essential thrombocythaemia and polycythaemia vera being the least. ABC analysis enables estimation of running costs of a SIHMDS model comprised of 'networked' laboratories. Similar cost analyses for other SIHMDS models covering varying populations are warranted to optimise quality and cost-effectiveness in delivery of modern haemato-oncology diagnostic services in the UK as well as internationally. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  5. 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

  6. 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.

  7. Research on Fault Diagnostic System in CVT Based on UDS

    Directory of Open Access Journals (Sweden)

    Jiande Wang

    2015-01-01

    Full Text Available A communication model of diagnostic network and implementation of unified diagnostic services (UDS based on controller area network (CAN bus are presented in this paper, and fault diagnostic function of transmission control unit (TCU, USB- (universal serial bus- CAN hardware and software modules, and fault diagnostic software based on personal computer (PC are designed. Model diagnostic method is applied on ratio control, and fault diagnostic system is tested in vehicle.

  8. 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.

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

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2010-01-01

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

  10. DNA Microarray-Based Diagnostics.

    Science.gov (United States)

    Marzancola, Mahsa Gharibi; Sedighi, Abootaleb; Li, Paul C H

    2016-01-01

    The DNA microarray technology is currently a useful biomedical tool which has been developed for a variety of diagnostic applications. However, the development pathway has not been smooth and the technology has faced some challenges. The reliability of the microarray data and also the clinical utility of the results in the early days were criticized. These criticisms added to the severe competition from other techniques, such as next-generation sequencing (NGS), impacting the growth of microarray-based tests in the molecular diagnostic market.Thanks to the advances in the underlying technologies as well as the tremendous effort offered by the research community and commercial vendors, these challenges have mostly been addressed. Nowadays, the microarray platform has achieved sufficient standardization and method validation as well as efficient probe printing, liquid handling and signal visualization. Integration of various steps of the microarray assay into a harmonized and miniaturized handheld lab-on-a-chip (LOC) device has been a goal for the microarray community. In this respect, notable progress has been achieved in coupling the DNA microarray with the liquid manipulation microsystem as well as the supporting subsystem that will generate the stand-alone LOC device.In this chapter, we discuss the major challenges that microarray technology has faced in its almost two decades of development and also describe the solutions to overcome the challenges. In addition, we review the advancements of the technology, especially the progress toward developing the LOC devices for DNA diagnostic applications.

  11. 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...

  12. NETWORK SERVICES FOR DIAGNOSTIC OPTODIGITAL COMPLEX FOR TELEMEDICINE

    OpenAIRE

    D. S. Kopylov; S. N. Ustinov; A. A. Skshidlevsky; A. V. Lyamin

    2014-01-01

    The paper deals with a result of the network services development for the optodigital complex for telemedicine diagnostics. This complex is designed for laboratory and clinical tests in health care facilities. Composition of network services includes the following: a client application for database of diagnostic test, a web-service, a web interface, a video server and microimage processing server. Structure of these services makes it possible to combine set of software for transferring depers...

  13. Diagnostic Neural Network Systems for the Electronic Circuits

    International Nuclear Information System (INIS)

    Mohamed, A.H.

    2014-01-01

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

  14. 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

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

    Science.gov (United States)

    Kobayashi, Takahisa; Simon, Donald L.

    2001-01-01

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

  16. NETWORK SERVICES FOR DIAGNOSTIC OPTODIGITAL COMPLEX FOR TELEMEDICINE

    Directory of Open Access Journals (Sweden)

    D. S. Kopylov

    2014-03-01

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

  17. Impact of precision of Bayesian networks parameters on accuracy of medical diagnostic systems

    Science.gov (United States)

    Oniśko, Agnieszka; Druzdzel, Marek J.

    2014-01-01

    Objective One of the hardest technical tasks in employing Bayesian network models in practice is obtaining their numerical parameters. In the light of this difficulty, a pressing question, one that has immediate implications on the knowledge engineering effort, is whether precision of these parameters is important. In this paper, we address experimentally the question whether medical diagnostic systems based on Bayesian networks are sensitive to precision of their parameters. Methods and Materials The test networks include Hepar II, a sizeable Bayesian network model for diagnosis of liver disorders and six other medical diagnostic networks constructed from medical data sets available through the Irvine Machine Learning Repository. Assuming that the original model parameters are perfectly accurate, we lower systematically their precision by rounding them to progressively courser scales and check the impact of this rounding on the models' accuracy. Results Our main result, consistent across all tested networks, is that imprecision in numerical parameters has minimal impact on the diagnostic accuracy of models, as long as we avoid zeroes among parameters. Conclusion The experiments' results provide evidence that as long as we avoid zeroes among model parameters, diagnostic accuracy of Bayesian network models does not suffer from decreased precision of their parameters. PMID:23466438

  18. 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...

  19. 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

  20. 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)

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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)

  6. 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)

  7. 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 clinical practice. The ANNs correctly classified all samples and identified the genes most relevant to the classification.

  8. 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....

  9. 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.

  10. 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.

  11. [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.

  12. 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)

  13. Evidence-based Diagnostics: Adult Septic Arthritis

    Science.gov (United States)

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

    2011-01-01

    Background 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. Objectives 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. Methods 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. Results The prevalence of nongonococcal septic arthritis in ED patients with a single acutely painful joint is approximately 27

  14. Droplet Microfluidics for Chip-Based Diagnostics

    Directory of Open Access Journals (Sweden)

    Karan V. I. S. Kaler

    2014-12-01

    Full Text Available Droplet microfluidics (DMF is a fluidic handling technology that enables precision control over dispensing and subsequent manipulation of droplets in the volume range of microliters to picoliters, on a micro-fabricated device. There are several different droplet actuation methods, all of which can generate external stimuli, to either actively or passively control the shape and positioning of fluidic droplets over patterned substrates. In this review article, we focus on the operation and utility of electro-actuation-based DMF devices, which utilize one or more micro-/nano-patterned substrates to facilitate electric field-based handling of chemical and/or biological samples. The underlying theory of DMF actuations, device fabrication methods and integration of optical and opto-electronic detectors is discussed in this review. Example applications of such electro-actuation-based DMF devices have also been included, illustrating the various actuation methods and their utility in conducting chip-based laboratory and clinical diagnostic assays.

  15. Network-Based Management Procedures.

    Science.gov (United States)

    Buckner, Allen L.

    Network-based management procedures serve as valuable aids in organizational management, achievement of objectives, problem solving, and decisionmaking. Network techniques especially applicable to educational management systems are the program evaluation and review technique (PERT) and the critical path method (CPM). Other network charting…

  16. 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

  17. 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

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

    Directory of Open Access Journals (Sweden)

    TAKURO ISODA

    2017-01-01

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

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

    Science.gov (United States)

    Isoda, Takuro; BaBa, Shingo; Maruoka, Yasuhiro; Kitamura, Yoshiyuki; Tahara, Keiichiro; Sasaki, Masayuki; Hatakenaka, Masamitsu; Honda, Hiroshi

    2017-01-01

    BONENAVI, a computer-aided diagnostic system, is used in bone scintigraphy. This system provides the artificial neural network (ANN) and bone scan index (BSI) values. ANN is associated with the possibility of bone metastasis, while BSI is related to the amount of bone metastasis. The degree of uptake on bone scintigraphy can be affected by the type of bone metastasis. Therefore, the ANN value provided by BONENAVI may be influenced by the characteristics of bone metastasis. In this study, we aimed to assess the relationship between ANN value and characteristics of bone metastasis. We analyzed 50 patients (36 males,14 females; age range: 87-42 yrs median age:72.5 yrs) with prostate, breast, or lung cancer who had undergone bone scintigraphy and were diagnosed with bone metastasis (32 cases of prostate cancer, nine cases of breast cancer, and nine cases of lung cancer). Those who had received systematic therapy over the past years were excluded. Bone metastases were diagnosed clinically, and the type of bone metastasis (osteoblastic, mildly osteoblastic, osteolytic, and mixed components) was decided visually by the agreement of two radiologists. We compared the ANN values (case-based and lesion-based) among the three primary cancers and four types of bone metastasis. There was no significant difference in case-based ANN values among prostate, breast, and lung cancers. However, the lesion-based ANN values were the highest in cases with prostate cancer and the lowest in cases of lung cancer (median values: prostate cancer, 0.980; breast cancer 0.909; and lung cancer, 0.864). Mildly osteoblastic lesions showed significantly lower ANN values than the other three types of bone metastasis (median values: osteoblastic,; 0.939 mildly osteoblastic; 0.788, mixed type; 0.991, and osteolytic. 0.969) The possibility of a lesion-based ANN value below 0.5 was %10.9 for bone metastasis in prostate cancer, %12.9 for breast cancer, and %37.2 for lung cancer. The corresponding

  20. Sinc-function based Network

    DEFF Research Database (Denmark)

    Madsen, Per Printz

    1999-01-01

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

  1. 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

  2. Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks

    OpenAIRE

    Xiao, Lin; Wu, Fahui; Yang, Dingcheng; Zhang, Tiankui; Zhu, Xiaoya

    2016-01-01

    The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has ...

  3. 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

  4. 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

  5. Evaluation of the Diagnostic Power of Thermography in Breast Cancer Using Bayesian Network Classifiers

    Science.gov (United States)

    Nicandro, Cruz-Ramírez; Efrén, Mezura-Montes; María Yaneli, Ameca-Alducin; Enrique, Martín-Del-Campo-Mena; Héctor Gabriel, Acosta-Mesa; Nancy, Pérez-Castro; Alejandro, Guerra-Hernández; Guillermo de Jesús, Hoyos-Rivera; Rocío Erandi, Barrientos-Martínez

    2013-01-01

    Breast cancer is one of the leading causes of death among women worldwide. There are a number of techniques used for diagnosing this disease: mammography, ultrasound, and biopsy, among others. Each of these has well-known advantages and disadvantages. A relatively new method, based on the temperature a tumor may produce, has recently been explored: thermography. In this paper, we will evaluate the diagnostic power of thermography in breast cancer using Bayesian network classifiers. We will show how the information provided by the thermal image can be used in order to characterize patients suspected of having cancer. Our main contribution is the proposal of a score, based on the aforementioned information, that could help distinguish sick patients from healthy ones. Our main results suggest the potential of this technique in such a goal but also show its main limitations that have to be overcome to consider it as an effective diagnosis complementary tool. PMID:23762182

  6. Computerized tongue diagnosis based on Bayesian networks.

    Science.gov (United States)

    Pang, Bo; Zhang, David; Li, Naimin; Wang, Kuanquan

    2004-10-01

    Tongue diagnosis is an important diagnostic method in traditional Chinese medicine (TCM). However, due to its qualitative, subjective and experience-based nature, traditional tongue diagnosis has a very limited-application in clinical medicine. Moreover, traditional tongue diagnosis is always concerned with the identification of syndromes rather than with the connection between tongue abnormal appearances and diseases. This is not well understood in Western medicine, thus greatly obstruct its wider use in the world. In this paper, we present a novel computerized tongue inspection method aiming to address these problems. First, two kinds of quantitative features, chromatic and textural measures, are extracted from tongue images by using popular digital image processing techniques. Then, Bayesian networks are employed to model the relationship between these quantitative features and diseases. The effectiveness of the method is tested on a group of 455 patients affected by 13 common diseases as well as other 70 healthy volunteers, and the diagnostic results predicted by the previously trained Bayesian network classifiers are reported.

  7. The system of technical diagnostics of the industrial safety information network

    Science.gov (United States)

    Repp, P. V.

    2017-01-01

    This research is devoted to problems of safety of the industrial information network. Basic sub-networks, ensuring reliable operation of the elements of the industrial Automatic Process Control System, were identified. The core tasks of technical diagnostics of industrial information safety were presented. The structure of the technical diagnostics system of the information safety was proposed. It includes two parts: a generator of cyber-attacks and the virtual model of the enterprise information network. The virtual model was obtained by scanning a real enterprise network. A new classification of cyber-attacks was proposed. This classification enables one to design an efficient generator of cyber-attacks sets for testing the virtual modes of the industrial information network. The numerical method of the Monte Carlo (with LPτ - sequences of Sobol), and Markov chain was considered as the design method for the cyber-attacks generation algorithm. The proposed system also includes a diagnostic analyzer, performing expert functions. As an integrative quantitative indicator of the network reliability the stability factor (Kstab) was selected. This factor is determined by the weight of sets of cyber-attacks, identifying the vulnerability of the network. The weight depends on the frequency and complexity of cyber-attacks, the degree of damage, complexity of remediation. The proposed Kstab is an effective integral quantitative measure of the information network reliability.

  8. 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

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

    African Journals Online (AJOL)

    They studied steel pipes filled with water, the surface of which 50x50 mm defect and the depth of thinning of 2 mm, 3 mm, and 5 mm. Using the ... The results of the classification by Kohonen's trained neural network show good abilities for the analysis of unknown samples and a high degree of their recognition reliability.

  10. Increasing Authenticity of Simulation-Based Assessment in Diagnostic Radiology

    NARCIS (Netherlands)

    van der Gijp, Anouk; Ravesloot, Cécile J.; Tipker, Corinne A.; de Crom, Kim; Rutgers, Dik R.; van der Schaaf, Marieke F.; van der Schaaf, Irene C.; Mol, Christian P.; Vincken, Koen L.; ten Cate, Olle Th J.; Maas, Mario; van Schaik, Jan P. J.

    2017-01-01

    Introduction: Clinical reasoning in diagnostic imaging professions is a complex skill that requires processing of visual information and image manipulation skills. We developed a digital simulation-based test method to increase authenticity of image interpretation skill assessment. Methods: A

  11. 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.

  12. 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.

  13. 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

  14. 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…

  15. 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.

  16. 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

  17. Diagnostics

    DEFF Research Database (Denmark)

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

    2007-01-01

    In order to support the operation of ITER and the planned experimental programme an extensive set of plasma and first wall measurements will be required. The number and type of required measurements will be similar to those made on the present-day large tokamaks while the specification...... of the measurements—time and spatial resolutions, etc—will in some cases be more stringent. Many of the measurements will be used in the real time control of the plasma driving a requirement for very high reliability in the systems (diagnostics) that provide the measurements. The implementation of diagnostic systems......&D is needed to prepare the systems. In some cases the environmental difficulties are so severe that new diagnostic techniques are required. The starting point in the development of diagnostics for ITER is to define the measurement requirements and develop their justification. It is necessary to include all...

  18. Global Accelerator Network, Control Systems And Beam Diagnostics

    CERN Document Server

    Raich, U

    2003-01-01

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

  19. Paper based microfluidic devices for environmental diagnostics

    CSIR Research Space (South Africa)

    Govindasamy, K

    2012-09-01

    Full Text Available such as elevated temperatures and mechanical stresses. Paper based microfluidic chips are patterned with micron sized hydrophobic barriers which penetrate the paper?s cross section. These barriers guide the capillary movement of fluids through the cellulose...

  20. 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

  1. Knowledge-Based Network Operations

    Science.gov (United States)

    Wu, Chuan-lin; Hung, Chaw-Kwei; Stedry, Steven P.; McClure, James P.; Yeh, Show-Way

    1988-03-01

    An expert system is being implemented for enhancing operability of the Ground Communication Facility (GCF) of Jet Propulsion Laboratory's (JPL) Deep Space Network (DSN). The DSN is a tracking network for all of JPL's spacecraft plus a subset of spacecrafts launched by other NASA centers. A GCF upgrade task is set to replace the current GCF aging system with new, modern equipments which are capable of using knowledge-based monitor and control approach. The expert system, implemented in terms of KEE and SUN workstation, is used for performing network fault management, configuration management, and performance management in real-time. Monitor data are collected from each processor and DSCC's in every five seconds. In addition to serving as input parameters of the expert system, extracted management information is used to update a management information database. For the monitor and control purpose, software of each processor is divided into layers following the OSI standard. Each layer is modeled as a finite state machine. A System Management Application Process (SMAP) is implemented at application layer, which coordinates layer managers of the same processor and communicates with peer SMAPs of other processors. The expert system will be tuned by augmenting the production rules as the operation is going on, and its performance will be measured.

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

    DEFF Research Database (Denmark)

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

    2014-01-01

    Insights about service improvement in a transit network can be gained by studying transit service reliability. In this paper, a general procedure for constructing a transit service reliability diagnostic (Tsrd) diagram based on a Bayesian network is proposed to automatically build a behavioural...... model from Automatic Vehicle Location (AVL) and Automatic Passenger Counters (APC) data. Our purpose is to discover the variability of transit service attributes and their effects on traveller behaviour. A Tsrd diagram describes and helps to analyse factors affecting public transport by combining domain...... knowledge with statistical data....

  3. 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.

  4. System of polarization phasometry of polycrystalline blood plasma networks in mammary gland pathology diagnostics

    Science.gov (United States)

    Zabolotna, Natalia I.; Oliinychenko, Bogdan P.; Radchenko, Kostiantyn O.; Krasnoshchoka, Anastasiia K.; Shcherba, Olga K.

    2015-09-01

    The polarizing phase meter system of polycrystalline networks of human blood plasma which is used for the mammary gland pathology diagnostics was proposed in this paper. Increasing the accuracy of the phase value determination was achieved using a combination of low coherent source of radiation and circularly polarized probing of biological object. Thus, high informativity of polarizing phase meter system for the diagnosis of breast pathology using the phase mapping of the human blood plasma films were determined, thereafter statistical, correlational, fractal structure analysis of the obtained phase maps was carried out and the quantitative criterias of the phase diagnostics and differentiation of the breast pathological conditions were determined too.

  5. 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

  6. Facial-paralysis diagnostic system based on 3D reconstruction

    Science.gov (United States)

    Khairunnisaa, Aida; Basah, Shafriza Nisha; Yazid, Haniza; Basri, Hassrizal Hassan; Yaacob, Sazali; Chin, Lim Chee

    2015-05-01

    The diagnostic process of facial paralysis requires qualitative assessment for the classification and treatment planning. This result is inconsistent assessment that potential affect treatment planning. We developed a facial-paralysis diagnostic system based on 3D reconstruction of RGB and depth data using a standard structured-light camera - Kinect 360 - and implementation of Active Appearance Models (AAM). We also proposed a quantitative assessment for facial paralysis based on triangular model. In this paper, we report on the design and development process, including preliminary experimental results. Our preliminary experimental results demonstrate the feasibility of our quantitative assessment system to diagnose facial paralysis.

  7. 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...

  8. 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.

  9. A network coding based routing protocol for underwater sensor networks.

    Science.gov (United States)

    Wu, Huayang; Chen, Min; Guan, Xin

    2012-01-01

    Due to the particularities of the underwater environment, some negative factors will seriously interfere with data transmission rates, reliability of data communication, communication range, and network throughput and energy consumption of underwater sensor networks (UWSNs). Thus, full consideration of node energy savings, while maintaining a quick, correct and effective data transmission, extending the network life cycle are essential when routing protocols for underwater sensor networks are studied. In this paper, we have proposed a novel routing algorithm for UWSNs. To increase energy consumption efficiency and extend network lifetime, we propose a time-slot based routing algorithm (TSR).We designed a probability balanced mechanism and applied it to TSR. The theory of network coding is introduced to TSBR to meet the requirement of further reducing node energy consumption and extending network lifetime. Hence, time-slot based balanced network coding (TSBNC) comes into being. We evaluated the proposed time-slot based balancing routing algorithm and compared it with other classical underwater routing protocols. The simulation results show that the proposed protocol can reduce the probability of node conflicts, shorten the process of routing construction, balance energy consumption of each node and effectively prolong the network lifetime.

  10. 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.

  11. 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)

  12. Energy Efficient Wireless Sensor Network Modelling Based on Complex Networks

    Directory of Open Access Journals (Sweden)

    Lin Xiao

    2016-01-01

    Full Text Available The power consumption and energy efficiency of wireless sensor network are the significant problems in Internet of Things network. In this paper, we consider the network topology optimization based on complex network theory to solve the energy efficiency problem of WSN. We propose the energy efficient model of WSN according to the basic principle of small world from complex networks. Small world network has clustering features that are similar to that of the rules of the network but also has similarity to random networks of small average path length. It can be utilized to optimize the energy efficiency of the whole network. Optimal number of multiple sink nodes of the WSN topology is proposed for optimizing energy efficiency. Then, the hierarchical clustering analysis is applied to implement this clustering of the sensor nodes and pick up the sink nodes from the sensor nodes as the clustering head. Meanwhile, the update method is proposed to determine the sink node when the death of certain sink node happened which can cause the paralysis of network. Simulation results verify the energy efficiency of the proposed model and validate the updating of the sink nodes to ensure the normal operation of the WSN.

  13. 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.

  14. 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.

  15. Outcome of Presumptive Versus Rapid Diagnostic Tests-Based ...

    African Journals Online (AJOL)

    First, 50 children with malaria-pneumonia symptom overlap were consecutively enrolled and treated presumptively with antibiotics and antimalarials irrespective of malaria test result (control arm).Then, another 50 eligible children were enrolled and treated with antibiotics with/out antimalarials based on rapid diagnostic test ...

  16. Breast Cancer Diagnostics Based on Spatial Genome Organization

    Science.gov (United States)

    2012-07-01

    breast tissues made up of invasive breast carcinomas, benign diseased tissues ( fibroadenoma and hyperplasia) and normal breast tissues [11]. To enable an...positioning patterns were also compared between benign disease ( fibroadenoma and hyperplasia; not including atypical hyperplasia, which is linked to breast ...AD_________________ Award Number: W81XWH-08-2-0098 TITLE: Breast Cancer Diagnostics Based on

  17. 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...

  18. 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

  19. Durer-pentagon-based complex network

    Directory of Open Access Journals (Sweden)

    Rui Hou

    2016-04-01

    Full Text Available A novel Durer-pentagon-based complex network was constructed by adding a centre node. The properties of the complex network including the average degree, clustering coefficient, average path length, and fractal dimension were determined. The proposed complex network is small-world and fractal.

  20. 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

  1. 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

  2. Location based Network Optimizations for Mobile Wireless Networks

    DEFF Research Database (Denmark)

    Nielsen, Jimmy Jessen

    The availability of location information in mobile devices, e.g., through built-in GPS receivers in smart phones, has motivated the investigation of the usefulness of location based network optimizations. Since the quality of input information is important for network optimizations, a main focus...... the achievable accuracy of future localization system in mobile settings, as well as quantifying the impact of having a realistic model of the required measurement exchanges. Secondly, this work has considered different large scale and small scale location based network optimizations, namely centralized relay...... and highly mobile scenarios....

  3. 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

  4. 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...

  5. Network reliability analysis based on percolation theory

    International Nuclear Information System (INIS)

    Li, Daqing; Zhang, Qiong; Zio, Enrico; Havlin, Shlomo; Kang, Rui

    2015-01-01

    In this paper, we propose a new way of looking at the reliability of a network using percolation theory. In this new view, a network failure can be regarded as a percolation process and the critical threshold of percolation can be used as network failure criterion linked to the operational settings under control. To demonstrate our approach, we consider both random network models and real networks with different nodes and/or edges lifetime distributions. We study numerically and theoretically the network reliability and find that the network reliability can be solved as a voting system with threshold given by percolation theory. Then we find that the average lifetime of random network increases linearly with the average lifetime of its nodes with uniform life distributions. Furthermore, the average lifetime of the network becomes saturated when system size is increased. Finally, we demonstrate our method on the transmission network system of IEEE 14 bus. - Highlights: • Based on percolation theory, we address questions of practical interest such as “how many failed nodes/edges will break down the whole network?” • The percolation threshold naturally gives a network failure criterion. • The approach based on percolation theory is suited for calculations of large-scale networks

  6. 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

  7. Caries detection using light-based diagnostic tools.

    Science.gov (United States)

    Rechmann, Peter; Rechmann, Beate M T; Featherstone, John D B

    2012-09-01

    Modern caries treatment concepts like caries management by risk assessment--CAMBRA--entail diagnosing early caries lesions in a precavitated stage to make it possible to reverse the caries process with remineralization and bacteria reduction efforts. Newer, sensitive caries diagnostic tools can serve not only for early detection but also for monitoring of caries lesions to confirm the success of prevention and remineralization efforts. This article describes light-based caries diagnostic tools, with emphasis on fluorescence-based techniques, and compares the most common available fluorescence-based tools with a standardized visual caries inspection system-the International Caries Detection and Assessment System (ICDAS II). Fluorescence tools that provide high-resolution fluorescence pictures are likely to provide more reliable scores than fluorescence devices that assess via a single spot. The better visibility of the high-resolution fluorescence imaging could prevent unnecessary operative interventions.

  8. 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.)

  9. 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.)

  10. 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.

  11. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    . 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.......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...... is hinged on a research aimed at understanding how and 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...

  12. 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.

  13. 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

  14. Diagnostic classification based on functional connectivity in chronic pain: model optimization in fibromyalgia and rheumatoid arthritis.

    Science.gov (United States)

    Sundermann, Benedikt; Burgmer, Markus; Pogatzki-Zahn, Esther; Gaubitz, Markus; Stüber, Christoph; Wessolleck, Erik; Heuft, Gereon; Pfleiderer, Bettina

    2014-03-01

    The combination of functional magnetic resonance imaging (fMRI) of the brain with multivariate pattern analysis (MVPA) has been proposed as a possible diagnostic tool. Goal of this investigation was to identify potential functional connectivity (FC) differences in the salience network (SN) and default mode network (DMN) between fibromyalgia syndrome (FMS), rheumatoid arthritis (RA), and controls (HC) and to evaluate the diagnostic applicability of derived pattern classification approaches. The resting period during an fMRI examination was retrospectively analyzed in women with FMS (n = 17), RA (n = 16), and HC (n = 17). FC was calculated for SN and DMN subregions. Classification accuracies of discriminative MVPA models were evaluated with cross-validation: (1) inferential test of a single method, (2) explorative model optimization. No inferentially tested model was able to classify subjects with statistically significant accuracy. However, the diagnostic ability for the differential diagnostic problem exhibited a trend to significance (accuracy: 69.7%, P = .086). Optimized models in the explorative analysis reached accuracies up to 73.5% (FMS vs. HC), 78.8% (RA vs. HC), and 78.8% (FMS vs. RA) whereas other models performed at or below chance level. Comparable support vector machine approaches performed above average for all three problems. Observed accuracies are not sufficient to reliably differentiate between FMS and RA for diagnostic purposes. However, some indirect evidence in support of the feasibility of this approach is provided. This exploratory analysis constitutes a fundamental model optimization effort to be based on in further investigations. Copyright © 2014 AUR. Published by Elsevier Inc. All rights reserved.

  15. Cloud-based Networked Visual Servo Control

    DEFF Research Database (Denmark)

    Wu, Haiyan; Lu, Lei; Chen, Chih-Chung

    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...... feedback, ii) a stabilizing control law for the networked visual servo control system with time-varying feedback time delay, and iii) a sending rate scheduling strategy aiming at reducing the communication network load. The performance of the networked visual servo control system with sending rate...

  16. 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.

  17. Electron Beam Diagnostic Based on a Short Seeded FEL

    CERN Document Server

    Graves, W; Kaertner, Franz X; Zwart, T

    2005-01-01

    The optical properties of an FEL amplifier are sensitively dependent on the electron beam current profile, energy spread, and transverse emittance. In this paper we consider using a short FEL amplifier operating on a low harmonic of a visible-IR input seed as a mildly destructive electron beam diagnostic able to measure these properties for sub-ps time slices. The optical methods are described as well as a planned implementation of the device for the FERMI@Elettra XUV FEL under construction at Sincrotrone Trieste, including its fiber-based seed laser closely coupled with the facility timing system, undulator parameters, and requirements on the electron and FEL pulses. This diagnostic is conveniently integrated with a "laser heater" designed to increase the very low electron beam energy spread produced by a photoinjector in order to avoid space charge and coherent synchrotron radiation instabilities.

  18. Increasing Authenticity of Simulation-Based Assessment in Diagnostic Radiology.

    Science.gov (United States)

    van der Gijp, Anouk; Ravesloot, Cécile J; Tipker, Corinne A; de Crom, Kim; Rutgers, Dik R; van der Schaaf, Marieke F; van der Schaaf, Irene C; Mol, Christian P; Vincken, Koen L; Ten Cate, Olle Th J; Maas, Mario; van Schaik, Jan P J

    2017-12-01

    Clinical reasoning in diagnostic imaging professions is a complex skill that requires processing of visual information and image manipulation skills. We developed a digital simulation-based test method to increase authenticity of image interpretation skill assessment. A digital application, allowing volumetric image viewing and manipulation, was used for three test administrations of the national Dutch Radiology Progress Test for residents. This study describes the development and implementation process in three phases. To assess authenticity of the digital tests, perceived image quality and correspondence to clinical practice were evaluated and compared with previous paper-based tests (PTs). Quantitative and qualitative evaluation results were used to improve subsequent tests. Authenticity of the first digital test was not rated higher than the PTs. Test characteristics and environmental conditions, such as image manipulation options and ambient lighting, were optimized based on participants' comments. After adjustments in the third digital test, participants favored the image quality and clinical correspondence of the digital image questions over paper-based image questions. Digital simulations can increase authenticity of diagnostic radiology assessments compared with paper-based testing. However, authenticity does not necessarily increase with higher fidelity. It can be challenging to simulate the image interpretation task of clinical practice in a large-scale assessment setting, because of technological limitations. Optimizing image manipulation options, the level of ambient light, time limits, and question types can help improve authenticity of simulation-based radiology assessments.

  19. 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

  20. 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.

  1. 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. PMID:25392692

  2. 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.

  3. Distribution network topology identification based on synchrophasor

    Directory of Open Access Journals (Sweden)

    Stefania Conti

    2018-03-01

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

  4. A Diagnostic HIV-1 Tropism System Based on Sequence Relatedness

    Science.gov (United States)

    Edwards, Suzanne; Stucki, Heinz; Bader, Joëlle; Vidal, Vincent; Kaiser, Rolf; Battegay, Manuel

    2014-01-01

    Key clinical studies for HIV coreceptor antagonists have used the phenotyping-based Trofile test. Meanwhile various simpler-to-do genotypic tests have become available that are compatible with standard laboratory equipment and Web-based interpretation tools. However, these systems typically analyze only the most prominent virus sequence in a specimen. We present a new diagnostic HIV tropism test not needing DNA sequencing. The system, XTrack, uses physical properties of DNA duplexes after hybridization of single-stranded HIV-1 env V3 loop probes to the clinical specimen. Resulting “heteroduplexes” possess unique properties driven by sequence relatedness to the reference and resulting in a discrete electrophoretic mobility. A detailed optimization process identified diagnostic probe candidates relating best to a large number of HIV-1 sequences with known tropism. From over 500 V3 sequences representing all main HIV-1 subtypes (Los Alamos database), we obtained a small set of probes to determine the tropism in clinical samples. We found a high concordance with the commercial TrofileES test (84.9%) and the Web-based tool Geno2Pheno (83.0%). Moreover, the new system reveals mixed virus populations, and it was successful on specimens with low virus loads or on provirus from leukocytes. A replicative phenotyping system was used for validation. Our data show that the XTrack test is favorably suitable for routine diagnostics. It detects and dissects mixed virus populations and viral minorities; samples with viral loads (VL) of <200 copies/ml are successfully analyzed. We further expect that the principles of the platform can be adapted also to other sequence-divergent pathogens, such as hepatitis B and C viruses. PMID:25502529

  5. 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

  6. 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.

  7. 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

  8. 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.

  9. Building a Network Based Laboratory Environment

    Directory of Open Access Journals (Sweden)

    Sea Shuan Luo

    2009-12-01

    Full Text Available This paper presents a comparative study about the development of a network based laboratory environment in the “Unix introduction” course for the undergraduate students. The study results and the response from the students from 2005 to 2006 will be used to better understand what kind of method is more suitable for students. We also use the data collected to adjust our teaching strategy and try to build up a network based laboratory environment.

  10. 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.

  11. Network Experiences from a Cross-Sector Biosafety Level-3 Laboratory Collaboration: A Swedish Forum for Biopreparedness Diagnostics.

    Science.gov (United States)

    Thelaus, Johanna; Lindberg, Anna; Thisted Lambertz, Susanne; Byström, Mona; Forsman, Mats; Lindmark, Hans; Knutsson, Rickard; Båverud, Viveca; Bråve, Andreas; Jureen, Pontus; Lundin Zumpe, Annelie; Melefors, Öjar

    The Swedish Forum for Biopreparedness Diagnostics (FBD) is a network that fosters collaboration among the 4 agencies with responsibility for the laboratory diagnostics of high-consequence pathogens, covering animal health and feed safety, food safety, public health and biodefense, and security. The aim of the network is to strengthen capabilities and capacities for diagnostics at the national biosafety level-3 (BSL-3) laboratories to improve Sweden's biopreparedness, in line with recommendations from the EU and WHO. Since forming in 2007, the FBD network has contributed to the harmonization of diagnostic methods, equipment, quality assurance protocols, and biosafety practices among the national BSL-3 laboratories. Lessons learned from the network include: (1) conducting joint projects with activities such as method development and validation, ring trials, exercises, and audits has helped to build trust and improve communication among participating agencies; (2) rotating the presidency of the network steering committee has fostered trust and commitment from all agencies involved; and (3) planning for the implementation of project outcomes is important to maintain gained competencies in the agencies over time. Contacts have now been established with national agencies of the other Nordic countries, with an aim to expanding the collaboration, broadening the network, finding synergies in new areas, strengthening the ability to share resources, and consolidating long-term financing in the context of harmonized European biopreparedness.

  12. Network Experiences from a Cross-Sector Biosafety Level-3 Laboratory Collaboration: A Swedish Forum for Biopreparedness Diagnostics

    Science.gov (United States)

    Lindberg, Anna; Thisted Lambertz, Susanne; Byström, Mona; Forsman, Mats; Lindmark, Hans; Knutsson, Rickard; Båverud, Viveca; Bråve, Andreas; Jureen, Pontus; Lundin Zumpe, Annelie; Melefors, Öjar

    2017-01-01

    The Swedish Forum for Biopreparedness Diagnostics (FBD) is a network that fosters collaboration among the 4 agencies with responsibility for the laboratory diagnostics of high-consequence pathogens, covering animal health and feed safety, food safety, public health and biodefense, and security. The aim of the network is to strengthen capabilities and capacities for diagnostics at the national biosafety level-3 (BSL-3) laboratories to improve Sweden's biopreparedness, in line with recommendations from the EU and WHO. Since forming in 2007, the FBD network has contributed to the harmonization of diagnostic methods, equipment, quality assurance protocols, and biosafety practices among the national BSL-3 laboratories. Lessons learned from the network include: (1) conducting joint projects with activities such as method development and validation, ring trials, exercises, and audits has helped to build trust and improve communication among participating agencies; (2) rotating the presidency of the network steering committee has fostered trust and commitment from all agencies involved; and (3) planning for the implementation of project outcomes is important to maintain gained competencies in the agencies over time. Contacts have now been established with national agencies of the other Nordic countries, with an aim to expanding the collaboration, broadening the network, finding synergies in new areas, strengthening the ability to share resources, and consolidating long-term financing in the context of harmonized European biopreparedness. PMID:28805472

  13. Network-based Database Course

    DEFF Research Database (Denmark)

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

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

  14. 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.

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

    Science.gov (United States)

    Hamidi, Reza Jalilzadeh

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

  16. 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.

  17. 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

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

    International Nuclear Information System (INIS)

    Basu, A.

    1992-01-01

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

  19. Identifying network public opinion leaders based on Markov Logic Networks.

    Science.gov (United States)

    Zhang, Weizhe; Li, Xiaoqiang; He, Hui; Wang, Xing

    2014-01-01

    Public opinion emergencies have important effect on social activities. Recognition of special communities like opinion leaders can contribute to a comprehensive understanding of the development trend of public opinion. In this paper, a network opinion leader recognition method based on relational data was put forward, and an opinion leader recognition system integrating public opinion data acquisition module, data characteristic selection, and fusion module as well as opinion leader discovery module based on Markov Logic Networks was designed. The designed opinion leader recognition system not only can overcome the incomplete data acquisition and isolated task of traditional methods, but also can recognize opinion leaders comprehensively with considerations to multiple problems by using the relational model. Experimental results demonstrated that, compared with the traditional methods, the proposed method can provide a more accurate opinion leader recognition and has good noise immunity.

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

    Science.gov (United States)

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

    2015-01-01

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

  1. Designing Network-based Business Model Ontology

    DEFF Research Database (Denmark)

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

    2015-01-01

    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....

  2. 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)

  3. [Signaling network-based functional cell design].

    Science.gov (United States)

    Ju, Jianqi; Wei, Ping

    2017-03-25

    Cellular signaling networks act as the central processor to deal with environmental signals and regulate cell function, and determine cell fate. Using synthetic biology approach to engineer cell signaling networks is crucial for ultimately constructing man-made "cell machines". Cellular signaling networks can encode sophisticated cell information by processing quantitatively signaling dynamics, which enables multi-dimensional regulation of functional sub-circuits. Here, we first review the research progresses on the signaling coding mechanisms; and then elaborate the methodologies and applications of cells signaling engineering; finally, we envision that signaling-based cell engineering are important for the increasingly-complicated next generation synthetic biology.

  4. Toward Measuring Network Aesthetics Based on Symmetry

    Directory of Open Access Journals (Sweden)

    Zengqiang Chen

    2017-05-01

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

  5. Translating sanger-based routine DNA diagnostics into generic massive parallel ion semiconductor sequencing

    NARCIS (Netherlands)

    Diekstra, A.; Bosgoed, E.A.J.; Rikken, A.; Lier, B. van; Kamsteeg, E.J.; Tychon, M.W.J.; Derks, R.C.; Soest, R.A.; Mensenkamp, A.R.; Scheffer, H.; Neveling, K.; Nelen, M.R.

    2015-01-01

    BACKGROUND: Dideoxy-based chain termination sequencing developed by Sanger is the gold standard sequencing approach and allows clinical diagnostics of disorders with relatively low genetic heterogeneity. Recently, new next generation sequencing (NGS) technologies have found their way into diagnostic

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

    Science.gov (United States)

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

    2015-01-01

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

  7. Clinical experience, infection control practices and diagnostic algorithms for poxvirus infections - an Emerging Infections Network survey

    Directory of Open Access Journals (Sweden)

    Lash R Ryan

    2010-02-01

    Full Text Available Abstract Background In order to determine how best to tailor outreach messages about poxvirus diagnosis and infection control for health practitioners, we surveyed infectious disease physicians in the Infectious Diseases Society of America's Emerging Infections Network. Findings Surveys consisting of two unknown case scenarios designed to raise suspicion for monkeypox and orf were distributed to the 1,080 members of the EIN. The surveys contained questions pertaining to which diagnostic tests, points of contact, and transmission precautions they would likely utilize during patient evaluation. Basic response rates and frequencies of responses were calculated. Comparisons of the survey responses were made using the chi-square test. Of the 212 members who responded (20% response rate, significantly more respondents indicated that they would request diagnostic testing in the context of the monkeypox case scenario as compared to the orf case scenario. A significantly higher number of respondents indicated they would institute droplet or airborne precautions for the monkeypox case as opposed to the orf case scenario. Conclusions This survey provided an opportunity for public health practitioners to gain insight into physician approaches to evaluation, diagnosis and reporting of suspected poxvirus-associated infections. This survey identified key areas in which public health practitioners can better serve physicians by focusing on education. As a result we were able to identify potential knowledge gaps and deficits in the availability of useful resources to facilitate accurate case identification and management.

  8. Point-of-care diagnostics: extending the laboratory network to reach the last mile.

    Science.gov (United States)

    Drain, Paul K; Rousseau, Christine

    2017-03-01

    More point-of-care (POC) diagnostic tests are becoming available for HIV diagnosis and treatment in resource-limited settings. These novel technologies have the potential to foster decentralized HIV care and treatment for the benefit of clinical laboratories, HIV clinics, and HIV-infected patients. There continue to be many business, technological, and operational challenges that limit product development and regulatory approval, which limits products available for the required operational and cost-effectiveness studies and delays policy adoption and implementation. Although the rapid HIV diagnostic test has been widely successful, the pathways for POC CD4 cell count and HIV viral load assay analyzers have been more challenging. We describe significant hurdles for product development, approval, and implementation, which include the business case, technical development, clinical impact, and integrating laboratory and clinical networks. The objective of this review is to highlight the obstacles for developing and implementing appropriate strategies for POC HIV testing assays to improve the clinical services for HIV-infected patients in resource-limited settings.

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

    Directory of Open Access Journals (Sweden)

    Vollmer Ekkehard

    2008-04-01

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

  10. Fuzzy logic-based diagnostic algorithm for implantable cardioverter defibrillators.

    Science.gov (United States)

    Bárdossy, András; Blinowska, Aleksandra; Kuzmicz, Wieslaw; Ollitrault, Jacky; Lewandowski, Michał; Przybylski, Andrzej; Jaworski, Zbigniew

    2014-02-01

    The paper presents a diagnostic algorithm for classifying cardiac tachyarrhythmias for implantable cardioverter defibrillators (ICDs). The main aim was to develop an algorithm that could reduce the rate of occurrence of inappropriate therapies, which are often observed in existing ICDs. To achieve low energy consumption, which is a critical factor for implantable medical devices, very low computational complexity of the algorithm was crucial. The study describes and validates such an algorithm and estimates its clinical value. The algorithm was based on the heart rate variability (HRV) analysis. The input data for our algorithm were: RR-interval (I), as extracted from raw intracardiac electrogram (EGM), and in addition two other features of HRV called here onset (ONS) and instability (INST). 6 diagnostic categories were considered: ventricular fibrillation (VF), ventricular tachycardia (VT), sinus tachycardia (ST), detection artifacts and irregularities (including extrasystoles) (DAI), atrial tachyarrhythmias (ATF) and no tachycardia (i.e. normal sinus rhythm) (NT). The initial set of fuzzy rules based on the distributions of I, ONS and INST in the 6 categories was optimized by means of a software tool for automatic rule assessment using simulated annealing. A training data set with 74 EGM recordings was used during optimization, and the algorithm was validated with a validation data set with 58 EGM recordings. Real life recordings stored in defibrillator memories were used. Additionally the algorithm was tested on 2 sets of recordings from the PhysioBank databases: MIT-BIH Arrhythmia Database and MIT-BIH Supraventricular Arrhythmia Database. A custom CMOS integrated circuit implementing the diagnostic algorithm was designed in order to estimate the power consumption. A dedicated Web site, which provides public online access to the algorithm, has been created and is available for testing it. The total number of events in our training and validation sets was 132. In

  11. Network-based analysis of complex diseases.

    Science.gov (United States)

    Liu, Z-P; Wang, Y; Zhang, X-S; Chen, L

    2012-02-01

    Complex diseases are commonly believed to be caused by the breakdown of several correlated genes rather than individual genes. The availability of genome-wide data of high-throughput experiments provides us with new opportunity to explore this hypothesis by analysing the disease-related biomolecular networks, which are expected to bridge genotypes and disease phenotypes and further reveal the biological mechanisms of complex diseases. In this study, the authors review the existing network biology efforts to study complex diseases, such as breast cancer, diabetes and Alzheimer's disease, using high-throughput data and computational tools. Specifically, the authors categorise these existing methods into several classes based on the research topics, that is, disease genes, dysfunctional pathways, network signatures and drug-target networks. The authors also summarise the pros and cons of those methods from both computation and application perspectives, and further discuss research trends and future topics of this promising field.

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

    Science.gov (United States)

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

    2017-04-01

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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-04-15

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

  14. FPGA-based network intrusion detection for IEC 61850-based industrial network

    Directory of Open Access Journals (Sweden)

    Junsik Kim

    2018-03-01

    Full Text Available This paper proposes an FPGA-based network intrusion detection system for the IEC 61850-based industrial network that is specially designed for substation automation. The proposed system uses the Shift-And algorithm for detecting malicious network packets within IEC 61850 messages. To implement a complex rule matching module with a limited memory size of FPGA, a specially designed rule matching module was proposed in this paper. For feasibility evaluation, a prototype with 265 regular expression matching modules was implemented using Xilinx Zynq-7030 FPGA and its performance is presented in this paper. Keywords: Network intrusion detection system (NIDS, IEC 61850, Regular expression, Substation automation, FPGA

  15. Medical diagnostics by laser-based analysis of exhaled breath

    Science.gov (United States)

    Giubileo, Gianfranco

    2002-08-01

    IMany trace gases can be found in the exhaled breath, some of them giving the possibility of a non invasive diagnosis of related diseases or allowing the monitoring of the disease in the course of its therapy. In the present lecture the principle of medical diagnosis based on the breath analysis will be introduced and the detection of trace gases in exhaled breath by high- resolution molecular spectroscopy in the IR spectral region will be discussed. A number of substrates and the optical systems for their laser detection will be reported. The following laser based experimental systems has been realised in the Molecular Spectroscopy Laboratory in ENEA in Frascati for the analysis of specific substances in the exhaled breath. A tuneable diode laser absorption spectroscopy (TDLAS) appartus for the measurement of 13C/12C isotopic ratio in carbon dioxide, a TDLAS apparatus for the detection of CH4 and a CO2 laser based photoacoustic system to detect trace ethylene at atmospheric pressure. The experimental set-up for each one of the a.m. optical systems will be shown and the related medical applications will be illustrated. The concluding remarks will be focuses on chemical species that are of major interest for medical people today and their diagnostic ability.

  16. Application of cluster analysis and autoregressive neural networks for the noise diagnostics of the IBR-2M reactor

    Science.gov (United States)

    Pepelyshev, Yu. N.; Tsogtsaikhan, Ts.; Ososkov, G. A.

    2016-09-01

    The pattern recognition methodologies and artificial neural networks were used widely for the IBR-2M pulsed reactor noise diagnostics. The cluster analysis allows a detailed study of the structure and fast reactivity effects of IBR-2M and nonlinear autoregressive neural network (NAR) with local feedback connection allows predicting slow reactivity effects. In this work we present results of a study on pulse energy noise dynamics and prediction of liquid sodium flow rate through the core of the IBR-2M reactor using cluster analysis and an artificial neural network.

  17. 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.

  18. 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.

  19. Changing Histopathological Diagnostics by Genome-Based Tumor Classification

    Directory of Open Access Journals (Sweden)

    Michael Kloth

    2014-05-01

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

  20. 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

  1. 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.

  2. Location-based Forwarding in Vehicular Networks

    NARCIS (Netherlands)

    Klein Wolterink, W.

    2013-01-01

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

  3. Effects of Computer-Based Diagnostic Instruction and Non-Diagnostic Instruction on Laboratory Achievement in General Science.

    Science.gov (United States)

    McKenzie, Danny L.; Karnau, Sally A.

    The effects of computer-based diagnostic testing on the laboratory achievement of 91 preservice elementary teachers were assessed. These teachers were enrolled in one of four laboratory sections of a general science course. Intact classes were randomly assigned to one of two treatment groups. All students completed the same laboratory activities…

  4. Bayesian networks and statistical analysis application to analyze the diagnostic test accuracy

    Science.gov (United States)

    Orzechowski, P.; Makal, Jaroslaw; Onisko, A.

    2005-02-01

    The computer aided BPH diagnosis system based on Bayesian network is described in the paper. First result are compared to a given statistical method. Different statistical methods are used successfully in medicine for years. However, the undoubted advantages of probabilistic methods make them useful in application in newly created systems which are frequent in medicine, but do not have full and competent knowledge. The article presents advantages of the computer aided BPH diagnosis system in clinical practice for urologists.

  5. 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...

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

    CERN Document Server

    Kuroda, R

    2005-01-01

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

  7. Smartphone-Based Food Diagnostic Technologies: A Review

    Directory of Open Access Journals (Sweden)

    Giovanni Rateni

    2017-06-01

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

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

    Science.gov (United States)

    Rateni, Giovanni; Dario, Paolo; Cavallo, Filippo

    2017-06-20

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

  9. Network Based High Speed Product Innovation

    DEFF Research Database (Denmark)

    Lindgren, Peter

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

  10. Quantum networks based on cavity QED

    Energy Technology Data Exchange (ETDEWEB)

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

    2014-07-01

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

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

    International Nuclear Information System (INIS)

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

    1989-01-01

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

  12. Dynamic social networks based on movement

    Science.gov (United States)

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

    2016-01-01

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

  13. Neural network based multiscale image restoration approach

    Science.gov (United States)

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

    2007-02-01

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

  14. Medical diagnostic system based on image receivers of various spectral ranges

    Science.gov (United States)

    Siniakova, Olga G.; Ishmuhametov, Airat I.

    1996-04-01

    The medical diagnostics is one of the most important spheres of application of measuring and diagnostic systems based on introscopy methods. The modern medical introscopy diagnostics has in its arsenal many various devices using x-ray radiation with energy from 10 to 100 keV (roentgenological diagnostics, x-ray computer tomography), gamma radiation of radionuclides with energy 10 - 300 keV (radionuclide diagnostics), infrared radiation of human body (thermovision), optical radiation range (endoscopic diagnostics). The application of high- frequency sound fluctuations (ultrasonography) is also effective for tasks of medical diagnostics. The microwave sources based on nuclear magnetic resonance (magnetic resonance imaging) are used for reception the images of internal structures of human body. The prompt development of modern medical introscopy diagnostics observable in last years is connected first of all with wide application of computer facilities for receiving, processing, restoration and analysis of images. It gives the additional opportunity to increase the reliability, accuracy, sensitivity and timeliness of diagnostic decisions. The images received both by scintillation gamma camera and by specialized x-ray or gamma radiation video camera based on charge- coupled devices can be used for evaluation of structural and functional state of vital organs and systems. One of the main tasks at development of medical diagnostic systems is the reduction of optical image to form that maximally facilitates its analysis to the doctor. The article considers the diagnostic system oriented on receiving, processing and evaluating data of radionuclide imaging studies.

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

    NARCIS (Netherlands)

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

    2015-01-01

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

  16. Model-based Diagnostics for Propellant Loading Systems

    Data.gov (United States)

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

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

    International Nuclear Information System (INIS)

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

    1995-01-01

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

  18. Development of genomic based diagnostics in various application domains

    DEFF Research Database (Denmark)

    Szallasi, Zoltan Imre

    2017-01-01

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

  19. On-board fault diagnostics for fly-by-light flight control systems using neural network flight processors

    Science.gov (United States)

    Urnes, James M., Sr.; Cushing, John; Bond, William E.; Nunes, Steve

    1996-10-01

    Fly-by-Light control systems offer higher performance for fighter and transport aircraft, with efficient fiber optic data transmission, electric control surface actuation, and multi-channel high capacity centralized processing combining to provide maximum aircraft flight control system handling qualities and safety. The key to efficient support for these vehicles is timely and accurate fault diagnostics of all control system components. These diagnostic tests are best conducted during flight when all facts relating to the failure are present. The resulting data can be used by the ground crew for efficient repair and turnaround of the aircraft, saving time and money in support costs. These difficult to diagnose (Cannot Duplicate) fault indications average 40 - 50% of maintenance activities on today's fighter and transport aircraft, adding significantly to fleet support cost. Fiber optic data transmission can support a wealth of data for fault monitoring; the most efficient method of fault diagnostics is accurate modeling of the component response under normal and failed conditions for use in comparison with the actual component flight data. Neural Network hardware processors offer an efficient and cost-effective method to install fault diagnostics in flight systems, permitting on-board diagnostic modeling of very complex subsystems. Task 2C of the ARPA FLASH program is a design demonstration of this diagnostics approach, using the very high speed computation of the Adaptive Solutions Neural Network processor to monitor an advanced Electrohydrostatic control surface actuator linked through a AS-1773A fiber optic bus. This paper describes the design approach and projected performance of this on-line diagnostics system.

  20. Fast infectious diseases diagnostics based on microfluidic biochip system

    Directory of Open Access Journals (Sweden)

    Qin Huang

    2017-03-01

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

  1. Neural network based facial recognition system

    Science.gov (United States)

    Luebbers, Paul G.; Uwechue, Okechukwu A.; Pandya, Abhijit S.

    1994-03-01

    Researchers have for many years tried to develop machine recognition systems using video images of the human face as the input, with limited success. This paper presents a technique for recognizing individuals based on facial features using a novel multi-layer neural network architecture called `PWRNET'. We envision a real-time version of this technique to be used for high security applications. Two systems are proposed. One involves taking a grayscale video image and using it directly, the other involves decomposing the grayscale image into a series of binary images using the isodensity regions of the image. Isodensity regions are the areas within an image where the intensity is within a certain range. The binary image is produced by setting the pixels inside this intensity range to one, and the rest of the pixels in the image to zero. Features based on moments are subsequently extracted from these grayscale images. These features are then used for classification of the image. The classification is accomplished using an artificial neural network called `PWRNET', which produces a polynomial expression of the trained network. There is one neural network for each individual to be identified, with an output value which is either positive or negative identification. A detailed development of the design is presented, and identification for small population of individuals is presented. It is shown that the system is effective for variations in both scale and translation, which are considered to be reasonable variations for this type of facial identification.

  2. Quantitative learning strategies based on word networks

    Science.gov (United States)

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

    2018-02-01

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

  3. 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.

  4. 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...... limited in their networking, while some even reject the notion of networking as a useful activity....

  5. Modeling acquaintance networks based on balance theory

    Directory of Open Access Journals (Sweden)

    Vukašinović Vida

    2014-09-01

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

  6. Network-based recommendation algorithms: A review

    Science.gov (United States)

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

    2016-06-01

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

  7. Research on Network Scanning Strategy Based on Information Granularity

    Science.gov (United States)

    Qin, Futong; Shi, Pengfei; Du, Jing; Cheng, Ruosi; Zhou, Yunyan

    2017-10-01

    As the basic mean to obtain the information of the targets network, network scanning is often used to discover the security risks and vulnerabilities existing on the network. However, with the development of network technology, the scale of network is more and more large, and the network scanning efficiency put forward higher requirements. In this paper, the concept of network scanning information granularity is proposed, and the design method of network scanning strategy based on information granularity is proposed. Based on single information granularity and hybrid information granularity, four network scanning strategies were designed and verified experimentally. Experiments show that the network scanning strategies based on hybrid information granularity can improve the efficiency of network scanning.

  8. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  9. Wireless Sensor Network Based Smart Parking System

    Directory of Open Access Journals (Sweden)

    Jeffrey JOSEPH

    2014-01-01

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

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

    Science.gov (United States)

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

    2000-05-01

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

  11. Intraoral fiber optic-based diagnostic for periodontal disease

    Energy Technology Data Exchange (ETDEWEB)

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

    2000-01-21

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

  12. Distributed CAN-bus Based Beam Diagnostic System for Pulse Race-Track Microtron

    Science.gov (United States)

    Chepurnov, Alexander

    Very compact 70 MeV pulse race-track microtron is under construction now. To acquire outputs of beam-current transformers on every orbit and pulses of high voltage and RF field a distributed multi-channel beam diagnostic system was developed. Each acquisition controller consists of four fast differential amplifiers and one DSP-based micro-controller with on-chip ADC and CAN-bus controller. Each amplifier is coupled with beam-current transformer and has bandwidth of up to 150MHz and gain of up to 10. One of four channels is acquired during a measurement cycle. Another channel could be selected between two following pulses. All the controllers are connected via optically coupled CAN-bus with a host diskless PC running under Linux with the RTLinux extension. Dedicated software of the system consists of low level acquisition software for DSP, network software for controllers and host PC, application software for PC to present date for operator and control system. Standard CAN application layers were considered but refused because of the closed character of the whole system and centralised synchronisation of the whole system. Dedicated software of the system consists from low level acquisition software for DSP, network software for controllers and host PC, application software for PC to present date for operator and control system. Standard CAN application layers were considered but refused because of closed character of the whole system and centralised synchronisation of the whole system.

  13. Research of ad hoc network based on SINCGARS network

    Science.gov (United States)

    Nie, Hao; Cai, Xiaoxia; Chen, Hong; Chen, Jian; Weng, Pengfei

    2016-03-01

    In today's world, science and technology make a spurt of progress, so society has entered the era of information technology, network. Only the comprehensive use of electronic warfare and network warfare means can we maximize their access to information and maintain the information superiority. Combined with the specific combat mission and operational requirements, the research design and construction in accordance with the actual military which are Suitable for the future of information technology needs of the tactical Adhoc network, tactical internet, will greatly improve the operational efficiency of the command of the army. Through the study of the network of the U.S. military SINCGARS network, it can explore the routing protocol and mobile model, to provide a reference for the research of our army network.

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

    Science.gov (United States)

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

    2018-02-21

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

  15. Diagnostic radiographic examinations in Saudi Arabia based on ...

    African Journals Online (AJOL)

    Variations in patient dose arising from a specific X-ray examination may emerge from complex causes, but in general, low peak kilovolt and high milli Amperes were associated with the higher doses. The results of this study will prove useful information for the formulation of NDRLs and also provide local diagnostic reference ...

  16. Early diagnostic method for sepsis based on neutrophil MR imaging

    Directory of Open Access Journals (Sweden)

    Shanhua Han

    2015-06-01

    Conclusion: Mouse and human neutrophils could be more effectively labelled by Mannan-coated SPION in vitro than Feridex. Sepsis analog neutrophils labelled by Mannan-coated SPIONs could be efficiently detected on MR images, which may serve as an early diagnostic method for sepsis.

  17. 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.

  18. Receiver Based Traffic Control Mechanism to Protect Low Capacity Network in Infrastructure Based Wireless Mesh Network

    Science.gov (United States)

    Gilani, Syed Sherjeel Ahmad; Zubair, Muhammad; Khan, Zeeshan Shafi

    Infrastructure-based Wireless Mesh Networks are emerging as an affordable, robust, flexible and scalable technology. With the advent of Wireless Mesh Networks (WMNs) the dream of connecting multiple technology based networks seems to come true. A fully secure WMN is still a challenge for the researchers. In infrastructure-based WMNs almost all types of existing Wireless Networks like Wi-Fi, Cellular, WiMAX, and Sensor etc can be connected through Wireless Mesh Routers (WMRs). This situation can lead to a security problem. Some nodes can be part of the network with high processing power, large memory and least energy issues while others may belong to a network having low processing power, small memory and serious energy limitations. The later type of the nodes is very much vulnerable to targeted attacks. In our research we have suggested to set some rules on the WMR to mitigate these kinds of targeted flooding attacks. The WMR will then share those set of rules with other WMRs for Effective Utilization of Resources.

  19. 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.

  20. Network-based analysis of proteomic profiles

    KAUST Repository

    Wong, Limsoon

    2016-01-26

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

  1. Serology-Based Diagnostics for the Control of Bovine Neosporosis.

    Science.gov (United States)

    Guido, Stefano; Katzer, Frank; Nanjiani, Ian; Milne, Elspeth; Innes, Elisabeth A

    2016-02-01

    The protozoan Neospora caninum is a primary infectious cause of abortion in cattle that causes significant economic losses worldwide. Because effective vaccines and licensed pharmacological treatments are currently unavailable, control measures rely on biosecurity and management practice. Serological diagnosis plays a crucial role in the identification of infected animals and several tests have been developed. However, owing to the particular dynamics of the host-parasite interaction and to the characteristics of the currently used diagnostic tools, a proportion of infected cattle may not be reliably identified, and can potentially undermine efforts towards the control of bovine neosporosis. Current diagnostic methods for N. caninum infection in cattle and the advances necessary to support effective control strategies are discussed. Copyright © 2015 Elsevier Ltd. All rights reserved.

  2. Flexible substrate-based devices for point-of-care diagnostics

    Science.gov (United States)

    Wang, ShuQi; Chinnasamy, Thiruppathiraja; Lifson, Mark; Inci, Fatih; Demirci, Utkan

    2016-01-01

    Point-of-care (POC) diagnostics play an important role in delivering healthcare, particularly for clinical management and disease surveillance in both developed and developing countries. Currently, the majority of POC diagnostics utilize paper substrates owing to their affordability, disposability, and mass production capability. Recently, flexible polymer substrates have been investigated due to their enhanced physicochemical properties, potential to be integrated into wearable devices with wireless communications for personalized health monitoring, and ability to be customized for POC diagnostics. Here, we focus on the latest advances in developing flexible substrate-based diagnostic devices, including paper and polymers, and their clinical applications at the POC. PMID:27344425

  3. 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.

  4. Insulator recognition based on convolution neural network

    Directory of Open Access Journals (Sweden)

    Yang Yanli

    2017-01-01

    Full Text Available Insulator fault detection plays an important role in maintaining the safety of transmission lines. Insulator recognition is a prerequisite for its fault detection. An insulator recognition algorithm based on convolution neural network (CNN is proposed. A dataset is established to train the constructed CNN. The correct rate is 98.52% for 1220 training samples and the accuracy rate of testing is 89.04% on 1305 samples. The classification result of the CNN is further used to segment the insulator image. The test results show that the proposed method can realize the effective segmentation of insulators.

  5. WEB BASED LEARNING OF COMPUTER NETWORK COURSE

    Directory of Open Access Journals (Sweden)

    Hakan KAPTAN

    2004-04-01

    Full Text Available As a result of developing on Internet and computer fields, web based education becomes one of the area that many improving and research studies are done. In this study, web based education materials have been explained for multimedia animation and simulation aided Computer Networks course in Technical Education Faculties. Course content is formed by use of university course books, web based education materials and technology web pages of companies. Course content is formed by texts, pictures and figures to increase motivation of students and facilities of learning some topics are supported by animations. Furthermore to help working principles of routing algorithms and congestion control algorithms simulators are constructed in order to interactive learning

  6. 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…

  7. 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

  8. Diagnostic performance of bone scintigraphy analyzed by three artificial neural network systems.

    Science.gov (United States)

    Kikushima, Shoichi; Hanawa, Noboru; Kotake, Fumio

    2015-02-01

    The accuracy of bone scintigraphy analyzed by computer-assisted diagnosis (CAD) software involving multiple artificial neural network (ANN) systems has not been well established. We conducted a retrospective study to examine the accuracy of bone scintigraphy analyzed by CAD software, BONENAVI(®) version 2 (BN2; FUJIFILM RI Pharma Co., Ltd.), in patients with suspected bone metastases. In 399 patients, bone metastases were analyzed by means of the BN2 focused on balance of sensitivity and specificity ANN system (BN2-B), focused on specificity-ANN system (BN2-Sp), and focused on sensitivity-ANN system (BN2-Sen). The ANN presented an output between 0 and 1 for each patient. A cutoff value of 0.5 was chosen to provide BN2 with the binary classification of "bone metastasis" or "no bone metastasis". The area under the receiver operating characteristic curve (AUC) was used to evaluate diagnostic accuracy. A total of 18 % of male patients (36/196) and 12 % of female patients (24/203) had bone metastases. BN2-Sp and BN2-Sen were similar to BN2-B in the ability to identify patients who had bone metastases; the AUC values were 0.87 [95 % confidence interval (CI) 0.79-0.95], 0.92 (95 % CI 0.85-0.97), and 0.90 (95 % CI 0.83-0.97), respectively, in male patients. In female patients, the AUC values were 0.81 (95 % CI 0.71-0.91), 0.85 (95 % CI 0.78-0.93), and 0.81 (95 % CI 0.71-0.92), respectively. A total of 65.4 % of patients were classified as concordance of "bone metastases" (17.8 %) or "no bone metastases" (47.6 %), and 34.6 % were classified as mismatch. In the concordance group, BN2-B revealed an AUC of 0.94 (95 % CI 0.88-0.99), with a sensitivity of 94 % (95 % CI 79-98 %) and a specificity of 88 % (95 % CI 79-93 %) in 120 male patients and an AUC of 0.89 (95 % CI 0.78-1.00), with a sensitivity of 86 % (95 % CI 60-96 %) and a specificity of 85 % (95 % CI 78-90 %) in 141 female patients. Bone scintigraphy analyzed by BN2-B accurately identifies the presence of bone

  9. 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

  10. 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.

  11. 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

  12. UPM: unified policy-based network management

    Science.gov (United States)

    Law, Eddie; Saxena, Achint

    2001-07-01

    Besides providing network management to the Internet, it has become essential to offer different Quality of Service (QoS) to users. Policy-based management provides control on network routers to achieve this goal. The Internet Engineering Task Force (IETF) has proposed a two-tier architecture whose implementation is based on the Common Open Policy Service (COPS) protocol and Lightweight Directory Access Protocol (LDAP). However, there are several limitations to this design such as scalability and cross-vendor hardware compatibility. To address these issues, we present a functionally enhanced multi-tier policy management architecture design in this paper. Several extensions are introduced thereby adding flexibility and scalability. In particular, an intermediate entity between the policy server and policy rule database called the Policy Enforcement Agent (PEA) is introduced. By keeping internal data in a common format, using a standard protocol, and by interpreting and translating request and decision messages from multi-vendor hardware, this agent allows a dynamic Unified Information Model throughout the architecture. We have tailor-made this unique information system to save policy rules in the directory server and allow executions of policy rules with dynamic addition of new equipment during run-time.

  13. Artificial organic networks artificial intelligence based on carbon networks

    CERN Document Server

    Ponce-Espinosa, Hiram; Molina, Arturo

    2014-01-01

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

  14. Rumor diffusion in an interests-based dynamic social network.

    Science.gov (United States)

    Tang, Mingsheng; Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping

    2013-01-01

    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.

  15. Developing computational model-based diagnostics to analyse clinical chemistry data

    NARCIS (Netherlands)

    Schalkwijk, D.B. van; Bochove, K. van; Ommen, B. van; Freidig, A.P.; Someren, E.P. van; Greef, J. van der; Graaf, A.A. de

    2010-01-01

    This article provides methodological and technical considerations to researchers starting to develop computational model-based diagnostics using clinical chemistry data.These models are of increasing importance, since novel metabolomics and proteomics measuring technologies are able to produce large

  16. A Laser-Based Diagnostic Suite for Hypersonic Test Facilities, Phase II

    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....

  17. 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....

  18. Paper-based smart microfluidics for education and low-cost diagnostics

    CSIR Research Space (South Africa)

    Smith, S

    2015-11-01

    Full Text Available to develop PoC diagnostic solutions. Specifically, the emerging field of paper-based microfluidics, with advantages such as low-cost, disposability and minimal external equipment requirements, provides unique opportunities for addressing healthcare issues...

  19. Community-Driven Standards-Based Electronic Laboratory Data-Sharing Networks

    OpenAIRE

    Zarcone, Patina; Nordenberg, Dale; Meigs, Michelle; Merrick, Ulrike; Jernigan, Daniel; Hinrichs, Steven H.

    2010-01-01

    Public health laboratories (PHLs) are critical components of the nation's health-care system, serving as stewards of valuable specimens, delivering important diagnostic results to support clinical and public health programs, supporting public health policy, and conducting research. This article discusses the need for and challenges of creating standards-based data-sharing networks across the PHL community, which led to the development of the PHL Interoperability Project (PHLIP). Launched by t...

  20. A fluctuation-induced plasma transport diagnostic based upon fast-Fourier transform spectral analysis

    Science.gov (United States)

    Powers, E. J.; Kim, Y. C.; Hong, J. Y.; Roth, J. R.; Krawczonek, W. M.

    1978-01-01

    A diagnostic, based on fast Fourier-transform spectral analysis techniques, that provides experimental insight into the relationship between the experimentally observable spectral characteristics of the fluctuations and the fluctuation-induced plasma transport is described. The model upon which the diagnostic technique is based and its experimental implementation is discussed. Some characteristic results obtained during the course of an experimental study of fluctuation-induced transport in the electric field dominated NASA Lewis bumpy torus plasma are presented.

  1. Network-Based and Binless Frequency Analyses.

    Directory of Open Access Journals (Sweden)

    Sybil Derrible

    Full Text Available We introduce and develop a new network-based and binless methodology to perform frequency analyses and produce histograms. In contrast with traditional frequency analysis techniques that use fixed intervals to bin values, we place a range ±ζ around each individual value in a data set and count the number of values within that range, which allows us to compare every single value of a data set with one another. In essence, the methodology is identical to the construction of a network, where two values are connected if they lie within a given a range (±ζ. The value with the highest degree (i.e., most connections is therefore assimilated to the mode of the distribution. To select an optimal range, we look at the stability of the proportion of nodes in the largest cluster. The methodology is validated by sampling 12 typical distributions, and it is applied to a number of real-world data sets with both spatial and temporal components. The methodology can be applied to any data set and provides a robust means to uncover meaningful patterns and trends. A free python script and a tutorial are also made available to facilitate the application of the method.

  2. Object Classification Using Substance Based Neural Network

    Directory of Open Access Journals (Sweden)

    P. Sengottuvelan

    2014-01-01

    Full Text Available Object recognition has shown tremendous increase in the field of image analysis. The required set of image objects is identified and retrieved on the basis of object recognition. In this paper, we propose a novel classification technique called substance based image classification (SIC using a wavelet neural network. The foremost task of SIC is to remove the surrounding regions from an image to reduce the misclassified portion and to effectively reflect the shape of an object. At first, the image to be extracted is performed with SIC system through the segmentation of the image. Next, in order to attain more accurate information, with the extracted set of regions, the wavelet transform is applied for extracting the configured set of features. Finally, using the neural network classifier model, misclassification over the given natural images and further background images are removed from the given natural image using the LSEG segmentation. Moreover, to increase the accuracy of object classification, SIC system involves the removal of the regions in the surrounding image. Performance evaluation reveals that the proposed SIC system reduces the occurrence of misclassification and reflects the exact shape of an object to approximately 10–15%.

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

    Directory of Open Access Journals (Sweden)

    Mohamed A. Ahmed

    2016-03-01

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

  4. Molecular diagnostics based on clustering dynamics of magnetic nanobeads

    DEFF Research Database (Denmark)

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

    2014-01-01

    or on the polymerase chain reaction (PCR) [1]. In this work we demonstrate detection of DNA coils formed from a Vibrio Cholerae DNA target at pM concentrations using a novel opto-magnetic approach exploiting the dynamic collective behavior of magnetic nanobeads. The technique relies on measurements of the light...... transmission modulation caused by the AC magnetic field-stimulated reversible formation and disruption of elongated MNB supra-structures during a cycle of the uniaxial applied magnetic field. As a specific clinically relevant diagnostic case, we detect DNA coils formed via padlock probe recognition...... and isothermal rolling circle amplification from Vibrio cholerae DNA. The detection method is shown in Figure 1. MNBs which specifically bind to the micrometric sized DNA coil cannot rotate under the field action as free beads and form chains; this results in a strongly modified opto-magnetic signal. As a core...

  5. 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.

  6. 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.

  7. Computer network management based on TMN

    Science.gov (United States)

    Li, Yanpeng; Li, Xiang; Zhuang, Zhaowen

    2002-09-01

    Computer Network Management is the most important work of the computer network. Two problems in managing of a professional network must be solved, one is to standardize the relationship between the professional network and the management network, and the other is to ensure the security of the network. The first problem will be discussed here. We put forward a new idea here, that is, to build standard computer management network and standard professional network. First, we give a standard model of the relationship between the management network and its subordinate professional network. This model is different with the TMN. It distinguishes the management network and the subordinate professional network clearly in the notion, the place and the property of all the sockets is determined. Subsequently, the corresponding function structure and physical architecture are given together. For each kind of architecture, we define the related rules respectively. These rules are: the relationship between the function module, the standard of the interface, protocol architecture. Then, the procedure of communication signal processing is analyzed. The requirements of all the programming sockets are explained. Finally, we explain some related topics briefly.

  8. Analysis of neural networks through base functions

    NARCIS (Netherlands)

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

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

  9. Personalized Network-Based Treatments in Oncology

    DEFF Research Database (Denmark)

    Robin, Xavier; Creixell, Pau; Radetskaya, Oxana

    2013-01-01

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

  10. 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.”

  11. 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)

  12. 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

  13. 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

  14. Network analysis based on large deviation statistics

    Science.gov (United States)

    Miyazaki, Syuji

    2007-07-01

    A chaotic piecewise linear map whose statistical properties are identical to those of a random walk on directed graphs such as the world wide web (WWW) is constructed, and the dynamic quantity is analyzed in the framework of large deviation statistics. Gibbs measures include the weight factor appearing in the weighted average of the dynamic quantity, which can also quantitatively measure the importance of web sites. Currently used levels of importance in the commercial search engines are independent of search terms, which correspond to the stationary visiting frequency of each node obtained from a random walk on the network or equivalent chaotic dynamics. Levels of importance based on the Gibbs measure depend on each search term which is specified by the searcher. The topological conjugate transformation between one dynamical system with a Gibbs measure and another dynamical system whose standard invariant probability measure is identical to the Gibbs measure is also discussed.

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

    Science.gov (United States)

    Yu, Tao; Cai, Weiwei; Liu, Yingzheng

    2018-04-01

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

  16. 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.

  17. Graphlet-based Characterization of Directed Networks

    Science.gov (United States)

    Sarajlić, Anida; Malod-Dognin, Noël; Yaveroğlu, Ömer Nebil; Pržulj, Nataša

    2016-10-01

    We are flooded with large-scale, dynamic, directed, networked data. Analyses requiring exact comparisons between networks are computationally intractable, so new methodologies are sought. To analyse directed networks, we extend graphlets (small induced sub-graphs) and their degrees to directed data. Using these directed graphlets, we generalise state-of-the-art network distance measures (RGF, GDDA and GCD) to directed networks and show their superiority for comparing directed networks. Also, we extend the canonical correlation analysis framework that enables uncovering the relationships between the wiring patterns around nodes in a directed network and their expert annotations. On directed World Trade Networks (WTNs), our methodology allows uncovering the core-broker-periphery structure of the WTN, predicting the economic attributes of a country, such as its gross domestic product, from its wiring patterns in the WTN for up-to ten years in the future. It does so by enabling us to track the dynamics of a country’s positioning in the WTN over years. On directed metabolic networks, our framework yields insights into preservation of enzyme function from the network wiring patterns rather than from sequence data. Overall, our methodology enables advanced analyses of directed networked data from any area of science, allowing domain-specific interpretation of a directed network’s topology.

  18. Network Medicine: A Network-based Approach to Human Disease

    Science.gov (United States)

    Barabási, Albert-László; Gulbahce, Natali; Loscalzo, Joseph

    2011-01-01

    Given the functional interdependencies between the molecular components in a human cell, a disease is rarely a consequence of an abnormality in a single gene, but reflects the perturbations of the complex intracellular network. The emerging tools of network medicine offer a platform to explore systematically not only the molecular complexity of a particular disease, leading to the identification of disease modules and pathways, but also the molecular relationships between apparently distinct (patho)phenotypes. Advances in this direction are essential to identify new diseases genes, to uncover the biological significance of disease-associated mutations identified by genome-wide association studies and full genome sequencing, and to identify drug targets and biomarkers for complex diseases. PMID:21164525

  19. Classification of complex networks based on similarity of topological network features

    Science.gov (United States)

    Attar, Niousha; Aliakbary, Sadegh

    2017-09-01

    Over the past few decades, networks have been widely used to model real-world phenomena. Real-world networks exhibit nontrivial topological characteristics and therefore, many network models are proposed in the literature for generating graphs that are similar to real networks. Network models reproduce nontrivial properties such as long-tail degree distributions or high clustering coefficients. In this context, we encounter the problem of selecting the network model that best fits a given real-world network. The need for a model selection method reveals the network classification problem, in which a target-network is classified into one of the candidate network models. In this paper, we propose a novel network classification method which is independent of the network size and employs an alignment-free metric of network comparison. The proposed method is based on supervised machine learning algorithms and utilizes the topological similarities of networks for the classification task. The experiments show that the proposed method outperforms state-of-the-art methods with respect to classification accuracy, time efficiency, and robustness to noise.

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

    NARCIS (Netherlands)

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

    2003-01-01

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

  1. Document categorization using multilingual associative networks based on Wikipedia

    NARCIS (Netherlands)

    Bloom, Niels; Theune, Mariet; de Jong, Franciska M.G.

    Associative networks are a connectionist language model with the ability to categorize large sets of documents. In this research we combine monolingual associative networks based on Wikipedia to create a larger, multilingual associative network, using the cross-lingual connections between Wikipedia

  2. Survey-Based Measurement of Public Management and Policy Networks

    Science.gov (United States)

    Henry, Adam Douglas; Lubell, Mark; McCoy, Michael

    2012-01-01

    Networks have become a central concept in the policy and public management literature; however, theoretical development is hindered by a lack of attention to the empirical properties of network measurement methods. This paper compares three survey-based methods for measuring organizational networks: the roster, the free-recall name generator, and…

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

    Science.gov (United States)

    Li, Tianli

    2017-11-01

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

  4. A Cascade-Based Emergency Model for Water Distribution Network

    Directory of Open Access Journals (Sweden)

    Qing Shuang

    2015-01-01

    Full Text Available Water distribution network is important in the critical physical infrastructure systems. The paper studies the emergency resource strategies on water distribution network with the approach of complex network and cascading failures. The model of cascade-based emergency for water distribution network is built. The cascade-based model considers the network topology analysis and hydraulic analysis to provide a more realistic result. A load redistribution function with emergency recovery mechanisms is established. From the aspects of uniform distribution, node betweenness, and node pressure, six recovery strategies are given to reflect the network topology and the failure information, respectively. The recovery strategies are evaluated with the complex network indicators to describe the failure scale and failure velocity. The proposed method is applied by an illustrative example. The results showed that the recovery strategy considering the node pressure can enhance the network robustness effectively. Besides, this strategy can reduce the failure nodes and generate the least failure nodes per time.

  5. Optimising TCP for cloud-based mobile networks

    DEFF Research Database (Denmark)

    Artuso, Matteo; Christiansen, Henrik Lehrmann

    2016-01-01

    protocols should therefore be adjusted to cope with the peculiarities of these networks. In this paper, we investigate an optimized design of the Transmission Control Protocol (TCP), as it plays a central role in all-IP mobile networks to ensure optimal performances for application-layer services. TCP......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....

  6. Chaotic Extension Neural Network-Based Fault Diagnosis Method for Solar Photovoltaic Systems

    Directory of Open Access Journals (Sweden)

    Kuo-Nan Yu

    2014-01-01

    Full Text Available At present, the solar photovoltaic system is extensively used. However, once a fault occurs, it is inspected manually, which is not economical. In order to remedy the defect of unavailable fault diagnosis at any irradiance and temperature in the literature with chaos synchronization based intelligent fault diagnosis for photovoltaic systems proposed by Hsieh et al., this study proposed a chaotic extension fault diagnosis method combined with error back propagation neural network to overcome this problem. It used the nn toolbox of matlab 2010 for simulation and comparison, measured current irradiance and temperature, and used the maximum power point tracking (MPPT for chaotic extraction of eigenvalue. The range of extension field was determined by neural network. Finally, the voltage eigenvalue obtained from current temperature and irradiance was used for the fault diagnosis. Comparing the diagnostic rates with the results by Hsieh et al., this scheme can obtain better diagnostic rates when the irradiances or the temperatures are changed.

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

    Directory of Open Access Journals (Sweden)

    Junqing Yuan

    2013-01-01

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

  8. 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.

  9. Evolution of RNA-Based Networks.

    Science.gov (United States)

    Stadler, Peter F

    2016-01-01

    RNA molecules have served for decades as a paradigmatic example of molecular evolution that is tractable both in in vitro experiments and in detailed computer simulation. The adaptation of RNA sequences to external selection pressures is well studied and well understood. The de novo innovation or optimization of RNA aptamers and riboswitches in SELEX experiments serves as a case in point. Likewise, fitness landscapes building upon the efficiently computable RNA secondary structures have been a key toward understanding realistic fitness landscapes. Much less is known, however, on models in which multiple RNAs interact with each other, thus actively influencing the selection pressures acting on them. From a computational perspective, RNA-RNA interactions can be dealt with by same basic methods as the folding of a single RNA molecule, although many details become more complicated. RNA-RNA interactions are frequently employed in cellular regulation networks, e.g., as miRNA bases mRNA silencing or in the modulation of bacterial mRNAs by small, often highly structured sRNAs. In this chapter, we summarize the key features of networks of replicators. We highlight the differences between quasispecies-like models describing templates copied by an external replicase and hypercycle similar to autocatalytic replicators. Two aspects are of importance: the dynamics of selection within a population, usually described by conventional dynamical systems, and the evolution of replicating species in the space of chemical types. Product inhibition plays a key role in modulating selection dynamics from survival of the fittest to extinction of unfittest. The sequence evolution of replicators is rather well understood as approximate optimization in a fitness landscape for templates that is shaped by the sequence-structure map of RNA. Some of the properties of this map, in particular shape space covering and extensive neutral networks, give rise to evolutionary patterns such as drift

  10. 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...

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

    NARCIS (Netherlands)

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

    2015-01-01

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

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

    NARCIS (Netherlands)

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

    2015-01-01

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

  13. 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...

  14. Ontology- and graph-based similarity assessment in biological networks.

    Science.gov (United States)

    Wang, Haiying; Zheng, Huiru; Azuaje, Francisco

    2010-10-15

    A standard systems-based approach to biomarker and drug target discovery consists of placing putative biomarkers in the context of a network of biological interactions, followed by different 'guilt-by-association' analyses. The latter is typically done based on network structural features. Here, an alternative analysis approach in which the networks are analyzed on a 'semantic similarity' space is reported. Such information is extracted from ontology-based functional annotations. We present SimTrek, a Cytoscape plugin for ontology-based similarity assessment in biological networks. http://rosalind.infj.ulst.ac.uk/SimTrek.html francisco.azuaje@crp-sante.lu Supplementary data are available at Bioinformatics online.

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

    International Nuclear Information System (INIS)

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

    2005-01-01

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

  16. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...... to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models........ In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us...

  17. 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.

  18. Network-based production quality control

    Science.gov (United States)

    Kwon, Yongjin; Tseng, Bill; Chiou, Richard

    2007-09-01

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

  19. Automation of Network-Based Scientific Workflows

    Energy Technology Data Exchange (ETDEWEB)

    Altintas, I. [University of California, La Jolla; Barreto, R. [Oak Ridge National Laboratory (ORNL); Blondin, J. M. [North Carolina State University; Cheng, Z. [North Carolina State University; Critchlow, T. [Lawrence Livermore National Laboratory (LLNL); Khan, A. [University of Utah; Klasky, Scott A [ORNL; Ligon, J. [North Carolina State University; Ludaescher, B. [University of California, Davis; Mouallem, P. A. [North Carolina State University; Parker, S. [University of Utah; Podhorszki, Norbert [University of California, Davis; Shoshani, A. [Lawrence Berkeley National Laboratory (LBNL); Silva, C. [University of Utah; Vouk, M. A. [North Carolina State University

    2007-01-01

    Comprehensive, end-to-end, data and workflow management solutions are needed to handle the increasing complexity of processes and data volumes associated with modern distributed scientific problem solving, such as ultra-scale simulations and high-throughput experiments. The key to the solution is an integrated network-based framework that is functional, dependable, fault-tolerant, and supports data and process provenance. Such a framework needs to make development and use of application workflows dramatically easier so that scientists' efforts can shift away from data management and utility software development to scientific research and discovery An integrated view of these activities is provided by the notion of scientific workflows - a series of structured activities and computations that arise in scientific problem-solving. An information technology framework that supports scientific workflows is the Ptolemy II based environment called Kepler. This paper discusses the issues associated with practical automation of scientific processes and workflows and illustrates this with workflows developed using the Kepler framework and tools.

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

    International Nuclear Information System (INIS)

    Korsah, K.; Uhrig, R.E.

    1991-01-01

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

  1. Neural network based system for equipment surveillance

    Science.gov (United States)

    Vilim, R.B.; Gross, K.C.; Wegerich, S.W.

    1998-04-28

    A method and system are disclosed for performing surveillance of transient signals of an industrial device to ascertain the operating state. The method and system involves the steps of reading into a memory training data, determining neural network weighting values until achieving target outputs close to the neural network output. If the target outputs are inadequate, wavelet parameters are determined to yield neural network outputs close to the desired set of target outputs and then providing signals characteristic of an industrial process and comparing the neural network output to the industrial process signals to evaluate the operating state of the industrial process. 33 figs.

  2. 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.

  3. A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

    Science.gov (United States)

    Kojić, Nenad; Reljin, Irini; Reljin, Branimir

    2012-01-01

    The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

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

    Science.gov (United States)

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

    2017-08-01

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

  5. A signal-detection-based diagnostic-feature-detection model of eyewitness identification.

    Science.gov (United States)

    Wixted, John T; Mickes, Laura

    2014-04-01

    The theoretical understanding of eyewitness identifications made from a police lineup has long been guided by the distinction between absolute and relative decision strategies. In addition, the accuracy of identifications associated with different eyewitness memory procedures has long been evaluated using measures like the diagnosticity ratio (the correct identification rate divided by the false identification rate). Framed in terms of signal-detection theory, both the absolute/relative distinction and the diagnosticity ratio are mainly relevant to response bias while remaining silent about the key issue of diagnostic accuracy, or discriminability (i.e., the ability to tell the difference between innocent and guilty suspects in a lineup). Here, we propose a signal-detection-based model of eyewitness identification, one that encourages the use of (and helps to conceptualize) receiver operating characteristic (ROC) analysis to measure discriminability. Recent ROC analyses indicate that the simultaneous presentation of faces in a lineup yields higher discriminability than the presentation of faces in isolation, and we propose a diagnostic feature-detection hypothesis to account for that result. According to this hypothesis, the simultaneous presentation of faces allows the eyewitness to appreciate that certain facial features (viz., those that are shared by everyone in the lineup) are non-diagnostic of guilt. To the extent that those non-diagnostic features are discounted in favor of potentially more diagnostic features, the ability to discriminate innocent from guilty suspects will be enhanced.

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

    International Nuclear Information System (INIS)

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

    2009-01-01

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

  7. Diagnostic system based on condition turbogenerator Petri nets

    International Nuclear Information System (INIS)

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

    2016-01-01

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

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

    Czech Academy of Sciences Publication Activity Database

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

    2007-01-01

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

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

    Science.gov (United States)

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

    2016-02-01

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

  10. Dynamics of subway networks based on vehicles operation timetable

    Science.gov (United States)

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

    2017-05-01

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

  11. 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.

  12. 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)

  13. Neural Network Classifier Based on Growing Hyperspheres

    Czech Academy of Sciences Publication Activity Database

    Jiřina Jr., Marcel; Jiřina, Marcel

    2000-01-01

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

  14. Community-Based Networks for Challenged Environments

    Science.gov (United States)

    Vigil-Hayes, Morgan Ashlee

    2017-01-01

    The Internet as a networked system has been rendered more complex than ever before as human endpoints are grafted into the system via increasingly pervasive and personalized networked devices. According to the United Nations, the Internet is a transnational enabler of a number of human rights, and as such, access to the Internet has been…

  15. 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)

  16. 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.

  17. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

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

  18. 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 relevan...... 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 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...

  19. 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.

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

    Science.gov (United States)

    Jones, Robert E.; Liberman, Eugene M.

    1992-01-01

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

  1. 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...... 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...... 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...

  2. Magnetic-particle-sensing based diagnostic protocols and applications.

    Science.gov (United States)

    Takamura, Tsukasa; Ko, Pil Ju; Sharma, Jaiyam; Yukino, Ryoji; Ishizawa, Shunji; Sandhu, Adarsh

    2015-06-04

    Magnetic particle-labeled biomaterial detection has attracted much attention in recent years for a number of reasons; easy manipulation by external magnetic fields, easy functionalization of the surface, and large surface-to-volume ratio, to name but a few. In this review, we report on our recent investigations into the detection of nano-sized magnetic particles. First, the detection by Hall magnetic sensor with lock-in amplifier and alternative magnetic field is summarized. Then, our approach to detect sub-200 nm diameter target magnetic particles via relatively large micoro-sized "columnar particles" by optical microscopy is described. Subsequently, we summarize magnetic particle detection based on optical techniques; one method is based on the scattering of the magnetically-assembled nano-sized magnetic bead chain in rotating magnetic fields and the other one is based on the reflection of magnetic target particles and porous silicon. Finally, we report recent works with reference to more familiar industrial products (such as smartphone-based medical diagnosis systems and magnetic removal of unspecific-binded nano-sized particles, or "magnetic washing").

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

    Science.gov (United States)

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

    2015-01-01

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

  4. Cancer classification based on gene expression using neural networks.

    Science.gov (United States)

    Hu, H P; Niu, Z J; Bai, Y P; Tan, X H

    2015-12-21

    Based on gene expression, we have classified 53 colon cancer patients with UICC II into two groups: relapse and no relapse. Samples were taken from each patient, and gene information was extracted. Of the 53 samples examined, 500 genes were considered proper through analyses by S-Kohonen, BP, and SVM neural networks. Classification accuracy obtained by S-Kohonen neural network reaches 91%, which was more accurate than classification by BP and SVM neural networks. The results show that S-Kohonen neural network is more plausible for classification and has a certain feasibility and validity as compared with BP and SVM neural networks.

  5. 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.

  6. Index : A Rule Based Expert System For Computer Network Maintenance

    Science.gov (United States)

    Chaganty, Srinivas; Pitchai, Anandhi; Morgan, Thomas W.

    1988-03-01

    Communications is an expert intensive discipline. The application of expert systems for maintenance of large and complex networks, mainly as an aid in trouble shooting, can simplify the task of network management. The important steps involved in troubleshooting are fault detection, fault reporting, fault interpretation and fault isolation. At present, Network Maintenance Facilities are capable of detecting and reporting the faults to network personnel. Fault interpretation refers to the next step in the process, which involves coming up with reasons for the failure. Fault interpretation can be characterized in two ways. First, it involves such a diversity of facts that it is difficult to predict. Secondly, it embodies a wealth of knowledge in the form of network management personnel. The application of expert systems in these interpretive tasks is an important step towards automation of network maintenance. In this paper, INDEX (Intelligent Network Diagnosis Expediter), a rule based production system for computer network alarm interpretation is described. It acts as an intelligent filter for people analyzing network alarms. INDEX analyzes the alarms in the network and identifies proper maintenance action to be taken.The important feature of this production system is that it is data driven. Working memory is the principal data repository of production systems and its contents represent the current state of the problem. Control is based upon which productions match the constantly changing working memory elements. Implementation of the prototype is in OPS83. Major issues in rule based system development such as rule base organization, implementation and efficiency are discussed.

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

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-09-01

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

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

    Science.gov (United States)

    Briggs, Derek C.; Circi, Ruhan

    2017-01-01

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

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

    Directory of Open Access Journals (Sweden)

    Amir Momeni Boroujeni

    2012-12-01

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

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

    Directory of Open Access Journals (Sweden)

    О.С. Якушенко

    2006-01-01

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

  11. Neural Network Based Intelligent Sootblowing System

    Energy Technology Data Exchange (ETDEWEB)

    Mark Rhode

    2005-04-01

    . Due to the composition of coal, particulate matter is also a by-product of coal combustion. Modern day utility boilers are usually fitted with electrostatic precipitators to aid in the collection of particulate matter. Although extremely efficient, these devices are sensitive to rapid changes in inlet mass concentration as well as total mass loading. Traditionally, utility boilers are equipped with devices known as sootblowers, which use, steam, water or air to dislodge and clean the surfaces within the boiler and are operated based upon established rule or operator's judgment. Poor sootblowing regimes can influence particulate mass loading to the electrostatic precipitators. The project applied a neural network intelligent sootblowing system in conjunction with state-of-the-art controls and instruments to optimize the operation of a utility boiler and systematically control boiler slagging/fouling. This optimization process targeted reduction of NOx of 30%, improved efficiency of 2% and a reduction in opacity of 5%. The neural network system proved to be a non-invasive system which can readily be adapted to virtually any utility boiler. Specific conclusions from this neural network application are listed below. These conclusions should be used in conjunction with the specific details provided in the technical discussions of this report to develop a thorough understanding of the process.

  12. Improving Student Engagement Using Course-Based Social Networks

    Science.gov (United States)

    Imlawi, Jehad Mohammad

    2013-01-01

    This study proposes an engagement model that supports use of course-based online social networks for engaging student, and hence, improving their educational outcomes. This research demonstrates that instructors who create course-based online social networks to communicate with students can increase the student engagement in these online social…

  13. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Zhi Chen

    2014-01-01

    Full Text Available Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs. Self-organization feature map (SOFM neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the competitive learning among nodes, and takes the node residual energy and the distance to the neighbor nodes into account in the clustering process. In addition, the approach of dynamically adjusting the transmitting power of the cluster head nodes is adopted to optimize the network topology. Simulation results show that SOFMHTC may get a better energy-efficient performance and make more balanced energy consumption compared with some existing algorithms in WSNs.

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

    International Nuclear Information System (INIS)

    Korteniemi, A.

    1990-01-01

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

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

    International Nuclear Information System (INIS)

    Luccio, A.

    1993-01-01

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

  16. MicroTCA based Platform for advanced particle accelerators diagnostics

    CERN Document Server

    Juszczyk, Bartłomiej

    2014-01-01

    All over the world there are many research centers that are conducting researches with use of particle accelerators. Thanks to various experiments we could better understand surrounding world. But, there are still a lot of unknowns to explore which science needs better instruments. One of these tools are measurement systems. Unfortunately currently used solutions do not provide sufficient performance to satisfy growing needs. This implies the search for new solutions. One of such solution is a modern uTCA architecture. In this document an open source project of a base card (AFC -AMC to FMC carrier board) based on this standard has been described. This card is equipped with two FMC connectors, which allow to connect wide variety of extension cards. In combination with a powerful FPGA device this card is an universal base circuit for variety of projects. Among the others it allows to implement algorithms which are collecting data from fast ADCs and to process these data. Moreover the applied uTCA architecture p...

  17. Image-Based Structural Modeling of the Cardiac Purkinje Network

    Directory of Open Access Journals (Sweden)

    Benjamin R. Liu

    2015-01-01

    Full Text Available The Purkinje network is a specialized conduction system within the heart that ensures the proper activation of the ventricles to produce effective contraction. Its role during ventricular arrhythmias is less clear, but some experimental studies have suggested that the Purkinje network may significantly affect the genesis and maintenance of ventricular arrhythmias. Despite its importance, few structural models of the Purkinje network have been developed, primarily because current physical limitations prevent examination of the intact Purkinje network. In previous modeling efforts Purkinje-like structures have been developed through either automated or hand-drawn procedures, but these networks have been created according to general principles rather than based on real networks. To allow for greater realism in Purkinje structural models, we present a method for creating three-dimensional Purkinje networks based directly on imaging data. Our approach uses Purkinje network structures extracted from photographs of dissected ventricles and projects these flat networks onto realistic endocardial surfaces. Using this method, we create models for the combined ventricle-Purkinje system that can fully activate the ventricles through a stimulus delivered to the Purkinje network and can produce simulated activation sequences that match experimental observations. The combined models have the potential to help elucidate Purkinje network contributions during ventricular arrhythmias.

  18. Diagnostic accuracy of three-dimensional CT reconstruction and cephalometry for lateral skull base tumors.

    Science.gov (United States)

    Xie, X Z; Huo, X K

    2015-10-01

    To explore the diagnostic accuracy of three-dimensional CT reconstruction and cephalometry in lateral skull base tumors. Fifty-eight patients with lateral skull base tumors were randomly divided into control group (n = 29, examined with conventional diagnostic technique) or study group (n = 29, examined with three-dimensional CT reconstruction and cephalometry). The diagnostic accuracy, tumor distribution and image characteristics were compared between both patient groups. In control group, preoperative tumor diagnosis was consistent with intraoperative diagnosis in 20 patients, similar in 7 patients and discrepant in 2 patients. In study group, there were 24 consistent, 4 similar, and 1 discrepant diagnoses (p cephalometry provides accurate diagnosis of lateral skull base tumors, which is helpful for subsequent surgical treatment.

  19. Diagnostics of gas turbines based on changes in thermodynamics parameters

    Science.gov (United States)

    Hocko, Marián; Klimko, Marek

    2016-03-01

    This article is focused on solving the problems of determining the true state of gas turbine based on measured changes in thermodynamic parameters. Dependence between the real individual parts for gas turbines and changing the thermodynamic parameters were experimentally verified and confirmed on a small jet engine MPM-20 in the laboratory of the Department of Aviation Engineering at Technical University in Košice. The results of experiments confirm that the wear and tear of basic parts for gas turbines (turbo-compressor engines) to effect the change of thermodynamic parameters of the engine.

  20. Diagnostics of gas turbines based on changes in thermodynamics parameters

    Directory of Open Access Journals (Sweden)

    Hocko Marián

    2016-01-01

    Full Text Available This article is focused on solving the problems of determining the true state of gas turbine based on measured changes in thermodynamic parameters. Dependence between the real individual parts for gas turbines and changing the thermodynamic parameters were experimentally verified and confirmed on a small jet engine MPM-20 in the laboratory of the Department of Aviation Engineering at Technical University in Košice. The results of experiments confirm that the wear and tear of basic parts for gas turbines (turbo-compressor engines to effect the change of thermodynamic parameters of the engine.

  1. ETHERNET BASED EMBEDDED SYSTEM FOR FEL DIAGNOSTICS AND CONTROLS

    Energy Technology Data Exchange (ETDEWEB)

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

    2006-10-24

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

  2. Posture control with a neural network of a diagnostic robot manipulator; Neural network ni yoru haikan shindan robot manipulator no shisei seigyo

    Energy Technology Data Exchange (ETDEWEB)

    Abe, Y. [Shinryo Corp., Tokyo (Japan); Hara, F.; Hosokai, H.; Kojima, T. [Science University of Tokyo, Tokyo (Japan)

    1997-09-25

    A robot manipulator has been investigated, by which non-destructive diagnostic tests of a pipe-line can be automatically conducted. This robot manipulator has five degrees of freedom, and can set up an ultrasonic probe for diagnosis attached at the surface of pipe at various angles and with various forces. The manipulator is controlled through the feedback of variation of reflected ultrasonic wave with the contact force of probe. Using a neural network, the manipulator can smoothly shift its posture in the optimum sensing state from the initial posture. For the measuring experiment using a pipe, the posture control and the force control were evaluated. The posture control error was around 0.4 degree. The target allowable error 0.5 degree was achieved. For the force control, it was demonstrated that the follow-up property of contact force to the target value can be enhanced by increasing the intermediate posture number. 3 refs., 13 figs.

  3. Credit scoring based on social network data

    OpenAIRE

    MASYUTIN A.A.

    2015-01-01

    Social networks accumulate huge amounts of information, which can provide valuable insights on people s behavior. In this paper, we use social data from Vkontakte, Russias most popular social network, to discriminate between the solvent and delinquent debtors of credit organizations. Firstly, we present the datacenter architecture for social data retrieval. It has several functions, such as client matching, user profile parsing, API communication and data storing. Secondly, we develop two cre...

  4. 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.

  5. 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

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

    Energy Technology Data Exchange (ETDEWEB)

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

    2016-03-15

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

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

    Science.gov (United States)

    van der Voort, D. D.; Maes, N. C. J.; Lamberts, T.; Sweep, A. M.; van de Water, W.; Kunnen, R. P. J.; Clercx, H. J. H.; van Heijst, G. J. F.; Dam, N. J.

    2016-03-01

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

  8. Multiagent Based Information Dissemination in Vehicular Ad Hoc Networks

    Directory of Open Access Journals (Sweden)

    S.S. Manvi

    2009-01-01

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

  9. Time Series Prediction based on Hybrid Neural Networks

    Directory of Open Access Journals (Sweden)

    S. A. Yarushev

    2016-01-01

    Full Text Available In this paper, we suggest to use hybrid approach to time series forecasting problem. In first part of paper, we create a literature review of time series forecasting methods based on hybrid neural networks and neuro-fuzzy approaches. Hybrid neural networks especially effective for specific types of applications such as forecasting or classification problem, in contrast to traditional monolithic neural networks. These classes of problems include problems with different characteristics in different modules. The main part of paper create a detailed overview of hybrid networks benefits, its architectures and performance under traditional neural networks. Hybrid neural networks models for time series forecasting are discussed in the paper. Experiments with modular neural networks are given.

  10. Underwater Sensor Network Redeployment Algorithm Based on Wolf Search.

    Science.gov (United States)

    Jiang, Peng; Feng, Yang; Wu, Feng

    2016-10-21

    This study addresses the optimization of node redeployment coverage in underwater wireless sensor networks. Given that nodes could easily become invalid under a poor environment and the large scale of underwater wireless sensor networks, an underwater sensor network redeployment algorithm was developed based on wolf search. This study is to apply the wolf search algorithm combined with crowded degree control in the deployment of underwater wireless sensor networks. The proposed algorithm uses nodes to ensure coverage of the events, and it avoids the prematurity of the nodes. The algorithm has good coverage effects. In addition, considering that obstacles exist in the underwater environment, nodes are prevented from being invalid by imitating the mechanism of avoiding predators. Thus, the energy consumption of the network is reduced. Comparative analysis shows that the algorithm is simple and effective in wireless sensor network deployment. Compared with the optimized artificial fish swarm algorithm, the proposed algorithm exhibits advantages in network coverage, energy conservation, and obstacle avoidance.

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

    Directory of Open Access Journals (Sweden)

    Yunpeng Wang

    2014-01-01

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

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

    Science.gov (United States)

    Tu, Chengyi

    2014-05-01

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

  13. Hydro-fracture in the laboratory: matching diagnostic seismic signals to fracture networks

    Science.gov (United States)

    Gehne, S.; Benson, P. M.; Koor, N.; Dobson, K. J.; Enfield, M.; Barber, A.

    2017-12-01

    Hydraulic fracturing is a key process in both natural (e.g. dyke intrusion) and engineered environments (e.g. shale gas). To better understand this process, we present new data from simulated hydraulic fracturing in a controlled laboratory environment in order to track fracture nucleation (location) and propagation (velocity) in space and time to assess the fracture mechanics and developing fracture network. Fluid overpressure is used to generate a permeable network of micro tensile fractures in an anisotropic sandstone and a highly anisotropic shale. A newly developed technique, using a steel guide arrangement to direct pressurised fluid into a sealed section of an axially drilled conduit, allows the pore fluid to contact the rock directly and to initiate tensile fractures from a pre-defined zone inside the sample. Acoustic emission location is used to record and map the nucleation and development of the micro-fracture network. For both rock types, fractures progresses parallel to the bedding plane (short-transverse) if the bedding plane is aligned with the direction of σ1 requiring breakdown pressures of approximately 7 and 13MPa respectively at a confining pressure of 8MPa. The data also indicates a more ductile behaviour of the shale than expected. We use X-Ray Computed Tomography (CT) to evaluate the evolved fracture network in terms of fracture pattern and aperture. Hydraulic fracturing produces very planar fractures in the shale, with axial fractures over the entire length of the sample broadly following the bedding. In contrast, fractures in the sandstone are more diffuse, linking pore spaces as they propagate. However, secondary micro cracking, branching of the main fracture, are also observed. These new experiments suggest that fracture pattern, fracture propagation trajectories, and fracturing fluid pressures are predominantly controlled by the interaction between the anisotropic mechanical properties of the rock and the anisotropic stress environment.

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

    Science.gov (United States)

    Dande, Payal; Samant, Purva

    2018-01-01

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

  15. Network-based analysis reveals distinct association patterns in a semantic MEDLINE-based drug-disease-gene network.

    Science.gov (United States)

    Zhang, Yuji; Tao, Cui; Jiang, Guoqian; Nair, Asha A; Su, Jian; Chute, Christopher G; Liu, Hongfang

    2014-01-01

    A huge amount of associations among different biological entities (e.g., disease, drug, and gene) are scattered in millions of biomedical articles. Systematic analysis of such heterogeneous data can infer novel associations among different biological entities in the context of personalized medicine and translational research. Recently, network-based computational approaches have gained popularity in investigating such heterogeneous data, proposing novel therapeutic targets and deciphering disease mechanisms. However, little effort has been devoted to investigating associations among drugs, diseases, and genes in an integrative manner. We propose a novel network-based computational framework to identify statistically over-expressed subnetwork patterns, called network motifs, in an integrated disease-drug-gene network extracted from Semantic MEDLINE. The framework consists of two steps. The first step is to construct an association network by extracting pair-wise associations between diseases, drugs and genes in Semantic MEDLINE using a domain pattern driven strategy. A Resource Description Framework (RDF)-linked data approach is used to re-organize the data to increase the flexibility of data integration, the interoperability within domain ontologies, and the efficiency of data storage. Unique associations among drugs, diseases, and genes are extracted for downstream network-based analysis. The second step is to apply a network-based approach to mine the local network structure of this heterogeneous network. Significant network motifs are then identified as the backbone of the network. A simplified network based on those significant motifs is then constructed to facilitate discovery. We implemented our computational framework and identified five network motifs, each of which corresponds to specific biological meanings. Three case studies demonstrate that novel associations are derived from the network topology analysis of reconstructed networks of significant

  16. 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.

  17. 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.

  18. Dynamical complexity in the perception-based network formation model

    Science.gov (United States)

    Jo, Hang-Hyun; Moon, Eunyoung

    2016-12-01

    Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.

  19. Fluorescence multispectral imaging-based diagnostic system for atherosclerosis.

    Science.gov (United States)

    Ho, Cassandra Su Lyn; Horiuchi, Toshikatsu; Taniguchi, Hiroaki; Umetsu, Araya; Hagisawa, Kohsuke; Iwaya, Keiichi; Nakai, Kanji; Azmi, Amalina; Zulaziz, Natasha; Azhim, Azran; Shinomiya, Nariyoshi; Morimoto, Yuji

    2016-08-20

    Composition of atherosclerotic arterial walls is rich in lipids such as cholesterol, unlike normal arterial walls. In this study, we aimed to utilize this difference to diagnose atherosclerosis via multispectral fluorescence imaging, which allows for identification of fluorescence originating from the substance in the arterial wall. The inner surface of extracted arteries (rabbit abdominal aorta, human coronary artery) was illuminated by 405 nm excitation light and multispectral fluorescence images were obtained. Pathological examination of human coronary artery samples were carried out and thickness of arteries were calculated by measuring combined media and intima thickness. The fluorescence spectra in atherosclerotic sites were different from those in normal sites. Multiple regions of interest (ROI) were selected within each sample and a ratio between two fluorescence intensity differences (where each intensity difference is calculated between an identifier wavelength and a base wavelength) from each ROI was determined, allowing for discrimination of atherosclerotic sites. Fluorescence intensity and thickness of artery were found to be significantly correlated. These results indicate that multispectral fluorescence imaging provides qualitative and quantitative evaluations of atherosclerosis and is therefore a viable method of diagnosing the disease.

  20. Vibration-based monitoring and diagnostics using compressive sensing

    Science.gov (United States)

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

    2017-04-01

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

  1. Vibration-based Fault Diagnostic of a Spur Gearbox

    Directory of Open Access Journals (Sweden)

    Hartono Dennis

    2016-01-01

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

  2. 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.

  3. Finite-sample based learning algorithms for feedforward networks

    Energy Technology Data Exchange (ETDEWEB)

    Rao, N.S.V.; Protopopescu, V.; Mann, R.C.; Oblow, E.M. [Oak Ridge National Lab., TN (United States); Iyengar, S.S. [Louisiana State Univ., Baton Rouge, LA (United States). Dept. of Computer Science

    1995-04-01

    We discuss two classes of convergent algorithms for learning continuous functions (and also regression functions) that are represented by FeedForward Networks (FFN). The first class of algorithms, applicable to networks with unknown weights located only in the output layer, is obtained by utilizing the potential function methods of Aizerman et al. The second class, applicable to general feedforward networks, is obtained by utilizing the classical Robbins-Monro style stochastic approximation methods. Conditions relating the sample sizes to the error bounds are derived for both classes of algorithms using martingale-type inequalities. For concreteness, the discussion is presented in terms of neural networks, but the results are applicable to general feedforward networks, in particular to wavelet networks. The algorithms can also be directly applied to concept learning problems. A main distinguishing feature of the this work is that the sample sizes are based on explicit algorithms rather than information-based methods.

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

    Directory of Open Access Journals (Sweden)

    Kormeier Benjamin

    2010-06-01

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

  5. A modulator based regulatory network for ERα signaling pathway.

    Science.gov (United States)

    Wu, Heng-Yi; Zheng, Pengyue; Jiang, Guanglong; Liu, Yunlong; Nephew, Kenneth P; Huang, Tim H M; Li, Lang

    2012-01-01

    Estrogens control multiple functions of hormone-responsive breast cancer cells. They regulate diverse physiological processes in various tissues through genomic and non-genomic mechanisms that result in activation or repression of gene expression. Transcription regulation upon estrogen stimulation is a critical biological process underlying the onset and progress of the majority of breast cancer. ERα requires distinct co-regulator or modulators for efficient transcriptional regulation, and they form a regulatory network. Knowing this regulatory network will enable systematic study of the effect of ERα on breast cancer. To investigate the regulatory network of ERα and discover novel modulators of ERα functions, we proposed an analytical method based on a linear regression model to identify translational modulators and their network relationships. In the network analysis, a group of specific modulator and target genes were selected according to the functionality of modulator and the ERα binding. Network formed from targets genes with ERα binding was called ERα genomic regulatory network; while network formed from targets genes without ERα binding was called ERα non-genomic regulatory network. Considering the active or repressive function of ERα, active or repressive function of a modulator, and agonist or antagonist effect of a modulator on ERα, the ERα/modulator/target relationships were categorized into 27 classes. Using the gene expression data and ERα Chip-seq data from the MCF-7 cell line, the ERα genomic/non-genomic regulatory networks were built by merging ERα/ modulator/target triplets (TF, M, T), where TF refers to the ERα, M refers to the modulator, and T refers to the target. Comparing these two networks, ERα non-genomic network has lower FDR than the genomic network. In order to validate these two networks, the same network analysis was performed in the gene expression data from the ZR-75.1 cell. The network overlap analysis between two

  6. 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.

  7. A random spatial network model based on elementary postulates

    Science.gov (United States)

    Karlinger, Michael R.; Troutman, Brent M.

    1989-01-01

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

  8. 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.

  9. Competence–Based Support of Interaction between Business Network Members

    OpenAIRE

    Smirnov, Alexander; Kashevnik, Alexey; Shilov, Nikolay

    2008-01-01

    In a complicated business network finding a supplier can be a very time consuming task. The technology of competence management is aimed to support such kind of tasks. The paper presents an approach to support interaction between business network members based on such technologies as competence management and knowledge management. The conceptual models of the context-driven competence management system and production network member competence profile are described. The usage of th...

  10. Traffic allocation strategies in WSS-based dynamic optical networks

    OpenAIRE

    Shakeri, Ali; Garrich, Miquel; Bravalheri, Anderson; Careglio, Davide; Solé Pareta, Josep; Fumagalli, Andrea

    2017-01-01

    Elastic optical networking (EON) is a viable solution to meet future dynamic capacity requirements of Internet service provider and inter-datacenter networks. At the core of EON, wavelength selective switches (WSSs) are applied to individually route optical circuits, while assigning an arbitrary bandwidth to each circuit. Critically, the WSS control scheme and configuration time may delay the creation time of each circuit in the network. In this paper, we first detail the WSS-based optical da...

  11. 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.

  12. Community-driven standards-based electronic laboratory data-sharing networks.

    Science.gov (United States)

    Zarcone, Patina; Nordenberg, Dale; Meigs, Michelle; Merrick, Ulrike; Jernigan, Daniel; Hinrichs, Steven H

    2010-01-01

    Public health laboratories (PHLs) are critical components of the nation's healthcare system, serving as stewards of valuable specimens, delivering important diagnostic results to support clinical and public health programs, supporting public health policy, and conducting research. This article discusses the need for and challenges of creating standards-based data-sharing networks across the PHL community, which led to the development of the PHL Interoperability Project (PHLIP). Launched by the Association of Public Health Laboratories and the Centers for Disease Control and Prevention in September 2006, PHLIP has leveraged a unique community-based collaborative process, catalyzing national capabilities to more effectively share electronic laboratory-generated diagnostic information and bolster the nation's health security. PHLIP is emerging as a model of accelerated innovation for the fields of laboratory science, technology, and public health.

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

    Directory of Open Access Journals (Sweden)

    Raúl Eusebio-Grande

    2015-12-01

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

  14. ANOMALY NETWORK INTRUSION DETECTION SYSTEM BASED ON DISTRIBUTED TIME-DELAY NEURAL NETWORK (DTDNN

    Directory of Open Access Journals (Sweden)

    LAHEEB MOHAMMAD IBRAHIM

    2010-12-01

    Full Text Available In this research, a hierarchical off-line anomaly network intrusion detection system based on Distributed Time-Delay Artificial Neural Network is introduced. This research aims to solve a hierarchical multi class problem in which the type of attack (DoS, U2R, R2L and Probe attack detected by dynamic neural network. The results indicate that dynamic neural nets (Distributed Time-Delay Artificial Neural Network can achieve a high detection rate, where the overall accuracy classification rate average is equal to 97.24%.

  15. 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.

  16. 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.

  17. Anomaly-based Network Intrusion Detection Methods

    Directory of Open Access Journals (Sweden)

    Pavel Nevlud

    2013-01-01

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

  18. A novel interacting multiple model based network intrusion detection scheme

    Science.gov (United States)

    Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

    2006-04-01

    In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

  19. Behavior of gadolinium-based diagnostics in water treatment

    Energy Technology Data Exchange (ETDEWEB)

    Cyris, Maike

    2013-04-25

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

  20. Behavior of gadolinium-based diagnostics in water treatment

    International Nuclear Information System (INIS)

    Cyris, Maike

    2013-01-01

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

  1. The Technical Problems of Anti-theft Diagnostics in a Traction Network

    Science.gov (United States)

    Mikulski, Jerzy; Młynczak, Jakub

    2012-02-01

    The paper presents an analysis of traction lines theft in the Katowice division of the Railroad Development Company (Zakład Linii Kolejowych - ZLK) as well as the principles for the anti-theft protection system, currently in development. The problem of theft is a very important issue concerning the safety of rail transportation. It is also a significant economic problem, as the cost of recreating a stolen network is very high. Moreover, the Administrator of the infrastructure bears the cost of compensation for any delays in train schedules.

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

    Directory of Open Access Journals (Sweden)

    Mohammad Taghi Ameli

    2012-01-01

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

  3. Target recognition based on convolutional neural network

    Science.gov (United States)

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

    2017-11-01

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

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

    International Nuclear Information System (INIS)

    Yang, Hui; Liu, Gang

    2013-01-01

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

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

    Science.gov (United States)

    Yang, Hui; Liu, Gang

    2013-12-01

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

  6. Image Restoration Technology Based on Discrete Neural network

    Directory of Open Access Journals (Sweden)

    Zhou Duoying

    2015-01-01

    Full Text Available With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, this paper verifies that the discrete neural network has a good convergence and identification capability in the image restoration technology with a better effect than that of the feedforward network. The restoration technology based on the discrete neural network can provide a reliable mathematical model for this field.

  7. 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)

  8. 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.

  9. Identifying key nodes in multilayer networks based on tensor decomposition

    Science.gov (United States)

    Wang, Dingjie; Wang, Haitao; Zou, Xiufen

    2017-06-01

    The identification of essential agents in multilayer networks characterized by different types of interactions is a crucial and challenging topic, one that is essential for understanding the topological structure and dynamic processes of multilayer networks. In this paper, we use the fourth-order tensor to represent multilayer networks and propose a novel method to identify essential nodes based on CANDECOMP/PARAFAC (CP) tensor decomposition, referred to as the EDCPTD centrality. This method is based on the perspective of multilayer networked structures, which integrate the information of edges among nodes and links between different layers to quantify the importance of nodes in multilayer networks. Three real-world multilayer biological networks are used to evaluate the performance of the EDCPTD centrality. The bar chart and ROC curves of these multilayer networks indicate that the proposed approach is a good alternative index to identify real important nodes. Meanwhile, by comparing the behavior of both the proposed method and the aggregated single-layer methods, we demonstrate that neglecting the multiple relationships between nodes may lead to incorrect identification of the most versatile nodes. Furthermore, the Gene Ontology functional annotation demonstrates that the identified top nodes based on the proposed approach play a significant role in many vital biological processes. Finally, we have implemented many centrality methods of multilayer networks (including our method and the published methods) and created a visual software based on the MATLAB GUI, called ENMNFinder, which can be used by other researchers.

  10. Connected Dominating Set Based Topology Control in Wireless Sensor Networks

    Science.gov (United States)

    He, Jing

    2012-01-01

    Wireless Sensor Networks (WSNs) are now widely used for monitoring and controlling of systems where human intervention is not desirable or possible. Connected Dominating Sets (CDSs) based topology control in WSNs is one kind of hierarchical method to ensure sufficient coverage while reducing redundant connections in a relatively crowded network.…

  11. Distributed network generation based on preferential attachment in ABS

    NARCIS (Netherlands)

    K. Azadbakht (Keyvan); N. Bezirgiannis (Nikolaos); F.S. de Boer (Frank)

    2017-01-01

    textabstractGeneration of social networks using Preferential Attachment (PA) mechanism is proposed in the Barabasi-Albert model. In this mechanism, new nodes are introduced to the network sequentially and they attach to the existing nodes preferentially where the preference can be based on the

  12. Machine learning for network-based malware detection

    DEFF Research Database (Denmark)

    Stevanovic, Matija

    using traffic traces from honeypots and malware testing environments as well as operational ISP networks. Based on the evaluation, the novel detection methods provide accurate and efficient identification of malicious network traffic, thus being promising in the light of operational deployment...

  13. RBF neural network based H∞ H∞ H∞ synchronization for ...

    Indian Academy of Sciences (India)

    In this paper, we propose a new H ∞ synchronization strategy, called a Radial Basis Function Neural Network H ∞ synchronization (RBFNNHS) strategy, for ... Based on this neural network and linear matrix inequality (LMI) formulation, the RBFNNHS controller and the learning laws are presented to reduce the effect of ...

  14. A fuzzy art neural network based color image processing and ...

    African Journals Online (AJOL)

    A fuzzy art neural network based color image processing and recognition scheme. ... color image pixels, which enables a Fuzzy ART neural network to process the RGB color images. The application of the algorithm was implemented and tested on a set of RGB color face images. Keywords: Color image processing, RGB, ...

  15. Rate adaptation in ad hoc networks based on pricing

    CSIR Research Space (South Africa)

    Awuor, F

    2011-09-01

    Full Text Available to transmit at high power leading to abnormal interference in the network hence degrades network performance (i.e. low data rates, loss of connectivity among others). In this paper, the authors propose rate adaptation based on pricing (RAP) algorithm...

  16. Validation of Learning Effort Algorithm for Real-Time Non-Interfering Based Diagnostic Technique

    Science.gov (United States)

    Hsu, Pi-Shan; Chang, Te-Jeng

    2011-01-01

    The objective of this research is to validate the algorithm of learning effort which is an indicator of a new real-time and non-interfering based diagnostic technique. IC3 Mentor, the adaptive e-learning platform fulfilling the requirements of intelligent tutor system, was applied to 165 university students. The learning records of the subjects…

  17. 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).

  18. Approach to building knowledge bases in information-measuring systems diagnostics of acute leukemias

    Science.gov (United States)

    Nikitaev, V. G.; Pronichev, A. N.; Polyakov, E. V.; Dmitrieva, V. V.

    2018-01-01

    The paper describes an approach for the formation of the reference base of peripheral blood cells and bone marrow in information-measuring systems of acute leukemia diagnostics. The proposed approach has allowed to create a system, that is enable peer evaluation of blood cells needed for the training of recognition systems when carrying out microscopic studies.

  19. Social networking for web-based communities

    NARCIS (Netherlands)

    Issa, T.; Kommers, Petrus A.M.

    2013-01-01

    In the 21st century, a new technology was introduced to facilitate communication, collaboration, and interaction between individuals and businesses. This technology is called social networking; this technology is now part of Internet commodities like email, browsing and blogging. From the 20th

  20. Based on BP Neural Network Stock Prediction

    Science.gov (United States)

    Liu, Xiangwei; Ma, Xin

    2012-01-01

    The stock market has a high profit and high risk features, on the stock market analysis and prediction research has been paid attention to by people. Stock price trend is a complex nonlinear function, so the price has certain predictability. This article mainly with improved BP neural network (BPNN) to set up the stock market prediction model, and…

  1. Network-Based Models in Molecular Biology

    Science.gov (United States)

    Beyer, Andreas

    Biological systems are characterized by a large number of diverse interactions. Interaction maps have been used to abstract those interactions at all biological scales ranging from food webs at the ecosystem level down to protein interaction networks at the molecular scale.

  2. 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

  3. Epidemic spreading on networks based on stress response

    Science.gov (United States)

    Nian, Fuzhong; Yao, Shuanglong

    2017-06-01

    Based on the stress responses of individuals, the susceptible-infected-susceptible epidemic model was improved on the small-world networks and BA scale-free networks and the simulations were implemented and analyzed. Results indicate that the behaviors of individual’s stress responses could induce the epidemic spreading resistance and adaptation at the network level. This phenomenon showed that networks were learning how to adapt to the disease and the evolution process could improve their immunization to future infectious diseases and would effectively prevent the spreading of infectious diseases.

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

    OpenAIRE

    Coscia, Michele; Hausmann, Ricardo

    2015-01-01

    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 a...

  5. An image segmentation method based on network clustering model

    Science.gov (United States)

    Jiao, Yang; Wu, Jianshe; Jiao, Licheng

    2018-01-01

    Network clustering phenomena are ubiquitous in nature and human society. In this paper, a method involving a network clustering model is proposed for mass segmentation in mammograms. First, the watershed transform is used to divide an image into regions, and features of the image are computed. Then a graph is constructed from the obtained regions and features. The network clustering model is applied to realize clustering of nodes in the graph. Compared with two classic methods, the algorithm based on the network clustering model performs more effectively in experiments.

  6. Two port network analysis for three impedance based oscillators

    KAUST Repository

    Said, Lobna A.

    2011-12-01

    Two-port network representations are applied to analyze complex networks which can be dissolved into sub-networks connected in series, parallel or cascade. In this paper, the concept of two-port network has been studied for oscillators. Three impedance oscillator based on two port concept has been analyzed using different impedance structures. The effect of each structure on the oscillation condition and the frequency of oscillation have been introduced. Two different implementations using MOS and BJT have been introduced. © 2011 IEEE.

  7. 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.

  8. Development and Testing of Atomic Beam-Based Plasma Edge Diagnostics in the CIEMAT Fusion Devices

    International Nuclear Information System (INIS)

    Tafalla, D.; Tabares, F.L.; Ortiz, P.; Herrero, V.J.; Tanarro, I.

    1998-01-01

    In this report the development of plasma edge diagnostic based on atomic beam techniques fir their application in the CIEMAT fusion devices is described. The characterisation of the beams in laboratory experiments at the CSIC, together with first results in the Torsatron TJ-II are reported. Two types of beam diagnostics have been developed: a thermal (effusive) Li and a supersonic, pulsed He beams. This work has been carried out in collaboration between the institutions mentioned above under partial financial support by EURATOM. (Author) 17 refs

  9. Hybrid network defense model based on fuzzy evaluation.

    Science.gov (United States)

    Cho, Ying-Chiang; Pan, Jen-Yi

    2014-01-01

    With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network's existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter's inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  10. Face recognition based on improved BP neural network

    Directory of Open Access Journals (Sweden)

    Yue Gaili

    2017-01-01

    Full Text Available In order to improve the recognition rate of face recognition, face recognition algorithm based on histogram equalization, PCA and BP neural network is proposed. First, the face image is preprocessed by histogram equalization. Then, the classical PCA algorithm is used to extract the features of the histogram equalization image, and extract the principal component of the image. And then train the BP neural network using the trained training samples. This improved BP neural network weight adjustment method is used to train the network because the conventional BP algorithm has the disadvantages of slow convergence, easy to fall into local minima and training process. Finally, the BP neural network with the test sample input is trained to classify and identify the face images, and the recognition rate is obtained. Through the use of ORL database face image simulation experiment, the analysis results show that the improved BP neural network face recognition method can effectively improve the recognition rate of face recognition.

  11. Web-based networking within the framework of ANENT

    International Nuclear Information System (INIS)

    Han, K.W.; Lee, E.J.; Kim, Y.T.; Nam, Y.M.; Kim, H.K.

    2004-01-01

    The Korea Atomic Energy Research Institute (KAERI) is actively participating in the Asian Network for Education in Nuclear Technology (ANENT), which is an IAEA activity to promote nuclear knowledge management. This has led KAERI to conduct a web-based networking for nuclear education and training in Asia. The networking encompasses the establishment of a relevant website and a system for a sustainable operation of the website. The established ANENT website features function as a database providing collected information, a link facilitating a systematic worldwide access to relevant websites, and an activity implementation for supporting the individual tasks of ANENT. The required information is being collected and loaded onto the database, and the website will be improved step by step. Consequently, networking is expected to play an important role, through cooperating with other networks, and thus contributing to a future global network for a sustainable development of nuclear technology. (author)

  12. The need for culture collections to support plant pathogen diagnostic networks.

    Science.gov (United States)

    Barba, Marina; Van den Bergh, Inge; Belisario, Alessandra; Beed, Fen

    2010-01-01

    Plant-pathogenic microorganisms, by virtue of their size, similarity in disease symptoms and closely related morphologies, are notoriously difficult to diagnose and detect. Diagnosis gives proof as to the causal agent of disease and is important for developing appropriate control measures. Detection shows the presence of a microorganism and is of importance for safeguarding national and international trade. Live reference collections are required to characterize the taxonomy and function of microorganisms as a prerequisite to development of tools for diagnosis and detection. Two case studies will be presented in this paper to demonstrate the importance of microorganism collections for facilitating knowledge sharing and the development of identification methods. Fusarium wilt of banana caused by Fusarium oxysporum f. sp. cubense and sharka disease of stone fruits caused by plum pox virus (PPV) are considered. Both diseases consist of different races/strains with different host specificities, but Fusarium wilt poses a threat to food security, while PPV poses a threat to trade due to its classification as a quarantine pest, since there is no anti-virus treatment available to control sharka disease in orchards. It is only through comprehensive collections of correctly identified and well-maintained strains representing the genetic diversity of a target organism that robust, specific, reliable and efficient diagnostic and detection tools can be developed. (c) 2010 Elsevier Masson SAS. All rights reserved.

  13. 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.

  14. Spatial enhancement of ECG using diagnostic similarity score based lead selective multi-scale linear model.

    Science.gov (United States)

    Nallikuzhy, Jiss J; Dandapat, S

    2017-06-01

    In this work, a new patient-specific approach to enhance the spatial resolution of ECG is proposed and evaluated. The proposed model transforms a three-lead ECG into a standard twelve-lead ECG thereby enhancing its spatial resolution. The three leads used for prediction are obtained from the standard twelve-lead ECG. The proposed model takes advantage of the improved inter-lead correlation in wavelet domain. Since the model is patient-specific, it also selects the optimal predictor leads for a given patient using a lead selection algorithm. The lead selection algorithm is based on a new diagnostic similarity score which computes the diagnostic closeness between the original and the spatially enhanced leads. Standard closeness measures are used to assess the performance of the model. The similarity in diagnostic information between the original and the spatially enhanced leads are evaluated using various diagnostic measures. Repeatability and diagnosability are performed to quantify the applicability of the model. A comparison of the proposed model is performed with existing models that transform a subset of standard twelve-lead ECG into the standard twelve-lead ECG. From the analysis of the results, it is evident that the proposed model preserves diagnostic information better compared to other models. Copyright © 2017 Elsevier Ltd. All rights reserved.

  15. A sparse sensor network topologized for cylindrical wave-based identification of damage in pipeline structures

    Science.gov (United States)

    Wang, Qiang; Hong, Ming; Su, Zhongqing

    2016-07-01

    A sparse sensor network, based on the concept of semi-decentralized and standardized sensing, is developed, to actively excite and acquire cylindrical waves for damage identification and health monitoring of pipe structures. Differentiating itself from conventional ‘ring-style’ transducer arrays which attempt to steer longitudinal axisymmetric cylindrical waves via transducer synchronism, this sparse sensor network shows advantages in some aspects, including the use of fewer sensors, simpler manipulation, quicker configuration, less mutual dependence among sensors, and an improved signal-to-noise ratio. The sparse network is expanded topologically, aimed at eliminating the presence of ‘blind zones’ and the challenges associated with multi-path propagation of cylindrical waves. Theoretical analysis is implemented to comprehend propagation characteristics of waves guided by a cylindrical structure. A probability-based diagnostic imaging algorithm is introduced to visualize damage in pixelated images in an intuitive, prompt, and automatic manner. A self-contained health monitoring system is configured for experimental validation, via which quantitative identification of mono- and multi-damage in a steel cylinder is demonstrated. The results highlight an expanded sensing coverage of the sparse sensor network and its enhanced capacity of acquiring rich information, avoiding the cost of augmenting the number of sensors in a sensor network.

  16. Case-based reasoning combined with statistics for diagnostics and prognosis

    Science.gov (United States)

    Olsson, T.; Funk, P.

    2012-05-01

    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.

  17. 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.

  18. The Quake-Catcher Network: An Innovative Community-Based Seismic Network

    Science.gov (United States)

    Saltzman, J.; Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.

    2009-12-01

    The Quake-Catcher Network (QCN) is a volunteer computing seismic network that engages citizen scientists, teachers, and museums to participate in the detection of earthquakes. In less than two years, the network has grown to over 1000 participants globally and continues to expand. QCN utilizes Micro-Electro-Mechanical System (MEMS) accelerometers, in laptops and external to desktop computers, to detect moderate to large earthquakes. One goal of the network is to involve K-12 classrooms and museums by providing sensors and software to introduce participants to seismology and community-based scientific data collection. The Quake-Catcher Network provides a unique opportunity to engage participants directly in the scientific process, through hands-on activities that link activities and outcomes to their daily lives. Partnerships with teachers and museum staff are critical to growth of the Quake Catcher Network. Each participating institution receives a MEMS accelerometer to connect, via USB, to a computer that can be used for hands-on activities and to record earthquakes through a distributed computing system. We developed interactive software (QCNLive) that allows participants to view sensor readings in real time. Participants can also record earthquakes and download earthquake data that was collected by their sensor or other QCN sensors. The Quake-Catcher Network combines research and outreach to improve seismic networks and increase awareness and participation in science-based research in K-12 schools.

  19. A network-based dynamical ranking system for competitive sports

    Science.gov (United States)

    Motegi, Shun; Masuda, Naoki

    2012-12-01

    From the viewpoint of networks, a ranking system for players or teams in sports is equivalent to a centrality measure for sports networks, whereby a directed link represents the result of a single game. Previously proposed network-based ranking systems are derived from static networks, i.e., aggregation of the results of games over time. However, the score of a player (or team) fluctuates over time. Defeating a renowned player in the peak performance is intuitively more rewarding than defeating the same player in other periods. To account for this factor, we propose a dynamic variant of such a network-based ranking system and apply it to professional men's tennis data. We derive a set of linear online update equations for the score of each player. The proposed ranking system predicts the outcome of the future games with a higher accuracy than the static counterparts.

  20. Ionic liquid based multifunctional double network gel

    Science.gov (United States)

    Ahmed, Kumkum; Higashihara, Tomoya; Arafune, Hiroyuki; Kamijo, Toshio; Morinaga, Takashi; Sato, Takaya; Furukawa, Hidemitsu

    2015-04-01

    Gels are a promising class of soft and wet materials with diverse application in tissue engineering and bio-medical purpose. In order to accelerate the development of gels, it is required to synthesize multi-functional gels of high mechanical strength, ultra low surface friction and suitable elastic modulus with a variety of methods and new materials. Among many types of gel ionic gel made from ionic liquids (ILs) could be used for diverse applications in electrochemical devices and in the field of tribology. IL, a promising materials for lubrication, is a salt with a melting point lower than 100 °C. As a lubricant, ILs are characterized by an extremely low vapor pressure, high thermal stability and high ion conductivity. In this work a novel approach of making double network DN ionic gel using IL has been made utilizing photo polymerization process. A hydrophobic monomer Methyl methacrylate (MMA) has been used as a first network and a hydrophobic IL monomer, N,N-diethyl-N-(2-mthacryloylethyl)-N-methylammonium bistrifluoromethylsulfonyl)imide (DEMM-TFSI) has been used as a second network using photo initiator benzophenon and crosslinker triethylene glycol dimethacrylate (TEGDMA). The resulting DN ionic gel shows transparency, flexibility, high thermal stability, good mechanical toughness and low friction coefficient value which can be a potential candidate as a gel slider in different mechanical devices and can open a new area in the field of gel tribology.

  1. Flexible optical network components based on densely integrated microring resonators

    NARCIS (Netherlands)

    Geuzebroek, D.H.

    2005-01-01

    This thesis addresses the design, realization and characterization of reconfigurable optical network components based on multiple microring resonators. Since thermally tunable microring resonators can be used as wavelength selective space switches, very compact devices with high complexity and

  2. Ensemble of classifiers based network intrusion detection system performance bound

    CSIR Research Space (South Africa)

    Mkuzangwe, Nenekazi NP

    2017-11-01

    Full Text Available This paper provides a performance bound of a network intrusion detection system (NIDS) that uses an ensemble of classifiers. Currently researchers rely on implementing the ensemble of classifiers based NIDS before they can determine the performance...

  3. Planning diagnostic imaging work-up strategies using case-based reasoning.

    OpenAIRE

    Kahn, C. E.

    1994-01-01

    ISIS is a developmental decision support system that helps physicians select diagnostic imaging procedures. It uses case-based reasoning, an artificial-intelligence approach that emphasizes reasoning and planning from prior experience. The development, training, and evaluation of a prototype system were used to guide the development of ISIS. To realize a clinically useful system, particular emphasis has been placed on increasing the depth and breadth of case-based knowledge, enhancing the exp...

  4. Building a satellite climate diagnostics data base for real-time climate monitoring

    International Nuclear Information System (INIS)

    Ropelewski, C.F.

    1991-01-01

    The paper discusses the development of a data base, the Satellite Climate Diagnostic Data Base (SCDDB), for real time operational climate monitoring utilizing current satellite data. Special attention is given to the satellite-derived quantities useful for monitoring global climate changes, the requirements of SCDDB, and the use of conventional meteorological data and model assimilated data in developing the SCDDB. Examples of prototype SCDDB products are presented. 10 refs

  5. Paper-based smart microfluidics for education and low-cost diagnostics

    Directory of Open Access Journals (Sweden)

    Suzanne Smith

    2015-11-01

    Full Text Available Current centralised healthcare models pose many challenges, particularly for developing countries such as South Africa, where travel and time costs make it difficult for patients to seek healthcare, even when urgently needed. To address this issue, point-of-care (PoC tests, which are performed at or near the site of clinical care, have gained popularity and are actively being developed. Microfluidic systems, in which small volumes of fluids can be processed, provide an ideal platform on which to develop PoC diagnostic solutions. Specifically, the emerging field of paper-based microfluidics, with advantages such as low-cost, disposability and minimal external equipment requirements, provides unique opportunities for addressing healthcare issues in developing countries. This work explores the field of paper-based microfluidics, with step-by-step instructions on the design, manufacture and testing processes to realise paper-based devices towards diagnostic applications. Paper-based microfluidic and electronic components are presented, as well as the integration of these components to provide smart paper-based devices. This serves as an educational tool, enabling both beginners and experts in the field to fast-track development of unique paper-based solutions towards PoC diagnostics, with emphasis on the South African context, where both the need for and impact of these solutions are great.

  6. Diagnostic Support for Selected Paediatric Pulmonary Diseases Using Answer-Pattern Recognition in Questionnaires Based on Combined Data Mining Applications--A Monocentric Observational Pilot Study.

    Directory of Open Access Journals (Sweden)

    Ann-Katrin Rother

    Full Text Available Clinical symptoms in children with pulmonary diseases are frequently non-specific. Rare diseases such as primary ciliary dyskinesia (PCD, cystic fibrosis (CF or protracted bacterial bronchitis (PBB can be easily missed at the general practitioner (GP.To develop and test a questionnaire-based and data mining-supported tool providing diagnostic support for selected pulmonary diseases.First, interviews with parents of affected children were conducted and analysed. These parental observations during the pre-diagnostic time formed the basis for a new questionnaire addressing the parents' view on the disease. Secondly, parents with a sick child (e.g. PCD, PBB answered the questionnaire and a data base was set up. Finally, a computer program consisting of eight different classifiers (support vector machine (SVM, artificial neural network (ANN, fuzzy rule-based, random forest, logistic regression, linear discriminant analysis, naive Bayes and nearest neighbour and an ensemble classifier was developed and trained to categorise any given new questionnaire and suggest a diagnosis. For estimating the diagnostic accuracy, we applied ten-fold stratified cross validation.All questionnaires of patients suffering from CF, asthma (AS, PCD, acute bronchitis (AB and the healthy control group were correctly diagnosed by the fusion algorithm. For the pneumonia (PM group 19/21 (90.5% and for the PBB group 17/18 (94.4% correct diagnoses could be reached. The program detected the correct diagnoses with an overall sensitivity of 98.8%. Receiver operating characteristics (ROC analyses confirmed the accuracy of this diagnostic tool. Case studies highlighted the applicability of the tool in the daily work of a GP.For children with symptoms of pulmonary diseases a questionnaire-based diagnostic support tool using data mining techniques exhibited good results in arriving at diagnostic suggestions. In the hands of a doctor, this tool could be of value in arousing awareness for

  7. The Efficiency of Constraint Based Routing in MPLS Networks

    Directory of Open Access Journals (Sweden)

    Martin Medvecky

    2011-01-01

    Full Text Available The paper presents the simulation results and evaluates the efficiency of constraint base routing algorithms used in MPLS network from the point of their usability in Next Generation Networks. The efficiency of constraint based routing is evaluated according following criteria: optimal path selection, routing priority of traffic flows selected for constraint routing and bandwidth allocation by MAM or RDM bandwidth constraints models.

  8. Image Restoration Technology Based on Discrete Neural network

    OpenAIRE

    Zhou Duoying

    2015-01-01

    With the development of computer science and technology, the development of artificial intelligence advances rapidly in the field of image restoration. Based on the MATLAB platform, this paper constructs a kind of image restoration technology of artificial intelligence based on the discrete neural network and feedforward network, and carries out simulation and contrast of the restoration process by the use of the bionic algorithm. Through the application of simulation restoration technology, ...

  9. Content-Based Multi-Channel Network Coding Algorithm in the Millimeter-Wave Sensor Network.

    Science.gov (United States)

    Lin, Kai; Wang, Di; Hu, Long

    2016-07-01

    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.

  10. 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.

  11. Design and Simulation Analysis for Integrated Vehicle Chassis-Network Control System Based on CAN Network

    Directory of Open Access Journals (Sweden)

    Wei Yu

    2016-01-01

    Full Text Available Due to the different functions of the system used in the vehicle chassis control, the hierarchical control strategy also leads to many kinds of the network topology structure. According to the hierarchical control principle, this research puts forward the integrated control strategy of the chassis based on supervision mechanism. The purpose is to consider how the integrated control architecture affects the control performance of the system after the intervention of CAN network. Based on the principle of hierarchical control and fuzzy control, a fuzzy controller is designed, which is used to monitor and coordinate the ESP, AFS, and ARS. And the IVC system is constructed with the upper supervisory controller and three subcontrol systems on the Simulink platform. The network topology structure of IVC is proposed, and the IVC communication matrix based on CAN network communication is designed. With the common sensors and the subcontrollers as the CAN network independent nodes, the network induced delay and packet loss rate on the system control performance are studied by simulation. The results show that the simulation method can be used for designing the communication network of the vehicle.

  12. A family of quantization based piecewise linear filter networks

    DEFF Research Database (Denmark)

    Sørensen, John Aasted

    1992-01-01

    A family of quantization-based piecewise linear filter networks is proposed. For stationary signals, a filter network from this family is a generalization of the classical Wiener filter with an input signal and a desired response. The construction of the filter network is based on quantization...... of the input signal x(n) into quantization classes. With each quantization class is associated a linear filter. The filtering at time n is carried out by the filter belonging to the actual quantization class of x(n ) and the filters belonging to the neighbor quantization classes of x(n) (regularization...

  13. Theory of fractional order elements based impedance matching networks

    KAUST Repository

    Radwan, Ahmed G.

    2011-03-01

    Fractional order circuit elements (inductors and capacitors) based impedance matching networks are introduced for the first time. In comparison to the conventional integer based L-type matching networks, fractional matching networks are much simpler and versatile. Any complex load can be matched utilizing a single series fractional element, which generally requires two elements for matching in the conventional approach. It is shown that all the Smith chart circles (resistance and reactance) are actually pairs of completely identical circles. They appear to be single for the conventional integer order case, where the identical circles completely overlap each other. The concept is supported by design equations and impedance matching examples. © 2010 IEEE.

  14. Hybrid Network Defense Model Based on Fuzzy Evaluation

    Directory of Open Access Journals (Sweden)

    Ying-Chiang Cho

    2014-01-01

    Full Text Available With sustained and rapid developments in the field of information technology, the issue of network security has become increasingly prominent. The theme of this study is network data security, with the test subject being a classified and sensitive network laboratory that belongs to the academic network. The analysis is based on the deficiencies and potential risks of the network’s existing defense technology, characteristics of cyber attacks, and network security technologies. Subsequently, a distributed network security architecture using the technology of an intrusion prevention system is designed and implemented. In this paper, first, the overall design approach is presented. This design is used as the basis to establish a network defense model, an improvement over the traditional single-technology model that addresses the latter’s inadequacies. Next, a distributed network security architecture is implemented, comprising a hybrid firewall, intrusion detection, virtual honeynet projects, and connectivity and interactivity between these three components. Finally, the proposed security system is tested. A statistical analysis of the test results verifies the feasibility and reliability of the proposed architecture. The findings of this study will potentially provide new ideas and stimuli for future designs of network security architecture.

  15. [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.

  16. Grid-Based Hybrid Network Deployment Approach for Energy Efficient Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Haleem Farman

    2016-01-01

    Full Text Available Wireless sensor networks (WSN empower applications for critical decision-making through collaborative computing, communications, and distributed sensing. However, they face several challenges due to their peculiar use in a wide variety of applications. One of the inherent challenges with any battery operated sensor is the efficient consumption of energy and its effect on network lifetime. In this paper, we introduce a novel grid-based hybrid network deployment (GHND framework which ensures energy efficiency and load balancing in wireless sensor networks. This research is particularly focused on the merge and split technique to achieve even distribution of sensor nodes across the grid. Low density neighboring zones are merged together whereas high density zones are strategically split to achieve optimum balance. Extensive simulations reveal that the proposed method outperforms state-of-the-art techniques in terms of load balancing, network lifetime, and total energy consumption.

  17. Topological Embedding Feature Based Resource Allocation in Network Virtualization

    Directory of Open Access Journals (Sweden)

    Hongyan Cui

    2014-01-01

    Full Text Available Virtualization provides a powerful way to run multiple virtual networks on a shared substrate network, which needs accurate and efficient mathematical models. Virtual network embedding is a challenge in network virtualization. In this paper, considering the degree of convergence when mapping a virtual network onto substrate network, we propose a new embedding algorithm based on topology mapping convergence-degree. Convergence-degree means the adjacent degree of virtual network’s nodes when they are mapped onto a substrate network. The contributions of our method are as below. Firstly, we map virtual nodes onto the substrate nodes with the maximum convergence-degree. The simulation results show that our proposed algorithm largely enhances the network utilization efficiency and decreases the complexity of the embedding problem. Secondly, we define the load balance rate to reflect the load balance of substrate links. The simulation results show our proposed algorithm achieves better load balance. Finally, based on the feature of star topology, we further improve our embedding algorithm and make it suitable for application in the star topology. The test result shows it gets better performance than previous works.

  18. The competitive dynamics of network-based businesses.

    Science.gov (United States)

    Coyne, K P; Dye, R

    1998-01-01

    Telecommunications carriers, transportation companies, and banks are among the many network-based businesses--companies that move people, goods, or information from various points to various other points. Managers have long assumed that customers valued all links in these networks equally. It was thought that banking customers, for example, sought access to all of the branches throughout the network or that shipping customers wanted to be able to send packages everywhere. Intuitively, managers thought that many of their customers' needs were, in reality, narrower, but they had no way of knowing which links were most important. New computing power and robust mapping software now make it possible to understand network customers better. In applying this technology, the authors, both consultants from McKinsey & Company, have uncovered three distinct usage patterns: one in which all links are, indeed, valued equally; another in which customers concentrate their use in particular zones; and a third in which customers value only individual links. Each of these patterns requires a different strategy to direct executives in making the decisions fundamental to managing any network-based business: whether to open or close outlets, whether to connect their network to others, and how to organize business units so that they reflect the network's structure. Those who don't spot the patterns or understand their strategic implications will find themselves on the losing end of the network battle.

  19. Streamlined ion torrent PGM-based diagnostics: BRCA1 and BRCA2 genes as a model.

    Science.gov (United States)

    Tarabeux, Julien; Zeitouni, Bruno; Moncoutier, Virginie; Tenreiro, Henrique; Abidallah, Khadija; Lair, Séverine; Legoix-Né, Patricia; Leroy, Quentin; Rouleau, Etienne; Golmard, Lisa; Barillot, Emmanuel; Stern, Marc-Henri; Rio-Frio, Thomas; Stoppa-Lyonnet, Dominique; Houdayer, Claude

    2014-04-01

    To meet challenges in terms of throughput and turnaround time, many diagnostic laboratories are shifting from Sanger sequencing to higher throughput next-generation sequencing (NGS) platforms. Bearing in mind that the performance and quality criteria expected from NGS in diagnostic or research settings are strikingly different, we have developed an Ion Torrent's PGM-based routine diagnostic procedure for BRCA1/2 sequencing. The procedure was first tested on a training set of 62 control samples, and then blindly validated on 77 samples in parallel with our routine technique. The training set was composed of difficult cases, for example, insertions and/or deletions of various sizes, large-scale rearrangements and, obviously, mutations occurring in homopolymer regions. We also compared two bioinformatic solutions in this diagnostic context, an in-house academic pipeline and the commercially available NextGene software (Softgenetics). NextGene analysis provided higher sensitivity, as four previously undetected single-nucleotide variations were found. Regarding specificity, an average of 1.5 confirmatory Sanger sequencings per patient was needed for complete BRCA1/2 screening. Large-scale rearrangements were identified by two distinct analyses, that is, bioinformatics and fragment analysis with electrophoresis profile comparison. Turnaround time was enhanced, as a series of 30 patients were sequenced by one technician, making the results available for the clinician in 10 working days following blood sampling. BRCA1/2 genes are a good model, representative of the difficulties commonly encountered in diagnostic settings, which is why we believe our findings are of interest for the whole community, and the pipeline described can be adapted by any user of PGM for diagnostic purposes.

  20. 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

  1. 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.

  2. Polyurethane networks based on poly (ethylene oxide)

    International Nuclear Information System (INIS)

    Zulfiqar, S.; Quddos, A.

    1993-01-01

    A wide range of infinite urethane polymer networks were prepared from poly (ethylene glycol) (PEG) and hexamethylene disocyanate (HMDI) using 1,1,1, tris (hydroxymethyl) ethane (THME) as crosslinking agent. Differential scanning calorimetry showed that the hydrate formation depends quantitatively upon the weight fraction on poly(ethylene oxide) in the gel. It has been shown that the crosslinked polyurethanes crystallize only with definite ranges of chain length between the knots. In this range with the increase of the degree of crosslinking and crystallinity on the swelling character of the polymeric materials has also been discussed. (author)

  3. 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)

  4. 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

  5. A Model-Based Expert System for Space Power Distribution Diagnostics

    Science.gov (United States)

    Quinn, Todd M.; Schlegelmilch, Richard F.

    1994-01-01

    When engineers diagnose system failures, they often use models to confirm system operation. This concept has produced a class of advanced expert systems that perform model-based diagnosis. A model-based diagnostic expert system for the Space Station Freedom electrical power distribution test bed is currently being developed at the NASA Lewis Research Center. The objective of this expert system is to autonomously detect and isolate electrical fault conditions. Marple, a software package developed at TRW, provides a model-based environment utilizing constraint suspension. Originally, constraint suspension techniques were developed for digital systems. However, Marple provides the mechanisms for applying this approach to analog systems such as the test bed, as well. The expert system was developed using Marple and Lucid Common Lisp running on a Sun Sparc-2 workstation. The Marple modeling environment has proved to be a useful tool for investigating the various aspects of model-based diagnostics. This report describes work completed to date and lessons learned while employing model-based diagnostics using constraint suspension within an analog system.

  6. High performance network and channel-based storage

    Science.gov (United States)

    Katz, Randy H.

    1991-01-01

    In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called input/output (I/O) channels. With the dramatic shift towards workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. We discuss the underlying technology trends that are leading to high performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high performance computing based on network-attached storage.

  7. High-performance network and channel based storage

    Science.gov (United States)

    Katz, Randy H.

    1992-01-01

    In the traditional mainframe-centered view of a computer system, storage devices are coupled to the system through complex hardware subsystems called I/O channels. With the dramatic shift toward workstation-based computing, and its associated client/server model of computation, storage facilities are now found attached to file servers and distributed throughout the network. In this paper, we discuss the underlying technology trends that are leading to high-performance network-based storage, namely advances in networks, storage devices, and I/O controller and server architectures. We review several commercial systems and research prototypes that are leading to a new approach to high-performance computing based on network-attached storage.

  8. 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.

  9. 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 ...

  10. Cellulose-based diagnostic devices for diagnosing serotype-2 dengue fever in human serum.

    Science.gov (United States)

    Wang, Hsi-Kai; Tsai, Cheng-Han; Chen, Kuan-Hung; Tang, Chung-Tao; Leou, Jiun-Shyang; Li, Pi-Chun; Tang, Yin-Liang; Hsieh, Hsyue-Jen; Wu, Han-Chung; Cheng, Chao-Min

    2014-02-01

    Here, two types of cellulose-based in vitro diagnostic devices are demonstrated for the diagnosis of dengue virus infection in both buffer system and human serum: 1) paper-based ELISA for providing the semiquantitative information of the disease activity of serotype-2 dengue fever to healthcare persons (i.e., monitoring the disease activity with a specific serotype in single patients); 2) lateral flow immunoassays to screen for infection with serotype-2 dengue fever (i.e., rapid YES or NO diagnosis prepared for large populations, in terms of global public health). Paper-based ELISA (specific to serotype-2 dengue fever), which builds off of our previous studies and a revised previous ELISA procedure, owns multiple advantages: 1) high sensitivity (about 40 times higher than the current ELISA-based approaches, due to our therapeutic-based monoclonal antibody) and specificity (specific to dengue virus serotype-2 nonstructural protein-1 antigens); 2) tiny amount of sample and reagent used for single tests; 3) short operating duration (i.e., rapid diagnostic device); and, 4) inexpensiveness (appropriate for use in all developing and underdeveloped nations of the world). Due to the higher sensitivity and shorter operating duration of paper-based ELISA (compared with conventional ELISA, and lateral flow immunoassays also performed in this study), this study has not only been able to perform the diagnosis of dengue virus serotype-2 nonstructural protein-1 antigens in both buffer system and human serum but also to evaluate dengue virus serotype-2 envelope proteins in the buffer system, thus successfully achieving the first such use of these proteins as the target antigen for the development of diagnostic tools. These results provide a more comprehensive understanding for the genesis of dengue fever diagnostic tools (through antibody-antigen recognition). Copyright © 2014 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

  11. The conceptual foundations of network-based diffusion analysis: choosing networks and interpreting results.

    Science.gov (United States)

    Hoppitt, Will

    2017-12-05

    Network-based diffusion analysis (NBDA) is a statistical technique for detecting the social transmission of behavioural innovations in groups of animals, including humans. The strength of social transmission is inferred from the extent to which the diffusion (spread) of the innovation follows a social network. NBDA can have two goals: (a) to establish whether social transmission is occurring and how strong its effects are; and/or (b) to establish the typical pathways of information transfer. The technique has been used in a range of taxa, including primates, cetaceans, birds and fish, using a range of different types of network. Here I investigate the conceptual underpinnings of NBDA, in order to establish the meaning of results using different networks. I develop a model of the social transmission process where each individual observation of the target behaviour affects the rate at which the observer learns that behaviour. I then establish how NBDAs using different networks relate to this underlying process, and thus how we can interpret the results of each. My analysis shows that a different network or networks are appropriate depending on the specific goal or goals of the study, and establishes how the parameter estimates yielded from an NBDA can be interpreted for different networks.This article is part of the themed issue 'Process and pattern in innovations from cells to societies'. © 2017 The Author(s).

  12. Cooperative and Adaptive Network Coding for Gradient Based Routing in Wireless Sensor Networks with Multiple Sinks

    Directory of Open Access Journals (Sweden)

    M. E. Migabo

    2017-01-01

    Full Text Available Despite its low computational cost, the Gradient Based Routing (GBR broadcast of interest messages in Wireless Sensor Networks (WSNs causes significant packets duplications and unnecessary packets transmissions. This results in energy wastage, traffic load imbalance, high network traffic, and low throughput. Thanks to the emergence of fast and powerful processors, the development of efficient network coding strategies is expected to enable efficient packets aggregations and reduce packets retransmissions. For multiple sinks WSNs, the challenge consists of efficiently selecting a suitable network coding scheme. This article proposes a Cooperative and Adaptive Network Coding for GBR (CoAdNC-GBR technique which considers the network density as dynamically defined by the average number of neighbouring nodes, to efficiently aggregate interest messages. The aggregation is performed by means of linear combinations of random coefficients of a finite Galois Field of variable size GF(2S at each node and the decoding is performed by means of Gaussian elimination. The obtained results reveal that, by exploiting the cooperation of the multiple sinks, the CoAdNC-GBR not only improves the transmission reliability of links and lowers the number of transmissions and the propagation latency, but also enhances the energy efficiency of the network when compared to the GBR-network coding (GBR-NC techniques.

  13. 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)

  14. FUZZY LOGIC BASED ENERGY EFFICIENT PROTOCOL IN WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Zhan Wei Siew

    2012-12-01

    Full Text Available Wireless sensor networks (WSNs have been vastly developed due to the advances in microelectromechanical systems (MEMS using WSN to study and monitor the environments towards climates changes. In environmental monitoring, sensors are randomly deployed over the interest area to periodically sense the physical environments for a few months or even a year. Therefore, to prolong the network lifetime with limited battery capacity becomes a challenging issue. Low energy adaptive cluster hierarchical (LEACH is the common clustering protocol that aim to reduce the energy consumption by rotating the heavy workload cluster heads (CHs. The CHs election in LEACH is based on probability model which will lead to inefficient in energy consumption due to least desired CHs location in the network. In WSNs, the CHs location can directly influence the network energy consumption and further affect the network lifetime. In this paper, factors which will affect the network lifetime will be presented and the demonstration of fuzzy logic based CH selection conducted in base station (BS will also be carried out. To select suitable CHs that will prolong the network first node dies (FND round and consistent throughput to the BS, energy level and distance to the BS are selected as fuzzy inputs.

  15. CD-Based Indices for Link Prediction in Complex Network.

    Directory of Open Access Journals (Sweden)

    Tao Wang

    Full Text Available Lots of similarity-based algorithms have been designed to deal with the problem of link prediction in the past decade. In order to improve prediction accuracy, a novel cosine similarity index CD based on distance between nodes and cosine value between vectors is proposed in this paper. Firstly, node coordinate matrix can be obtained by node distances which are different from distance matrix and row vectors of the matrix are regarded as coordinates of nodes. Then, cosine value between node coordinates is used as their similarity index. A local community density index LD is also proposed. Then, a series of CD-based indices include CD-LD-k, CD*LD-k, CD-k and CDI are presented and applied in ten real networks. Experimental results demonstrate the effectiveness of CD-based indices. The effects of network clustering coefficient and assortative coefficient on prediction accuracy of indices are analyzed. CD-LD-k and CD*LD-k can improve prediction accuracy without considering the assortative coefficient of network is negative or positive. According to analysis of relative precision of each method on each network, CD-LD-k and CD*LD-k indices have excellent average performance and robustness. CD and CD-k indices perform better on positive assortative networks than on negative assortative networks. For negative assortative networks, we improve and refine CD index, referred as CDI index, combining the advantages of CD index and evolutionary mechanism of the network model BA. Experimental results reveal that CDI index can increase prediction accuracy of CD on negative assortative networks.

  16. GENETIC ALGORITHM BASED CONCEPT DESIGN TO OPTIMIZE NETWORK LOAD BALANCE

    Directory of Open Access Journals (Sweden)

    Ashish Jain

    2012-07-01

    Full Text Available Multiconstraints optimal network load balancing is an NP-hard problem and it is an important part of traffic engineering. In this research we balance the network load using classical method (brute force approach and dynamic programming is used but result shows the limitation of this method but at a certain level we recognized that the optimization of balanced network load with increased number of nodes and demands is intractable using the classical method because the solution set increases exponentially. In such case the optimization techniques like evolutionary techniques can employ for optimizing network load balance. In this paper we analyzed proposed classical algorithm and evolutionary based genetic approach is devise as well as proposed in this paper for optimizing the balance network load.

  17. 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.

  18. Adaptive Control for Robotic Manipulators Base on RBF Neural Network

    Directory of Open Access Journals (Sweden)

    MA Jing

    2013-09-01

    Full Text Available An adaptive neural network controller is brought forward by the paper to solve trajectory tracking problems of robotic manipulators with uncertainties. The first scheme consists of a PD feedback and a dynamic compensator which is composed by neural network controller and variable structure controller. Neutral network controller is designed to adaptive learn and compensate the unknown uncertainties, variable structure controller is designed to eliminate approach errors of neutral network. The adaptive weight learning algorithm of neural network is designed to ensure online real-time adjustment, offline learning phase is not need; Global asymptotic stability (GAS of system base on Lyapunov theory is analysised to ensure the convergence of the algorithm. The simulation result s show that the kind of the control scheme is effective and has good robustness.

  19. Incentive-Based Voltage Regulation in Distribution Networks: Preprint

    Energy Technology Data Exchange (ETDEWEB)

    Zhou, Xinyang; Chen, Lijun; Dall' Anese, Emiliano; Baker, Kyri

    2017-03-03

    This paper considers distribution networks fea- turing distributed energy resources, and designs incentive-based mechanisms that allow the network operator and end-customers to pursue given operational and economic objectives, while concurrently ensuring that voltages are within prescribed limits. Two different network-customer coordination mechanisms that require different amounts of information shared between the network operator and end-customers are developed to identify a solution of a well-defined social-welfare maximization prob- lem. Notably, the signals broadcast by the network operator assume the connotation of prices/incentives that induce the end- customers to adjust the generated/consumed powers in order to avoid the violation of the voltage constraints. Stability of the proposed schemes is analytically established and numerically corroborated.

  20. A complex network-based importance measure for mechatronics systems

    Science.gov (United States)

    Wang, Yanhui; Bi, Lifeng; Lin, Shuai; Li, Man; Shi, Hao

    2017-01-01

    In view of the negative impact of functional dependency, this paper attempts to provide an alternative importance measure called Improved-PageRank (IPR) for measuring the importance of components in mechatronics systems. IPR is a meaningful extension of the centrality measures in complex network, which considers usage reliability of components and functional dependency between components to increase importance measures usefulness. Our work makes two important contributions. First, this paper integrates the literature of mechatronic architecture and complex networks theory to define component network. Second, based on the notion of component network, a meaningful IPR is brought into the identifying of important components. In addition, the IPR component importance measures, and an algorithm to perform stochastic ordering of components due to the time-varying nature of usage reliability of components and functional dependency between components, are illustrated with a component network of bogie system that consists of 27 components.

  1. Development of Novel Random Network Theory-Based Approaches to Identify Network Interactions among Nitrifying Bacteria

    Energy Technology Data Exchange (ETDEWEB)

    Shi, Cindy

    2015-07-17

    The interactions among different microbial populations in a community could play more important roles in determining ecosystem functioning than species numbers and their abundances, but very little is known about such network interactions at a community level. The goal of this project is to develop novel framework approaches and associated software tools to characterize the network interactions in microbial communities based on high throughput, large scale high-throughput metagenomics data and apply these approaches to understand the impacts of environmental changes (e.g., climate change, contamination) on network interactions among different nitrifying populations and associated microbial communities.

  2. 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.

  3. 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

  4. Constructing financial network based on PMFG and threshold method

    Science.gov (United States)

    Nie, Chun-Xiao; Song, Fu-Tie

    2018-04-01

    Based on planar maximally filtered graph (PMFG) and threshold method, we introduced a correlation-based network named PMFG-based threshold network (PTN). We studied the community structure of PTN and applied ISOMAP algorithm to represent PTN in low-dimensional Euclidean space. The results show that the community corresponds well to the cluster in the Euclidean space. Further, we studied the dynamics of the community structure and constructed the normalized mutual information (NMI) matrix. Based on the real data in the market, we found that the volatility of the market can lead to dramatic changes in the community structure, and the structure is more stable during the financial crisis.

  5. VoIP attacks detection engine based on neural network

    Science.gov (United States)

    Safarik, Jakub; Slachta, Jiri

    2015-05-01

    The security is crucial for any system nowadays, especially communications. One of the most successful protocols in the field of communication over IP networks is Session Initiation Protocol. It is an open-source project used by different kinds of applications, both open-source and proprietary. High penetration and text-based principle made SIP number one target in IP telephony infrastructure, so security of SIP server is essential. To keep up with hackers and to detect potential malicious attacks, security administrator needs to monitor and evaluate SIP traffic in the network. But monitoring and following evaluation could easily overwhelm the security administrator in networks, typically in networks with a number of SIP servers, users and logically or geographically separated networks. The proposed solution lies in automatic attack detection systems. The article covers detection of VoIP attacks through a distributed network of nodes. Then the gathered data analyze aggregation server with artificial neural network. Artificial neural network means multilayer perceptron network trained with a set of collected attacks. Attack data could also be preprocessed and verified with a self-organizing map. The source data is detected by distributed network of detection nodes. Each node contains a honeypot application and traffic monitoring mechanism. Aggregation of data from each node creates an input for neural networks. The automatic classification on a centralized server with low false positive detection reduce the cost of attack detection resources. The detection system uses modular design for easy deployment in final infrastructure. The centralized server collects and process detected traffic. It also maintains all detection nodes.

  6. Improving diagnostic capability for HPV disease internationally within the NIH-NIAID Division of AIDS Clinical Trial Networks.

    Science.gov (United States)

    Godfrey, Catherine C; Michelow, Pamela M; Godard, Mandana; Sahasrabuddhe, Vikrant V; Darden, Janice; Firnhaber, Cynthia S; Wetherall, Neal T; Bremer, James; Coombs, Robert W; Wilkin, Timothy

    2013-12-01

    To evaluate an external quality assurance (EQA) program for the laboratory diagnosis of human papillomavirus (HPV) disease that was established to improve international research capability within the Division of AIDS at the National Institute of Allergy and Infectious Disease-supported Adult AIDS Clinical Trials Group network. A three-component EQA scheme was devised comprising assessments of diagnostic accuracy of cytotechnologists and pathologists using available EQA panels, review of quality and accuracy of clinical slides from local sites by an outside expert, and HPV DNA detection using a commercially available HPV test kit. Seven laboratories and 17 pathologists in Africa, India, and South America participated. EQA scores were suboptimal for EQA proficiency testing panels in three of seven laboratories. There was good agreement between the local laboratory and the central reader 70% of the time (90% confidence interval, 42%-98%). Performance on the College of American Pathologists' HPV DNA testing panel was successful in all laboratories tested. The prequalifying EQA round identified correctable issues that will improve the laboratory diagnosis of HPV-related cervical disease at the participating international study sites and will provide a mechanism for ongoing education and continuous quality improvement.

  7. Appraising and applying evidence about a diagnostic test during a performance-based assessment

    Directory of Open Access Journals (Sweden)

    Franklin Ellen

    2004-10-01

    Full Text Available Abstract Background The practice of Evidence-based Medicine requires that clinicians assess the validity of published research and then apply the results to patient care. We wanted to assess whether our soon-to-graduate medical students could appraise and apply research about a diagnostic test within a clinical context and to compare our students with peers trained at other institutions. Methods 4th year medical students who previously had demonstrated competency at probability revision and just starting first-year Internal Medicine residents were used for this research. Following an encounter with a simulated patient, subjects critically appraised a paper about an applicable diagnostic test and revised the patient's pretest probability given the test result. Results The medical students and residents demonstrated similar skills at critical appraisal, correctly answering 4.7 and 4.9, respectively, of 6 questions (p = 0.67. Only one out of 28 (3% medical students and none of the 15 residents were able to correctly complete the probability revision task (p = 1.00. Conclusions This study found that most students completing medical school are able to appraise an article about a diagnostic test but few are able to apply the information from the article to a patient. These findings raise questions about the clinical usefulness of the EBM skills possessed by graduating medical students within the area of diagnostic testing.

  8. Quantum-dot based photonic quantum networks

    Science.gov (United States)

    Lodahl, Peter

    2018-01-01

    Quantum dots (QDs) embedded in photonic nanostructures have in recent years proven to be a very powerful solid-state platform for quantum optics experiments. The combination of near-unity radiative coupling of a single QD to a photonic mode and the ability to eliminate decoherence processes imply that an unprecedent light-matter interface can be obtained. As a result, high-cooperativity photon-emitter quantum interfaces can be constructed opening a path-way to deterministic photonic quantum gates for quantum-information processing applications. In the present manuscript, I review current state-of-the-art on QD devices and their applications for quantum technology. The overarching long-term goal of the research field is to construct photonic quantum networks where remote entanglement can be distributed over long distances by photons.

  9. 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.

  10. EEG Analysis during complex diagnostic tasks in Nuclear Power Plants - Simulator-based Experimental Study

    International Nuclear Information System (INIS)

    Ha, Jun Su; Seong, Poong Hyun

    2005-01-01

    In literature, there are a lot of studies based on EEG signals during cognitive activities of human-beings but most of them dealt with simple cognitive activities such as transforming letters into Morse code, subtraction, reading, semantic memory search, visual search, memorizing a set of words and so on. In this work, EEG signals were analyzed during complex diagnostic tasks in NPP simulator-based environment. Investigated are the theta, alpha, beta, and gamma band EEG powers during the diagnostic tasks. The experimental design and procedure are represented in section 2 and the results are shown in section 3. Finally some considerations are discussed and the direction for the further work is proposed in section 4

  11. Alternative methods of fuel consumption metering based on the on-board diagnostics outputs

    Directory of Open Access Journals (Sweden)

    Jiří Čupera

    2005-01-01

    Full Text Available The article describes alternative methods of fuel consumption measurement based on model with using the diagnostic outputs of engine control unit. On-board diagnosis (the second level, known as OBD-2 has been mandated by government regulation because of advanced damage control systems in newer cars. However, its signals can be used for accurate analyses of power or torque measurement. On-board diagnostics offers many various parameters such a spark advance, intake air temperature, coolant temperature, throttle position, air flow mass and so on. Many of them have been unavailable without using sophisticated and expensive instrumentation. In the article are described two ways of fuel consumption measuring which are based on intake air consumption and knowledge about air-fuel ratio. First of them is founded on voltage output of oxygen sensor, the second on short (long term fuel trim. As is shown at the end the second way gives more accurately results.

  12. Diagnostic of the temperature and differential emission measure (DEM based on Hinode/XRT data

    Directory of Open Access Journals (Sweden)

    P. Rudawy

    2008-10-01

    Full Text Available We discuss here various methodologies and an optimal strategy of the temperature and emission measure diagnostics based on Hinode X-Ray Telescope data. As an example of our results we present the determination of the temperature distribution of the X-rays emitting plasma using a filters ratio method and three various methods of the calculation of the differential emission measure (DEM. We have found that all these methods give results similar to the two filters ratio method. Additionally, all methods of the DEM calculation gave similar solutions. We can state that the majority of the pairs of the Hinode filters allows one to derive the temperature and emission measure in the isothermal plasma approximation using standard diagnostics based on the two filters ratio method. In cases of strong flares one can also expect good conformity of the results obtained using a Withbroe – Sylwester, genetic algorithm and least-squares methods of the DEM evaluation.

  13. Networking

    OpenAIRE

    Rauno Lindholm, Daniel; Boisen Devantier, Lykke; Nyborg, Karoline Lykke; Høgsbro, Andreas; Fries, de; Skovlund, Louise

    2016-01-01

    The purpose of this project was to examine what influencing factor that has had an impact on the presumed increasement of the use of networking among academics on the labour market and how it is expressed. On the basis of the influence from globalization on the labour market it can be concluded that the globalization has transformed the labour market into a market based on the organization of networks. In this new organization there is a greater emphasis on employees having social qualificati...

  14. Parrondo's games based on complex networks and the paradoxical effect.

    Directory of Open Access Journals (Sweden)

    Ye Ye

    Full Text Available Parrondo's games were first constructed using a simple tossing scenario, which demonstrates the following paradoxical situation: in sequences of games, a winning expectation may be obtained by playing the games in a random order, although each game (game A or game B in the sequence may result in losing when played individually. The available Parrondo's games based on the spatial niche (the neighboring environment are applied in the regular networks. The neighbors of each node are the same in the regular graphs, whereas they are different in the complex networks. Here, Parrondo's model based on complex networks is proposed, and a structure of game B applied in arbitrary topologies is constructed. The results confirm that Parrondo's paradox occurs. Moreover, the size of the region of the parameter space that elicits Parrondo's paradox depends on the heterogeneity of the degree distributions of the networks. The higher heterogeneity yields a larger region of the parameter space where the strong paradox occurs. In addition, we use scale-free networks to show that the network size has no significant influence on the region of the parameter space where the strong or weak Parrondo's paradox occurs. The region of the parameter space where the strong Parrondo's paradox occurs reduces slightly when the average degree of the network increases.

  15. Parrondo's games based on complex networks and the paradoxical effect.

    Science.gov (United States)

    Ye, Ye; Wang, Lu; Xie, Nenggang

    2013-01-01

    Parrondo's games were first constructed using a simple tossing scenario, which demonstrates the following paradoxical situation: in sequences of games, a winning expectation may be obtained by playing the games in a random order, although each game (game A or game B) in the sequence may result in losing when played individually. The available Parrondo's games based on the spatial niche (the neighboring environment) are applied in the regular networks. The neighbors of each node are the same in the regular graphs, whereas they are different in the complex networks. Here, Parrondo's model based on complex networks is proposed, and a structure of game B applied in arbitrary topologies is constructed. The results confirm that Parrondo's paradox occurs. Moreover, the size of the region of the parameter space that elicits Parrondo's paradox depends on the heterogeneity of the degree distributions of the networks. The higher heterogeneity yields a larger region of the parameter space where the strong paradox occurs. In addition, we use scale-free networks to show that the network size has no significant influence on the region of the parameter space where the strong or weak Parrondo's paradox occurs. The region of the parameter space where the strong Parrondo's paradox occurs reduces slightly when the average degree of the network increases.

  16. Node-Dependence-Based Dynamic Incentive Algorithm in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Ruiyun Yu

    2014-01-01

    Full Text Available Opportunistic networks lack end-to-end paths between source nodes and destination nodes, so the communications are mainly carried out by the “store-carry-forward” strategy. Selfish behaviors of rejecting packet relay requests will severely worsen the network performance. Incentive is an efficient way to reduce selfish behaviors and hence improves the reliability and robustness of the networks. In this paper, we propose the node-dependence-based dynamic gaming incentive (NDI algorithm, which exploits the dynamic repeated gaming to motivate nodes relaying packets for other nodes. The NDI algorithm presents a mechanism of tolerating selfish behaviors of nodes. Reward and punishment methods are also designed based on the node dependence degree. Simulation results show that the NDI algorithm is effective in increasing the delivery ratio and decreasing average latency when there are a lot of selfish nodes in the opportunistic networks.

  17. Greening radio access networks using distributed base station architectures

    DEFF Research Database (Denmark)

    Kardaras, Georgios; Soler, José; Dittmann, Lars

    2010-01-01

    energy saving. Different subsystems have to be coordinated real-time and intelligent network nodes supporting complicated functionalities are necessary. Distributed base station architectures are ideal for this purpose mainly because of their high degree of configurability and self......Several actions for developing environmentally friendly technologies have been taken in most industrial fields. Significant resources have also been devoted in mobile communications industry. Moving towards eco-friendly alternatives is primarily a social responsibility for network operators....... However besides this, increasing energy efficiency represents a key factor for reducing operating expenses and deploying cost effective mobile networks. This paper presents how distributed base station architectures can contribute in greening radio access networks. More specifically, the advantages...

  18. Morphometric relations of fractal-skeletal based channel network model

    Directory of Open Access Journals (Sweden)

    B. S. Daya Sagar

    1998-01-01

    Full Text Available A fractal-skeletal based channel network (F-SCN model is proposed. Four regular sided initiator-basins are transformed as second order fractal basins by following a specific generating mechanism with non-random rule. The morphological skeletons, hereafter referred to as channel networks, are extracted from these fractal basins. The morphometric and fractal relationships of these F-SCNs are shown. The fractal dimensions of these fractal basins, channel networks, and main channel lengths (computed through box counting method are compared with those of estimated length–area measures. Certain morphometric order ratios to show fractal relations are also highlighted.

  19. Facial expression recognition based on improved deep belief networks

    Science.gov (United States)

    Wu, Yao; Qiu, Weigen

    2017-08-01

    In order to improve the robustness of facial expression recognition, a method of face expression recognition based on Local Binary Pattern (LBP) combined with improved deep belief networks (DBNs) is proposed. This method uses LBP to extract the feature, and then uses the improved deep belief networks as the detector and classifier to extract the LBP feature. The combination of LBP and improved deep belief networks is realized in facial expression recognition. In the JAFFE (Japanese Female Facial Expression) database on the recognition rate has improved significantly.

  20. Water Pollution Detection Based on Hypothesis Testing in Sensor Networks

    Directory of Open Access Journals (Sweden)

    Xu Luo

    2017-01-01

    Full Text Available Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

  1. Bulk Restoration for SDN-Based Transport Network

    Directory of Open Access Journals (Sweden)

    Yang Zhao

    2016-01-01

    Full Text Available We propose a bulk restoration scheme for software defined networking- (SDN- based transport network. To enhance the network survivability and improve the throughput, we allow disrupted flows to be recovered synchronously in dynamic order. In addition backup paths are scheduled globally by applying the principles of load balance. We model the bulk restoration problem using a mixed integer linear programming (MILP formulation. Then, a heuristic algorithm is devised. The proposed algorithm is verified by simulation and the results are analyzed comparing with sequential restoration schemes.

  2. 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

    architecture. The approaches to QoS mapping, label distribution and connection and admission control (CAC) are specified here. At last, a simple T-MPLS based Carrier Ethernet network model with three kinds of users (VoIP, VoD and HTTP) and a RACE based control module is simulated in OPNET. The model is aiming...

  3. 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.

  4. Application of visually based, computerised diagnostic decision support system in dermatological medical education: a pilot study.

    Science.gov (United States)

    Chou, Wan-Yi; Tien, Peng-Tai; Lin, Fang-Yu; Chiu, Pin-Chi

    2017-05-01

    Medical education has shifted from memory-based practice to evidence-based decisions. The question arises: how can we ensure that all students get correct and systematic information? Visually based, computerised diagnostic decision support system (VCDDSS, VisualDx) may just fit our needs. A pilot study was conducted to investigate its role in medical education and clinical practice. This was a prospective study, including one consultant dermatologist, 51 medical students and 13 dermatology residents, conducted in the dermatology teaching clinic at China Medical University Hospital from 30 December 2014 to 21 April 2015. Clinical diagnoses of 13 patients were made before and after using VCDDSS. Questionnaires were filled out at the end. The consultant dermatologist's diagnosis was defined as the standard answer; the Sign test was used to analyse diagnostic accuracy and the Fisher exact test to analyse questionnaires. There was an 18.75% increase in diagnostic accuracy after use of VCDDSS (62.5-81.25%; p value system in clinical practice, medical education, residency training, and patient education in the future. Further large-scale studies should be planned to confirm its application. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/.

  5. Mechanism-based diagnostic reasoning: thoughts on teaching introductory clinical pathology.

    Science.gov (United States)

    Bender, Holly S.; Lockee, Barbara B.; Danielson, Jared A.; Mills, Eric M.; Boon, G. Daniel; Burton, John K.; Vermeer, Pamela J.; Zimmerman, Kurt L.; Hilmer, Kelly M.

    2000-01-01

    Teaching introductory clinical pathology to veterinary students is a challenging endeavor that requires a shift in learning strategies from rote memorization to diagnostic reasoning. Educational research has identified discrete cognitive stages required to achieve the automated, unconscious thinking process used by experts. Building on this knowledge, we developed a case-based approach to clinical pathology instruction that actively engages students in the learning process and links performance with positive reward. Simulated cases provide context and create a structure, or "schema", which enhances the learning process by enabling students to synthesize facts and link them with their causal mechanism to reach a defensible diagnostic conclusion. Web-based tools, including the "Problem List Generator" and tutorials, have been developed to facilitate this process. Through the collaborative Biomedical Informatics Research Group, we are working to further develop and evaluate Web-based instructional tools and new educational methods, to clarify the diagnostic reasoning processes used by experienced clinical pathologists, and, ultimately, to better educate our future students to be effective diagnosticians.

  6. Wavenumber selection based analysis in Raman spectroscopy improves skin cancer diagnostic specificity.

    Science.gov (United States)

    Zhao, Jianhua; Zeng, Haishan; Kalia, Sunil; Lui, Harvey

    2016-02-07

    Real-time Raman spectroscopy can be used to assist in assessing skin lesions suspicious for cancer. Most of the diagnostic algorithms are based on full band of the Raman spectra, either in the fingerprint region or the high wavenumber region. In this paper we explored wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Wavenumber selection was implemented using windows of wavenumber and leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected windows of wavenumber using multivariate statistical analyses, including principal component and general discriminate analysis (PC-GDA) and partial least squares (PLS). In total a combined cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included, which were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. It was found that the area under the receiver operating characteristic curve (ROC) was improved from 0.861-0.891 to 0.891-0.911 and the diagnostic specificity for fixed sensitivity 0.99-0.90 was improved from 0.17-0.65 to 0.20-0.75 with wavenumber selection based analysis.

  7. Severe childhood asthma and allergy to furry animals: refined assessment using molecular-based allergy diagnostics.

    Science.gov (United States)

    Konradsen, Jon R; Nordlund, Björn; Onell, Annica; Borres, Magnus P; Grönlund, Hans; Hedlin, Gunilla

    2014-03-01

    Allergy to cats and dogs and polysensitization towards these animals are associated with severe childhood asthma. Molecular-based allergy diagnostics offers new opportunities for improved characterization and has been suggested to be particularly useful in patients with polysensitization and/or severe asthma. The aim was to use extract- and molecular-based allergy diagnostics to compare patterns of IgE sensitization towards aeroallergens in children with problematic severe and controlled asthma. Children with a positive ImmunoCAP towards any furry animal (cat, dog or horse) were recruited from a Nationwide Swedish study on severe childhood asthma. Severe (n = 37, age 13 years) and controlled (n = 28, age 14 years) asthmatics underwent assessment of allergic sensitization by ImmunoCap (kUA /l) and immunosolid-phase allergen chip (ISAC). In addition, Asthma Control Test, spirometry and a methacholine challenge were performed. Children with severe asthma had lower asthma control (p Molecular-based allergy diagnostics revealed a more complex molecular spreading of allergen components in children with the most severe disease. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  8. 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.

  9. PRIORITY BASED PACKET SCHEDULING APPROACH FOR WIRELESS SENSOR NETWORKS

    OpenAIRE

    K. K. Kannan

    2017-01-01

    A priority based packet scheduling scheme is proposed which aims at scheduling different types of data packets, such as real time and non-real-time data packets at sensor nodes with resource constraints in Wireless Sensor Networks. Most of the existing packet-scheduling mechanisms of Wireless Sensor Networks use First Come First Served (FCFS), non-preemptive priority and preemptive priority scheduling algorithms. These algorithms results in long end-to-end data transmission delay, high energy...

  10. Research on Network Defense Strategy Based on Honey Pot Technology

    Science.gov (United States)

    Hong, Jianchao; Hua, Ying

    2018-03-01

    As a new network security technology of active defense, The honeypot technology has become a very effective and practical method of decoy attackers. The thesis discusses the theory, structure, characteristic, design and implementation of Honeypot in detail. Aiming at the development of means of attack, put forward a kind of network defense technology based on honeypot technology, constructing a virtual Honeypot demonstrate the honeypot’s functions.

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

    OpenAIRE

    ISLAM, M. R.; KIM, J.

    2009-01-01

    An energy efficient cooperative technique is proposed for the IEEE 1451 based Wireless Sensor Networks. Selected numbers of Wireless Transducer Interface Modules (WTIMs) are used to form a Multiple Input Single Output (MISO) structure wirelessly connected with a Network Capable Application Processor (NCAP). Energy efficiency and delay of the proposed architecture are derived for different combination of cluster size and selected number of WTIMs. Optimized constellation parameters are used for...

  12. Novel gas sensors based on carbon nanotube networks

    International Nuclear Information System (INIS)

    Sayago, I; Aleixandre, M; Horrillo, M C; Fernandez, M J; Gutierrez, J; Terrado, E; Lafuente, E; Maser, W K; Benito, A M; Martinez, M T; Munoz, E; Urriolabeitia, E P; Navarro, R

    2008-01-01

    Novel resistive gas sensors based on single-walled carbon nanotube (SWNT) networks as the active sensing element nave been investigated for gas detection. SWNTs networks were fabricated by airbrushing on alumina substrates. As-produced- and Pd-decorated SWNT materials were used as sensitive layers for the detection of NO 2 and H 2 , respectively. The studied sensors provided good response to NO 2 and H 2 as well as excellent selectivities to interfering gases.

  13. A Proposal for Kelly CriterionBased Lossy Network Compression

    Science.gov (United States)

    2016-03-01

    Criterion–Based Lossy Network Compression Sidney C Smith and Robert J Hammell II ARL-TR-7614 Approved for public release; distribution is unlimited...mail.mil>. This proposal describes the development of a Kelly criterion–inspired compression algorithm to be used in distributed network intrusion...detection applications. Most of these applications only send alerts to the central analysis servers. These alerts do not provide the forensic capability

  14. Strengthening regional innovation through network-based innovation brokering

    OpenAIRE

    Svare, Helge; Gausdal, Anne Haugen

    2015-01-01

    The primary objective of this paper is to demonstrate how regional innovation system theory may be translated into manageable micro-level methods with the potential for strengthening the productive dynamics of a regional innovation system. The paper meets this objective by presenting network-based innovation brokering (NBIB), a practical method designed using insights from regional innovation system theory and trust theory. Five cases from two Norwegian regional innovation networks show that ...

  15. A Novel Cross-Layer Routing Protocol Based on Network Coding for Underwater Sensor Networks.

    Science.gov (United States)

    Wang, Hao; Wang, Shilian; Bu, Renfei; Zhang, Eryang

    2017-08-08

    Underwater wireless sensor networks (UWSNs) have attracted increasing attention in recent years because of their numerous applications in ocean monitoring, resource discovery and tactical surveillance. However, the design of reliable and efficient transmission and routing protocols is a challenge due to the low acoustic propagation speed and complex channel environment in UWSNs. In this paper, we propose a novel cross-layer routing protocol based on network coding (NCRP) for UWSNs, which utilizes network coding and cross-layer design to greedily forward data packets to sink nodes efficiently. The proposed NCRP takes full advantages of multicast transmission and decode packets jointly with encoded packets received from multiple potential nodes in the entire network. The transmission power is optimized in our design to extend the life cycle of the network. Moreover, we design a real-time routing maintenance protocol to update the route when detecting inefficient relay nodes. Substantial simulations in underwater environment by Network Simulator 3 (NS-3) show that NCRP significantly improves the network performance in terms of energy consumption, end-to-end delay and packet delivery ratio compared with other routing protocols for UWSNs.

  16. Data Dissemination Based on Fuzzy Logic and Network Coding in Vehicular Networks

    Directory of Open Access Journals (Sweden)

    Xiaolan Tang

    2017-01-01

    Full Text Available Vehicular networks, as a significant technology in intelligent transportation systems, improve the convenience, efficiency, and safety of driving in smart cities. However, because of the high velocity, the frequent topology change, and the limited bandwidth, it is difficult to efficiently propagate data in vehicular networks. This paper proposes a data dissemination scheme based on fuzzy logic and network coding for vehicular networks, named SFN. It uses fuzzy logic to compute a transmission ability for each vehicle by comprehensively considering the effects of three factors: the velocity change rate, the velocity optimization degree, and the channel quality. Then, two nodes with high abilities are selected as primary backbone and slave backbone in every road segment, which propagate data to other vehicles in this segment and forward them to the backbones in the next segment. The backbone network helps to increase the delivery ratio and avoid invalid transmissions. Additionally, network coding is utilized to reduce transmission overhead and accelerate data retransmission in interbackbone forwarding and intrasegment broadcasting. Experiments show that, compared with existing schemes, SFN has a high delivery ratio and a short dissemination delay, while the backbone network keeps high reliability.

  17. A network security situation prediction model based on wavelet neural network with optimized parameters

    Directory of Open Access Journals (Sweden)

    Haibo Zhang

    2016-08-01

    Full Text Available The security incidents ion networks are sudden and uncertain, it is very hard to precisely predict the network security situation by traditional methods. In order to improve the prediction accuracy of the network security situation, we build a network security situation prediction model based on Wavelet Neural Network (WNN with optimized parameters by the Improved Niche Genetic Algorithm (INGA. The proposed model adopts WNN which has strong nonlinear ability and fault-tolerance performance. Also, the parameters for WNN are optimized through the adaptive genetic algorithm (GA so that WNN searches more effectively. Considering the problem that the adaptive GA converges slowly and easily turns to the premature problem, we introduce a novel niche technology with a dynamic fuzzy clustering and elimination mechanism to solve the premature convergence of the GA. Our final simulation results show that the proposed INGA-WNN prediction model is more reliable and effective, and it achieves faster convergence-speed and higher prediction accuracy than the Genetic Algorithm-Wavelet Neural Network (GA-WNN, Genetic Algorithm-Back Propagation Neural Network (GA-BPNN and WNN.

  18. A systematic review on diagnostic accuracy of CT-based detection of significant coronary artery disease

    International Nuclear Information System (INIS)

    Janne d'Othee, Bertrand; Siebert, Uwe; Cury, Ricardo; Jadvar, Hossein; Dunn, Edward J.; Hoffmann, Udo

    2008-01-01

    Objectives: Systematic review of diagnostic accuracy of contrast enhanced coronary computed tomography (CE-CCT). Background: Noninvasive detection of coronary artery stenosis (CAS) by CE-CCT as an alternative to catheter-based coronary angiography (CCA) may improve patient management. Methods: Forty-one articles published between 1997 and 2006 were included that evaluated native coronary arteries for significant stenosis and used CE-CCT as diagnostic test and CCA as reference standard. Study group characteristics, study methodology and diagnostic outcomes were extracted. Pooled summary sensitivity and specificity of CE-CCT were calculated using a random effects model (1) for all coronary segments, (2) assessable segments, and (3) per patient. Results: The 41 studies totaled 2515 patients (75% males; mean age: 59 years, CAS prevalence: 59%). Analysis of all coronary segments yielded a sensitivity of 95% (80%, 89%, 86%, 98% for electron beam CT, 4/8-slice, 16-slice and 64-slice MDCT, respectively) for a specificity of 85% (77%, 84%, 95%, 91%). Analysis limited to segments deemed assessable by CT showed sensitivity of 96% (86%, 85%, 98%, 97%) for a specificity of 95% (90%, 96%, 96%, 96%). Per patient, sensitivity was 99% (90%, 97%, 99%, 98%) and specificity was 76% (59%, 81%, 83%, 92%). Heterogeneity was quantitatively important but not explainable by patient group characteristics or study methodology. Conclusions: Current diagnostic accuracy of CE-CCT is high. Advances in CT technology have resulted in increases in diagnostic accuracy and proportion of assessable coronary segments. However, per patient, accuracy may be lower and CT may have more limited clinical utility in populations at high risk for CAD

  19. Chain-Based Communication in Cylindrical Underwater Wireless Sensor Networks

    Science.gov (United States)

    Javaid, Nadeem; Jafri, Mohsin Raza; Khan, Zahoor Ali; Alrajeh, Nabil; Imran, Muhammad; Vasilakos, Athanasios

    2015-01-01

    Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate. PMID:25658394

  20. Learning Control Over Emotion Networks Through Connectivity-Based Neurofeedback.

    Science.gov (United States)

    Koush, Yury; Meskaldji, Djalel-E; Pichon, Swann; Rey, Gwladys; Rieger, Sebastian W; Linden, David E J; Van De Ville, Dimitri; Vuilleumier, Patrik; Scharnowski, Frank

    2017-02-01

    Most mental functions are associated with dynamic interactions within functional brain networks. Thus, training individuals to alter functional brain networks might provide novel and powerful means to improve cognitive performance and emotions. Using a novel connectivity-neurofeedback approach based on functional magnetic resonance imaging (fMRI), we show for the first time that participants can learn to change functional brain networks. Specifically, we taught participants control over a key component of the emotion regulation network, in that they learned to increase top-down connectivity from the dorsomedial prefrontal cortex, which is involved in cognitive control, onto the amygdala, which is involved in emotion processing. After training, participants successfully self-regulated the top-down connectivity between these brain areas even without neurofeedback, and this was associated with concomitant increases in subjective valence ratings of emotional stimuli of the participants. Connectivity-based neurofeedback goes beyond previous neurofeedback approaches, which were limited to training localized activity within a brain region. It allows to noninvasively and nonpharmacologically change interconnected functional brain networks directly, thereby resulting in specific behavioral changes. Our results demonstrate that connectivity-based neurofeedback training of emotion regulation networks enhances emotion regulation capabilities. This approach can potentially lead to powerful therapeutic emotion regulation protocols for neuropsychiatric disorders. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  1. Chain-based communication in cylindrical underwater wireless sensor networks.

    Science.gov (United States)

    Javaid, Nadeem; Jafri, Mohsin Raza; Khan, Zahoor Ali; Alrajeh, Nabil; Imran, Muhammad; Vasilakos, Athanasios

    2015-02-04

    Appropriate network design is very significant for Underwater Wireless Sensor Networks (UWSNs). Application-oriented UWSNs are planned to achieve certain objectives. Therefore, there is always a demand for efficient data routing schemes, which can fulfill certain requirements of application-oriented UWSNs. These networks can be of any shape, i.e., rectangular, cylindrical or square. In this paper, we propose chain-based routing schemes for application-oriented cylindrical networks and also formulate mathematical models to find a global optimum path for data transmission. In the first scheme, we devise four interconnected chains of sensor nodes to perform data communication. In the second scheme, we propose routing scheme in which two chains of sensor nodes are interconnected, whereas in third scheme single-chain based routing is done in cylindrical networks. After finding local optimum paths in separate chains, we find global optimum paths through their interconnection. Moreover, we develop a computational model for the analysis of end-to-end delay. We compare the performance of the above three proposed schemes with that of Power Efficient Gathering System in Sensor Information Systems (PEGASIS) and Congestion adjusted PEGASIS (C-PEGASIS). Simulation results show that our proposed 4-chain based scheme performs better than the other selected schemes in terms of network lifetime, end-to-end delay, path loss, transmission loss, and packet sending rate.

  2. 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.

  3. Implementation of neural network based non-linear predictive control

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1999-01-01

    This paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems, including open-loop unstable and non-minimum phase systems, but has also been proposed to be extended for the control...... of non-linear systems. GPC is model based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model, a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis...

  4. Implementation of neural network based non-linear predictive

    DEFF Research Database (Denmark)

    Sørensen, Paul Haase; Nørgård, Peter Magnus; Ravn, Ole

    1998-01-01

    The paper describes a control method for non-linear systems based on generalized predictive control. Generalized predictive control (GPC) was developed to control linear systems including open loop unstable and non-minimum phase systems, but has also been proposed extended for the control of non-linear...... systems. GPC is model-based and in this paper we propose the use of a neural network for the modeling of the system. Based on the neural network model a controller with extended control horizon is developed and the implementation issues are discussed, with particular emphasis on an efficient Quasi...

  5. Restoration in multi-domain GMPLS-based networks

    DEFF Research Database (Denmark)

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

    2011-01-01

    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......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...

  6. 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.

  7. Power Network Fault Diagnosis Based on Multi - source Network Message Data

    Science.gov (United States)

    Li, Peng; Wen, Bo; Li, Hengheng; Li, Jin

    2018-01-01

    In the event of a grid failure, the network message data (SMV, GOOSE) contains all the information about the primary and secondary devices. The amount of information data is large, and it is difficult for the personnel to make a quick and accurate judgment about the fault type from the massive data, affecting fault handling and safe grid operation. In this paper, modeling is unified based on network message data about the primary equipment. The comprehensive data analysis between multiple substations can be achieved by acquiring alarm information, network message information and wave recording information through network message analysis devices; meanwhile, the longitudinal differential principle is applied to fault diagnosis, which can realize accurate fault diagnosis of power grid, and provide data basis for accident treatment.

  8. Advanced Signaling Support for IP-based Networks

    OpenAIRE

    Röhricht, Martin

    2013-01-01

    This work develops a set of advanced signaling concepts for IP-based networks. It proposes a design for secure and authentic signaling and provides QoS signaling support for mobile users. Furthermore, this work develops methods which allow for scalable QoS signaling by realizing QoS-based group communication mechanisms and through aggregation of resource reservations.

  9. IPTV inter-destination synchronization: A network-based approach

    NARCIS (Netherlands)

    Stokking, H.M.; Deventer, M.O. van; Niamut, O.A.; Walraven, F.A.; Mekuria, R.N.

    2010-01-01

    This paper introduces a novel network-based approach to inter-destination media synchronization. The approach meets the need for synchronization in advanced TV concepts like social TV and offers high scalability, unlike conventional end-point based approaches. The solution for interdestination media

  10. Decoding signalling networks by mass spectrometry-based proteomics

    DEFF Research Database (Denmark)

    Choudhary, Chuna Ram; Mann, Matthias

    2010-01-01

    Signalling networks regulate essentially all of the biology of cells and organisms in normal and disease states. Signalling is often studied using antibody-based techniques such as western blots. Large-scale 'precision proteomics' based on mass spectrometry now enables the system...

  11. Dynamic QoS Provisioning for Ethernet-based Networks

    Science.gov (United States)

    Angelopoulos, J.; Kanonakis, K.; Leligou, H. C.; Orfanoudakis, Th.; Katsigiannis, M.

    2008-11-01

    The evolution towards packet-based access networks and the importance of quality of experience brings the need for access networks that support the offer of a wide range of multimedia services not currently available to the desired extent. Legacy networks based on circuit switching used explicit signalling that travelled to all nodes along the path to book resources before the launce of the media stream. This approach does not scale well and is not in line with the philosophy of packet networks. Still, the need to reserve resources in advance remains since real-time services have limited if any means of adjusting their rates to the prevailing network conditions and to preserve customer satisfaction the traditional preventive approach that needs accurate estimates of resource needs for the duration of the session is the only option. The paper describes a possible CAC solution based on measuring flows and enriches the network with implicit admission control (without obviating explicit control if available) and can manage resource allocation to protect quality-demanding services from degradation. The basis is a flow measurement system, which will estimate the traffic load produced by the flow and activate admission control. However, because in most cases these initial indication may well be misleading, it will be cross checked against a database of previously recorded flows per customer interface which can provide long term data on the flows leaving only a few cases that have to be corrected on the fly. The overall product is a self-learning autonomic system that supports QoS in the access network for services that do not communicate with the network layer such as, for example, peer-to-peer real-time multimedia applications.

  12. DMA Reference Base Station Network Data

    Data.gov (United States)

    National Oceanic and Atmospheric Administration, Department of Commerce — The data (15,904 records documenting 9,090 worldwide gravity base stations) were gathered by various governmental organizations (and academia) using a variety of...

  13. Traffic sign recognition based on deep convolutional neural network

    Science.gov (United States)

    Yin, Shi-hao; Deng, Ji-cai; Zhang, Da-wei; Du, Jing-yuan

    2017-11-01

    Traffic sign recognition (TSR) is an important component of automated driving systems. It is a rather challenging task to design a high-performance classifier for the TSR system. In this paper, we propose a new method for TSR system based on deep convolutional neural network. In order to enhance the expression of the network, a novel structure (dubbed block-layer below) which combines network-in-network and residual connection is designed. Our network has 10 layers with parameters (block-layer seen as a single layer): the first seven are alternate convolutional layers and block-layers, and the remaining three are fully-connected layers. We train our TSR network on the German traffic sign recognition benchmark (GTSRB) dataset. To reduce overfitting, we perform data augmentation on the training images and employ a regularization method named "dropout". The activation function we employ in our network adopts scaled exponential linear units (SELUs), which can induce self-normalizing properties. To speed up the training, we use an efficient GPU to accelerate the convolutional operation. On the test dataset of GTSRB, we achieve the accuracy rate of 99.67%, exceeding the state-of-the-art results.

  14. Ontology-Oriented Diagnostic System for Traditional Chinese Medicine Based on Relation Refinement

    Directory of Open Access Journals (Sweden)

    Peiqin Gu

    2013-01-01

    Full Text Available Although Chinese medicine treatments have become popular recently, the complicated Chinese medical knowledge has made it difficult to be applied in computer-aided diagnostics. The ability to model and use the knowledge becomes an important issue. In this paper, we define the diagnosis in Traditional Chinese Medicine (TCM as discovering the fuzzy relations between symptoms and syndromes. An Ontology-oriented Diagnosis System (ODS is created to address the knowledge-based diagnosis based on a well-defined ontology of syndromes. The ontology transforms the implicit relationships among syndromes into a machine-interpretable model. The clinical data used for feature selection is collected from a national TCM research institute in China, which serves as a training source for syndrome differentiation. The ODS analyzes the clinical cases to obtain a statistical mapping relation between each syndrome and associated symptom set, before rechecking the completeness of related symptoms via ontology refinement. Our diagnostic system provides an online web interface to interact with users, so that users can perform self-diagnosis. We tested 12 common clinical cases on the diagnosis system, and it turned out that, given the agree metric, the system achieved better diagnostic accuracy compared to nonontology method—92% of the results fit perfectly with the experts’ expectations.

  15. Ontology-oriented diagnostic system for traditional Chinese medicine based on relation refinement.

    Science.gov (United States)

    Gu, Peiqin; Chen, Huajun; Yu, Tong

    2013-01-01

    Although Chinese medicine treatments have become popular recently, the complicated Chinese medical knowledge has made it difficult to be applied in computer-aided diagnostics. The ability to model and use the knowledge becomes an important issue. In this paper, we define the diagnosis in Traditional Chinese Medicine (TCM) as discovering the fuzzy relations between symptoms and syndromes. An Ontology-oriented Diagnosis System (ODS) is created to address the knowledge-based diagnosis based on a well-defined ontology of syndromes. The ontology transforms the implicit relationships among syndromes into a machine-interpretable model. The clinical data used for feature selection is collected from a national TCM research institute in China, which serves as a training source for syndrome differentiation. The ODS analyzes the clinical cases to obtain a statistical mapping relation between each syndrome and associated symptom set, before rechecking the completeness of related symptoms via ontology refinement. Our diagnostic system provides an online web interface to interact with users, so that users can perform self-diagnosis. We tested 12 common clinical cases on the diagnosis system, and it turned out that, given the agree metric, the system achieved better diagnostic accuracy compared to nonontology method-92% of the results fit perfectly with the experts' expectations.

  16. 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.

  17. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  19. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    Science.gov (United States)

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  20. Mobile phone-based biosensing: An emerging "diagnostic and communication" technology.

    Science.gov (United States)

    Quesada-González, Daniel; Merkoçi, Arben

    2017-06-15

    In this review we discuss recent developments on the use of mobile phones and similar devices for biosensing applications in which diagnostics and communications are coupled. Owing to the capabilities of mobile phones (their cameras, connectivity, portability, etc.) and to advances in biosensing, the coupling of these two technologies is enabling portable and user-friendly analytical devices. Any user can now perform quick, robust and easy (bio)assays anywhere and at any time. Among the most widely reported of such devices are paper-based platforms. Herein we provide an overview of a broad range of biosensing possibilities, from optical to electrochemical measurements; explore the various reported designs for adapters; and consider future opportunities for this technology in fields such as health diagnostics, safety & security, and environment monitoring. Copyright © 2016 Elsevier B.V. All rights reserved.

  1. An XML-based loose-schema approach to managing diagnostic data in heterogeneous formats

    International Nuclear Information System (INIS)

    Naito, O.

    2010-01-01

    An approach to managing diagnostic data in heterogenous formats by using XML-based (eXtensible Markup Language) tag files is discussed. The tag file functions like header information in ordinary data formats but it is separate from the main body of data, human readable, and self-descriptive. Thus all the necessary information for reading the contents of data can be obtained without prior information or reading the data body itself. In this paper, modeling of diagnostic data and its representation in XML are studied and a very primitive implementation of this approach in C++ is presented. The overhead of manipulating XML in a proof-of-principle code was found to be small. The merits, demerits, and possible extensions of this approach are also discussed.

  2. Scintillator Based Energetic Ion Loss Diagnostic for the National Spherical Torus Experiment

    Energy Technology Data Exchange (ETDEWEB)

    D.S. Darrow

    2007-07-02

    A scintillator based energetic ion loss detector has been built and installed on the National Spherical Torus Experiment (NSTX) to measure the loss of neutral beam ions. The detector is able to resolve the pitch angle and gyroradius of the lost energetic ions. It has a wide acceptance range in pitch angle and energy, and is able to resolve the full, one-half, and one-third energy components of the 80 keV D neutral beams up to the maximum toroidal magnetic field of NSTX. Multiple Faraday cups have been embedded behind the scintillator to allow easy absolute calibration of the diagnostic and to measure the energetic ion loss to several ranges of pitch angle with good time resolution. Several small, vacuum compatible lamps allow simple calibration of the scintillator position within the field of view of the diagnostic's video camera.

  3. Development and Optimisation of the SPS and LHC beam diagnostics based on Synchrotron Radiation monitors

    CERN Document Server

    AUTHOR|(CDS)2081364; Roncarolo, Federico

    Measuring the beam transverse emittance is fundamental in every accelerator, in particular for colliders, where its precise determination is essential to maximize the luminosity and thus the performance of the colliding beams.
 Synchrotron Radiation (SR) is a versatile tool for non-destructive beam diagnostics, since its characteristics are closely related to those of the source beam. At CERN, being the only available diagnostics at high beam intensity and energy, SR monitors are exploited as the proton beam size monitor of the two higher energy machines, the Super Proton Synchrotron (SPS) and the Large Hadron Collider (LHC). The thesis work documented in this report focused on the design, development, characterization and optimization of these beam size monitors. Such studies were based on a comprehensive set of theoretical calculations, numerical simulations and experiments. A powerful simulation tool has been developed combining conventional softwares for SR simulation and optics design, thus allowing t...

  4. Diagnostics of oral lichen planus based on analysis of volatile organic compounds in saliva

    Science.gov (United States)

    Kistenev, Yury; Borisov, Alexey; Shapovalov, Alexander; Baydik, Olga; Titarenko, Maria

    2017-03-01

    The ability of diagnostics of oral lichen planus (OLP) based on spectral analysis of saliva using the THz spectroscopy is presented. The study included 8 patients with clinically proven OLP. The comparison group consisted of 8 healthy volunteers. Absorption spectra of the saliva was measured using time-domain spectrometer T-spec (EXPLA) in the range 0.2-3THz and have been considered as the feature vectors of the state. The spatial distribution of the objects under study in the feature space was analyzed using principle component analysis. The groups under study were shown to separate in full. Thus, the saliva analysis by the THz spectroscopy technique can be potentially used as a method of noninvasive diagnostics of the OLP.

  5. A Chinese literature overview on ultra-weak photon emission as promising technology for studying system-based diagnostics

    NARCIS (Netherlands)

    He, M.; Sun, M.; Wijk, E. van; Wietmarschen, H. van; Wijk, R. van; Wang, Z.; Wang, M.; Hankemeier, T.; Greef, J. van der

    2016-01-01

    To present the possibilities pertaining to linking ultra-weak photon emission (UPE) with Chinese medicine-based diagnostics principles, we conducted a review of Chinese literature regarding UPE with respect to a systems view of diagnostics. Data were summarized from human clinical studies and animal

  6. 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.

  7. Deep Belief Network-Based Approaches for Link Prediction in Signed Social Networks

    Directory of Open Access Journals (Sweden)

    Feng Liu

    2015-04-01

    Full Text Available In some online social network services (SNSs, the members are allowed to label their relationships with others, and such relationships can be represented as the links with signed values (positive or negative. The networks containing such relations are named signed social networks (SSNs, and some real-world complex systems can be also modeled with SSNs. Given the information of the observed structure of an SSN, the link prediction aims to estimate the values of the unobserved links. Noticing that most of the previous approaches for link prediction are based on the members’ similarity and the supervised learning method, however, research work on the investigation of the hidden principles that drive the behaviors of social members are rarely conducted. In this paper, the deep belief network (DBN-based approaches for link prediction are proposed. Including an unsupervised link prediction model, a feature representation method and a DBN-based link prediction method are introduced. The experiments are done on the datasets from three SNSs (social networking services in different domains, and the results show that our methods can predict the values of the links with high performance and have a good generalization ability across these datasets.

  8. 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.

  9. Management system of personnel dosimetry based on ISO 9001:2008 for medical diagnostic

    International Nuclear Information System (INIS)

    Queiroz, Carlos E.B.; Gerber Junior, Walmoli; Jahn, Tiago R.; Hahn, Tiago T.; Fontana, Thiago S.; Bolzan, Vagner

    2013-01-01

    MDose is a computer management system of personal dosimetry in diagnostic radiology services physician based on ISO 9001:9008 management system. According to Brazilian law all service radiology should implement a control of personal dosimetry in addition to radiation doses greater than 1.5 mSv/year service should do research of high dose, which is to identify the causes the resulting dose increase professional. This work is based on the use of the PDCA cycle in a JAVA software developed as a management method in the analysis of high doses in order to promote systematic and continuous improvement within the organization of radiological protection of workers

  10. Router Agent Technology for Policy-Based Network Management

    Science.gov (United States)

    Chow, Edward T.; Sudhir, Gurusham; Chang, Hsin-Ping; James, Mark; Liu, Yih-Chiao J.; Chiang, Winston

    2011-01-01

    This innovation can be run as a standalone network application on any computer in a networked environment. This design can be configured to control one or more routers (one instance per router), and can also be configured to listen to a policy server over the network to receive new policies based on the policy- based network management technology. The Router Agent Technology transforms the received policies into suitable Access Control List syntax for the routers it is configured to control. It commits the newly generated access control lists to the routers and provides feedback regarding any errors that were faced. The innovation also automatically generates a time-stamped log file regarding all updates to the router it is configured to control. This technology, once installed on a local network computer and started, is autonomous because it has the capability to keep listening to new policies from the policy server, transforming those policies to router-compliant access lists, and committing those access lists to a specified interface on the specified router on the network with any error feedback regarding commitment process. The stand-alone application is named RouterAgent and is currently realized as a fully functional (version 1) implementation for the Windows operating system and for CISCO routers.

  11. Contingent approach to Internet-based supply network integration

    Science.gov (United States)

    Ho, Jessica; Boughton, Nick; Kehoe, Dennis; Michaelides, Zenon

    2001-10-01

    The Internet is playing an increasingly important role in enhancing the operations of supply networks as many organizations begin to recognize the benefits of Internet- enabled supply arrangements. However, the developments and applications to-date do not extend significantly beyond the dyadic model, whereas the real advantages are to be made with the external and network models to support a coordinated and collaborative based approach. The DOMAIN research group at the University of Liverpool is currently defining new Internet- enabled approaches to enable greater collaboration across supply chains. Different e-business models and tools are focusing on different applications. Using inappropriate e- business models, tools or techniques will bring negative results instead of benefits to all the tiers in the supply network. Thus there are a number of issues to be considered before addressing Internet based supply network integration, in particular an understanding of supply chain management, the emergent business models and evaluating the effects of deploying e-business to the supply network or a particular tier. It is important to utilize a contingent approach to selecting the right e-business model to meet the specific supply chain requirements. This paper addresses the issues and provides a case study on the indirect materials supply networks.

  12. Simulation-based education leads to decreased use of fluoroscopy in diagnostic coronary angiography.

    Science.gov (United States)

    Prenner, Stuart B; Wayne, Diane B; Sweis, Ranya N; Cohen, Elaine R; Feinglass, Joe M; Schimmel, Daniel R

    2017-08-02

    The aim of this study is to determine whether simulation-based education (SBE) translates into reduced procedure time, radiation, and contrast use in actual clinical care. As a high volume procedure often performed by novice cardiology fellows, diagnostic coronary angiography represents an excellent target for SBE. Reports of SBE in interventional cardiology are limited and there is little understanding of the potential downstream clinical impact of these interventions. All diagnostic coronary angiograms performed at a single center between January 1, 2011 and June 30, 2015 were analyzed. Random effects linear regression models were used to compare outcomes between procedures performed by 12 cardiology fellows who underwent simulation-based training and those performed by 20 traditionally trained fellows. Thirty-two cardiology fellows performed 2,783 diagnostic coronary angiograms. Procedures performed by fellows trained with SBE were shorter (mean of 23.98 min vs. 24.94 min, P = 0.034) and were performed with decreased radiation (mean of 56,348 mGycm 2 vs. 66,120 mGycm 2 , P < 0.001). After controlling for year in training, procedure year, access site, and supervising attending physician, training on the simulator was independently associated with 117 fewer seconds of fluoroscopy time per procedure (P = 0.04). Diagnostic coronary angiography SBE is associated with decreased use of fluoroscopy in downstream clinical care. SBE may be a useful tool to reduce radiation exposure in the cardiac catheterization laboratory. © 2017 Wiley Periodicals, Inc.

  13. 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.

  14. Formal Specification Based Automatic Test Generation for Embedded Network Systems

    Directory of Open Access Journals (Sweden)

    Eun Hye Choi

    2014-01-01

    Full Text Available Embedded systems have become increasingly connected and communicate with each other, forming large-scaled and complicated network systems. To make their design and testing more reliable and robust, this paper proposes a formal specification language called SENS and a SENS-based automatic test generation tool called TGSENS. Our approach is summarized as follows: (1 A user describes requirements of target embedded network systems by logical property-based constraints using SENS. (2 Given SENS specifications, test cases are automatically generated using a SAT-based solver. Filtering mechanisms to select efficient test cases are also available in our tool. (3 In addition, given a testing goal by the user, test sequences are automatically extracted from exhaustive test cases. We’ve implemented our approach and conducted several experiments on practical case studies. Through the experiments, we confirmed the efficiency of our approach in design and test generation of real embedded air-conditioning network systems.

  15. Structure-based control of complex networks with nonlinear dynamics

    Science.gov (United States)

    Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

    2017-01-01

    What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework’s applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances. PMID:28655847

  16. Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

    Directory of Open Access Journals (Sweden)

    Jin Qi

    2015-01-01

    Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

  17. Seismic noise filtering based on Generalized Regression Neural Networks

    Science.gov (United States)

    Djarfour, Nouredine; Ferahtia, Jalal; Babaia, Foudel; Baddari, Kamel; Said, El-adj; Farfour, Mohammed

    2014-08-01

    This paper deals with the application of Generalized Regression Neural Networks to the seismic data filtering. The proposed system is a class of neural networks widely used for the continuous function mapping. They are based on the well known nonparametric kernel statistical estimators. The main advantages of this neural network include adaptability, simplicity and rapid training. Several synthetic tests are performed in order to highlight the merit of the proposed topology of neural network. In this work, the filtering strategy has been applied to remove random noises as well as source-related noises from real seismic data extracted from a field in the South of Algeria. The obtained results are very promising and indicate the high performance of the proposed filter in comparison to the well known frequency-wavenumber filter.

  18. Architecture Analysis of an FPGA-Based Hopfield Neural Network

    Directory of Open Access Journals (Sweden)

    Miguel Angelo de Abreu de Sousa

    2014-01-01

    Full Text Available Interconnections between electronic circuits and neural computation have been a strongly researched topic in the machine learning field in order to approach several practical requirements, including decreasing training and operation times in high performance applications and reducing cost, size, and energy consumption for autonomous or embedded developments. Field programmable gate array (FPGA hardware shows some inherent features typically associated with neural networks, such as, parallel processing, modular executions, and dynamic adaptation, and works on different types of FPGA-based neural networks were presented in recent years. This paper aims to address different aspects of architectural characteristics analysis on a Hopfield Neural Network implemented in FPGA, such as maximum operating frequency and chip-area occupancy according to the network capacity. Also, the FPGA implementation methodology, which does not employ multipliers in the architecture developed for the Hopfield neural model, is presented, in detail.

  19. Numeral eddy current sensor modelling based on genetic neural network

    International Nuclear Information System (INIS)

    Yu Along

    2008-01-01

    This paper presents a method used to the numeral eddy current sensor modelling based on the genetic neural network to settle its nonlinear problem. The principle and algorithms of genetic neural network are introduced. In this method, the nonlinear model parameters of the numeral eddy current sensor are optimized by genetic neural network (GNN) according to measurement data. So the method remains both the global searching ability of genetic algorithm and the good local searching ability of neural network. The nonlinear model has the advantages of strong robustness, on-line modelling and high precision. The maximum nonlinearity error can be reduced to 0.037% by using GNN. However, the maximum nonlinearity error is 0.075% using the least square method

  20. Whitelists Based Multiple Filtering Techniques in SCADA Sensor Networks

    Directory of Open Access Journals (Sweden)

    DongHo Kang

    2014-01-01

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

  1. Hierarchical-control-based output synchronization of coexisting attractor networks

    International Nuclear Information System (INIS)

    Yun-Zhong, Song; Yi-Fa, Tang

    2010-01-01

    This paper introduces the concept of hierarchical-control-based output synchronization of coexisting attractor networks. Within the new framework, each dynamic node is made passive at first utilizing intra-control around its own arena. Then each dynamic node is viewed as one agent, and on account of that, the solution of output synchronization of coexisting attractor networks is transformed into a multi-agent consensus problem, which is made possible by virtue of local interaction between individual neighbours; this distributed working way of coordination is coined as inter-control, which is only specified by the topological structure of the network. Provided that the network is connected and balanced, the output synchronization would come true naturally via synergy between intra and inter-control actions, where the Tightness is proved theoretically via convex composite Lyapunov functions. For completeness, several illustrative examples are presented to further elucidate the novelty and efficacy of the proposed scheme. (general)

  2. Structure-based control of complex networks with nonlinear dynamics

    Science.gov (United States)

    Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

    What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

  3. Applying Model Based Systems Engineering to NASA's Space Communications Networks

    Science.gov (United States)

    Bhasin, Kul; Barnes, Patrick; Reinert, Jessica; Golden, Bert

    2013-01-01

    System engineering practices for complex systems and networks now require that requirement, architecture, and concept of operations product development teams, simultaneously harmonize their activities to provide timely, useful and cost-effective products. When dealing with complex systems of systems, traditional systems engineering methodology quickly falls short of achieving project objectives. This approach is encumbered by the use of a number of disparate hardware and software tools, spreadsheets and documents to grasp the concept of the network design and operation. In case of NASA's space communication networks, since the networks are geographically distributed, and so are its subject matter experts, the team is challenged to create a common language and tools to produce its products. Using Model Based Systems Engineering methods and tools allows for a unified representation of the system in a model that enables a highly related level of detail. To date, Program System Engineering (PSE) team has been able to model each network from their top-level operational activities and system functions down to the atomic level through relational modeling decomposition. These models allow for a better understanding of the relationships between NASA's stakeholders, internal organizations, and impacts to all related entities due to integration and sustainment of existing systems. Understanding the existing systems is essential to accurate and detailed study of integration options being considered. In this paper, we identify the challenges the PSE team faced in its quest to unify complex legacy space communications networks and their operational processes. We describe the initial approaches undertaken and the evolution toward model based system engineering applied to produce Space Communication and Navigation (SCaN) PSE products. We will demonstrate the practice of Model Based System Engineering applied to integrating space communication networks and the summary of its

  4. Robustness of TAP-based Scan Networks

    OpenAIRE

    Ghani Zadegan, Farrokh; Carlsson, Gunnar; Larsson, Erik

    2014-01-01

    It is common to embed instruments when developing integrated circuits (ICs). These instruments are accessed at post-silicon validation, debugging, wafer sort, package test, burn-in, printed circuit board bring-up, printed circuit board assembly manufacturing test, power-on self-test, and operator-driven in-field test. At any of these scenarios, it is of interest to access some but not all of the instruments. IEEE 1149.1-2013 and IEEE 1687 propose Test Access Port based (TAP-based) mechanisms ...

  5. Dynamic Network-Based Epistasis Analysis: Boolean Examples

    Science.gov (United States)

    Azpeitia, Eugenio; Benítez, Mariana; Padilla-Longoria, Pablo; Espinosa-Soto, Carlos; Alvarez-Buylla, Elena R.

    2011-01-01

    In this article we focus on how the hierarchical and single-path assumptions of epistasis analysis can bias the inference of gene regulatory networks. Here we emphasize the critical importance of dynamic analyses, and specifically illustrate the use of Boolean network models. Epistasis in a broad sense refers to gene interactions, however, as originally proposed by Bateson, epistasis is defined as the blocking of a particular allelic effect due to the effect of another allele at a different locus (herein, classical epistasis). Classical epistasis analysis has proven powerful and useful, allowing researchers to infer and assign directionality to gene interactions. As larger data sets are becoming available, the analysis of classical epistasis is being complemented with computer science tools and system biology approaches. We show that when the hierarchical and single-path assumptions are not met in classical epistasis analysis, the access to relevant information and the correct inference of gene interaction topologies is hindered, and it becomes necessary to consider the temporal dynamics of gene interactions. The use of dynamical networks can overcome these limitations. We particularly focus on the use of Boolean networks that, like classical epistasis analysis, relies on logical formalisms, and hence can complement classical epistasis analysis and relax its assumptions. We develop a couple of theoretical examples and analyze them from a dynamic Boolean network model perspective. Boolean networks could help to guide additional experiments and discern among alternative regulatory schemes that would be impossible or difficult to infer without the elimination of these assumption from the classical epistasis analysis. We also use examples from the literature to show how a Boolean network-based approach has resolved ambiguities and guided epistasis analysis. Our article complements previous accounts, not only by focusing on the implications of the hierarchical and

  6. A Network-Based Electrical Engineering Laboratory

    Science.gov (United States)

    Asimopoulos, Nikolaos D.; Kyriakos, Nathanail; Mpatzakis, Vlasios

    2007-01-01

    Technical education is, by definition, a field that requires hands-on practice and experience by the student. When it comes to distant learning, technical education suffers from lack of such practical study, given the fact that e-learning is based on theoretical material being provided remotely to the student. This article presents the idea of…

  7. AMERICA’S BASE NETWORK: CREDIBLE DETERRENCE

    Science.gov (United States)

    2017-04-06

    been a focal point of the US grand strategy for nearly 120 years dating back to the conclusion of the Spanish American War.3 As such the evolution can...domestic political landscapes are changing around the globe. This global rules based international order is under pressure and governing and

  8. Global profiling and rapid matching of natural products using diagnostic product ion network and in silico analogue database: Gastrodia elata as a case study.

    Science.gov (United States)

    Lai, Chang-Jiang-Sheng; Zha, Liangping; Liu, Da-Hui; Kang, Liping; Ma, Xiaojing; Zhan, Zhi-Lai; Nan, Tie-Gui; Yang, Jian; Li, Fajie; Yuan, Yuan; Huang, Lu-Qi

    2016-07-22

    Rapid discovery of novel compounds of a traditional herbal medicine is of vital significance for pharmaceutical industry and plant metabolic pathway analysis. However, discovery of unknown or trace natural products is an ongoing challenge. This study presents a universal targeted data-independent acquisition and mining strategy to globally profile and effectively match novel natural product analogues from an herbal extract. The famous medical plant Gastrodia elata was selected as an example. This strategy consists of three steps: (i) acquisition of accurate parent and adduct ions (PAIs) and the product ions data of all eluting compounds by untargeted full-scan MS(E) mode; (ii) rapid compound screening using diagnostic product ions (DPIs) network and in silico analogue database with SUMPRODUCT function to find novel candidates; and (iii) identification and isomerism discrimination of multiple types of compounds using ClogP and ions fragment behavior analyses. Using above data mining methods, a total of 152 compounds were characterized, and 70 were discovered for the first time, including series of phospholipids and novel gastroxyl derivatives. Furthermore, a number of gastronucleosides and phase II metabolites of gastrodin and parishins were discovered, including glutathionylated, cysteinylglycinated and cysteinated compounds, and phosphatidylserine analogues. This study extended the application of classical DPIs filter strategy and developed a structure-based screening approach with the potential for significant increase of efficiency for discovery and identification of trace novel natural products. Copyright © 2016 Elsevier B.V. All rights reserved.

  9. Functional networks inference from rule-based machine learning models.

    Science.gov (United States)

    Lazzarini, Nicola; Widera, Paweł; Williamson, Stuart; Heer, Rakesh; Krasnogor, Natalio; Bacardit, Jaume

    2016-01-01

    Functional networks play an important role in the analysis of biological processes and systems. The inference of these networks from high-throughput (-omics) data is an area of intense research. So far, the similarity-based inference paradigm (e.g. gene co-expression) has been the most popular approach. It assumes a functional relationship between genes which are expressed at similar levels across different samples. An alternative to this paradigm is the inference of relationships from the structure of machine learning models. These models are able to capture complex relationships between variables, that often are different/complementary to the similarity-based methods. We propose a protocol to infer functional networks from machine learning models, called FuNeL. It assumes, that genes used together within a rule-based machine learning model to classify the samples, might also be functionally related at a biological level. The protocol is first tested on synthetic datasets and then evaluated on a test suite of 8 real-world datasets related to human cancer. The networks inferred from the real-world data are compared against gene co-expression networks of equal size, generated with 3 different methods. The comparison is performed from two different points of view. We analyse the enriched biological terms in the set of network nodes and the relationships between known disease-associated genes in a context of the network topology. The comparison confirms both the biological relevance and the complementary character of the knowledge captured by the FuNeL networks in relation to similarity-based methods and demonstrates its potential to identify known disease associations as core elements of the network. Finally, using a prostate cancer dataset as a case study, we confirm that the biological knowledge captured by our method is relevant to the disease and consistent with the specialised literature and with an independent dataset not used in the inference process. The

  10. Symptom diagnostics based on clinical records: a tool for scientific research in child psychiatry?

    Science.gov (United States)

    de Jong, Marianne; Punt, Marja; de Groot, Erik; Hielkema, Tjitske; Struik, Marianne; Minderaa, Ruud B; Hadders-Algra, Mijna

    2009-05-01

    Child psychiatric diagnoses are generally based on a clinical examination and not on standardized questionnaires. The present study assessed whether symptom diagnostics based on clinical records facilitates the use of non-standardized clinical material for research. Six hundred and eighty-five children, referred to a third level child psychiatric centre in the Netherlands, were, after extensive multidisciplinary examination, classified according to the multi-axial classification scheme for psychiatric disorders in childhood and adolescence (MAC-ICD-9). By two raters 44 behavioural symptoms were scored based on the clinical records of these children. Interrater agreement on symptoms in 50 records was performed. Principal components analysis on symptom scores of all children was performed; factor scores were related with MAC-ICD-9 classifications. Interrater reliability for behavioural symptoms was excellent (kappa = 0.88). Many children with psychiatric problems suffer from a large number of behavioural symptoms. Factor scores of the symptoms revealed recognizable and well interpretable entities and indicated overlap in symptomatology and comorbidity. A symptom-based diagnostic approach based on extensive clinical patient files may provide a special dimension to improve the reliability of psychiatric classification.

  11. 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.

    Science.gov (United States)

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

    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 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.

  12. A reconsideration of negative ratings for network-based recommendation

    Science.gov (United States)

    Hu, Liang; Ren, Liang; Lin, Wenbin

    2018-01-01

    Recommendation algorithms based on bipartite networks have become increasingly popular, thanks to their accuracy and flexibility. Currently, many of these methods ignore users' negative ratings. In this work, we propose a method to exploit negative ratings for the network-based inference algorithm. We find that negative ratings play a positive role regardless of sparsity of data sets. Furthermore, we improve the efficiency of our method and compare it with the state-of-the-art algorithms. Experimental results show that the present method outperforms the existing algorithms.

  13. Heterogeneous Community-based mobility model for human opportunistic network

    DEFF Research Database (Denmark)

    Hu, Liang; Dittmann, Lars

    2009-01-01

    Human opportunistic networks can facilitate wireless content dissemination while humans are on the move. In such a network, content is disseminated via nodes relaying and nodes mobility (human mobility). Thus it is essential to understand and model the real human mobility. We present...... a heterogeneous community-based random way-point (HC-RWP) mobility model that captures the four important properties of real human mobility. These properties are based on both intuitive observations of daily human mobility and analysis of empirical mobility traces. By discrete event simulation, we show HC......-RWP captures essential statistic features of wide range of real human mobility traces reported in previous studies....

  14. Market Split based Congestion Management for Networks with Loops

    Science.gov (United States)

    Marmiroli, Marta; Tanimoto, Masahiko; Tsukamoto, Yukitoki; Yokoyama, Ryuichi

    Market splitting is one of the methods to solve the transmission congestion problem associated with the introduction of competitive electricity market and transmission access. Based on the concept of price difference among congested areas, the market splitting approach produces a solution that strongly informs market participants of congestion path. In this paper, an algorithm to solve the market splitting problem for complex networks including loop structures is proposed. The method, based on an algebraic approach, ensures a feasible optimal solution verifiable and easily understandable by the market participants. Complex networks are transformed into simple radial ones using the delta-star approach. The method was tested on large problems to evaluate the performances.

  15. Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network.

    Science.gov (United States)

    Bhatia, Udit; Kumar, Devashish; Kodra, Evan; Ganguly, Auroop R

    2015-01-01

    The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.

  16. Network Science Based Quantification of Resilience Demonstrated on the Indian Railways Network.

    Directory of Open Access Journals (Sweden)

    Udit Bhatia

    Full Text Available The structure, interdependence, and fragility of systems ranging from power-grids and transportation to ecology, climate, biology and even human communities and the Internet have been examined through network science. While response to perturbations has been quantified, recovery strategies for perturbed networks have usually been either discussed conceptually or through anecdotal case studies. Here we develop a network science based quantitative framework for measuring, comparing and interpreting hazard responses as well as recovery strategies. The framework, motivated by the recently proposed temporal resilience paradigm, is demonstrated with the Indian Railways Network. Simulations inspired by the 2004 Indian Ocean Tsunami and the 2012 North Indian blackout as well as a cyber-physical attack scenario illustrate hazard responses and effectiveness of proposed recovery strategies. Multiple metrics are used to generate various recovery strategies, which are simply sequences in which system components should be recovered after a disruption. Quantitative evaluation of these strategies suggests that faster and more efficient recovery is possible through network centrality measures. Optimal recovery strategies may be different per hazard, per community within a network, and for different measures of partial recovery. In addition, topological characterization provides a means for interpreting the comparative performance of proposed recovery strategies. The methods can be directly extended to other Large-Scale Critical Lifeline Infrastructure Networks including transportation, water, energy and communications systems that are threatened by natural or human-induced hazards, including cascading failures. Furthermore, the quantitative framework developed here can generalize across natural, engineered and human systems, offering an actionable and generalizable approach for emergency management in particular as well as for network resilience in general.

  17. Connecting Land-Based Networks to Ships

    Science.gov (United States)

    2013-06-01

    VSAT ) system. The stations, equipped with VSAT antennas, share the satellite’s transmission capacity to transmit to the station having the central hub...and base stations, which are connected to the ship’s VSAT system by which the voice and data connections from the phones are routed from the ship to...Maritime VSAT Communications Solutions, June 2010. [17] MCP Maritime Communications Partner, “Technology overview,” August 2012, http

  18. 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).

  19. Analytical network process based optimum cluster head selection in wireless sensor network.

    Science.gov (United States)

    Farman, Haleem; Javed, Huma; Jan, Bilal; Ahmad, Jamil; Ali, Shaukat; Khalil, Falak Naz; Khan, Murad

    2017-01-01

    Wireless Sensor Networks (WSNs) are becoming ubiquitous in everyday life due to their applications in weather forecasting, surveillance, implantable sensors for health monitoring and other plethora of applications. WSN is equipped with hundreds and thousands of small sensor nodes. As the size of a sensor node decreases, critical issues such as limited energy, computation time and limited memory become even more highlighted. In such a case, network lifetime mainly depends on efficient use of available resources. Organizing nearby nodes into clusters make it convenient to efficiently manage each cluster as well as the overall network. In this paper, we extend our previous work of grid-based hybrid network deployment approach, in which merge and split technique has been proposed to construct network topology. Constructing topology through our proposed technique, in this paper we have used analytical network process (ANP) model for cluster head selection in WSN. Five distinct parameters: distance from nodes (DistNode), residual energy level (REL), distance from centroid (DistCent), number of times the node has been selected as cluster head (TCH) and merged node (MN) are considered for CH selection. The problem of CH selection based on these parameters is tackled as a multi criteria decision system, for which ANP method is used for optimum cluster head selection. Main contribution of this work is to check the applicability of ANP model for cluster head selection in WSN. In addition, sensitivity analysis is carried out to check the stability of alternatives (available candidate nodes) and their ranking for different scenarios. The simulation results show that the proposed method outperforms existing energy efficient clustering protocols in terms of optimum CH selection and minimizing CH reselection process that results in extending overall network lifetime. This paper analyzes that ANP method used for CH selection with better understanding of the dependencies of

  20. Bacterial clonal diagnostics as a tool for evidence-based empiric antibiotic selection.

    Directory of Open Access Journals (Sweden)

    Veronika Tchesnokova

    Full Text Available Despite the known clonal distribution of antibiotic resistance in many bacteria, empiric (pre-culture antibiotic selection still relies heavily on species-level cumulative antibiograms, resulting in overuse of broad-spectrum agents and excessive antibiotic/pathogen mismatch. Urinary tract infections (UTIs, which account for a large share of antibiotic use, are caused predominantly by Escherichia coli, a highly clonal pathogen. In an observational clinical cohort study of urgent care patients with suspected UTI, we assessed the potential for E. coli clonal-level antibiograms to improve empiric antibiotic selection. A novel PCR-based clonotyping assay was applied to fresh urine samples to rapidly detect E. coli and the urine strain's clonotype. Based on a database of clonotype-specific antibiograms, the acceptability of various antibiotics for empiric therapy was inferred using a 20%, 10%, and 30% allowed resistance threshold. The test's performance characteristics and possible effects on prescribing were assessed. The rapid test identified E. coli clonotypes directly in patients' urine within 25-35 minutes, with high specificity and sensitivity compared to culture. Antibiotic selection based on a clonotype-specific antibiogram could reduce the relative likelihood of antibiotic/pathogen mismatch by ≥ 60%. Compared to observed prescribing patterns, clonal diagnostics-guided antibiotic selection could safely double the use of trimethoprim/sulfamethoxazole and minimize fluoroquinolone use. In summary, a rapid clonotyping test showed promise for improving empiric antibiotic prescribing for E. coli UTI, including reversing preferential use of fluoroquinolones over trimethoprim/sulfamethoxazole. The clonal diagnostics approach merges epidemiologic surveillance, antimicrobial stewardship, and molecular diagnostics to bring evidence-based medicine directly to the point of care.